Abstracts of papers presented at the 2016 Cold Spring Harbor Asia Conference
FRONTIERS IN SINGLE CELL GENOMICS November 7–November 11, 2016
Cold Spring Harbor Conferences Asia Cold Spring Harbor Laboratory
Abstracts of papers presented at the 2016 Cold Spring Harbor Asia Conference
FRONTIERS IN SINGLE CELL GENOMICS November 7–November 11, 2016 Arranged by John Marioni, European Molecular Biology Laboratory, UK Nicolas Navin, University of Texas MD Anderson Cancer Center, USA Paul Robson, The Jackson Laboratory for Genomic Medicine, USA Fuchou Tang, Peking University, China Angela Wu, Stanford University, USA
Cold Spring Harbor Conferences Asia
Cold Spring Harbor Laboratory
Cold Spring Harbor Asia 2016 Sponsors
Genome Level
苏州工业园区科技发展局
Suzhou Industrial Park Science and Technology Development Bureau
Chromsome Level
DNA Level
Cell Level
Protein Level
__________________________________________________________
Cover: Image by Nicholas Navin Lab, University of Texas MD Anderson Cancer Center.
SINGLE CELLS Monday, November 7 – Friday, November 11, 2016
Monday 7:00 pm 1 Opening Keynote Session Free Open Bar sponsored by Frontline BioVentures
Tuesday 9:00 am 2 Computational Biology Tuesday 2:00 pm 3 Poster Session Tuesday 4:30 pm Chinese Tea and Beer Tasting Tuesday 7:00 pm 4 Evening Keynote Session Wednesday 9:00 am 5 Development / Neurobiology /
Immunology I Wednesday 2:00 pm Visit to Old Suzhou Wednesday 7:00 pm 6 Technology I
Free open bar sponsored by BioBAY
Thursday 9:00 am 7 Cancer Biology Thursday 2:00 pm 8 Development / Neurobiology /
Immunology II Thursday 6:00 pm Cocktails and Banquet Friday 9:00 am 9 Technology II
Oral presentation sessions are located in the Watson Auditorium
Poster session and Chinese Tea & Beer Tasting are in the Poster Hall. Cocktail social hour is held in the Long Hall. Old Suzhou visits depart from the hotel lobby
*optional tour requires additional fee.
Meal locations and times are as follows: Breakfast Octagon 7:00am - 9:00am Lunch Octagon 12:00am - 1:30pm Dinner Octagon 6:00pm - 7:30pm
Banquet Suz Garden 7:00pm
Abstracts are the responsibility of the author(s) and publication of an abstract does not imply endorsement by Cold Spring Harbor Asia of the studies reported in the abstract. These abstracts should not be cited in bibliographies. Material herein should be treated as personal communications and should be cited as such only with the consent of the author. Please note that ANY photography or video/audio recording of oral presentations or individual posters is strictly prohibited except with the advance permission of the author(s), the organizers, and Cold Spring Harbor Asia.
ix
PROGRAM
MONDAY, November 7—7:00 PM SESSION 1 OPENING KEYNOTE SESSION Chairperson: Fuchou Tang, Peking University, Beijing, China Single-cell whole genome application—Coming of ageXiaoliang Sunney Xie, Chongyi Chen, Dong Xing, Longzhi Tan, Heng Li [35’+10’]
Presenter affiliation: Harvard University, Cambridge, Massachusetts.
1
Stephen Quake [35’+10’] Presenter affiliation: Stanford University, Stanford, California
Free Open Bar Sponsored by
Frontline BioVentures
TUESDAY, November 8—9:00 AM SESSION 2 COMPUTATIONAL BIOLOGY Chairperson: John Marioni, European Molecular Biology Laboratory, Hinxton, United Kingdom Using single-cell genomics to understand cell fate decisionsJohn C. Marioni [20’+10’]
Presenter affiliation: EMBL, Cambridge, United Kingdom.
2
x
Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm Li-Fang Chu, Ning Leng, Ron Stewart, James A. Thomoson [10’+5’]
Presenter affiliation: Morgridge Institute for Research, Madison, Wisconsin.
3
Rickard Sandberg [20’+10’] Presenter affiliation: Karolinska Institutet, Stockholm, Sweden
Coffee Break
Lineage segregation of post-implantation mouse embryo revealed by spatial and single cell transcriptome Guangdun Peng, Shengbao Suo, Guizhong Cui, Na Sun, Patrick P. Tam, Jingdong J. Han, Naihe Jing [20’+10’]
Presenter affiliation: Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
4
HYSIC—A noise robust cell categorization algorithm for single-cell transcriptomics Xin Zou, Jie Hao, Ze-Guang Han [10’+5’]
Presenter affiliation: Key Laboratory of Systems Biomedicine, Shanghai, China.
5
Contrasting transcriptional and genetic heterogeneity within tumors Peter V. Kharchenko [20’+10’]
Presenter affiliation: Harvard Medical School, Boston, Massachusetts.
6
Single cell biology—From exploring heterogeneity to functional analysis Jay West [20’+10’] Presenter affiliation: Fluidigm R&D, USA.
xi
TUESDAY, November 8—2:00 PM SESSION 3 POSTER SESSION Transcriptomic study of HPV infected cells using the new high-throuput single cell nanoCAGE protocol Ophelie Arnaud, Stephane G. Poulain, Sachi Kato, Kazunori Nagasaka, Mickael Mendez, Charles G. Plessy
Presenter affiliation: Riken Institute, Center For Life Science and Technologies , Yokohama, Japan.
7
Tumor-selective replication herpes simplex virus-based technology significantly improves clinical detection and prognostication of viable circulating tumor cells Wen Zhang, Li Bao, Zhaoyang Qian, Hongjun Gao, Kaitai Zhang, Binlei Liu
Presenter affiliation: BGI-Shenzhen, Shenzhen, China
8
Single-cell comprehensive omics analysis highlight heterogeneity of colorectal cancer Shuhui Bian, Yu Hou, Jun Yong, Xin Zhou, Xianlong Li, Lu Wen, Fuchou Tang, Wei Fu
Presenter affiliation: Third Hospital, Peking University, Beijing, China.
9
Single cell and spatial transcriptomics reveal cellular vulnerability in the early stage of Alzheimer' disease Wei-Ting Chen, Katleen Craessaerts, Mark Fiers, Bart De Strooper
Presenter affiliation: VIB-Leuven, Leuven, Belgium.
10
Single cell gene expression profiling identifies population dynamics during T cell exhaustion Zeyu Chen, Zhicheng Ji, Laura A. Vella, Ramin S. Herati, Bertram Bengsch, Erietta Stelekati, Hongkai Ji, E. J. Wherry
Presenter affiliation: University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
11
NRSF regulates hippocampal CA3-CA1 LTP via transcriptional repression of tissue plasminogen activator Xuewen Cheng, Ya Shen, Zhi-Qi Xiong
Presenter affiliation: Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
12
xii
Single cell transcriptome analysis of pituitary adenomasYueli Cui, Lin Li, Chao Li, Lu Wen, Fuchou Tang, Dabiao Zhou
Presenter affiliation: Biodynamic Optical Imaging Center, Beijing, China.
13
Single cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer Marco L. Leung, Alexander Davis, Ruli Gao, Anna Casasent, Yong Wang, Emi Sei, Dipen Maru, Scott Kopetz, Nicholas E. Navin
Presenter affiliation: The University of Texas MD Anderson Cancer Center, Houston, Texas; Graduate School of Biomedical Sciences, Houston, Texas.
14
Single cell transcriptomes of human germline cells and their niche cells Ji Dong, Li Li, Liying Yan, Lu Wen, Jie Qiao, Fuchou Tang
Presenter affiliation: Peking University, Beijing, China.
15
A SMARTer approach to profiling the human T-cell receptor repertoire Suvarna Gandlur, Sarah Taylor, Thomas Schaal, Nao Yasuyama, Jude Dunne, Maithreyan Srinivasan, Andrew Farmer
Presenter affiliation: Takara Bio USA, Inc., Mountain View, California.
16
Single cell total RNA sequencing through isothermal ampli-fication in picoliter-droplet emulsion Yusi Fu, He Chen, Lu Liu, Yanyi Huang
Presenter affiliation: Peking University, Beijing, China.
17
Illumina-ready strand-specific RNA-seq library preparation from single cells Suvarna Gandlur, Nathalie Bolduc, Simon Lee, Andrew Farmer
Presenter affiliation: Takara Bio USA, Mountain View, California.
18
Early hematopoietic development revealed by single-cell transcriptome analysis of mouse embryos Siyuan Hou, Yun Gao, Ji Dong, Bing Liu, Fuchou Tang, Yu Lan
Presenter affiliation: ; Biomedical Institute for Pioneering Investigation via Convergence (BIOPIC), Beijing, China.
19
Using genetic features to identify subpopulations in single-cell RNA-seq data Olivier Poirion, Xun Zhu, Travers Ching, Lana Garmire
Presenter affiliation: University of Hawaii Cancer Center, Honolulu, Hawaii; University of Hawaii at Manoa, Honolulu, Hawaii.
20
xiii
Single cell mRNA-seq unveils non-autonomous effect of epidermal Wnt/β-catenin signalling Arsham Ghahramani, Giacomo Donati, Fiona M. Watt, Nicholas M. Luscombe
Presenter affiliation: Francis Crick Institute, London, United Kingdom; King's College London, London, United Kingdom.
21
Single-cell multiplexed profiling of protein-level changes induced by EGFR inhibitor gefitinib Haibiao Gong, Ilona Holcomb, Gajalakshmi Dakshinamoorthy, Benjamin Liu, Marc Unger, Ramesh Ramakrishnan
Presenter affiliation: Fluidigm Corporation, South San Francisco, California.
22
Massive population genetic diversity in ETV6-RUNX1 acute lymphoblastic leukemia Veronica Gonzalez-Pena, John Easton, Charles Gawad
Presenter affiliation: St. Jude Children's Research Hospital, Memphis, Tennessee.
23
Microbead-mediated simultaneous isolation of DNA and total RNA from single cells Kyung Yeon Han, Kyu-Tae Kim, Donghyun Park, Hae-Ock Lee, Woong-Yang Park
Presenter affiliation: Samsung Medical Center, Seoul, South Korea.
24
Single-cell triple omics sequencing reveals multi-omics heterogeneity in hepatocellular carcinomas Yu Hou, Huahu Guo, Chen Cao, Xianlong Li, Boqiang Hu, Ping Zhu, Xinglong Wu, Lu Wen, Fuchou Tang, Yanyi Huang, Jirun Peng
Presenter affiliation: Biodynamic Optical Imaging Center, College of Life Sciences, Beijing, China.
25
Epigenomic landscape of human fetal brain, heart and liverBoqiang Hu, Hongshan Guo, Liying Yan, JIe Qiao, Fuchou Tang
Presenter affiliation: Peking University, Beijing, China.
26
Single-cell RNA-seq reveals distinct injury responses in different types of DRG sensory neurons Ganlu Hu, Kevin Huang, Youjin Hu, Guizhen Du, Zhigang Xue, Xianmin Zhu, Guoping Fan
Presenter affiliation: Tongji University, Shanghai, China; University of California Los Angeles, Los Angeles, California; Tongji University School of Medicine, Shanghai, China.
27
xiv
Single cell transcriptome analysis of mouse organogenesisYuqiong Hu, Xiaoying Fan, Jing Dong, Xinglong Wu, Fuchou Tang
Presenter affiliation: Peking University, Beijing, China.
28
Genetic alternations in circulating tumor cells of lung cancer adenocarcinoma with their implication in metastasis and clinical practice Jicheng Yao, Shuo Mu, Jinwei Hu, Gongwei Qin, Ming Yao, Hui Kang, Kai Wang
Presenter affiliation: OrigiMed Inc., Shanghai, China.
29
Histopathology linked single-cell genomics by high-throughput laser discharging system Sungsik Kim, Amos C. Lee, Han-Byoel Lee, Jinhyun Kim, Yushin Jung, Yongju Lee, Sangwook Bae, Wonshik Han, Sunghoon Kwon
Presenter affiliation: Interdisciplinary Program for Bioengineering, Seoul, South Korea.
30
Detecting aberrant hypermethylated CpG islands in circulating cell-free DNA of colorectalcancer patients using MCTA-seq Jingyi Li, Xiaomeng Liu, Xin Zhou, Jie Ren, Jilian Wang, Lu Wen, Liying Yan, Fuchou Tang, Wei Fu
Presenter affiliation: Peking University, Beijing, China.
31
Using microdose cell nucleosome occupancy and methylome sequencing to analyze the chromatin state landscapes of mouse preimplantation embryos Fan Guo, Lin Li, Jingyun Li, Fuchou Tang
Presenter affiliation: Biodynamic Optical Imaging Center, Beijing, China; Peking-Tsinghua Center for Life Sciences, Beijing, China; Academy for Advanced Interdisciplinary Studies, Peking University, China.
32
Single cell RNA-seq analysis of human PGCs Li Li, Ji Dong, Liying Yan, Fuchou Tang, Jie Qiao
Presenter affiliation: Peking University, Beijing, China.
33
The transcriptome and DNA methylome landscapes of human primordial germ cells Fan Guo, Liying Yan, Hongshan Guo, Lin Li, Fuchou Tang, Jie Qiao
Presenter affiliation: Peking University, Beijing, China.
34
Single-cell triple omics sequencing reveals the molecular dynamics during lung cancer development and metastasis Qingqing Li, Yu Hou, Lin Li, Hua Bai, Lu Wen, Fuchou Tang, Jie Wang Presenter affiliation: Peking University, Beijing, China.
35
xv
Representation learning for single cell RNA-seq dataXiangyu Li, Weizheng Chen, Yang Chen, Xuegong Zhang, Michael Q. Zhang
Presenter affiliation: Tsinghua University, Bejing, China.
36
Single cell RNA sequencing of the mouse dorsal root ganglia in response to injury Veronique Lisi, Michel Giroux, Elmer Guzman, Bhagat Singh, Clifford Woolf, Kenneth S. Kosik
Presenter affiliation: University of California Santa Barbara, Santa Barbara, California.
37
Development of microfluidics-based advanced workflow for high-throughput single-cell RNA sequencing Benjamin Liu, Michael Phelan, Tze-Howe Charn, Jing Wang, Devin Do, Larry Wang, Hoan Phan, Benjamin Lacar, David Wang, Joel Brockman, Manisha Ray, Shaun Cordes, Marc Unger, Richard Fekete Presenter affiliation: Fluidigm Corporation, South San Francisco, California.
38
Human adenosine deaminases ADA1 and ADA2 bind to different subsets of immune cells Chengqian Liu, Julia Kaljas, Maksym Skaldin, Chengxiang Wu, Qing Zhou, Yuanan Lu, Ivona Aksentijevich, Andrey V. Zavialov
Presenter affiliation: University of Turku, Turku, Finland.
39
PTEN deficiency reprogrammes human neural stem cells towards a glioblastoma stem cell-like phenotype Xiaomeng Liu, Shunlei Duan, Guohong Yuan, Jingyi Li, Jing Qu, Fuchou Tang, Guang-Hui Liu
Presenter affiliation: Peking University, Beijing, China.
40
PCR amplification from encapsulated single-cell within protein based hydrogel micro-beads Xiaotian Liu, Fei Sun
Presenter affiliation: Hong Kong University of Science and Technology, Hong Kong, China.
41
Single cell transcriptome analysis reveals two pre-DC sub-populations in differentiation trajectory towards distinct types of dendritic cells Wenji Ma, Jaeyop Lee, Daniel Backenroth, Yu Zhou, Erin Bush, Peter Sims, Kang Liu, Yufeng Shen
Presenter affiliation: Columbia University Medical Center, New York, New York.
42
xvi
Mechanisms of dermal niche control of hair follicle development and carcinogenesis Peggy Myung, Thomas Sun, Valentina Greco
Presenter affiliation: Yale University School of Medicine, New Haven, Connecticut; Yale University School of Medicine, New Haven, Connecticut.
43
Effective detection of variations in single cell transcriptomeKuanwei Sheng, Wenjian Cao, Qing Deng, Chenghang Zong
Presenter affiliation: Baylor College of Medicine, Houston, Texas.
44
Single-cell RNA-Seq analysis reveals the dynamic trajectories during mouse liver development Xianbin Su, Yi Shi, Xin Zou, Zhao-Ning Lu, Chong-Chao Wu, Xiao-Fang Cui, Lan Wang, Kun-Yan He, Ze-Guang Han
Presenter affiliation: Shanghai Jiao Tong University, Shanghai, China.
45
Single-cell nanobiopsy, a novel platform for the multiplexed analysis of mRNA compartmentalization in neuronal cells Eszter Toth, Akshar Lohith, Akiyoshi Fukamizu, Nader Pourmand
Presenter affiliation: University of Tsukuba, Tsukuba, Japan; University of Tsukuba, Tsukuba, Japan; University of California at Santa Cruz, Santa Cruz, California.
46
Trancriptome and DNA methylome analysis of human embryos at eight-cell stage Rui Wang, Lu Yang, Liying Yan, Hao Ge, Fuchou Tang
Presenter affiliation: Biodynamic Optical Imaging Center, Beijing, China.
47
Identification of lineage marker genes with single-cell RNA sequencing Xi Wang, Thomas Höfer
Presenter affiliation: German Cancer Research Center, Heidelberg, Germany.
48
A novel technique for genome-scale detection of hypermethylated CpG islands in circulating cell-free DNA. Lu Wen, Jingyi Li, Huahu Guo, Xiaomeng Liu, Fuchou Tang, Yanyi Huang, Jirun Peng
Presenter affiliation: Peking University, Beijing, China.
49
xvii
A novel and reliable approach for whole genome amplification from individual mammalian cells improves amplification consistency and genome sequence coverage Gang Zhang, Edouard Hatton, Anjali Hinch, Rory Bowden, Peter Donnelly
Presenter affiliation: Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom.
50
Intratumor heterogeneity and branched evolution of early-stage primary acral melanoma revealed by microdissectional multiregion sequencing Xiannian Zhang, Yang Peng, Chunmei Li, Yuhong Pang, Yanyi Huang, Hang Li
Presenter affiliation: Peking University, Beijing, China.
51
Evolution of multiple cell clones over a 29 year period of a CLL patient Zhikun Zhao, Liang Wu, Shiping Liu, Yong Hou, Michael Dean
Presenter affiliation: BGI-shenzhen, Shenzhen, China.
52
CircRNA analysis of HEK293T single-cell data has revealed novel distribution patterns for CircRNAs Chaofang Zhong, Maozhen Han
Presenter affiliation: Huazhong University of Science and Technology, Wuhan, China.
53
Tracing the formation of hematopoietic stem cells in mouse embryos by single-cell functional and RNA-Seq analyses Fan Zhou, Xianlong Li, Weili Wang, Ping Zhu, Jie Zhou, Wenyan He, Weiping Yuan, Fuchou Tang, Bing Liu
Presenter affiliation: Academy of Military Medical Sciences, Beijing, Colombia.
54
Highly multiplexed single mRNA measurements reveal distinct molecular regions in the mouse hippocampus Wen Zhou, Sheel Shah, Eric Lubeck, Long Cai
Presenter affiliation: California Institute of Technology, Pasadena, California.
55
Tracing haematopoietic stem cell formation at single-cell resolution Ping Zhu, Fan Zhou, Xian L. Li, Wei L. Wang, Ji Zhou, Wen Y. He, Meng Ding, Fu Y. Xiong, Xiao N. Zheng, Zhuan Li, Yan L. N, Xiao H. Mu, Lu Wen, Tao Cheng, Yu Lan, Wei P. Yuan, Bing Liu, Fu C. Tang
Presenter affiliation: Peking University, Beijing, China.
56
xviii
TUESDAY, November 8—4:30 PM
Chinese Tea and Beer Tasting
TUESDAY, November 8—7:00 PM SESSION 4 EVENING KEYNOTE SESSION Chairperson: John Marioni, European Molecular Biology Laboratory, Hinxton, United Kingdom Revealing novel cell types, cell-cell interactions, and cell lineages by single-cell sequencing Alexander van Oudenaarden [35’+10’]
Presenter affiliation: Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences), Utrecht, the Netherlands; University Medical Center Utrecht, Utrecht, the Netherlands.
57
Differential expression analyses for single-cell RNA-Seq—Old questions on new data Zhun Miao, Xuegong Zhang [10’+5’]
Presenter affiliation: MOE Key Laboratory of Bioinformatics, Beijing, China; TNLIST, Beijing, China; Tsinghua University, Beijing, China.
58
An overview of the NIH Single Cell Analysis and Brain Cell Census programs Yong Yao [20’+10’]
Presenter affiliation: National Institute of Mental Health, Bethesda, Maryland.
59
Interrogating the robustness of gene regulatory circuits by RACIPE Huang Bin, Mingyang Lu, Eshel Ben-Jacob, Herbert Levine, Jose Onuchic [10’+5’]
Presenter affiliation: Rice University, Houston, Texas; Jackson Laboratory, Bar Harbor, Maine.
60
Single cell RNA-seq reveals cell cycle dependent differential potential of hematopoietic stem cell progenitor Xinghua Pan [20’+10’] Presenter affiliation: Southern Medical University, Guangzhou, China.
xix
WEDNESDAY, November 9—9:00 AM SESSION 5 DEVELOPMENT / NEUROBIOLOGY / IMMUNOLOGY I Chairperson: Paul Robson, The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA Epigenetic reprogramming in mammalian developmentWolf Reik [20’+10’]
Presenter affiliation: Babraham Institute, Cambridge, United Kingdom.
61
Engineering cells and tissues—Insights from single cell transcriptomics Barbara Treutlein, Gray Camp [20’+10’]
Presenter affiliation: Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany; Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany; Technical University Munich, Munich, Germany.
62
James Eberwine [20’+10’] Presenter affiliation: University of Pennsylvania Medical School, Philadelphila, Pennsylvania.
Coffee Break
Unpeeling the layers of post-transcriptional gene regulation: intron retention coordinates functional gene networks Justin J L. Wong, Ulf Schmitz, Natalia Pinello, Fangzhi Jia, Dadi Gao, Trung Nguyen, Sultan Alasmari, William Ritchie, Maria-Cristina Keightley, Shaniko Shini, Graham J. Lieschke, John E J. Rasko [20’+10’]
Presenter affiliation: University of Sydney, Sydney, Australia.
63
Single-cell dynamics of chromatin accessibility during forebrain development Sebastian Preissl, Rongxin Fang, Hui Huang, Yuan Zhao, David Gorkin, Brandon Sos, Veena Afzal, Diane Dickel, Samantha Kuan, Axel Visel, Len A. Pennachio, Kun Zhang, Bing Ren [10’+5’]
Presenter affiliation: Ludwig Institute for Cancer Research, La Jolla, California.
64
Paul Robson [20’+10’] Presenter affiliation: The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut.
xx
WEDNESDAY, November 9—2:00 PM
Visit to Old Suzhou
WEDNESDAY, November 9—7:00 PM SESSION 6 TECHNOLOGY I Chairperson: Angela Wu, Hong Kong University of Science and Technology, Hong Kong, China Development of new techniques for single-cell analysisJianbin Wang [20’+10’] Presenter affiliation: Tsinghua University, Beijing, China.
65
Dissecting gene regulation network in human early embryos at single-cell and single-base resolution Fuchou Tang [20’+10’]
Presenter affiliation: BIOPIC, Beijing, China.
66
Single-cell miRNA sequencing of the human hematopoietic hierarchy Michael VanInsberghe, David J. Knapp, Michelle Moksa, Hans Zahn, Martin Hirst, Connie J. Eaves, Carl L. Hansen [10’+5’]
Presenter affiliation: University of British Columbia, Vancouver, Canada.
67
Microfluidic single cell whole genome and whole transcriptome amplification and sequencing Yanyi Huang [20’+10’]
Presenter affiliation: Peking University, Beijing, China.
68
Noninvasive chromosome screening of human embryos by genome sequencing of embryo culture medium for in vitro fertilization Sijia Lu [10’+5’] Presenter affiliation: Yikon Genomics, Shanghai, China.
Free Open Bar Sponsored by BioBAY
xxi
THURSDAY, November 10—9:00 AM SESSION 7 CANCER BIOLOGY Chairperson: Nicholas Navin, University of Texas MD Anderson Cancer Center, Houston, Texas, USA Tumor evolution at single cell genomic resolutionNicholas Navin [20’+10’]
Presenter affiliation: MD Anderson Cancer Center, Houston, Texas.
69
Enriched EMT and related pathways for castration resistant prostate cancer detection and therapy revealed by single-cell qRT-PCR, RNA-seq and biophysical assessment Chun-Liang Chen [10’+5’]
Presenter affiliation: University of Texas Health Science Center at San Antonio, San Antonio, Texas.
70
Tracing back the past and predicting the future of childhood leukemias with single-cell genomics Veronica Gonzalez, Robert Carter, Sivaraman Natarajan, Yuki Inaba, John Easton, Charles Gawad [20’+10’]
Presenter affiliation: St. Jude Children's Hospital, Memphis, Tennessee.
71
Coffee Break
Immune profiling of breast cancer by single cell genome analysisWoong-Yang Park, Hae-Ock Lee [20’+10’]
Presenter affiliation: Samsung Medical Center, Seoul, South Korea.
72
Single-cell transcriptomics of oncogene-induced senescenceYee Voan Teo, Kristina Kirschner, Anthony Green, Nicola Neretti, Tamir Chandra [10’+5’]
Presenter affiliation: Brown University, Providence, Rhode Island.
73
Single cell genome sequencing of circulating tumor cellsFan Bai [10’+5’] Presenter affiliation: School of Life Science, Peking University, Beijing, China.
xxii
Effective detection of variations in single cell transcriptomeKuanwei Sheng, Wenjian Cao, Qing Deng, Chenghang Zong [20’+10’]
Presenter affiliation: Baylor College of Medicine, Houston, Texas.
74
THURSDAY, November 10—2:00 PM SESSION 8 DEVELOPMENT / NEUROBIOLOGY / IMMUNOLOGY II Chairperson: Paul Robson, The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA Single cell RNA sequencing reveals T cell patterns in hepatocellular carcinoma Chunhong Zheng, Xinyi Guo, Liangtao Zheng, Zemin Zhang [20’+10’]
Presenter affiliation: Peking University, Beijing, China; Peking University, Beijing, China.
75
Immune repertoire profiling by high-throughput sequencing and single cell analysis Jenny Jiang [20’+10’]
Presenter affiliation: The University of Texas at Austin, Austin, Texas.
76
Coffee Break
Tracing haematopoietic stem cell formation at single-cell resolution Fan Zhou, Xianlong Li, Weili Wang, Ping Zhu, Jie Zhou, Wenyan He, Meng Ding, Fuyin Xiong, Xiaona Zheng, Zhuan Li, Yanli Ni, Xiaohuan Mu, Lu Wen, Tao Cheng, Yu Lan, Weiping Yuan, Fuchou Tang, Bing Liu [20’+10’]
Presenter affiliation: Academy of Military Medical Sciences, Beijing, China; Chinese Academy of Medical Sciences, Tianjin, China.
77
Interrogating stem cell heterogeneity by single cell transcriptomics Ana Martin-Villalba [20’+10’]
Presenter affiliation: German Cancer Research Center (DKFZ), Heidelberg, Germany.
78
Single-cell trajectory analysis with reverse graph embeddingXiaojie Qiu, Hannah Pliner, Qi Mao, Cole Trapnell [20’+10’]
Presenter affiliation: University of Washington, Seattle, Washington.
79
xxiii
Single-cell analyses of X Chromosome inactivation dynamics and pluripotency during differentiation Qiaolin Deng [10’+5’]
Presenter affiliation: Karolinska Institutet, Stockholm, Sweden.
80
THURSDAY, November 10—6:00 PM
COCKTAILS and BANQUET
FRIDAY, November 11—9:00 AM SESSION 9 TECHNOLOGY II Chairperson: Angela Wu, Hong Kong University of Science and Technology, Hong Kong, China Sequencing is believing—Probing mutagenesis one cell at a timeCheng-Zhong Zhang, Neil T. Umbreit, Alexander Spektor, Yingying Zhang, Matthew Meyerson, David Pellman [20’+10’]
Presenter affiliation: Dana-Farber Cancer Institute, Boston, Massachusetts; Broad Institute of Harvard and MIT, Cambridge, Massachusetts.
81
Scalable whole-genome single-cell library preparation without pre-amplification Hans Zahn, Adi Steif, Emma Laks, Peter Eirew, Michael VanInsberghe, Sohrab P. Shah, Samuel Aparicio, Carl L. Hansen [10’+5’]
Presenter affiliation: ; BC Cancer Agency, Vancouver, Canada.
82
Mapping human hematopoietic hierarchy at single cell resolution by Microwell-seq Shujing Lai, Yang Xu, Guoji Guo [10’+5’]
Presenter affiliation: Zhejiang University, Hangzhou, China.
83
Coffee Break
xxiv
Diverse evolutionary dynamics in tumor and early neurodevelopment inferred by single cell sequencing Guibo Li, Kui Wu, Shiping Liu, Zhouchun Shang, Liang Wu, Luting Song, Bo Li, Yong Hou, Xun Xu [20’+10’]
Presenter affiliation: BGI-Research, Shenzhen, China.
84
Investigation of epithelial tissue heterogeneity using microfluidic single-cell transcriptomics Angela Wu, Norma F. Neff, Barbara Treutlein, Winston Koh, Michael E. Rothenberg, Yair J. Blumenfeld, Yasser Y. El-Sayed, Michael F. Clarke, Stephen R. Quake [20’+10’]
Presenter affiliation: The Hong Kong University of Science and Technology, Hong Kong, China.
85
Conventional and ‘virtual’ microfluidics for low-input and single-cell genomics Liyi Xu, Soohong Kim, Dwayne Vickers, Navpreet Ranu, Paul Blainey [20’+10’]
Presenter affiliation: The Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Massachusetts Institute of Technology, Cambridge, Massachusetts.
86
xxv
AUTHOR INDEX Afzal, Veena, 64 Aksentijevich, Ivona, 39 Alasmari, Sultan, 63 Aparicio, Samuel, 82 Arnaud, Ophelie, 7 Backenroth, Daniel, 42 Bae, Sangwook, 30 Bai, Hua, 35 Bao, Li, 8 Bengsch, Bertram, 11 Ben-Jacob, Eshel, 60 Bian, Shuhui, 9 Bin, Huang, 60 Blainey, Paul, 86 Blumenfeld, Yair J., 85 Bolduc, Nathalie, 18 Bowden, Rory, 50 Brockman, Joel, 38 Bush, Erin, 42 Cai, Long, 55 Camp, Gray, 62 Cao, Chen, 25 Cao, Wenjian, 44, 74 Carter, Robert, 71 Casasent, Anna, 14 Chandra, Tamir, 73 Charn, Tze-Howe, 38 Chen, Chongyi, 1 Chen, Chun-Liang, 70 Chen, He, 17 Chen, Wei-Ting, 10 Chen, Weizheng, 36 Chen, Yang, 36 Chen, Zeyu, 11 Cheng, Tao, 56, 77 Cheng, Xuewen, 12 Ching, Travers, 20 Chu, Li-Fang, 3 Clarke, Michael F., 85 Cordes, Shaun, 38 Craessaerts, Katleen, 10 Cui, Guizhong, 4 Cui, Xiao-Fang, 45
Cui, Yueli, 13 Dakshinamoorthy, Gajalakshmi, 22 Davis, Alexander, 14 De Strooper, Bart, 10 Dean, Michael, 52 Deng, Qiaolin, 80 Deng, Qing, 44, 74 Dickel, Diane, 64 Ding, Meng, 56, 77 Do, Devin, 38 Donati, Giacomo, 21 Dong, Ji, 15, 19, 33 Dong, Jing, 28 Donnelly, Peter, 50 Du, Guizhen, 27 Duan, Shunlei, 40 Dunne, Jude, 16 Easton, John, 23, 71 Eaves, Connie J., 67 Eirew, Peter, 82 El-Sayed, Yasser Y., 85 Fan, Guoping, 27 Fan, Xiaoying, 28 Fang, Rongxin, 64 Farmer, Andrew, 16, 18 Fekete, Richard, 38 Fiers, Mark, 10 Fu, Wei, 9, 31 Fu, Yusi, 17 Fukamizu, Akiyoshi, 46 Gandlur, Suvarna, 16, 18 Gao, Dadi, 63 Gao, Hongjun, 8 Gao, Ruli, 14 Gao, Yun, 19 Garmire, Lana, 20 Gawad, Charles, 23, 71 Ge, Hao, 47 Ghahramani, Arsham, 21 Giroux, Michel, 37
xxvi
Gong, Haibiao, 22 Gonzalez, Veronica, 71 Gonzalez-Pena, Veronica, 23 Gorkin, David, 64 Greco, Valentina, 43 Green, Anthony, 73 Guo, Fan, 32, 24 Guo, Guoji, 83 Guo, Hongshan, 26, 34 Guo, Huahu, 25, 49 Guo, Xinyi, 75 Guzman, Elmer, 37 Han, Jingdong J., 4 Han, Kyung Yeon, 24 Han, Maozhen, 53 Han, Wonshik, 30 Han, Ze-Guang, 5, 45 Hansen, Carl L., 67, 82 Hao, Jie, 5 Hatton, Edouard, 50 He, Kun-Yan, 45 He, Wenyan, 54, 56, 77 Herati, Ramin S., 11 Hinch, Anjali, 50 Hirst, Martin, 67 Höfer, Thomas, 48 Holcomb, Ilona, 22 Hou, Siyuan, 19 Hou, Yong, 52, 84 Hou, Yu, 9, 25, 35 Hu, Boqiang, 25, 26 Hu, Ganlu, 27 Hu, Jinwei, 29 Hu, Youjin, 27 Hu, Yuqiong, 28 Huang, Hui, 64 Huang, Kevin, 27 Huang, Yanyi, 17, 25, 49, 51, 68 Inaba, Yuki, 71 Ji, Hongkai, 11 Ji, Zhicheng, 11 Jia, Fangzhi, 63 Jiang, Jenny, 76 Jing, Naihe, 4 Jung, Yushin, 30
Kaljas, Julia, 39 Kang, Hui, 29 Kato, Sachi, 7 Keightley, Maria-Cristina, 63 Kharchenko, Peter V., 6 Kim, Jinhyun, 30 Kim, Kyu-Tae, 24 Kim, Soohong, 86 Kim, Sungsik, 30 Kirschner, Kristina, 73 Knapp, David J., 67 Koh, Winston, 85 Kopetz, Scott, 14 Kosik, Kenneth S., 37 Kuan, Samantha, 64 Kwon, Sunghoon, 30 Lacar, Benjamin, 38 Lai, Shujing, 83 Laks, Emma, 82 Lan, Yu, 19, 56, 77 Lee, Amos C., 30 Lee, Hae-Ock, 24, 72 Lee, Han-Byoel, 30 Lee, Jaeyop, 42 Lee, Simon, 18 Lee, Yongju, 30 Leng, Ning, 3 Leung, Marco L., 14 Levine, Herbert, 60 Li, Bo, 84 Li, Chao, 13 Li, Chunmei, 51 Li, Guibo, 84 Li, Hang, 51 Li, Heng, 1 Li, Jingyi, 31, 40, 49 Li, Jingyun, 32 Li, Li, 15, 33 Li, Lin, 13, 32, 34, 35 Li, Qingqing, 35 Li, Xiangyu, 36 Li, Xianlong, 9, 25, 54, 56, 77 Li, Zhuan, 56, 77 Lieschke, Graham J., 63 Lisi, Veronique, 37 Liu, Benjamin, 22, 38 Liu, Bing, 19, 54, 56, 77
xxvii
Liu, Binlei, 8 Liu, Chengqian, 39 Liu, Guang-Hui, 40 Liu, Kang, 42 Liu, Lu, 17 Liu, Shiping, 52, 84 Liu, Xiaomeng, 31, 40, 49 Liu, Xiaotian, 41 Lohith, Akshar, 46 Lu, Mingyang, 60 Lu, Yuanan, 39 Lu, Zhao-Ning, 45 Lubeck, Eric, 55 Luscombe, Nicholas M., 21 Ma, Wenji, 42 Mao, Qi, 79 Marioni, John C., 2 Martin-Villalba, Ana, 78 Maru, Dipen, 14 Mendez, Mickael, 7 Meyerson, Matthew, 81 Miao, Zhun, 58 Moksa, Michelle, 67 Mu, Shuo, 29 Mu, Xiaohuan, 56, 77 Myung, Peggy, 43 Nagasaka, Kazunori, 7 Natarajan, Sivaraman, 71 Navin, Nicholas, 14, 69 Neff, Norma F., 85 Neretti, Nicola, 73 Nguyen, Trung, 63 Ni, Yanli, 56, 77 Onuchic, Jose, 60 Pang, Yuhong, 51 Park, Donghyun, 24 Park, Woong-Yang, 24, 72 Pellman, David, 81 Peng, Guangdun, 4 Peng, Jirun, 25, 49 Peng, Yang, 51 Pennachio, Len A., 64 Phan, Hoan, 38 Phelan, Michael, 38
Pinello, Natalia, 63 Plessy, Charles G., 7 Pliner, Hannah, 79 Poirion, Olivier, 20 Poulain, Stephane G., 7 Pourmand, Nader, 46 Preissl, Sebastian, 64 Qian, Zhaoyang, 8 Qiao, Jie, 15, 26, 33, 34 Qin, Gongwei, 29 Qiu, Xiaojie, 79 Qu, Jing, 40 Quake, Stephen R., 85 Ramakrishnan, Ramesh, 22 Ranu, Navpreet, 86 Rasko, John E J., 63 Ray, Manisha, 38 Reik, Wolf, 61 Ren, Bing, 64 Ren, Jie, 31 Ritchie, William, 63 Rothenberg, Michael E., 85 Schaal, Thomas, 16 Schmitz, Ulf, 63 Sei, Emi, 14 Shah, Sheel, 55 Shah, Sohrab P., 82 Shang, Zhouchun, 84 Shen, Ya, 12 Shen, Yufeng, 42 Sheng, Kuanwei, 44, 74 Shi, Yi, 45 Shini, Shaniko, 63 Sims, Peter, 42 Singh, Bhagat, 37 Skaldin, Maksym, 39 Song, Luting, 84 Sos, Brandon, 64 Spektor, Alexander, 81 Srinivasan, Maithreyan, 16 Steif, Adi, 82 Stelekati, Erietta, 11 Stewart, Ron, 3 Su, Xianbin, 45 Sun, Fei, 41
xxviii
Sun, Na, 4 Sun, Thomas, 43 Suo, Shengbao, 4 Tam, Patrick P., 4 Tan, Longzhi, 1 Tang, Fuchou, 9, 13, 15, 19, 25, 26, 28, 31, 32, 33, 34, 35, 40, 47, 49, 54, 56, 66, 77 Taylor, Sarah, 16 Teo, Yee Voan, 73 Thomoson, James A., 3 Toth, Eszter, 46 Trapnell, Cole, 79 Treutlein, Barbara, 62, 85 Umbreit, Neil T., 81 Unger, Marc, 22, 38 van Oudenaarden, Alexander, 57 VanInsberghe, Michael, 67, 82 Vella, Laura A., 11 Vickers, Dwayne, 86 Visel, Axel, 64 Wang, David, 38 Wang, Jianbin, 65 Wang, Jie, 35 Wang, Jilian, 31 Wang, Jing, 38 Wang, Kai, 29 Wang, Lan, 45 Wang, Larry, 38 Wang, Rui, 47 Wang, Weili, 54, 56, 77 Wang, Xi, 48 Wang, Yong, 14 Watt, Fiona M., 21 Wen, Lu, 9, 13, 15, 25, 31, 35, 49, 56, 77 Wherry, E. J., 11 Wong, Justin J L., 63 Woolf, Clifford, 37 Wu, Angela R., 85 Wu, Chengxiang, 39 Wu, Chong-Chao, 45 Wu, Kui, 84 Wu, Liang, 52, 84
Wu, Xinglong, 25, 28 Xie, Xiaoliang Sunney, 1 Xing, Dong, 1 Xiong, Fuyin, 56, 77 Xiong, Zhi-Qi, 12 Xu, Liyi, 86 Xu, Xun, 84 Xu, Yang, 83 Xue, Zhigang, 27 Yan, Liying, 15, 26, 31, 33, 34, 47 Yang, Lu, 47 Yao, Jicheng, 29 Yao, Ming, 29 Yao, Yong, 59 Yasuyama, Nao, 16 Yong, Jun, 9 Yuan, Guohong, 40 Yuan, Weiping, 54,56, 77 Zahn, Hans, 67, 82 Zavialov, Andrey V., 39 Zhang, Cheng-Zhong, 81 Zhang, Gang, 50 Zhang, Kaitai, 8 Zhang, Kun, 64 Zhang, Michael Q., 36 Zhang, Wen, 8 Zhang, Xiannian, 51 Zhang, Xuegong, 36, 58 Zhang, Yingying, 81 Zhang, Zemin, 75 Zhao, Yuan, 64 Zhao, Zhikun, 52 Zheng, Chunhong, 75 Zheng, Liangtao, 75 Zheng, Xiaona, 56, 77 Zhong, Chaofang, 53 Zhou, Dabiao, 13 Zhou, Fan, 54, 56, 77 Zhou, Ji, 54, 56, 77 Zhou, Qing, 39 Zhou, Wen, 55 Zhou, Xin, 9, 31 Zhou, Yu, 42 Zhu, Ping, 25, 54, 56, 77
xxix
Zhu, Xianmin, 27 Zhu, Xun, 20 Zong, Chenghang, 44, 74 Zou, Xin, 5, 45
1
SINGLE-CELL WHOLE GENOME ANALYSES BY LINEAR AMPLIFICATION VIA TRANSPOSON INSERTION (LIANTI) Xiaoliang Sunney Xie1, Chongyi Chen1, Dong Xing1, Longzhi Tan1, Heng Li2 1Harvard University, Department of Chemistry and Chemical Biology , Cambridge, MA, 2Broad Institute, Bioinformatics, Cambridge, MA Single-cell genomics becomes increasingly important for both fundamental research and clinical practice. However, current whole genome amplification (WGA) methods do not accurately reflect unamplified single cell samples. Here we report a much improved single-cell WGA method, Linear Amplification via Transposon Insertion (LIANTI). By virtue of linear amplification, LIANTI supersedes existing methods in all key parameters, offering superb genome coverage, superior amplification uniformity, improved accuracy for single nucleotide variation (SNV) detection, reduced chimera rates for detection of structure variation, and especially higher spatial resolution for copy number variation (CNV) measurements, which enables detection of micro CNVs or deletion in a single cell. We prove that the predominant cytosine-to-thymine SNV false positives in single cells are caused by spontaneous cytosine deamination after cell lysing. Filtering out the false positive SNVs by sequencing at least kindred cells, we were able to determine the true point mutation spectrum of a single human cell after ultraviolet radiation, revealing new insights on the genome wide distributions of the point mutations.
2
USING SINGLE-CELL GENOMICS TO UNDERSTAND CELL FATE DECISIONS John C Marioni EMBL, EBI, Cambridge, United Kingdom With recent technological developments it has become possible to characterize a single cell’s genome, epigenome, transcriptome and proteome. However, to take advantage of such data it is critical that appropriate computational methods are applied and developed. In this presentation, I will describe some of the computational challenges and the solutions we have developed, focussing particularly on problems in single-cell transcriptomics. I will illustrate the utility of the methods using a variety of case studies, focusing particularly on understanding cell fate decisions during early mammalian development, in particular gastrulation, and in immunity.
3
SINGLE-CELL RNA-SEQ REVEALS NOVEL REGULATORS OF HUMAN EMBRYONIC STEM CELL DIFFERENTIATION TO DEFINITIVE ENDODERM Li-Fang Chu, Ning Leng, Ron Stewart, James A Thomoson Morgridge Institute for Research, Regenerative Biology, Madison, WI Human pluripotent stem cells offer the best available model to study the underlying cellular and molecular mechanisms of human embryonic lineage specification. However, it is not fully understood how individual stem cells exit the pluripotent state and transition towards their respective progenitor states. Herein, we analyzed the transcriptomes of human embryonic stem (ES) cell-derived lineage-specific progenitors by single-cell RNA-seq (scRNA-seq). Remarkably, a unique transcriptomic signature identified definitive endoderm (DE) cells, which led us to define a critical time window of their differentiation regulated by oxygen tension. The molecular mechanisms governing the emergence of DE cells are further dissected by time course scRNA-seq experiments. We developed two new statistical tools to identify stage-specific genes over time (SCPattern) and to reconstruct the differentiation trajectory from the pluripotent state through mesendoderm to DE (Wave-Crest). Importantly, presumptive DE cells can be detected during the transitory phase from Brachyury (T)+ mesendoderm toward a CXCR4+ DE state. Novel regulators were identified within this time window and functionally validated on a screening platform with a T-2A-EGFP knock-in reporter engineered by CRISPR/Cas9. Through loss- and gain-of-function experiments, we demonstrate that KLF8 plays a pivotal role modulating mesendoderm to DE differentiation. Altogether, we report the analysis of 1776 cells by scRNA-seq covering distinct human ES-derived progenitor states. We believe our strategy of combining single-cell analysis and genetic approaches can be applied to uncover novel regulators governing cell fate decisions in a variety of systems.
4
LINEAGE SEGREGATION OF POST-IMPLANTION MOUSE EMBRYO REVEALED BY SPATIAL AND SINGLE CELL TRANSCRIPTOME Guangdun Peng1, Shengbao Suo2, Guizhong Cui1, Na Sun2, Patrick P Tam3, Jingdong J Han2, Naihe Jing1
1Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China, 2Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, CAS-MPG Partner Institute for Computational Biology, Shanghai, China, 3University of Sydney, Children's Medical Research Institute and School of Medical Sciences, Sydney, Australia The paradigm and blueprint of lineage segregation of early mouse embryo is established during gastrulation in which progenitors of various cell fates are regionlizated and patterned in different embryo positions. The dynamic transcriptome landscape underpinning the cell fates determination is unknown. Here, we performed a spatial transcriptome study encompassing the whole gastrulation process. Combined with single-cell gene profiling at different time-points, we are able to reveal the lineage differentiating trajectories and molecular determinants that drive the pluriopotent epiblast cell commitment. Our study also established the spatial zip codes which can be used as a reference positioning system for stem cells differentiation and reprogramming.
5
HYSIC: A NOISE ROBUST CELL CATEGORIZATION ALGORITHM FOR SINGLE-CELL TRANSCRIPTOMICS Xin Zou, Jie Hao, Ze-Guang Han Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai, China Background: Transcriptomes derived from single-cell RNA-seq technique usually contain biological variations which can characterize cell subpopulations. However, the existing analysis methods may generate sub-optimal results as the biological variations are always mixed with random noises. Here, we proposed a novel algorithm, HYSIC, to reliably discriminate the biological variations from the random noises by modeling multiple characteristics of the variable transcripts. Method: In HYSIC, i) The noise corrupted transcripts are automatically identified and excluded by evaluating the variation of each transcript on the estimated noise variances, which are achieved by applying novel identification criteria for noisy transcripts constructed on the generalized linear model. ii) Then, a novel hybrid-clustering scheme is proposed to identify variable transcripts by evaluating the contribution of each transcript to cell categorization. iii) The hybrid-clustering is performed iteratively with the number of cell subpopulations being increased by 1 after each iteration, until low separability is observed between any two cell subpopulations. The separability is evaluated by making use of the bimodality property of the identified variable transcripts. The outputs of the HYSIC algorithm are: a list of variable transcripts, the estimated number of cell subpopulations and the cell categorization information. We have exemplified the HYSIC algorithm on a published single-cell RNA-seq dataset of mouse lung development. Results: HYSIC has revealed the cell subpopulation characteristics of the dataset; for comparison, the conventional methods, e.g., PCA, failed to show such information. The number of cell subpopulations was also estimated with high reliability, which was superior over the widely used gap statistic method. Conclusion: HYSIC iteratively ‘learns’ the characteristics best representing the cell subpopulations by modeling various properties of the variable transcripts. This enables more reliable and feasible single-cell datasets interpretations.
6
CONTRASTING TRANSCRIPTIONAL AND GENETIC HETEROGENEITY WITHIN TUMORS Peter V Kharchenko Harvard Medical School, Biomedical Informatics, Boston, MA Understanding intratumoral heterogeneity is critical to cancer therapy, as presence of phenotypically diverse cell populations commonly fuels drug resistance and relapse. Extensive studies in cancer genomics have demonstrated presence of genetic heterogeneity, that over time commonly manifests itself in expansion of particular subclonal populations that possess mutations conferring proliferative advantage, such as disruption of tumor suppressor genes. However the functional impact of most genetic alterations separating the subclonal populations remains unclear. Even less understood is the relative importance of other factors influencing heterogeneity, such as epigenetic state or tumor microenvironment. Arguing that all these and other potential heterogeneity mechanisms will likely affect the overall transcriptional state of the cell, we aimed to investigate the relationship between transcriptional and genetic heterogeneity in human tumors. To do so, we developed a Bayesian computational approach called BADGER to infer copy number variation and loss-of-heterozygosity in individual cells from single-cell RNA-sequencing data based on the combined analysis of allele frequencies and gene expression magnitudes. Our approach allowed to identify major genetic subclones, and characterize their transcriptional signatures, which we found to be relevant to known cancer progression pathways. We also fond that other prominent transcriptional subpopulations within these tumors did not line up with the genetic subclonal structure, and were likely driven by alternative, non-clonal mechanisms. These observations highlight the need for integrative analysis to understand the molecular and phenotypic heterogeneity in human cancers.
7
TRANSCRIPTOMIC STUDY OF HPV INFECTED CELLS USING THE NEW HIGH-THROUPUT SINGLE CELL NANOCAGE PROTOCOL Ophelie Arnaud1, Stephane G Poulain1, Sachi Kato1, Kazunori Nagasaka2, Mickael Mendez1, Charles G Plessy1 1Riken Institute, Center For Life Science and Technologies , Division of Genomic Technology, Yokohama, Japan, 2The University of Tokyo , Dep. of Obstetrics and Gynecology, Graduate School of Medicine , Tokyo, Japan The Human Papilloma Viruses (HPV) are responsible for almost all cervical cancers (WHO). There are more than 100 types of HPV. Among them, the HPV16 and the HPV18 can integrate the host genome and they are responsible for 70% of the cervical cancers. Here, using cells lines infected either by the HPV16 (CaSkI, SiHa and W12E cells) or HPV18 (HeLa and HeLa S3), we have studied the impact of the virus infection on single cell transcriptomes. The W12E cells are particularly interesting as the virus is an episomal form and can integrate during the cultured time. Thus, the cells are highly heterogeneous and it was indispensable to study each cell individually. Several protocols are designed to study the single cells transcriptome. However they are either limited in their coverage of the transcriptome because they use oligo-dT primers (STRT protocol (Islam et al., 2012, Nature Protocols) or Drop-seq (Macosko et al., 2015 Cell)), or they require a Fluidigm platform (Kouno et al., 2015, ScriptHub Fluidigm) increasing the cost of an experiment and limiting the number of single cells. Therefore, based on the nanoCAGE method (Plessy et al., 2010, Nature Methods), we have developed a new protocol allowing the detection of the poly(A) and non-poly(A) RNA at the single cells level. We have first used a flow cytometer to isolate the single cells in a 384-wells plate and then the Labcyte Echo 555 to dispense a reduce volume of the reverse-transcription (RT) mix. In the RT step, a barcode (96 barcodes available) and a Unique Molecular Identifier (UMI, allowing a direct transcript count in each single cell) are added to the cDNA. The tagged cDNA from 96 single cells are then pooled together. An index is then added during the tagmentation step (Nextera kit, Illumina); 24 indexes are available meaning that 2,300 single cells can be multiplexed together in one sequencing run. The libraries are sequenced paired-end, which we use for single-molecule transcript assembly using the UMIs (See the abstract of Plessy et al.). In a highly multiplexed experiment, we have studied more than 8,000 single cells obtaining around 50,000 read pairs per cell, which is in the range of best practices for shallow sequencing (between 10,000 to 50,000 reads, Pollen et al., 2014 Nat. Biotechnology). Our preliminary results suggest that this depth is sufficient for efficient clustering and population analysis. Therefore, our single cell nanoCAGE method opens new possibilities in the field of single cell “population transcriptomics” in a very high throughput way, including a direct transcript count in each single cell at a reduced cost.
8
TUMOR-SELECTIVE REPLICATION HERPES SIMPLEX VIRUS-BASED TECHNOLOGY SIGNIFICANTLY IMPROVES CLINICAL DETECTION AND PROGNOSTICATION OF VIABLE CIRCULATING TUMOR CELLS Wen Zhang1, Li Bao3, Zhaoyang Qian4, Hongjun Gao5, Kaitai Zhang2, Binlei Liu1 1Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Department of Immunology, Beijing, China, 2Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, State Key Laboratory of Molecular Oncology, Beijing, China, 3University of Copenhagen, Molecular Disease Biology, Copenhagen, Denmark, 4BGI-Shenzhen, RD, Shenzhen, China, 5Affiliated Hospital, Academy of Military Medical Science, Department of Pulmonary Oncology, Beijing, China Detection of circulating tumor cells remains a significant challenge due to their vast physical and biological heterogeneity. We developed a cell-surface-marker-independent technology based on telomerase-specific, replication-selective oncolytic herpes-simplex-virus-1 that targets telomerase-reverse-transcriptase-positive cancer cells and expresses green-fluorescent-protein that identifies viable CTCs from a broad spectrum of malignancies. Our method recovered 75.5–87.2% of tumor cells spiked into healthy donor blood, as validated by different methods, including single cell sequencing. CTCs were detected in 59–100% of 326 blood samples from patients with 6 different solid organ carcinomas and lymphomas. Significantly, CTC-positive rates increased remarkably with tumor progression from N0M0, N+M0 to M1 in each of 5 tested cancers (lung, colon, liver, gastric and pancreatic cancer, and glioma). Among 21 non-small cell lung cancer cases in which CTC values were consecutively monitored, 81% showed treatment-related decreases, which was also found after treatments in the other solid tumors. Moreover, monitoring CTC values provided an efficient treatment response indicator in hematological malignancies. Compared to CellSearch, our method detected significantly higher positive rates in 40 NSCLC in all stages, including N0M0, N+M0 and M1, and was less affected by chemotherapy. This simple, robust and clinically-applicable technology detects viable CTCs from solid and hematopoietic malignancies in early to late stages, and significantly improves clinical detection and treatment prognostication.
9
SINGLE-CELL COMPREHENSIVE OMICS ANALYSIS HIGHLIGHT HETEROGENEITY OF COLORECTAL CANCER Shuhui Bian1, Yu Hou1, Jun Yong1, Xin Zhou2, Xianlong Li1, Lu Wen1, Fuchou Tang1, Wei Fu2 1Third Hospital, Peking University, Biodynamic Optical Imaging Center, College of Life Sciences and Center for Reproductive Medicine, Beijing, China, 2Peking University Third Hospital, Department of General Surgery, Beijing, China Colorectal cancer is one of the most lethal tumor type with inherently variable features of genome instability, DNA methylation abnormity and gene expression dysregulation. Such tumor heterogeneity in genome, epigenome and transcriptome can lead to drug resistance and disease recurrence, which has posed big challenges to clinical treatment. Traditional cancer research has provided integrative molecular characterization at bulk level. However, intratumoral heterogeneity and mechanisms underlying tumor initiation and progression still remain obscure. Here, we did comprehensive analyses of genetic, epigenetic, and transcriptomic heterogeneity at single cell level, providing novel insights into the dynamics of tumor progression and may facilitate personalized therapy.
10
SINGLE CELL AND SPATIAL TRANSCRIPTOMICS REVEAL CELLULAR VULNERABILITY IN THE EARLY STAGE OF ALZHEIMER' DISEASE. Wei-Ting Chen, Katleen Craessaerts, Mark Fiers, Bart De Strooper VIB-Leuven, VIB Center for the Biology of Disease, Leuven, Belgium Several pathogenic mechanisms, such as inflammation, gliosis and neuronal loss, have been previously identified in the late stage of Alzheimer’s disease (AD), but the early cellular responses towards initial amyloid stress remain largely unknown. We want to chart the molecular responses at the transcriptome level in cells exposed to these stresses in brain at the early stage of the disease. Our hypothesis is that these initial subtle changes can be discovered by increasing resolution from “bulk tissue” into “single cell with spatial information” analysis. We apply “single cell RNAseq” and “in situ RNAseq” in the hippocampus of single human APPNL-G-F knock-in mice at 3 months of age. This AD mice model has normal expression of APP driven by an endogeneous promoter and develops progressively amyloid plaques. Our preliminary results indicate demyelination, imbalance of proteostasis, mitochondria dysfunction, and increased phosphorylation and synaptic transmission as characteristics of the initial amyloid stress. We ask questions with regard to cellular vulnerability, cellular compensation mechanisms and initial cellular alterations in early AD. We expect that this work will provide the basis for new insights and new drug targets in AD.
11
SINGLE CELL GENE EXPRESSION PROFILING IDENTIFIES POPULATION DYNAMICS DURING T CELL EXHAUSTION Zeyu Chen1, Zhicheng Ji2, Laura A Vella1,3, Ramin S Herati1, Bertram Bengsch1, Erietta Stelekati1, Hongkai Ji2, E. J Wherry1 1University of Pennsylvania Perelman School of Medicine, Institute of Immunology,Department of Microbiology , Philadelphia, PA, 2Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics, Baltimore, MD, 3The Children's Hospital of Philadelphia, Division of Infectious Diseases, Philadelphia, PA CD8+ T cells play a key role in antiviral and antitumor immunity. However, during chronic infections and cancer, CD8+ T cells become ‘exhausted’, lose function and fail to form functional memory cells. Heterogeneity in exhausted T cell populations (TEX) has been defined based on expression of the inhibitory receptor PD-1, the transcription factors T-bet and Eomes and more recently the chemokine receptor CXCR5. Since none of these subsets fully explains the observed in vivo biology, a major question in the field is how to map cell fates during development of exhaustion. Answering this question should identify subpopulations of cells to be targeted for re-invigoration in the treatment of cancer and chronic infections. In this study, we employed single cell transcriptional profiling coupled to cellular analysis to reconstruct the population dynamics during T cell exhaustion. We used acute and chronic Lymphocytic Choriomeningitis virus (LCMV) infections to trigger either memory (TM) or exhausted T cell (TEX) generation. To examine population heterogeneity, we sorted antigen specific CD8+ T cells and performed single cell multiplex qPCR to interrogate transcriptional profiles. These data identified three major cell states early in chronic infection with features similar to an activated effector, a quiescent naïve-like, and a unique “early exhausted” population (TeEx). The TeEx population displayed key exhaustion features, but also a signature of highly activated cells. Using pseudo-Time reconstruction in Single-Cell RNA-seq Analysis (TSCAN) to digitally map cell fate, the TeEx was not predicted to generate the more mature TEx population observed later in chronic infection. These data suggested that TeEx cells were terminal and that later TEx might arise from one of the two the less activated effector subsets. Using surface markers, we identified TeEx as KLRG1+CD39+CD200R+ and the less activated subsets as KLRG1+CD39-CD200R- and KLRG1-PD-1+. Lineage-tracing approaches revealed that the KLRG1+CD39+CD200R+ subset had poor capacity for long-term persistence and could not give rise to the other subsets of TEX. Further TSCAN analysis showed that reinvigorating TEx with antiPD-L1 treatment provoked an acquisition of effector-like features but not the TeEx state. In summary, these data identify a terminal and highly activated subset of developing TEX found only in the acute phase of chronic viral infection. Moreover, these data suggest that the stable TEX population found later in chronic infections arises from a less activated precursor found early and that re-invigoration of TEX fails to generate the same population diversity as found early in infection.
12
NRSF REGULATES HIPPOCAMPAL CA3-CA1 LTP VIA TRANSCRIPTIONAL REPRESSION OF TISSUE PLASMINOGEN ACTIVATOR Xuewen Cheng, Ya Shen, Zhi-Qi Xiong Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Institute of Neuroscience, State Key Laboratory of Neuroscience, Shanghai, China Learning and memory are known to involve significant changes in chromatin structure, but the precise molecular mechanisms that underlie these changes remain largely unclear. Here we report that NRSF inhibits long-term potentiation (LTP) in the hippocampal Schaffer-collateral pathway by repressing the expression of tissue plasminogen activator (tPA). Field recordings of hippocampal CA1 radiatum area revealed a strikingly enhanced in LTP in our nrsf-cKO mice. Furthermore, neuronal activation led to a marked elevation in tPA expression, which led to the increased rates of BDNF maturation in the cKO mice. Moreover, tPA was found to be transcriptionally repressed through the direct binding of NRSF to RE1 sequences within the tPA promoter. Inhibition of tPA protease activity prevented the enhancement in hippocampal LTP in nrsf-cKO mice. Thus, our results demonstrate that NRSF acts in the postnatal brain to transcriptionally repress tPA, thereby tempering the level of BDNF induction and reducing the amplitude and duration of hippocampal LTP.
13
SINGLE CELL TRANSCRIPTOME ANALYSIS OF PITUITARY ADENOMAS Yueli Cui1, Lin Li1, Chao Li2, Lu Wen1, Fuchou Tang1, Dabiao Zhou2
1Biodynamic Optical Imaging Center, College of Life Sciences, Peking University, Beijing, China, 2Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China The pituitary adenoma is a relatively high prevalent among human brain tumors. Although these tumors are usually benign, but they can affect the endocrine system by hormone hypersecretion, and the tumors often invade the brain that can cause blindness and nerve palsies, and even cause death in some patients. The classification of pituitary tumors is not very clear in clinic, and the mechanisms of tumorigenesis and the origin of cells for pituitary adenoma subtypes remain to be elucidated. Previous studies demonstrate that the somatic mutation rate is low in growth hormone-secreting pituitary adenomas and nonfunctioning pituitary adenomas, so the transcriptome analysis are needed to investigate possible mechanisms underlying pituitary tumor pathogenesis. Hear, we use the single cell RNA-seq technology to study the intratumor-heterogeneity and intertumor-heterogeneity, trying to find novel markers for distinguishing pituitary adenoma subtypes.
14
SINGLE CELL DNA SEQUENCING REVEALS A LATE-DISSEMINATION MODEL IN METASTATIC COLORECTAL CANCER Marco L Leung1,2, Alexander Davis1,3, Ruli Gao1, Anna Casasent1,2, Yong Wang1, Emi Sei1, Dipen Maru4, Scott Kopetz5, Nicholas E Navin1,2,5
1The University of Texas MD Anderson Cancer Center, Genetics, Houston, TX, 2Graduate School of Biomedical Sciences, Genes and Development Program, Houston, TX, 3Graduate School of Biomedical Sciences, Biostatistics, Bioinformatics, and Systems Biology Program, Houston, TX, 4The University of Texas MD Anderson Cancer Center, Pathology, Houston, TX, 5The University of Texas MD Anderson Cancer Center, Gastroinestinal Medical Oncology, Houston, TX, 6The University of Texas MD Anderson Cancer Center, Bioinformatics and Computational Biology, Houston, TX Metastasis is a complex process and has been difficult to study in human patients. A major technical obstacle has been the extensive intratumor heterogeneity at the primary and metastatic tumor sites. To address this problem, we developed a highly-multiplexed single cell DNA sequencing approach to trace the metastatic lineages of two colorectal cancer (CRC) patients with matched liver metastases. Single cell copy number and mutational profiling was performed on 444 cells, in addition to bulk exome and deep-targeted sequencing. In the first patient we observed monoclonal seeding, in which a single clone had evolved a large number of mutations prior to migrating to the liver to establish the metastatic tumor. In the second patient we observed polyclonal seeding, in which two independent clones seeded the metastatic tumor after having diverged at different time points from the primary tumor lineage. The single cell data also revealed an unexpected independent tumor lineage that did not metastasize, and early progenitor clones with the first hit in APC that subsequently gave rise to both the primary and metastatic tumors. Collectively, these data reveal a late-dissemination model of metastasis in two CRC patients, and provide an unprecedented view of metastasis at single cell genomic resolution.
15
SINGLE CELL TRANSCRIPTOMES OF HUMAN GERMLINE CELLS AND THEIR NICHE CELLS Ji Dong1, Li Li1, Liying Yan2, Lu Wen1, Jie Qiao2, Fuchou Tang1 1Peking University, College of Life Sciences, Beijing, China, 2Peking University, Department of Obstetrics and Gynecology Third Hospital, Beijing, China Human primordial germ cells (PGCs) are precursors of sperm and oocytes and are crucial for the maintenance of species. The single-cell RNA-seq technique is a powerful tool and has the ability to uncover the mystery veil of human PGCs. Here we performed single cell RNA-seq analysis for human PGCs as well as their gonadal niche cells. We found that the female and male PGCs both have several sequential yet distinct developmental phases. More importantly, one individual embryo usually contains several of these subpopulations simultaneously, highlighting the innate and highly entangled heterogeneity of PGC development in vivo. Moreover, we found the reciprocal signaling interactions between PGCs and their gonadal niche cells. Our works gives new insights of the crucial features of human early germ cells during their highly ordered mitotic, meiotic, and gametogenesis processes in vivo.
16
A SMARTER APPROACH TO PROFILING THE HUMAN T-CELL RECEPTOR REPERTOIRE Suvarna Gandlur1, Sarah Taylor1, Thomas Schaal2, Nao Yasuyama1, Jude Dunne2, Maithreyan Srinivasan2, Andrew Farmer1 1Takara Bio USA, Inc., R&D, Mountain View, CA, 2WaferGen Bio-Systems, Inc., R&D, Fremont, CA Profiling T-cell receptor (TCR) repertoires involves characterizing the diversity of TCR nucleotide sequences in a sample, and is an increasingly popular approach for analyzing the composition of the adaptive immune system in the context of human development and disease. While low-throughput approaches have yielded important insights concerning TCR repertoire dynamics, the development of next-generation sequencing (NGS) technologies has dramatically expanded the prospects for this research area. Using SMART® technology, we have developed an NGS library preparation kit for TCR profiling that employs a 5’ RACE-like approach to capture full-length variable regions of TCR-α and/or TCR-β subunits. Our approach enables the user to obtain sequencing-ready TCR libraries from RNA in ~2.5 hours of hands-on time. Whereas approaches that utilize genomic DNA require multiplexed PCR strategies, amplification of TCR sequences derived from RNA can be accomplished using single primers for each subunit. We initially utilized this approach for bulk cell or RNA inputs – requiring a minimum of 10 ng of RNA or 50 cells. With this method, starting with RNA obtained from human peripheral blood, libraries containing TCR-α and TCR-β sequences were generated and analyzed on an Illumina MiSeq® using 300-bp paired-end reads. For each experimental replicate, >70% of sequencing reads mapped to TCR variable regions, and the most highly represented clonotypes remained consistent across a range of input amounts. More recently, we have modified the protocol to allow for amplification of the TCR from single cells. This approach has the benefit of allowing researchers to understand the heterodimeric complex better, by identifying the alpha and beta chains that make up the receptor simultaneously in individual cells. Preliminary analysis with Jurkat cells has identified an average of 92% of reads mapping to TCR sequences, and 90% of reads being used in clonotype identification. To accommodate high throughput single-cell TCR analysis, we have been working with the WaferGen ICELL8 system to generate the cDNA libraries. With this platform, we can generate a pooled library of ~1000 single cells at a time, which can be sequenced in a single MiSeq run. The combination of the Clontech library preparation workflow and the iCELL8 system allows us to provide a high throughput approach for the generation of full length VDJ sequences for single-cell TCR profiling.
17
SINGLE CELL TOTAL RNA SEQUENCING THROUGH ISOTHERMAL AMPLI-FICATION IN PICOLITER-DROPLET EMULSION Yusi Fu1, He Chen1, Lu Liu1,2, Yanyi Huang1,2,3 1Peking University, Biodynamic Optical Imaging Center (BIOPIC), School of Life Sciences, and Beijing Advanced Innovation Center for Ge-nomic, Beijing, China, 2Peking University, College of Engineering, Beijing, China, 3Peking University, Peking-Tsinghua Center for Life Sciences, Beijing, China Prevalent single cell RNA amplification and sequencing chemistries mainly focus on polyadenylated RNAs in eukaryotic cells by using oligo(dT) primers for reverse transcription. We develop a new RNA amplification method, “easier-seq”, to reverse transcribe and amplify the total RNAs, both with and without polyadenylate tails, from a single cell for transcriptome sequencing with high efficiency, reproducibility, and accuracy. By distributing the reverse transcribed cDNA molecules into 1.5x105 aqueous droplets in oil, the cDNAs are isothermally amplified using random primers in each of these 65-picoliter reactors separately. This new method greatly improves the ease of single-cell RNA sequencing by reducing the experimental steps. Meanwhile, with less chance to induce errors, this method can easily maintain the quality of single-cell sequencing. In addition, this polyadenylate-tail-independent method can be seamlessly applied to prokaryotic cell RNA sequencing.
18
ILLUMINA-READY STRAND-SPECIFIC RNA-SEQ LIBRARY PREPARATION FROM SINGLE CELLS Suvarna Gandlur, Nathalie Bolduc*, Simon Lee*, Andrew Farmer Takara Bio USA, R&D, Mountain View, CA Next-generation sequencing is empowering a deeper understanding of biology by enabling RNA expression analysis over the entire transcriptome with high sensitivity and wide dynamic range. One powerful application within this field is stranded RNA-seq, which is necessary to distinguish overlapping genes and for comprehensive annotation and quantification of lncRNAs. Commonly used methods for generating strand-specific RNA-seq libraries require several rounds of enzymatic treatments and clean-up steps, making them incompatible with low RNA inputs. Moreover, generation of RNA-seq libraries from total RNA is challenged by the high amounts of ribosomal RNA (rRNA) in the starting material. To make the preparation of strand-specific RNA-seq libraries accessible to picogram amounts of total RNA while minimizing the sequencing of rRNA, we developed the SMARTer Stranded Total RNA-Seq Kit – Pico Input Mammalian (Pico kit). We started by blending Clontech’s patented SMART technology with locked nucleic acid (LNA) technology, and added random priming, which allows cDNA synthesis from both polyadenylated and non-polyadenylated RNA. In addition, we developed a novel technology allowing the removal of molecules originating from rRNA after reverse transcription and library amplification, ZapR. The Pico kit can accommodate high quality RNA as well as degraded samples such as RNA extracted from FFPE, with input ranging from 250 pg to 10 ng of total RNA. While the Pico kit enables strand-specific transcriptome analysis from a very small amount total RNA, it can only allow the analysis of a cell population, leaving intra-sample heterogeneity hidden. To circumvent this problem, we further improved the Pico kit to work with single cell equivalent inputs - as low as 10 pg. In addition, the protocol has been optimized to accommodate intact cells, thus providing a workflow that delivers a more accurate representation of single cells transcriptome than is achievable with current methods.
19
EARLY HEMATOPOIETIC DEVELOPMENT REVEALED BY SINGLE-CELL TRANSCRIPTOME ANALYSIS OF MOUSE EMBRYOS Siyuan Hou1, Yun Gao2, Ji Dong2, Bing Liu1, Fuchou Tang2, Yu Lan1
1Translational Medicine Center of Stem Cells, 307-Ivy Translational Medicine Center, Affiliated Hospital, Academy of Military Medical Sciences, Beijing, China, 2Biomedical Institute for Pioneering Investigation via Convergence (BIOPIC), School of Life Sciences, Peking University, Beijing, China During mouse development, blood cell generation occurs sequentially in three major waves. The first transient wave initiates around embryonic day (E) 7 in the yolk sac blood islands producing primitive erythrocytes and macrophages. The second wave also transiently emerges in the yolk sac, initiating around E8.5 and produces multi-potent and lineage-restricted hematopoietic cells with an adult-like morphology. The third wave is responsible for the generation of the adult-repopulating hematopoietic stem cells from E10.5 in development[1]. Tal1 is pivotal for the early hematopoietic fate choice of Flk1+ mesoderm in gastrulating mouse embryos. In contrast, Runx1 is only required for the latter two waves of hematopoiesis.[2]. However, the fate decision mechanisms of distinct hematopoietic waves still remain largely unknown. Therefore, we use the flow cytometric sorting followed by single-cell RNA sequencing technique to trace the blood developmental process of mouse embryos. We reveal several populations with distinct differentiating and cell cycle status existing within the somite-stage embryonic blood cells, suggesting the dynamic occurrence and differentiation of early hematopoietic products. Reference: 1. McGrath, K.E., J.M. Frame, and J. Palis, Early hematopoiesis and macrophage development. Semin Immunol, 2015. 27(6): p. 379-87. 2. Scialdone, A., et al., Resolving early mesoderm diversification through single-cell expression profiling. Nature, 2016. 535(7611): p. 289-93.
20
USING GENETIC FEATURES TO IDENTIFY SUBPOPULATIONS IN SINGLE-CELL RNA-SEQ DATA Olivier Poirion1, Xun Zhu1,2, Travers Ching1,2, Lana Garmire1,2 1University of Hawaii Cancer Center, Epidemiology Program, Honolulu, HI, 2University of Hawaii at Manoa, Molecular Biology and Bioengineering Graduate Program, Honolulu, HI Characterization of subpopulations is a key challenge in the emerging field of single-cell RNA-seq (scRNA-seq). In scRNA-seq data, gene expression (GE) is classically used as features to explore the heterogeneity among the single cells. However, GE features are subject to significant amount of noises. Single nucleotide variations (SNVs) are small genetic alterations occurring in specific cells as compared to the population background. We propose in this study to obtain useful SNV genetic information from scRNA-Seq data as predictive features for subpopulation identification. We identified SNVs from scRNA-seq FASTA files directly, and developed a linear modeling framework SSrGE (Sparse SNV inference to reflect Gene Expression) to detect valid SNVs (vSNVs), which are associated with gene expression profiles. In all the data sets tested, these SNVs show better accuracy at retrieving cell subpopulations, compared to GE. Moreover, bi-partite graphs of cells in combination with vSNVs, present less noisy representations of the different cell subpopulations compared to those using GE. We ranked vSNVs and genes according to their overall contributions in the linear models and discovered that several top-ranked genes were reported in the original study as “cancer relevant genes”. In summary, we emphasis that SNV features obtained from scRNA-seq dataset have the potential to identify subpopulations in single cell tumor evolution.
21
SINGLE CELL mRNA-SEQ UNVEILS NON-AUTONOMOUS EFFECT OF EPIDERMAL Wnt/β-CATENIN SIGNALLING Arsham Ghahramani1,2, Giacomo Donati2, Fiona M Watt2, Nicholas M Luscombe1 1Francis Crick Institute, London, United Kingdom, 2King's College London, Centre for Stem Cells and Regenerative Medicine, London, United Kingdom Wnt/β-catenin signalling regulates an array of cell processes in the epidermis including proliferation, migration and commitment to differentiation. Previous research has identified that β-catenin signalling in a follicular stem cell niche results in a non-cell autonomous effect on stem cell behaviour leading to tissue growth. The mechanism of this non-autonomous effect has until now remained unclear. Using high-content imaging we have characterised an inducible β-catenin signalling system in vitro in order to study cell-cell interactions at the single cell level. Combining this system with single cell mRNA-seq we have identified the transcriptomic perturbation to keratinocytes when presented with a β-catenin activated neighbour. We are using this integrated approach to identify the mechanism through which β-catenin activated cells influence the fate of neighbouring stem cells.
22
SINGLE-CELL MULTIPLEXED PROFILING OF PROTEIN-LEVEL CHANGES INDUCED BY EGFR INHIBITOR GEFITINIB Haibiao Gong, Ilona Holcomb, Gajalakshmi Dakshinamoorthy, Benjamin Liu, Marc Unger, Ramesh Ramakrishnan Fluidigm Corporation, Molecular Biology, South San Francisco, CA The ability to measure gene expression in single cells has become increasingly important in biomedical research, especially in the field of cancer research, where cell-to-cell heterogeneity is an intrinsic feature. We developed a multiplexed single-cell protein detection assay for the C1™ system that can isolate and process 96 individual cells in parallel. The assay uses oligonucleotide-conjugated antibodies, which produce a detectable DNA amplicon only when they bind to the target protein. The resulting DNA amplicons are quantified by qPCR on the Biomark™ HD system. Data are analyzed using the Singular™ Analysis Toolset. A total of 80 antibody binders were developed for this assay to target proteins related to apoptosis, cell cycle, cell proliferation, tumor suppressors, biomarkers, stem cells, growth factors and proteins associated with specific cancers, such as breast and prostate cancers. The assay can be performed in a 48-plex format, which yields over 4,600 datapoints in two days. Assay sensitivity and specificity were assessed using recombinant proteins and various cell lysates. To demonstrate the usefulness of this assay, we used it to profile the protein level changes in A431 cells treated with epidermal growth factor receptor (EGFR) inhibitor gefitinib. EGFR is a member of ErbB family of type I tyrosine kinases, whose overexpression and mutations are involved in a variety of cancers. While much effort has been made to investigate the consequences of EGFR activation or inhibition, hardly any information is available regarding how EGFR regulation changes protein expression in single cells. Using a multiplexed protein assay, we discovered some interesting co-expression patterns of proteins in single cells. We also demonstrated that TNFRSF10B was upregulated, while MET, GATA3, BRCA1, CCNB1, MKI67 and CCNA2 were downregulated by gefitinib treatment. Notably, the reduction of the cell cycle-related proteins CCNA2 and CCNB1 and the cell proliferation marker MKI67 was consistent with the inhibited cell growth by gefitinib. Single-cell analysis—determining protein changes and analyzing the correlation between different proteins at the single-cell level—yields valuable information that is not achievable using bulk-cell analysis.
23
MASSIVE POPULATION GENETIC DIVERSITY IN ETV6-RUNX1 ACUTE LYMPHOBLASTIC LEUKEMIA. Veronica Gonzalez-Pena1, John Easton2, Charles Gawad1,2 1St. Jude Children's Research Hospital, Oncology, Memphis, TN, 2St. Jude Children's Research Hospital, Computational Biology, Memphis, TN Leukemia is the most common childhood malignancy and remains a leading cause of pediatric cancer-related mortality. While we have made significant improvements in the outcomes of pediatric leukemia, we continue to have a limited understanding of how the disease evolves and the mechanisms behind the development of chemotherapy resistance. Acute leukemias are thought to harbor few somatic mutations compared to other cancers based on bulk sequencing of diagnostic tumor samples. However, by examining leukemia samples at single-cell resolution, we found about twice as many variants per clone than standard bulk sequencing, and multiple high frequency clones within each patient despite some clones having private mutations that are known to drive disease progression, such as activating ras mutations. Error-corrected sequencing identified additional activating ras mutations at even lower frequencies in a cohort of ETV6-RUNX1 ALL patients suggesting significant population genetic diversity. Single-cell exome sequencing also revealed that APOBEC mutagenesis is important in disease initiation but not progression and estimated that the total genetic diversity of each cell was much higher than previously appreciated. Furthermore, exposure of ALL diagnostic samples to chemotherapy drugs selected for clones with preexisting resistance mutations, including alterations in genes that provide insight into drug resistance. Taken together, these findings have important implications for understanding the formation and drug-specific treatment response of childhood leukemia while providing a framework for deeply interrogating cancer population genetics.
24
MICROBEAD-MEDIATED SIMULTANEOUS ISOLATION OF DNA AND TOTAL RNA FROM SINGLE CELLS Kyung Yeon Han1, Kyu-Tae Kim*1, Donghyun Park 1, Hae-Ock Lee1,2, Woong-Yang Park1,2 1Samsung Medical Center, Samsung Genome Institute, Seoul, South Korea, 2 Sungkyunkwan University School of Medicine, Department of Molecular Cell Biology, Suwon, South Korea The simultaneous sequencing of a single cell’s genome and transcriptome is a powerful tool to understand the genomic and transcriptomic variations, and their correlative relationships. However, technical obstacles have prohibited the simultaneous analysis of both genome and transcriptome derived from a single cell. Here, we report a method for simultaneous isolation of DNA and total RNA (SIDR) from single cells, especially for parallel genome and transcriptome sequencing. The method adopts a strategy to physically separate genomic DNA and total RNA from single cells, based on hypotonic lysis and subsequent separation of cell lysate associated with magnetic microbeads. Systematic performance evaluation by quantitative real-time PCR and single-cell sequencing demonstrated that the method efficiency recovered genomic DNA and total RNA. We also validated that genomic DNA and total RNA simultaneously fractionated by SIDR method were suitable for the genome and transcriptome sequencing analysis of single cells, based on various aspects of sequencing data quality. By applying SIDR to integrated genome and transcriptome sequencing, this platform may be potentially an enabling tool to merge biological genetic and phenotypic information at the single cell scale.
25
SINGLE-CELL TRIPLE OMICS SEQUENCING REVEALS MULTI-OMICS HETEROGENEITY IN HEPATOCELLULAR CARCINOMAS Yu Hou1, Huahu Guo2, Chen Cao1, Xianlong Li1, Boqiang Hu1, Ping Zhu1, Xinglong Wu1, Lu Wen1, Fuchou Tang1, Yanyi Huang1, Jirun Peng2
1Biodynamic Optical Imaging Center, College of Life Sciences, Peking University, Beijing, China, 2Department of Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, China Single-cell genome, DNA methylome, and transcriptome sequencing methods have been separately developed. However, to accurately analyze the mechanism by which transcriptome, genome and DNA methylome regulate each other, these omic methods need to be performed in the same single cell. Here we demonstrate a single-cell triple omics sequencing technique, scTrio-seq, that can be used to simultaneously analyze the genomic copy-number variations (CNVs), DNA methylome, and transcriptome of an individual mammalian cell. We show that large-scale CNVs cause proportional changes in RNA expression of genes within the gained or lost genomic regions, whereas these CNVs generally do not affect DNA methylation in these regions. Furthermore, we applied scTrio-seq to 25 single cancer cells derived from a human hepatocellular carcinoma tissue sample. We identified two subpopulations within these cells based on CNVs, DNA methylome, or transcriptome of individual cells. Further, by dissecting both the primary and metastasis cancer at single cell multi-omics level, we will get a deeper insight into cancer occurrence and metastasis. Our work offers a new avenue of dissecting the complex contribution of genomic and epigenomic heterogeneities to the transcriptomic heterogeneity within a population of cells.
26
EPIGENOMIC LANDSCAPE OF HUMAN FETAL BRAIN, HEART AND LIVER Boqiang Hu1, Hongshan Guo1, Liying Yan2, Jie Qiao2, Fuchou Tang1
1Peking University, Biodynamic Optical Imaging Center and Center for Reproductive Medicine, College of life science, Beijing, China, 2Peking University, Department of Obstetrics and Gynecology, Third Hospital, Beijing, China The epigenetic regulation of spatiotemporal gene expression is crucial for human development. Here, we present whole-genome chromatin immunoprecipitation (ChIP-seq) analyses of a wide variety of histone markers in the brain, heart, and liver of early human embryos shortly after their formation. We identified 40,181 active enhancers in the samples, and a large proportion of them showed tissue-specific and developmental stage-specific patterns, indicating that they orchestrate the ordered spatiotemporal expression of the developmental genes in early human embryos. Moreover, the epigenomic differences between human and mouse using H3K27ac signal were also compared and a species-dominant pattern were discovered. Our work illustrates the potentially critical roles of tissue- and developmental stage-specific epigenomes for regulating the spatiotemporal expression of developmental genes during early human embryonic development.
27
SINGLE-CELL RNA-SEQ REVEALS DISTINCT INJURY RESPONSES IN DIFFERENT TYPES OF DRG SENSORY NEURONS Ganlu Hu1,2,3, Kevin Huang2, Youjin Hu2,3, Guizhen Du2, Zhigang Xue3, Xianmin Zhu1,4, Guoping Fan1,2 1 Tongji University, School of Life Sciences and Technology, Shanghai, China, 2University of California Los Angeles, Department of Human Genetics, Los Angeles, CA, 3Tongji University School of Medicine, Translational Center for Stem Cell Research, Shanghai, China, 4Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China Peripheral nerve injury leads to various injury-induced responses in sensory neurons including physiological pain, neuronal cell death, and nerve regeneration. In this study, we performed single-cell RNA-sequencing (scRNA-seq) analysis of mouse nonpeptidergic nociceptors (NP), peptidergic nociceptors (PEP), and large myelinated sensory neurons (LM) under both control and injury conditions at 3 days post nerve injury (PNI). After performing principle component and weighted gene co-expression network analysis, we categorized dorsal root ganglion (DRG) neurons into different subtypes and discovered co-regulated injury-response genes including novel regeneration associated genes (RAGs) in association with neuronal development, protein translation and cytoplasm transportation. In addition, we found significant up-regulation of the genes associated with cell death such as Pdcd2 in small NP neurons after axotomy, implicating their roles in small fiber neuropathy. Furthermore, we detected conserved injury-induced gene co-expression networks in LM neurons at different time points PNI, which are distinct from those in other neuronal subtypes. Our study revealed the distinctive and sustained heterogeneity of transcriptomic response to injury in single neurons that may contribute to diverse injury outcomes towards either nerve regeneration and functional recovery or neuronal cell death and neuropathy.
28
SINGLE CELL TRANSCRIPTOME ANALYSIS OF MOUSE ORGANOGENESIS Yuqiong Hu2, Xiaoying Fan1, Jing Dong1, Xinglong Wu2, Fuchou Tang1,2
1Peking University, School of Life Science, Peking, China, 2Peking University, Peking-Tsinghua Center for Life Sciences, Peking, China The mammalian embryo develops from a single-cell zygote to a blastocyst, followed by gastrulation and organogenesis. The mouse is an excellent model for studying mammalian organogenesis and almost all organs have been present at embryonic day 9.5. We have analyzed more than 500 single cells by using the single-cell RNA-seq method from 2 mouse embryos at E9.5, which were derived from 3 ectoderm organs (forebrain, metencephalon and skin), 2 mesoderm organs (heart and somite), and 3 endoderm organs (intestine, liver and lung). The single-cell transcriptome profiles of these cells revealed layer markers at this stage, known and novel cell types in each organ and expression patterns of some interested gene families which are important for the development of these organs. Our results provide a resource for understanding gene expression of mouse organogenesis at single cell resolution.
29
GENETIC ALTERNATIONS IN CIRCULATING TUMOR CELLS OF LUNG CANCER ADENOCARCINOMA WITH THEIR IMPLICATION IN METASTASIS AND CLINICAL PRACTICE Jicheng Yao1*, Shuo Mu1*, Jinwei Hu1, Gongwei Qin1, Ming Yao1, Hui Kang1, Kai Wang1 1OrigiMed Inc, Shanghai, China * These authors contributed equally; Background. Circulating tumor cell (CTC) is well recognized for its role in colonization of its tumors of origin to metastatic sites of distant organs, which eventually leads to cancer-related deaths. Programmed death-ligand 1 (PD-L1), which is expressed on many tumor cells, blocks the antitumor immune response. Juxtaposition of CTC with its matching primary and metastatic tumors with regard to PD-L1 activity will certainly be of great interest and value to understand the critical role of CTC in escaping immune elimination and mediating successful metastasis. Methods. In order to detect somatic single nucleotide variants (SNVs), insertions/deletions (INDELs) and copy number variation (CNV) within CTC, we specifically developed a suite of in-house pipelines and tested it on 8 CTCs from each of 6 patients with lung cancer adenocarcinoma (Ni et al 2013 PNAS). In particular, amplification of PD-L1 was compared across the CTCs and their matched solid tumors. Results. Extensive variability in SNV/INDELs and reproducible CNV patterns from cell to cell within the same patients were observed, which was consistent with previous reports. The amplifications of BCL6, FGF12, ETV5, SOX2 and PIK3CA, whose frequencies were reported over 20% in lung cancers, were also highlighted in chr3q26.1 region of some studied CTCs (66.7%). PD-L1 showed approximately homogeneous amplification across different CTCs from the same patient, but its heterogeneity across different patients was also noticeable. Indeed, it was interesting to have observed the simultaneous amplifications of PD-L1 in both liver metastatic site and its CTCs for one patient in our study. Conclusion. CTCs exhibit a broad heterogeneity of SNV/INDEL within the same patient, but meanwhile some recurrent CNVs that are possibly selected for their survival and potential targets of treatment. Not all CTCs solely rely on amplification of PD-L1 for successful metastasis, which undermines its utilities as prognostic biomarker; but detection of this event in CTC at least provides the basis of anti-PD-L1 immunotherapies.
30
HISTOPATHOLOGY LINKED SINGLE-CELL GENOMICS BY HIGH-THROUGHPUT LASER DISCHARGING SYSTEM Sungsik Kim1, Amos C Lee1, Han-Byoel Lee5, Jinhyun Kim3, Yushin Jung3, Yongju Lee3, Sangwook Bae1, Wonshik Han5,6, Sunghoon Kwon2,3,4 1Interdisciplinary Program for Bioengineering, Seoul National University College of Engineering, Seoul, South Korea, 2Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, South Korea, 3Department of Electrical and Computer Engineering, Seoul National University College of Engineering, Seoul, South Korea, 4Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea, 5Department of Surgery, Seoul National University College of Medicine, Seoul, South Korea, 6Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea Populations of tumor cells display serious heterogeneity in their phenotypic traits, which may be a result from mutation induced genetic variability. Tumor heterogeneity is important in both scientific and clinical aspects as it is deeply related to carcinogenesis and clinical outcomes. However, profiling genetic information in tumor cells en masse averages out variability between each tumor cells. Therefore, heterogeneous genetic information in tumor cells should be accessed by isolating each single cell in tumor tissue into different reactors to separate their genetic information from that of surrounding populations. Histopathology linked genomics becomes important with increasing knowledge of tumor phenotypes and microenvironments. Additionally, as cancer cells may function differently according to their phenotypes and microenvironments in tumor mass, histopathological information of genetic data can affect clinical interpretation. In this reason, histopathology linked single-cell analysis platform can be applied to cutting-edge cancer biology. Also, the technology can connect conventional histopathology to the recently developing genomics. Here, we describe a method for analyzing the genetic information of cancer in single-cell resolution with a high-throughput manner. Also, this approach provides links between the genetic information and biological context by isolating and sequencing cells using microscopic imaging. The system is equipped with infra-red nanosecond pulse laser and discharging layer for single-cell isolation. Additionally, softwares to automatically operate the system are developed, by which pathologists in hospital or laboratory can target and analyze cells remotely. We demonstrate this approach with various kinds of sample types including breast cancer tissue for genome wide mutation or copy number variation analysis of targeted cells. This study potentially provides new ways to investigate heterogeneity of individual tumor cells with a goal of uncovering the molecular mechanism of carcinogenesis and establishing personalized cancer therapy.
31
DETECTING ABERRANT HYPERMETHYLATED CPG ISLANDS IN CIRCULATING CELL-FREE DNA OF COLORECTALCANCER PATIENTS USING MCTA-SEQ Jingyi Li1, Xiaomeng Liu1, Xin Zhou2, Jie Ren1, Jilian Wang2, Lu Wen1, Liying Yan2, Fuchou Tang1, Wei Fu2 1Peking University, Biodynamic Optical Imaging Center (BIOPIC), College of Life Sciences, Beijing, China, 2Peking University Third Hospital, Department of General Surgery, Beijing, China Liquid biopsy is a potential tool for monitoring cancer markers in the blood and facilitating clinical therapy. However, owing to high fragmentation and limited quantities of the circulating cell-free DNA (ccfDNA), it remains to be a challenge to detect DNA methylation at a genome scale for ccfDNA. Previously we developed a methylated CpG tandems amplification and sequencing (MCTA-Seq) method that can detect thousands of hypermethylated CpG islands simultaneously in ccfDNA using relatively low depth of sequencing. Here we applied MCTA-Seq for colorectalcancer (CRC) using a cohort of tissue and plasma samples obtained from CRC patients and control subjects. Preliminary data identified known and novel markers which are characteristic of different sources. Combination of these markers indicated better sensitivity and specificity than individuals for detecting CRC in blood. MCTA-seq shows promises for non-invasive diagnostic and monitoring of CRC through ccfDNA.
32
USING MICRODOSE CELL NUCLEOSOME OCCUPANCY AND METHYLOME SEQUENCING TO ANALYZE THE CHROMATIN STATE LANDSCAPES OF MOUSE PREIMPLANTATION EMBRYOS Fan Guo1, Lin Li1, Jingyun Li1,3,5, Fuchou Tang1,2,3,4 1Biodynamic Optical Imaging Center, College of Life Science, Peking University, Beijing, China, 2Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China, 3Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China, 4Center for Molecular and Translational Medicine, Peking University Health Science Center, Beijing, China, 5Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China Chromatin state is crucial for the regulation of gene transcription and expression in mammals. Little is known about the chromatin state dynamics during mouse preimplantation development because of limited embryonic cells. In order to explore this issue, we develop a micro-dose cell chromatin state analysis based on Nucleosome Occupancy and Methylome Sequencing (NOMe-Seq) which could analyze the DNA methylation, nucleosome positioning, and chromatin accessibility at the same time. The modified NOMe-seq can analyze the the DNA methylation, nucleosome positioning, and chromatin accessibility of as low as 100 mouse embryonic stem cells and cover more than 80% of the mouse genome. Moreover, many well reported chromatin state traits can be verified by our modified NOMe-seq. By applying this technique to analyzing the chromatin state landscapes of mouse preimplantation embryos, we can have a better understanding of the roles of chromatin state regulation in the establishment of totipotent during preimplantation development.
33
SINGLE CELL RNA-SEQ ANALYSIS OF HUMAN PGCs Li Li1,2, Ji Dong1,2, Liying Yan3,4, Fuchou Tang1,2, Jie Qiao3,4
1Biomedical Institute for Pioneering Investigation via Convergence, Peking University, Beijing, China, 2College of Life Sciences, Peking University, Beijing, China, 3Department of Obstetrics and Gynecology Third Hospital, Peking University, Beijing, China, 4Center for Reproductive Medicine, Peking University, Beijing, China Primordial germ cells (PGCs) are the first germ cell population established during development. Investigating the mechanisms of PGCs development is critical to dissect the germ cell related diseases such as infertility and teratoma. We analyzed the transcription profiling of human PGCs and gonadal somatic cells spanning a large development stage with multiplexed single cell RNA-seq technology. We found that the gene expression patterns of female PGCs and male PGCs were distinctly different and there was gene expression diversity between mouse and human. Moreover, we analyzed the transcription factor regulatory network and signal pathway network in human PGCs. Furthermore, we found unique transcription features of PGCs niche cells. Our work provides new insights of human PGCs development and gives valuable clues of germ cells differentiation in vitro.
34
THE TRANSCRIPTOME AND DNA METHYLOME LANDSCAPES OF HUMAN PRIMORDIAL GERM CELLS Fan Guo1, Liying Yan1,5, Hongshan Guo1, Lin Li1, Fuchou Tang1,2,3,4, Jie Qiao1,3,5,6 1Biodynamic Optical Imaging Center and Department of Obstetrics and Gynecology, College of Life Sciences, Third Hospital, Peking University, Beijing, China, 2Key Laboratory of Cell Proliferation and Differentiation, Ministry of Education, Beijing, China, 3Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China, 4Center for Molecular and Translational Medicine, Peking University Health Science Center, Beijing, China, 5Key Laboratory of Assisted Reproduction, Ministry of Education, Beijing, China, 6Reproductive Endocrinology and Assisted Reproduction, Beijing Key Laboratory, Beijing, China Germ cells are vital for transmitting genetic information from one generation to the next and for maintaining the continuation of species. Here, we analyze the transcriptome of human primordial germ cells (PGCs) from the migrating stage to the gonadal stage at single-cell and single-base resolutions. Human PGCs show unique transcription patterns involving the simultaneous expression of both pluripotency genes and germline-specific genes, with a subset of them displaying developmental-stage-specific features. Furthermore, we analyze the DNA methylome of human PGCs and find global demethylation of their genomes. Approximately 10 to 11 weeks after gestation, the PGCs are nearly devoid of any DNA methylation, with only 7.8% and 6.0% of the median methylation levels in male and female PGCs, respectively. Our work paves the way toward deciphering the complex epigenetic reprogramming of the germline with the aim of restoring totipotency in fertilized oocytes.
35
SINGLE-CELL TRIPLE OMICS SEQUENCING REVEALS THE MOLECULAR DYNAMICS DURING LUNG CANCER DEVELOPMENT AND METASTASIS Qingqing Li1, Yu Hou1, Lin Li1, Hua Bai2, Lu Wen1, Fuchou Tang1,3, Jie Wang2 1Biodynamic Optical Imaging Center, College of Life Sciences, Peking University, Beijing 100871, China, 2Department of Thoracic Medical Oncology, Beijing Cancer Hospital & Beijing Institute for Cancer Research, Beijing 100142, China, 3Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China Lung cancer is the leading cause of cancer death worldwide and is one of the malignant tumors seriously threatening human health. Every year, more than 1.3 million people die of lung cancer, exceeding the combined death of breast, colon and prostate cancers. Lung cancers have high metastatic potential, hard to be removed by surgery and show poor response to radiation and chemotherapy. The lung cancers display high heterogeneity in terms of pathology and molecular biomarkers. Such heterogeneity should stem from multiple levels including genome, transcriptome and epigenome. Previous studies are based on population of cancer cells due to technical limitations. However this would conceal important information from rare single cells. In this study, we applied a previously established single-cell triple omics sequencing technique for lung cancers. We uncovered the dynamics during cancer development, and described the heterogeneity within tumor tissues at single cell multi-omics level. Our study will be helpful to illustrate the molecular regulation of the development and metastasis of lung carcinoma and its resistance to target therapy.
36
REPRESENTATION LEARNING FOR SINGLE CELL RNA-SEQ DATA Xiangyu Li1, Weizheng Chen3, Yang Chen1, Xuegong Zhang1, Michael Q Zhang1,2 1MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology , TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China, 2Department of Molecular and Cell Biology, Center for Systems Biology, The University of Texas, Richardson, TX, 3Institute of Network Computing and Information System, Department of Computer Science, Peking university, Beijing 100871, China Single cell RNA-seq data provides valuable insights into cell heterogeneity. To deal with such high dimensional single cell data, a powerful strategy is to project the cells into a low-dimensional space. However, large proportion of technical noise leads to the high prevalence of zero counts and drop-out events. Even worse, some marker genes are undetected. It is a great computational challenge to find a reasonable low-dimensional space. To overcome the above problems, here we develop a novel single cell representation learning (SCRL) method based on neural network, which integrates different sources of information (e.g. gene-cell information and gene-gene information) to learn more meaningful low-dimensional representations for cells.
37
SINGLE CELL RNA SEQUENCING OF THE MOUSE DORSAL ROOT GANGLIA IN RESPONSE TO INJURY. Veronique Lisi1, Michel Giroux1, Elmer Guzman1, Bhagat Singh2, Clifford Woolf2, Kenneth S Kosik1 1University of California Santa Barbara, Neuroscience Research Institute, Santa Barbara, CA, 2Boston Children’s Hospital and Harvard Medical School, F.M. Kirby Neurobiology Center, Boston, MA Nerve regeneration after injury is a complex process involving a number of transcriptional changes whose consequences are poorly understood. To gain insight into how transcriptional rewiring affects the regenerative outcome, we have taken advantage of two mouse strains, C57BL/6 and CAST/Ei, that differ greatly in the regenerative capacity of their dorsal root ganglia (DRG) neurons. CAST/Ei strain mice show a much stronger regenerative phenotype. We performed single cell sequencing of over 500 neurons obtained from the DRGs of these strains, with or without an in vitro axotomy (IVA). Hierarchical clustering of the cells partitioned two populations, each containing cells from both strains and both treatments. The relative abundance of each of the populations was affected by injury but not to the same extent in each strain. The genes more abundantly expressed in each cell cluster differed in their biological processes, as assessed by GO term enrichment analysis. To understand how these two clusters of cells arose, we analyzed the putative targets of miR-7048-3p, a miRNA we found to positively regulate nerve regeneration. We observed significant overlap in the biological functions of the miR-7048-3p putative targets and the genes defining each cluster of cells. Clustering of cell populations based on their single cell RNAseq profiles highlighted a previously unappreciated clustering of DRG neurons and suggested miR-7048-3p as one potential driver of this clustering.
38
DEVELOPMENT OF MICROFLUIDICS-BASED ADVANCED WORKFLOW FOR HIGH-THROUGHPUT SINGLE-CELL RNA SEQUENCING Benjamin Liu, Michael Phelan, Tze-Howe Charn, Jing Wang, Devin Do, Larry Wang, Hoan Phan, Benjamin Lacar, David Wang, Joel Brockman, Manisha Ray, Shaun Cordes, Marc Unger, Richard Fekete Fluidigm Corporation, R & D, South San Francisco, CA High-throughput (HT) next-generation sequencing (NGS) of whole transcriptomes, or RNA-seq, has been used extensively to profile gene expression from bulk tissues. Recent advances of single-cell technologies are facilitating the opportunity to discern biological insights within individual cells and providing a means to reveal previously hidden relationships between individual cells within a population or to detect subpopulations. Here we present an optimized workflow, based on the C1™ system and HT integrated fluidic circuits (HT IFCs), to rapidly and reliably process up to 800 individual cells. The workflow applies a cell-specific barcode to all polyA+ RNA, converts polyA+ RNA into cDNA, and performs universal amplification of the cDNA for 3’ end-counting single-cell mRNA sequencing. This superior chemistry and workflow offers great cell capture rate, even coverage, and elevated gene detection sensitivity. Examples will be shown in which heterogeneous cell types (such as K562, hiPSCs, NIH 3T3, etc) are clearly separated using expression differences and subpopulations of cells are identified.
39
HUMAN ADENOSINE DEAMINASES ADA1 AND ADA2 BIND TO DIFFERENT SUBSETS OF IMMUNE CELLS Chengqian Liu1, Julia Kaljas1, Maksym Skaldin1, Chengxiang Wu2,3, Qing Zhou4, Yuanan Lu3, Ivona Aksentijevich4, Andrey V Zavialov1 1University of Turku, Turku Centre for Biotechnology, Turku, Finland, 2Tulane National Primate Research Center, Tulane National Primate Research Center, Covington, LA, 3University of Hawaii at Manoa, Department of Public Health, Honolulu, HI, 4National Institutes of Health, National Human Genome Research Institute, Bethesda, MD These authors contributed equally to this work. Chengqian Liu, Julia Kaljas, Maksym Skaldin, Chengxiang Wu At sites of inflammation and tumor growth, the local concentration of extracellular adenosine rapidly increases and plays a role in controlling the immune responses of nearby cells. Adenosine deaminases ADA1 and ADA2 (ADAs) decrease the level of adenosine by converting it to inosine, which serves as a negative feedback mechanism. Mutations in the genes encoding ADAs lead to impaired immune function, which suggests a crucial role for ADAs in immune system regulation. It is not clear why humans and other mammals possess two enzymes with adenosine deaminase activity. Here, we found that ADA2 binds to neutrophils, monocytes, NK cells and B cells that do not express CD26, a receptor for ADA1. Moreover, the analysis of CD4+ T cell subsets revealed that ADA2 specifically binds to regulatory T cells expressing CD39 and lacking the receptor for ADA1. In addition, it was found that ADA1 binds to CD16- monocytes, while CD16+ monocytes preferably bind ADA2. A study of the blood samples from ADA2 deficient patients showed a dramatic reduction in the number of lymphocyte subsets and increased concentration of TNF-α in plasma. Our results suggest the existence a new mechanism, where the activation and survival of immune cells is regulated through the activities of ADA2 or ADA1 anchored to the cell surface.
40
PTEN DEFICIENCY REPROGRAMMES HUMAN NEURAL STEM CELLS TOWARDS A GLIOBLASTOMA STEM CELL-LIKE PHENOTYPE Xiaomeng Liu2, Shunlei Duan1, Guohong Yuan1, Jingyi Li2, Jing Qu1, Fuchou Tang2, Guang-Hui Liu1
1Chinese Academy of Sciences, institute of Biophysics, Beijing, China, 2Peking University, BIOPIC, Beijing, China PTEN is a potent tumor suppressor frequently mutated in many types of cancer. However, it is unclear how PTEN safeguards human adult stem cells from oncogenic transformation. Here, we report that targeted PTEN-deletion leads to the neoplastic transformation of human neural stem cells (NSCs), but not of mesenchymal stem cells (MSCs). PTEN-deficient NSCs are able to form intracranial neoplasm in immunodeficient mice, exhibit oncogenic features including increased proliferation and migration, and have malignancy-associated metabolic profile and alterations in gene expression. PTEN localizes to the nucleus of NSCs, binds to PAX7 promoter by association with CREB/CBP and inhibits PAX7 transcription. PTEN-deletion upregulates PAX7, which in turn promotes the neoplastic transformation of NSCs, and also induces the “aggressiveness” of human glioblastoma stem cells (GSCs). In clinical large database, PAX7 is elevated in PTEN-deficient glioblastoma. Furthermore, mitomycin C was identified as a factor that preferentially triggered apoptosis in GSCs with PTEN-deficiency. Together, we uncovered a novel mechanism how PTEN safeguards NSC as an untransformed identity, and established a cellular platform to identify factors underlying NSC transformation, which opens a door towards personalized strategies for the treatment of glioblastoma.
41
PCR AMPLIFICATION FROM ENCAPSULATED SINGLE-CELL WITHIN PROTEIN BASED HYDROGEL MICRO-BEADS Xiaotian Liu, Fei Sun Hong Kong University of Science and Technology, Biomedical Engineering, Hong Kong, China Genetically-engineered protein hydrogels emerge as promising alternatives to synthetic hydrogels for biomedical applications thanks to precise control we have over their structural and functional properties. Our research focuses on developing an entirely protein-based hydrogel system with tunable biophysical properties. Our hydrogels are designed to combine two types of protein interactions, i) genetically encoded click chemistry (GECC), also known as SpyTag (A)/SpyCatcher (B) chemistry, that can stitch proteins/peptides together by forming a covalent isopeptide bond with remarkable efficiency and specificity, and ii) tyrosine-rich underwater adhesive mussel foot proteins (Mfp-3 and Mfp-5). Protein hydrogels are synthesized by photo-crosslinking the tyrosine-rich Mfp domains while SpyTag peptides within the protein network allow further functionalization by globular proteins. The resulting materials offer a versatile platform for various applications such as studying cell-matrix interactions, protein-drug delivery and cell transplantation. Keywords: hydrogels, Mfp, protein self-assembly, regenerative medicine
42
SINGLE CELL TRANSCRIPTOME ANALYSIS REVEALS TWO PRE-DC SUB-POPULATIONS IN DIFFERENTIATION TRAJECTORY TOWARDS DISTINCT TYPES OF DENDRITIC CELLS Wenji Ma1, Jaeyop Lee2, Daniel Backenroth3, Yu Zhou2, Erin Bush2, Peter Sims1,3, Kang Liu*2, Yufeng Shen*1,3,4 1Columbia University Medical Center, Department of Systems Biology, New York, NY, 2Columbia University Medical Center, Department of Microbiology and Immunology, New York, NY, 3Columbia University Medical Center, JP Sulzberger Columbia Genome Center, New York, NY, 4Columbia University Medical Center, Department of Biomedical Informatics, New York, NY * Correspondence: [email protected], [email protected] Classic dendritic cells (cDCs) play a central role in the immune system by processing and presenting antigens to activate T cells, and are composed of two major subsets with distinct phenotypes and functions, namely, CD141+ DC and CD1c+ DC. A population of migratory precursor cells, the pre-DCs, downstream of common dendritic cell progenitor (CDP) in the bone marrow or cord blood, are the immediate precursors to both cDC subsets. To understand the DC development trajectory and identify key drivers of the bifurcation process into two distinct DC subtypes, we performed single cell RNA-Seq of two purified DC subsets and pre-DCs that are mixed in a known ratio, and bulk RNA-Seq of CDP, pre-DCs and DCs. With multi-dimensional scaling (MDS), we show that global transcriptome can separate pre-DC and DCs independent of cell surface markers, indicating that pre-DCs and DCs are transcriptionally distinct. Highly biological variable genes in the single cell pre-DCs can distinguish the two single cell DC subsets, indicating that the transcriptional program towards terminal differentiation is already initiated in the pre-DC stage. We compiled a list of TFs that are themselves differentially expressed (BH p.adj < 0.05, two fold change) or have regulated targets enriched in the differentially expressed genes in bulk CD1c+ DCs and CD141+ DCs. Using t-Distributed Stochastic Neighbor Embedding (t-SNE) based on these TFs, the pre-DCs are clearly separated into two sub-populations, with one close to CD1c+ DCs and the other close to CD141+ DCs. IRF4 and IRF8 expression ratio contributes most to the separation of pre-DC sub-populations. Additionally, the pre-DC sub-population close to CD1c+ DCs have upregulated interleukin-1 signaling and transcription pathways. We also identified a list of candidate surface markers that may serve to sort pre-DC sub-populations. In summary, we demonstrate that pre-DCs can be separated from DCs with global transcriptome profiling, and have sub-populations committed to two DC subsets driven by IRF4 and IRF8 ratio.
43
MECHANISMS OF DERMAL NICHE CONTROL OF HAIR FOLLICLE DEVELOPMENT AND CARCINOGENESIS Peggy Myung1,3, Thomas Sun2, Valentina Greco1,2
1Yale University School of Medicine, Dermatology, New Haven, CT, 2Yale University School of Medicine, Genetics, New Haven, CT, 3Yale University School of Medicine, Pathology, New Haven, CT During development, cells are instructed to form organized tissue through cues provided by their native microenvironment, or niche, which is frequently altered or disabled in cancer. Despite the growing number of mutations shown to promote cancer, far less is known about how the niche influences cells once they acquire oncogenic mutations or if the native niche has built-in mechanisms to preserve tissue integrity by suppressing oncogenic cell growth. A major challenge to addressing this question is rooted in our incomplete understanding of how the niche itself is regulated to instruct proper tissue growth and differentiation. One of the most tractable models to study tissue development is the hair follicle, which forms through interactions between the hair follicle epithelium and underlying dermal condensate cells. These dermal condensate cells form the dermal niche essential for proper hair follicle epithelial growth and differentiation. Using live imaging techniques adapted for embryonic skin explants, we have captured key dermal niche cell behaviors, including cell divisions and migration/compaction, that occur during formation of the dermal condensate, providing a platform to examine how the dermal niche influences epithelial cell behaviors and cell fate at a single cell level. In parallel, we show in a transplant model that embryonic dermal niche cells can suppress the dysregulated growth of basal cell carcinoma, a tumor that recapitulates undifferentiated hair follicle epithelium but lacks a dermal niche. Specifically, we show that embryonic dermal niche cells induce organized terminal differentiation of oncogenic epithelial cells. With these tools in hand, we are now equipped to examine how dermal cells influence epithelial cell divisions and cell fate during development and during carcinogenesis using live imaging approaches combined with single-cell RNA sequencing technology. Collectively, this work aims to provide a unifying mechanism to explain how the tissue microenvironment can dominantly control epithelial behaviors and fate during development and regeneration as well as during tumorigenesis.
44
EFFECTIVE DETECTION OF VARIATIONS IN SINGLE CELL TRANSCRIPTOME Kuanwei Sheng1,2, Wenjian Cao1, Qing Deng1, Chenghang Zong1
1Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, TX, 2Baylor College of Medicine, Integrative Molecular and Biomedical Sciences Graduate Program, Houston, TX The development of single-cell RNA-seq methods has allowed the detection of gene expression at the microscopic scale that is not accessible by bulk RNA-seq approaches. While single-cell RNA-seq has been successfully used for the identification of new cell types in complex tissues, recent analyses have also indicated that technical noise still exists in single-cell RNA-seq assays. In contrast to the gene expression differences between different cell types, single cells of the same type possess subtle yet dynamic transcriptional variations due to intrinsic and extrinsic noises. The successful detection of these transcriptional variations at whole transcriptome level essentially requires a very sensitive and quantitative single-cell RNA-seq assay. Here we present a new single-cell RNA-seq assay --- Multiple Annealing and dC-Tailing based Quantitative single-cell RNA-seq (MATQ-seq). This method demonstrates ~90% sensitivity comparing to 50~60% sensitivity of current single-cell RNA-seq assays. More importantly, MATQ-seq provides the highly desired accuracy for detecting transcriptional variations existing in single cells of the same population. With the sequencing of pool-and-split averaged single-cell samples, the technical noise of MATQ-seq was systematically characterized to warrant the detected variations are biologically genuine. By mapping the reads to the exons and introns respectively, we measured the transcriptional noise in mature RNAs and premature RNAs separately. The observation of large transcriptional noise in premature RNA is consistent with the transcriptional burst dynamics that have been widely observed in biological systems. With the sensitivity and accuracy demonstrated in measuring transcriptional variations among single cells of the same population, we believe that MATQ-seq will have broad applications in biological and clinical research.
45
SINGLE-CELL RNA-SEQ ANALYSIS REVEALS THE DYNAMIC TRAJECTORIES DURING MOUSE LIVER DEVELOPMENT Xianbin Su*1, Yi Shi*1, Xin Zou*1, Zhao-Ning Lu*1, Chong-Chao Wu1, Xiao-Fang Cui1, Lan Wang1, Kun-Yan He1, Ze-Guang Han1,2
1Shanghai Jiao Tong University, Key Laboratory of Systems Biomedicine (Ministry of Education) and Collaborative Innovation Center of Systems Biomedicine, Shanghai, China, 2Chinese National Human Genome Center at Shanghai, Shanghai-MOST Key Laboratory for Disease and Health Genomics, Shanghai, China Corresponding author: Ze-Guang Han Fetal liver stem/progenitor cells (LSPCs) with some biomarkers such as Epcam from endoderm have been known to finally differentiate into hepatocyte and cholangiocyte within adult liver, however, the trajectories of differentiation and maturation of these cell lineages are not fully understood at single-cell resolution. Considering priori knowledge of limited biomarkers for LSPCs could restrict the trajectory tracking, therefore, in the present work we employed marker-free single-cell RNA-Seq to characterize comprehensive transcriptional profiles of 507 cells from seven stages during mouse liver development. Interestingly, our data demonstrated the existence of two types of stem/progenitor cells with distinct molecular patterns during liver development, and one is fetal hepatoblasts while the other could be early stem/progenitor cells (Early S/PCs) that express more naïve stem cell-like molecules. Significantly, both types of cells exhibit heterogeneity of transcriptional program within each cell population, suggesting they be in distinct status of self-renewal, cell proliferation and different sub-stages of differentiation and maturation. In addition to cell surface molecules, we reconstructed the developmental trajectories of Early S/PCs and hepatoblasts based on the transcriptional profiles at single-cell resolution, which exhibit contiguous but discrete genetic control by transcription factors and signaling pathways. In general, our data depicting the dynamic trajectories with transcriptional profiles at single-cell resolution during mouse liver development provides not only a valuable resource, but also novel insights into the fate decision and transcriptional control of self-renewal, differentiation and maturation of LSPCs.
46
SINGLE-CELL NANOBIOPSY, A NOVEL PLATFORM FOR THE MULTIPLEXED ANALYSIS OF mRNA COMPARTMENTALIZATION IN NEURONAL CELLS Eszter Toth1,2,3, Akshar Lohith3, Akiyoshi Fukamizu1,2, Nader Pourmand3
1University of Tsukuba, Ph.D. Program in Human Biology, School of Integrative and Global Majors, Tsukuba, Japan, 2University of Tsukuba, Life Science Center, Tsukuba Advanced Research Alliance, Department of Life and Environmental Sciences, Tsukuba, Japan, 3University of California at Santa Cruz, Department of Biomolecular Engineering, Jack Baskin School of Engineering, Santa Cruz, CA In highly polarized cells such as neurons, compartmentalization of mRNA and local protein synthesis may be implemented in remarkably fast, precise, local responses to external stimuli. This is of high importance for growth cone guidance, synapse and memory formation, regeneration following injury and nociception. Defects of mRNA localization lead to mental retardation or neurodegenerative diseases1. Subcellular transcriptome analysis of neurons faces many technical difficulties. Currently available techniques (such as in situ hybridization, bulk microarray or RNA Sequencing) require a tradeoff between spatial resolution and multiplexing. In addition, previous studies used different cell types for axonal and dendritic transcriptome analysis, making data comparison very difficult. Previously, there was no available method for multiplexed, comparative analysis of dendritic and axonal transcriptome at the single-cell-level. Our group has recently developed a label-free, single-cell nanobiopsy platform based on scanning ion conductance microscopy (SICM), that uses electrowetting within a quartz nanopipette to extract cellular material from living cells with minimal disruption of the cellular milieu2. In this study, we used our nanobiopsy platform to extract samples from the cell bodies, dendrites or axons of human neurons, and analyzed the multiplex mRNA pool with RNA Sequencing. The minute volume of a nanobiopsy sample made it is possible to extract samples from several locations in the same cell. In addition to the previously identified transcripts, we found a new set of mRNAs that specifically localize to dendrite or axon. Hence, our single-neuron nanobiopsy analysis can deepen our understanding of mRNA transport, neuronal growth and network formation. 1. Jung, H. et al. Cell 157, 26 (2014). 2. Actis, P. et al. ACS Nano 8, 546 (2014).
47
TRANCRIPTOME AND DNA METHYLOME ANALYSIS OF HUMAN EMBRYOS AT EIGHT-CELL STAGE Rui Wang1, Lu Yang1, Liying Yan2, Hao Ge1, Fuchou Tang1 1Biodynamic Optical Imaging Center, College of Life Sciences,Peking University, Beijing, China, 2Center for Reproductive Medicine, Third Hospital, Beijing, China The research of human early embryos is very important for the application of assisted reproductive technology and the stem cell-based therapy. Currently, the embryos were mainly transplanted into uterus at eight-cell or blastocyst stage.Due to the scarcity of human early embryos, the research of the cellular and molecular mechanisms of early human embryos is very limited. Moreover,the way to judge the embryos is mainly based on the morphological characteristics and the success rate of pregnancy and live-birth of healthy children is low. In order to deeply understand the cellular and molecular mechanisms of abnormal human early embryonic development, we use the single cell RNA sequencing technology and whole genome bisulfite sequencing technology Post-Bisulfite Adaptor Tagging(PBAT) to study the transcriptome and DNA methylome of normal and abnormal human eight-cell embryos and blastocysts. We found that most arrested eight-cell embryos haves one or more of the following defects including: the chromosome copy number abnormalities, zygote activation failure, maternal gene degradation failure, increased mitochondrial genes expression and reduced mitochondrial associated genes expression. We also found that part of the embryos that has the normal chromosome copy number at eight-cell stage may appear as chromosome copy number chimeric at blastocyst stage. In the future, we plan to study the chromosome copy number variations (CNVs) during human early embryonic development and corresponding transcriptome and epigenome changes
48
IDENTIFICATION OF LINEAGE MARKER GENES WITH SINGLE-CELL RNA SEQUENCING Xi Wang1, Thomas Höfer1,2 1German Cancer Research Center, Division of Theoretical Systems Biology, Heidelberg, Germany, 2University of Heidelberg, Bioquant Center, Heidelberg, Germany Single-cell transcriptome sequencing approaches are becoming a powerful tool for high-throughput, genome-wide transcriptomic analysis of cell identities and dynamics. During embryo development, transcriptome profiling of single cells collected from a variety of embryonic days has shown the differentiation pathway in formation of final cell types. One of the following major challenges is to identify lineage-specific marker genes, which are then useful to elucidate the underlying molecular mechanism in establishing the final cell types. Here we present an improved identification of lineage marker genes using a novel approach based on the inferred lineage tree. We believe this approach can be also applied to other systems undergoing cellular differentiation, such as haematopoiesis.
49
A NOVEL TECHNIQUE FOR GENOME-SCALE DETECTION OF HYPERMETHYLATED CPG ISLANDS IN CIRCULATING CELL-FREE DNA. Lu Wen*1, Jingyi Li*1, Huahu Guo*2, Xiaomeng Liu*1, Fuchou Tang#1, Yanyi Huang#1, Jirun Peng#2 1Peking University, Biodynamic Optical Imaging Center (BIOPIC), College of Life Sciences, Beijing, China, 2Capital Medical University, Department of Surgery, Beijing Shijitan Hospital, Beijing, China Dying cancer cells will shed their DNA to blood as circulating cell-free DNA (ccfDNA). The ccfDNA released by an early cancer is scanty and highly fragmented, which goes beyond the detection sensitivity of current DNA methylome technologies. Here we describe a Methylated CpG Tandems Amplification and Sequencing (MCTA-Seq) method to enable genome-scale detection of hypermethylated CpG islands in ccfDNA. This highly sensitive technique can work with genomic DNA as little as 7.5 pg, which is equivalent to 2.5 copies of the haploid genome. We have analyzed a cohort of tissue and plasma samples (n = 151) of hepatocellular carcinoma (HCC) patients and control subjects, identifying dozens of high-performance markers in blood for detecting small HCC (≤ 3 cm). Among these markers, 4 (RGS10, ST8SIA6, RUNX2 and VIM) are mostly specific for cancer detection, while the other 15, classified as a novel set, are already hypermethylated in the normal liver tissues. Two corresponding classifiers have been established, combination of which achieves a sensitivity of 94% with a specificity of 89% for the plasma samples from HCC patients (n = 36) and control subjects including cirrhosis patients (n = 17) and normal individuals (n = 38). Notably, all 15 alpha-fetoprotein-negative HCC patients were successfully identified. Comparison between matched plasma and tissue samples indicates that both the cancer and noncancerous tissues contribute to elevation of the methylation markers in plasma. MCTA-Seq is simple and cost-effective with a single-tube reaction and a relatively low sequencing depth. This novel technique will facilitate the development of ccfDNA methylation biomarkers and contribute to the improvement of cancer detection in a clinical setting. *: These authors contribute equally to this work. #: Corresponding authors
50
A NOVEL AND RELIABLE APPROACH FOR WHOLE GENOME AMPLIFICATION FROM INDIVIDUAL MAMMALIAN CELLS IMPROVES AMPLIFICATION CONSISTENCY AND GENOME SEQUENCE COVERAGE Gang Zhang1, Edouard Hatton1, Anjali Hinch1, Rory Bowden1, Peter Donnelly1,2 1Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom, 2Department of Statistics, University of Oxford, Oxford, United Kingdom Recent advances in single-cell transcriptomics have led to a revolution in the understanding of cellular heterogeneity. In contrast, single-cell genomic sequence analyses have been less influential, in part because of the limitations of available whole-genome amplification (WGA) techniques. Here, we have taken alternative approaches to develop a novel method for WGA from single cells. In proof-of-principle experiments, we demonstrate that the size range of amplification products is around 0.2 to 3kbp and show that amplification is truly target-dependent, generating undetectable product in no-input-DNA controls. We evaluated the genomic coverage capability of the method by Illumina sequencing of libraries produced directly from the shorter fragments (~250-600bp) amplified from 67 individual mouse sperm cells, with sequencing depth of ~3.6 – 8.5× (median 5.2x). Genomic coverage (≥1x) for the individual libraries was between 45 and 72% (median 62%) of that attained from a pool of sperm cells from the same mouse. At 45±1%, the GC content of amplified material was consistent and slightly higher than that of the mouse reference sequence, perhaps because of amplification biases during WGA or during library amplification. Our WGA method provides consistent and reliable amplification from single haploid mammalian cells with higher genome coverage than existing approaches, and therefore offers potential in facilitating future progress in single-cell genomics.
51
INTRATUMOR HETEROGENEITY AND BRANCHED EVOLUTION OF EARLY-STAGE PRIMARY ACRAL MELANOMA REVEALED BY MICRODISSECTIONAL MULTIREGION SEQUENCING Xiannian Zhang2,3, Yang Peng1, Chunmei Li2,3, Yuhong Pang2,4, Yanyi Huang2,4, Hang Li1 1Peking University First Hospital , Department of Dermatology, Beijing, China, 2Peking University, Department of Biodynamics Optical Imaging Center (BIOPIC), Beijing, China, 3Peking University , College of Life Science, Beijing, China, 4Peking University, College of Engineering, Beijing, China Malignant melanoma is one of the most lethal cancers. High recurrence rate and drug resistant of melanoma stem from the highly genetically heterogeneous cell composition. Recently by using multi-region sequencing of the same tumor, it is able to systematically investigate the intra-tumor heterogeneity. However most researches were taken in metastatic sites considering the low purity and low amount of tumor cells in early-stage melanoma. Here we used Laser Capture Micro dissection (LCM) to ensure the acquiring of around 20 cells from the same tumor nest and used MALBAC for whole genome amplification (WGS) with better coverage uniformity. Microdissectional multiregion sequencing of 7 tumors provided evidence of intra-tumor heterogeneity in early-stage primary acral melanoma. We confirmed the intra-tumor heterogeneity in early-stage primary acral melanoma at single tumor nest level, even in the microscopically adjacent tumor nests. We also develop a method to distinguish the extraneous source contaminations and indicating the coverage of exome using shallowly sequenced genome data, which makes the pipeline more reliable and practical. We investigate on the SNV errors and artifacts from MALBAC library and removed them. The CNVs and SNVs from the individual primary melanoma showed the tumor phylogenetic tree possibly indicating the branched evolution process of melanoma. We validated that the combination of LCM and MALBAC methods can provide the landscape of CNVs and SNVs of single primary tumor nests.
52
EVOLUTION OF MULTIPLE CELL CLONES OVER A 29 YEAR PERIOD OF A CLL PATIENT Zhikun Zhao1, Liang Wu1, Shiping Liu1, Yong Hou1, Michael Dean2,1
1BGI-shenzhen, BGI-research, Shenzhen, China, 2National Institutes of Health, National Cancer Institute, Bethesda, MD Chronic lymphocytic leukemia (CLL) is a frequent B-cell malignancy characterized by recurrent somatic chromosome alterations and a low level of point mutations. We performed SNP microarray analyses of a single chronic lymphocytic leukemia (CLL) patient over 29 years of observation and treatment, and transcriptome and whole genome sequencing at selected timepoints. We identified the chromosome alterations 13q14-, and 6q- and 12q+ in early cell clones, elimination of clonal populations following therapy, followed by the appearance of a clone containing trisomy 12 and a chromosome 10 copy neutral LOH that marks a major population dominant at death. Serial single cell RNA sequencing revealed an expression pattern with high FOS, JUN and KLF4 at presentation, which resolved following therapy, but reoccurred following relapse and death. The pseudo-temporal analysis further characterized the cancer initiation, proliferation and extinction, and relapse, indicating that complex changes in expression occur over time. Several signalings, including B cell receptor signaling, EGFR signaling, ERKs signaling and NF-kappa B signaling, showed expression changes along evolution. In conclusion, we performed a very detailed and extensive analysis of a single CLL patient across the evolutionary lifespan. And we provided the evidence that CLL can evolve gradually during indolent phases, and undergo rapid changes following therapy.
53
CircRNA ANALYSIS OF HEK293T SINGLE-CELL DATA HAS REVEALED NOVEL DISTRIBUTION PATTERNS FOR CircRNAs Chaofang Zhong, Maozhen Han Huazhong University of Science and Technology, College of Life Science and Technology, Wuhan, China Single-cell sequencing can reveal differences among single-cells in the population and their evolutionary relationships. Based on data generated by single-cell sequencing, it has become increasingly important for studying heterogeneities of circRNAs and its functions as well as gene regulations in single-cells. In this study, we have first summarized the present status of single-cell sequencing and circRNA researches. We summarized the research progress of circRNA in single cell, further expounded the single-cell research in non-coding RNA researches and their applications in precision medicine. To better understand the relationship between circRNA and mRNA, the single cell sequencing datasets of seven HEK293T samples were selected, and four circRNA predictive methods, including CIRI, circRNA_finder, CIRCexplorer and UROBORUS, were chosen for circRNA prediction and single-cell transcriptome data analysis. Our results have shown that (i) A total of 1,858 circRNAs were predicted by at least one of the four methods, among which 68 were identified as of high credibility (identified by all of the four methods). Based on these 68 circRNAs, (ii) The expression of circRNAs had obvious differences among the individual cells, indicating that circRNAs exhibited dynamic expression patterns in single cells. (iii) There were no obvious correlation in expression level between circRNAs and mRNAs based on the information of Pearson Correlation Coefficient (R=0.054, P=0.662). (iv) On the basis of subtypes of circRNA, we found that a gene can usually produce different subtypes of circRNA, and the expression levels of genes were positively associated with the numbers of their circRNA isoforms. (v) According to the prediction results of miRNA binding sites, we found that high expression circRNAs (top 10 genes with highest expression values) tend to have more miRNA binding sites. Hence, we speculated the regulation mechanism between circRNA and miRNA might be more complex than we previously think. Our findings have revealed the heterogeneities of circRNA patterns among single-cells, and such patterns for non-coding elements were not in accordance with gene expression patterns, indicating another distribution patterns for circRNAs.
54
TRACING THE FORMATION OF HEMATOPOIETIC STEM CELLS IN MOUSE EMBRYOS BY SINGLE-CELL FUNCTIONAL AND RNA-SEQ ANALYSES Fan Zhou1, Xianlong Li2, Weili Wang3, Ping Zhu2, Jie Zhou1, Wenyan He1, Weiping Yuan3, Fuchou Tang2, Bing Liu1 1Academy of Military Medical Sciences, 307-Ivy Translational MedicineCenter, Laboratory of Oncology, Affiliated Hospital, Beijing, Colombia, 2PekingUniversity, Biodynamic Optical ImagingCenter, College of Life Sciences, Beijing, China, 3Chinese Academy of Medical Sciences, State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Diseases Hospital, Tianjin, China Hematopoietic stem cells (HSCs) are derived early from embryonic precursor cells, such as hemogenic endothelial cells and pre-HSCs. However, the identity of precursor cells remains elusive due to their rareness, transience, and inability to be isolated efficiently. Here we employed potent surface markers to capture the nascent pre-HSCs at 30% purity, as rigorously validated by single-cell-initiated serial transplantation assay. Then we applied single-cell RNA-Seq technique to analyse five populations closely related to HSC formation: endothelial cells, CD45- and CD45+ pre-HSCs in E11 aorta-gonad-mesonephros (AGM) region, and mature HSCs in E12 and E14 fetal liver. In comparison, the pre-HSCs showed unique features in transcriptional machinery, arterial signature, apoptosis, metabolism state, signalling pathway, transcription factor network, and lncRNA expression pattern. Among signalling pathways enriched in pre-HSCs, the mTOR activation was uncovered indispensable for the emergence of HSCs but not hematopoietic progenitors from endothelial cellsin vivo. Transcriptome data-based functional analysis revealed de novo the remarkable heterogeneity in cell cycle status of pre-HSCs, with considerable proportion being actively proliferative.By comparing with proximal populations without HSC potential, the core molecular signature of pre-HSCs was identified. Collectively, our work paves the way for dissection of complex molecular mechanisms regulating the step-wise generation of HSCs in vivo, informing future efforts to engineer HSCs for clinical application.
55
HIGHLY MULTIPLEXED SINGLE mRNA MEASUREMENTS REVEAL DISTINCT MOLECULAR REGIONS IN THE MOUSE HIPPOCAMPUS Wen Zhou1, Sheel Shah1,2, Eric Lubeck1, Long Cai1 1California Institute of Technology, Division of Chemistry and Chemical Engineering, Pasadena, CA, 2University of California at Los Angeles, UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, Los Angeles, CA Identifying the spatial organization of tissues at cellular resolution from single cell gene expression profiles is essential to understanding many biological systems. In particular, there exist conflicting evidence on whether the hippocampus is organized into transcriptionally distinct subregions. Here, we demonstrate a generalizable in situ 3D multiplexed imaging method to quantify hundreds of genes with single cell resolution via Sequential barcoded Fluorescence in situ hybridization (seqFISH) (Lubeck et al., 2014). We used seqFISH to identify unique transcriptional states by quantifying and clustering up to 249 genes in 16,958 cells. By visualizing these clustered cells in situ, we identified distinct layers in the dentate gyrus corresponding to the granule cell layer, composed of predominantly a single cell class, and the subgranular zone, which contains cells involved in adult neurogenesis. Furthermore, we discovered that distinct subregions within the CA1 and CA3 are composed of unique combinations of cells in different transcriptional states, instead of a single state in each sub-region as previously proposed. In addition, we see that while the dorsal region of the CA1 is relatively homogenous at the single cell level, the ventral part of the CA1 has a high degree of cellular heterogeneity. These structures and patterns are observed in sections from different mice, as well as in seqFISH experiments with different sets of genes. Together, these results demonstrate the power of seqFISH in transcriptional profiling of complex tissues.
56
TRACING HAEMATOPOIETIC STEM CELL FORMATION AT SINGLE-CELL RESOLUTION Ping Zhu1,2, Fan Zhou3, Xian L Li1, Wei L Wang4, Ji Zhou3, Wen Y He3, Meng Ding3, Fu Y Xiong3, Xiao N Zheng3, Zhuan Li3, Yan L N3, Xiao H Mu3, Lu Wen1,5, Tao Cheng4,6, Yu Lan7, Wei P Yuan4, Bing Liu3,4,8, Fu C Tang1,2,5,9
1Biodynamic Optical Imaging Center, College of Life Sciences, Peking University, Beijing, China, 2Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China, 3State Key Laboratory of Proteomics, Translational Medicine Center of Stem Cells, 307-Ivy Translational Medicine Center, Academy of Military Medical Sciences, Beijing, China, 4State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences, Tianjin, China, 5Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China, 6Collaborative Innovation Center for Cancer Medicine, National Institute of Biological Sciences, Tianjin, China, 7State Key Laboratory of Proteomics, Genetic Laboratory of Development and Diseases, Institute of Biotechnology, Beijing, China, 8Institute of Haematology, Medical College of Jinan University, Guangzhou, China, 9Center for Molecular and Translational Medicine, CMTMBeijing, China Haematopoietic stem cells (HSCs) are derived early from embryonic precursors, such as haemogenic endothelial cells and pre-haematopoietic stem cells (pre-HSCs), the molecular identity of which still remains elusive. Here we use potent surface markers to capture the nascent pre-HSCs at high purity, as rigorously validated by single-cell-initiated serial transplantation. Then we apply single-cell RNA sequencing to analyse endothelial cells, CD45− and CD45+ pre-HSCs in the aorta–gonad–mesonephros region, and HSCs in fetal liver. Pre-HSCs show unique features in transcriptional machinery, arterial signature, metabolism state, signalling pathway, and transcription factor network. Functionally, activation of mechanistic targets of rapamycin (mTOR) is shown to be indispensable for the emergence of HSCs but not haematopoietic progenitors. Transcriptome data-based functional analysis reveals remarkable heterogeneity in cell-cycle status of pre-HSCs. Finally, the core molecular signature of pre-HSCs is identified. Collectively, our work paves the way for dissection of complex molecular mechanisms regulating stepwise generation of HSCs in vivo, informing future efforts to engineer HSCs for clinical applications.
57
REVEALING NOVEL CELL TYPES, CELL-CELL INTERACTIONS, AND CELL LINEAGES BY SINGLE-CELL SEQUENCING Alexander van Oudenaarden1,2 1Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences), 3584 CT, Utrecht, Netherlands, 2University Medical Center Utrecht, Cancer Genomics Netherlands, 3584 CX, Utrecht, Netherlands In this talk I will report on our progress on detecting new cell types, cell-cell interactions, and cell lineages using single-cell sequencing. I will present three recent approaches, RaceID, StemID, and ProximID, that are used to detect rare cells, stem cells, and cell-cell interactions respectively. These algorithms are applied to several experimental model systems including the mammalian intestine, pancreas, and bone marrow. I will also present new data on detecting 5-hydroxycytosine methylation (5hmC) in single cells. This method does not only reveal extensive cell-to-cell heterogeneity of this epigenetic mark but also provide a powerful tool to perform endogenous lineage tracing at the single cell level.
58
DIFFERENTIAL EXPRESSION ANALYSES FOR SINGLE-CELL RNA-SEQ: OLD QUESTIONS ON NEW DATA Zhun Miao1,2,3, Xuegong Zhang1,2,3,4 1MOE Key Laboratory of Bioinformatics, Beijing, China, 2TNLIST, Bioinformatics Division and Center for Synthetic & Systems Biology, Beijing, China, 3Tsinghua University, Department of Automation, Beijing, China, 4Tsinghua University, School of Life Sciences, Beijing, China Background: Single-cell RNA sequencing (scRNA-seq) is an emerging technology that enables high resolution detection of heterogeneities between cells. One important application of scRNA-seq data is to detect differential expression (DE) of genes. Currently, some researchers still use DE analysis methods developed for bulk RNA-Seq data on single-cell data, and some new methods for scRNA-seq data have also been developed. Bulk and single-cell RNA-seq data have different characteristics. A systematic evaluation of the two types of methods on scRNA-seq data is needed. Results: In this study, we conducted a series of experiments on scRNA-seq data to quantitatively evaluate 14 popular DE analysis methods, including both of traditional methods developed for bulk RNA-seq data and new methods specifically designed for scRNA-seq data. We obtained observations and recommendations for the methods under different situations. After 16 sets of experiments on 3 scRNA-seq datasets with the 14 methods designed based on bulk RNA-seq data or scRNA-seq data, we observed that the methods can behave differently in the number of DEG each method tend to report, and also in the variation in this number when the sample size of the compared groups changes. Some methods tend to give very different reports at different experiment settings and may thus be less reliable. When comparing the similarities between results of different methods, we found that some methods give very similar results when sample size is small. The overall similarity between results of different methods drops when sample size increases, which implies that the difference between models and implementations becomes more obvious. We assessed the consistency of each method on data of different sample sizes, and found that the consistency of most methods are good in general, especially when the difference between the compared samples is strong. We also checked the reproducibility of the same method on two random subsets of the data with equal size. We found that random sampling of the samples can affect the detection of DEG, which highlights the high heterogeneity in gene expression among single cells. The reproducibility is higher for most methods when the difference signal between the two groups is stronger and when the sample size is larger. We introduced an ROC-like curve and its AUC to quantitatively study the potential accuracy of each method at each sample size. We found that the performances of most methods are severely affected by the compared sample sizes and perform less satisfying overall when sample sizes are small. Conclusions: DE analysis methods should be chosen for scRNA-seq data with great caution with regard to different situations of data. Different strategies should be taken for data with different sample sizes and/or different strengths of the expected signals. Several methods for scRNA-seq data show advantages in some aspects, and DEGSeq tends to outperform other methods with respect to consistency, reproducibility and accuracy of predictions on scRNA-seq data.
59
AN OVERVIEW OF THE NIH SINGLE CELL ANALYSIS AND BRAIN CELL CENSUS PROGRAMS Yong Yao National Institute of Mental Health, NIH Common Fund Single Cell Analysis Program, Bethesda, MD The NIH Common Fund Single Cell Analysis Program (SCAP) aims to address key roadblocks in analyzing single cells, to catalyze the emerging field of single cell research, and to coordinate NIH efforts in advancing the next-generation of technologies for single cell analysis. About four years have passed since the SCAP made the first set of awards in summer of 2012. The SCAP has stimulated the development of a variety of single cell technologies. In particular, single cell genomics analysis has recently become a flourishing area of research to uncover fundamental biological principles behind cell diversity, which are often masked and not amenable to the population analysis of cells.Multiple single cell ‘omics approaches are emerging that provide unprecedented high resolution of molecular signatures of a cell. The ability to measure genomic, epigenomic, transcriptomic, and metabolic status in individual cells are expected to provide new insight into molecular pathways in health and disease.In addition, powerful technologies are being developed to isolate and analyze rare cells from a heterogeneous population and to examine distinct cellular states in complex tissue environments.Currentlythere are multiple efforts from public and private sectors tosupport cell census and cell atlases research. The NIH BRAIN initiative will leverage the global momentum to support the generation of brain cell reference atlases encompassing molecular, anatomical, and physiological annotations of brain cell types in mouse, human, and non-human primates.Cataloging brain cell types and their connectivity is a prerequisite to understanding how they are organized in circuits, and how they change in brain disorders. In addition, a detailed understanding of cell classes and subclasses will enable the development of novel tools that allow researchers to target specific cell types and manipulate circuits for further study. However, there is not yet a consensus on what a brain cell type is, since a variety of factors including experience, cell interaction, and neuromodulators can diversify the molecular, electrical, and structural properties of similar cells, and cell phenotypes may change over time. Nonetheless, there is general agreement that cell types can be defined provisionally by invariant and generally intrinsic properties, and that this classification can provide a good starting point for a cell atlas.A goal is to coordinate and work with the neuroscience research community and other interested parties to build a brain cell census resource that can be widely used throughout the research and education communities
60
INTERROGATING THE ROBUSTNESS OF GENE REGULATORY CIRCUITS BY RACIPE Huang Bin1, Mingyang Lu1,2, Eshel Ben-Jacob1,3, Herbert Levine1, Jose Onuchic1 1Rice University, Center for Theoretical Biological Physics, Houston, TX, 2Jackson Laboratory, Bar Harbor, ME, 3Tel Aviv University, Physics, Tel Aviv, Israel One of the most important roles of cells is to perform their cellular tasks properly for survival. Cells usually achieve robust functions, for example cell-fate decision-making and signal transduction, through multiple layers of regulation involving many genes. Despite the combinatorial complexity of gene regulation, its quantitative behavior have been typically studied on the basis of experimentally-verified core gene regulatory circuitry, composed of a small set of important elements. It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded noisy cellular environment. Here we report a new computational method, named Random Circuit Perturbation (RACIPE), for interrogating the robust dynamical behavior of a gene regulatory circuit. RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology, and utilizes statistical tools to identify generic properties of the circuit. By testing RACIPE on a proposed stem cell gene regulatory circuit, we found that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed. Compared to circuits with random topology, the stem cell circuit allows for more robust gene states and is less likely to have oscillatory/chaotic dynamics. From parametric perturbations for all the RACIPE models, we identified key genes and the concomitant hierarchical structure of stem cell differentiation. Our results suggest that dynamics of a gene circuit is mainly determined by its topology, not by detailed circuit parameters. Our work provides a theoretical foundation for circuit-based systems biology modeling approach. We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner, and to quantitatively evaluate the robustness and heterogeneity of gene expression.
61
EPIGENETIC REPROGRAMMING IN MAMMALIAN DEVELOPMENT Wolf Reik Babraham Institute, Epigenetics, Cambridge, United Kingdom Epigenetic information is relatively stable in somatic cells but is reprogrammed on a genome wide level in germ cells and early embryos. Epigenetic reprogramming appears to be conserved in mammals including humans. This reprogramming is essential for imprinting, and important for the return to pluripotency including the generation of iPS cells, the erasure of epimutations, and perhaps for the control of transposons in the genome. Following reprogramming, epigenetic marking occurs during lineage commitment in the embryo in order to ensure the stability of the differentiated state in adult tissues. Signalling and cell interactions that occur during these sensitive periods in development may have an impact on the epigenome with potentially long lasting effects. A key component of reprogramming is the erasure of DNA methylation which probably involves an intricate combination of passive and active demethylation mechanisms. We have identified signalling events which regulate DNA methylation dynamics during early development, and which connect reprogramming firmly with naïve pluripotency in mouse and human. This is probably important in order to disable epigenetic memory in pluripotent cells. We are investigating the roles of these pathways in natural and in experimental reprogramming. Altered reprogramming may also result in transgenerational epigenetic inheritance. Single cell methylome sequencing begins to reveal profound epigenetic heterogeneity at the exit from pluripotency which may underlie cell fate decision making in early development.
62
ENGINEERING CELLS AND TISSUES: INSIGHTS FROM SINGLE CELL TRANSCRIPTOMICS Barbara Treutlein1,2,3, Gray Camp1 1Max Planck Institute for Evolutionary Anthropology, Evolutionary Genetics, Leipzig, Germany, 2Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany, 3Technical University Munich, Biosciences, Munich, Germany Engineering specific cell types and complex tissues in vitro is an important goal for regenerative medicine. We are using single-cell transcriptomics to understand mechanisms underlying cell fate programming in 2-D cell cultures and 3-D organoids. The first part of my talk focuses on cerebral organoids, three-dimensional cultures of human cerebral tissue derived from pluripotent stem cells. We use single-cell RNA-seq to dissect and compare cell composition and progenitor-to-neuron lineage relationships in human cerebral organoids and fetal neocortex in order to find out how well these in vitro systems recapitulate neural progenitor cell proliferation and neuronal differentiation programs observed in vivo. We identify cells in the cerebral organoids that derived from regions resembling the fetal neocortex and find that these cells use gene expression programs remarkably similar to those of the fetal tissue. Covariance network analysis reveals known and novel interactions among transcriptional regulators central to neural progenitor proliferation and neuronal differentiation. Interestingly, among these transcriptional regulators are genes that have been shown to have the capacity to convert the fate of a non-neuronal somatic cell into a neuronal cell. In the second part, we analyze the direct reprogramming of fibroblasts to induced neuronal cells. We deconstruct heterogeneity at multiple time points in order to reconstruct the reprogramming path in high resolution. Surprisingly, we identify a competing myogenic program that emerges when using a single transcription factor (Ascl1), but this alternative fate is repressed by a combination of three factors (Ascl1, Brn2, Myt1l). Our analysis highlights major inefficiencies in the direct reprogramming process.
63
UNPEELING THE LAYERS OF POST-TRANSCRIPTIONAL GENE REGULATION: INTRON RETENTION COORDINATES FUNCTIONAL GENE NETWORKS Justin J L Wong1,2,3, Ulf Schmitz1,3, Natalia Pinello1,2,3, Fangzhi Jia1,2, Dadi Gao1,3,4, Trung Nguyen1,2,3, Sultan Alasmari5, William Ritchie1,3,4, Maria-Cristina Keightley5, Shaniko Shini6, Graham J Lieschke5, John E J Rasko1,3,4 1University of Sydney, Gene & Stem Cell Therapy Program, Sydney, Australia, 2University of Sydney, Gene Regulation in Cancer Laboratory, Sydney, Australia, 3University of Sydney, Sydney Medical School, Sydney, Australia, 4Royal Prince Alfred Hospital, Cell and Molecular Therapies, Camperdown, Australia, 5Monash University, Australian Regenerative Medicine Institute, Clayton, Australia, 6University of Queensland, School of Biomedical Sciences, Brisbane, Australia Our original discovery in white blood cells consolidated the idea that intron retention (IR) is a widespread and conserved mechanism of gene expression control in normal cells (Wong et al, Cell 2013). We have now explored the role of DNA methylation in IR regulation since this type of epigenetic modification is known to regulate other modes of alternative splicing. Using whole genome bisulfite sequencing (WGBS) and RNA-seq, we discovered significantly reduced DNA methylation levels (P<1.0e8, t-test) near the splice junctions of DNA encoding retained compared to non-retained introns in 8 primary mouse and human cells/tissues and cell lines. The number of IR events increased significantly in de novo methyltransferases knockout mouse embryonic stem cells and B cells, and in cell lines (MPRO and OCL-AM3) treated with the DNA methylation inhibitor, 5-aza-2′-deoxycytidine compared to controls (P<0.05 in all cases, t-test). Thus, reduced levels of DNA methylation near splice junctions distinguish between retained and non-retained introns, and directly regulate IR. Using MeCP2 ChIP-seq and confirmation by ChIP-qPCR, we further identified reduced occupancy of MeCP2 near splice junctions of retained introns, mirroring the reduced DNA methylation at these sites in mouse promyelocytes and granulocytes. In diverse cell types from MeCP2 knockout mice, the number of IR events increased compared to wild-type controls (P<0.05 in all cases, t-test), indicating that reduced MeCP2 levels facilitate IR. MeCP2 occupancy was also associated with increased RNA Pol II Ser2p occupancy, predominantly near splice junctions: thereby connecting IR with reduced rates of transcription elongation, which is also a sign of inefficient splicing factor recruitment. Immunoprecipitation followed by mass spectrometry identified many splicing factors that preferentially bind to MeCP2 in cells with increased IR, including Tra2b, which is known to alter IR following knockdown. Additionally we have analyzed IR in granulocytes of five vertebrate species spanning 430 million years of evolution. IR induces organismal complexity, indicated by a strong anti-correlation between the number of genes affected by IR and the number of protein-coding genes in the genome of individual species. IR affects many orthologous or otherwise functionally-related genes. Notably, intron-retaining transcripts harbor a large number of miRNA binding sites that are co-regulated in human and mouse. We have demonstrated that IR is epigenetically controlled and associated with reduced recruitment of splicing factors, such as Tra2b, to splice junctions, mirroring reduced MeCP2 occupancy. Our study of this special form of alternative splicing provides novel insights into gene regulatory networks and provides fertile ground for single cell and single molecule exploration.
64
SINGLE-CELL DYNAMICS OF CHROMATIN ACCESSIBILITY DURING FOREBRAIN DEVELOPMENT Sebastian Preissl*1, Rongxin Fang*1, Hui Huang1, Yuan Zhao1, David Gorkin1, Brandon Sos2, Veena Afzal3, Diane Dickel3, Samantha Kuan1, Axel Visel3,4,5, Len A Pennachio3,4, Kun Zhang2, Bing Ren1,6
1Ludwig Institute for Cancer Research, Laboratory of Gene Regulation , La Jolla, CA, 2University of California San Diego, Department of Bioengineering, La Jolla, CA, 3Lawrence Berkeley National Laboratory, Berkeley, CA, 4U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA, 5University of California Merced, School of Natural Sciences, Merced, CA, 6University of California San Diego, Department of Cellular and Molecular Medicine, La Jolla, CA *these authors contributed equally The forebrain plays a key role in multiple higher-order brain functions including memory and consciousness. Proper forebrain development relies on regulatory DNA regions including promoters and distal acting enhancers, which can be identified by the presence of open chromatin as measured by ATAC-seq (Assay for transposase-accessible chromatin using sequencing). However, unraveling the role of regulatory DNA elements in forebrain development has proven challenging for two main reasons: 1) regulatory DNA elements are highly dynamic during development and thus their activity must be studied across multiple developmental stages; 2) forebrain is a heterogeneous tissue comprised of different cell types with cell-type-dependent chromatin accessibility patterns. To overcome these challenges, we performed ATAC-seq to profile open chromatin in nuclei from mouse forebrains at six consecutive days of embryonic development (embryonic day 11.5 - postnatal day 0) and from adult forebrain tissue (8 weeks). We performed ATAC-seq on bulk tissue using a version of the standard protocol which we optimized for frozen tissues, as well as on single cells using combinatorial indexing and FACS in combination with our frozen tissue ATAC-seq protocol (scATAC). In total we generated single nuclear profiles for 113,885 nuclei from embryonic mouse forebrains across six developmental stages with a median read depth of 3,484 reads per nucleus. Aggregate single cell ATAC-seq experiments are highly reproducible between biological replicates (R = 0.97), and agree with bulk tissue ATAC-seq data (R = 0.94). In preliminary analysis, we identify more than 13,000 regions that show highly dynamic patterns of chromatin accessibility during forebrain development. We expect that our comprehensive dataset of single cell chromatin accessibility will enable us to elucidate the dynamics of open chromatin in distinct cell-types, and to identify the associated regulatory networks that underlie mammalian forebrain development. I will present the most recent insights gleaned from our analysis of these data.
65
DEVELOPMENT OF NEW TECHNIQUES FOR SINGLE-CELL ANALYSIS Jianbin Wang Tsinghua University, Beijing, China Advances in technologies have been a major driving force behind biological discoveries. Our lab focuses on the development of novel genomic technologies and their application on biomedical research. I will start with the development of an automated single-cell sorting system for clinical biopsy analysis, which enables the identification of colon stem cells from a colonoscopy sample. Then I will introduce a strategy to lineage trace the whole organism with cumulative single-cell barcoding. Preliminary data demonstrate its application on C. elegans.
66
DISSECTING GENE REGULATION NETWORK IN HUMAN EARLY EMBRYOS AT SINGLE-CELL AND SINGLE-BASE RESOLUTION Fuchou Tang BIOPIC, Peking University, Beijing, China Measuring gene expression in individual cells is crucial for understanding the gene regulatory network controlling human embryonic development. We applied single-cell RNA -Seq analysis to human preimplantation embryos, primordial germ cells (PGCs), and human embryonic stem cells (hESCs). We also systematically profiled the DNA methylome of human early embryos from the zygotic stage throughout to post-implantation. We showed that the major wave of genome-wide demethylation is complete at the 2-cell stage, contrary to previous observations in mice. Moreover, the demethylation of the paternal genome was much faster than that of the maternal genome, and by the end of the zygotic stage the genome-wide methylation level in male pronuclei was already lower than that in female pronuclei. Then we also showed that long interspersed nuclear elements (LINEs) or short interspersed nuclear elements (SINEs) that were evolutionarily young are demethylated to a milder extent compared to older elements in the same family and had higher abundance of transcripts, indicating that early embryos tend to retain higher residual methylation at the evolutionarily younger and more active transposable elements. Furthermore, we analyzed the DNA methylome of human PGCs and found global demethylation of their genomes. Approximately 10 to 11 weeks after gestation, the PGCs were nearly devoid of any DNA methylation, whereas the repeat elements still kept high level of residual methylation. Our work provides new insights of critical features of the transcriptome and DNA methylome landscapes of human early embryos and primordial germ cells, as well as the functional significance of DNA methylome to regulation of gene expression and repression of transposable elements.
67
SINGLE-CELL miRNA SEQUENCING OF THE HUMAN HEMATOPOIETIC HIERARCHY Michael VanInsberghe1, David J Knapp2, Michelle Moksa1, Hans Zahn1, Martin Hirst1, Connie J Eaves2, Carl L Hansen1 1University of British Columbia, Michael Smith Laboratories, Vancouver, Canada, 2British Columbia Cancer Agency, Terry Fox Laboratory, Vancouver, Canada Newly available tools for sensitive and scalable single-cell transcriptomics have enabled improved understanding of complex tissues. However, all available methods have been restricted to mRNA profiling, leaving non-coding RNAs such as microRNAs largely inaccessible. Here, we present a method for single-cell miRNA-seq (SCemiRS) that combines an optimized protocol with small-volume microfluidic processing to enable, for the first time, global miRNA profiling across hundreds of single cells. We applied this approach to examine miRNA expression dynamics in Human hematopoiesis. We first validated our method on K562 cells, demonstrating that the quality of our single-cell libraries is comparable to those prepared using high-input, bulk methods. Alignment metrics illustrated the low contribution of reaction side products (1% adapter-dimers), and high miRNA specificity (70%). Our observed average diversity of 142 miRNAs per cell was comparable to previously published bulk K562 miRNA-seq data at similar sequencing depths. Using a spike-in control, we established that SCemiRS provides both accurate miRNA quantification, as read counts increased linearly with the template concentration, and a detection efficiency of 6-15% of input molecules. Coefficients of variation on technical replicates were well below those measured in single cells. Together these results establish SCemiRS as the first high-performance method for single-cell miRNA sequencing. We next applied SCemiRS to a total of 2057 single cells isolated from 8 progenitor, and 4 mature populations purified from cord blood. Analysis of miRNA expression showed that mature cells tightly clustered into their respective populations, with clear differential expression of established markers (e.g. miRs-451a and -150 were upregulated in erythroid, and lymphoid cells, respectively). In contrast, we observed substantial overlap of cells purified into the classical progenitor phenotypes, with no clear delineation of subpopulations. Rather, we found that these cells were broadly organized along an axis of differentiation. Within the most primitive progenitor cells, we corroborated the increased expression of miRNAs previously associated with hematopoietic stem cells (e.g. miR-126, -125a) as well as identified additional miRNA with highly similar expression patterns. Finally, we observed a decrease in the total miRNA as cells underwent commitment, and discovered evidence of novel population-specific isomiR usage. Overall, these data provide the first comprehensive picture of miRNA expression during the early stages of Human hematopoiesis and further support a model characterized by rich cellular heterogeneity and smooth transitions between progenitor subtypes.
68
MICROFLUIDIC SINGLE CELL WHOLE GENOME AND WHOLE TRANSCRIPTOME AMPLIFICATION AND SEQUENCING Yanyi Huang Peking University, BIOPIC, Beijing, China Quantitative single-cell analysis enables the characterization of cellular systems with a level of detail that cannot be achieved with ensemble measurement. I am going to show some of our recent work on developing better approaches for single-cell sequencing, including whole genome amplification and whole transcriptome analysis. Whole-genome amplification (WGA) for next-generation sequencing has seen wide applications in biology and medicine when characterization of the genome of a single cell is required. High uniformity and fidelity of WGA is needed to accurately determine genomic variations, such as copy number variations (CNVs) and single- nucleotide variations (SNVs). Prevailing WGA methods have been limited by fluctuation of the amplification yield along the genome, as well as false-positive and -negative errors for SNV identification. I will present a new approach, emulsion WGA (eWGA), to overcome these problems. We divide single-cell genomic DNA into a large number (10^5) of picoliter aqueous droplets in oil. Containing only a few DNA fragments, each droplet is led to reach saturation of DNA amplification before demulsification such that the differences in amplification gain among the fragments are minimized. We demonstrate the proof-of-principle of eWGA with multiple displacement amplification (MDA), a popular WGA method. This easy-to-operate approach enables simultaneous detection of CNVs and SNVs in an individual human cell, exhibiting significantly improved amplification evenness and accuracy. Following similar design, we also develop a robust and simple single-cell RNA-seq method, named 'easier-seq' to perform whole transcriptome analysis. This new method is capable of capturing novel transcripts without polyA tails.
69
TUMOR EVOLUTION AT SINGLE CELL GENOMIC RESOLUTION Nicholas Navin1,2 1MD Anderson Cancer Center, Genetics, Houston, TX, 2MD Anderson Cancer Center, Bioinformatics and Computational Biology, Houston, TX Tumors evolve from single cells. As they evolve they acquire complex genomic mutations and diverge to form multiple subpopulations, resulting in intratumor heterogeneity. This genomic heterogeneity plays an important role in clonal evolution during the growth of the primary tumor and during invasion, metastasis and chemoresistance in cancer patients. In this talk I will provide an overview of the experimental technologies and computational methods that we have developed for performing single cell DNA sequencing and measuring copy number profiles, point mutations and indels. I will also discuss our efforts in applying these methods to study punctuated copy number evolution in triple-negative breast cancer patients and metastatic dissemination in colorectal cancers.
70
ENRICHED EMT AND RELATED PATHWAYS FOR CASTRATION RESISTANT PROSTATE CANCER DETECTION AND THERAPY REVEALED BY SINGLE-CELL qRT-PCR, RNA-SEQ AND BIOPHYSICAL ASSESSMENT Chun-Liang Chen University of Texas Health Science Center at San Antonio, Molecular Medicine, San Antonio, TX Our project is to develop new biomarkers and provide new paradigms for detection of metastatic castration-resistant prostate cancer (mCRPC). Moreover, those biomarkers may play an important role in the castration resistance development and potential therapeutic targets for the disease. The new biomarkers for this fatal disease are crucial for resolving a long-standing dilemma, to treat or not to treat, in prostate cancer patient care. About 30,000 men in USA succumb to fatal metastatic castration-resistant prostate cancer with 16-18 month median survival after chemical recurrence. A portion (~65-70%) of 2.9 millions prostate cancer patients after castration therapy may not lead to aggressive progression. The discrimination between fatal and indolent prostate cancer patients has been elusive even with combined current biomarkers. Overdiagnosis and overtreatment of prostate cancer represent major concerns for clinicians with psychological and financial burdens to their patients and the society. There is an urgent need for early non-invasive biomarkers predicting this aggressive metastatic castration-resistant prostate cancer mCRPC and ensuring timely treatment decisions. In response to the urgent need of non-invasive biomarkers, we have developed a platform to collect and isolate prostate cancer cells and circulating tumor cells (CTCs) from clinical blood samples for “liquid biopsies” including single-cell gene expression, and biophysical assessment. Accumulative evidence showed that epithelial-mesenchymal transition (EMT) contributes significantly to advanced malignancy, metastasis, drug resistance and cancer stem cell characteristics. We hypothesize that EMT gene expression and biophysical features are biomarkers for the detection of mCRPC. Cancer cells and CTCs from prostate cancer patients’ blood samples were isolated using a C2M system (Microfilter and micromanipulator). Subsequently, cancer cells and CTCs were subject to molecular profiling of epithelial-mesenchymal transition (EMT) related genes using single cell microfluidic qRT-PCR, RNA sequencing, atomic force microscopy and Tracking of Single particles Using Nonlinear and Multiplexed Illumination (TSUNAMI) analysis for molecular and nanomechanical evaluation. Several lines of evidence based on single cell qRT-PCR and RNA-seq indicate that mCRPC and CTCs harbor enriched expression of EMT genes and EMT-related pathways and biophysical features as potential biomarkers for early detection of mCRPC. The EMT gene expression and behaviors in cancer cells were significantly prohibited by second line androgen signaling inhibitors, abiraterone acetate and enzalutamide. Additionally, small molecule inhibitors targeting EMT-related pathways show significant blocking effects of cancer cell proliferation, cell migration and invasion. In our study novel biomarkers, increased EMT gene expression and changes of nanomechanical properties of CTCs and cancer cells have been identified as early predictors for mCRPC. Those genes and pathways could be potential therapeutic targets for drug development for mCRPC treatment.
71
TRACING BACK THE PAST AND PREDICTING THE FUTURE OF CHILDHOOD LEUKEMIAS WITH SINGLE-CELL GENOMICS Veronica Gonzalez1, Robert Carter1, Sivaraman Natarajan1, Yuki Inaba1, John Easton2, Charles Gawad1,2 1St. Jude Children's Hospital, Oncology, Memphis, TN, 2St. Jude Children's Hospital, Computational Biology, Memphis, TN Acute lymphoblastic leukemia is the most common pediatric malignancy and remains a leading cause of pediatric cancer-associated morbidity and mortality. Previous studies have shown that childhood leukemias are initiated in utero, followed by evolution over years before transforming into clinical disease. We previously used single-cell DNA sequencing to deconvolute that evolutionary history where we found single nucleotide variants are acquired after structural variants, resulting in the development of co-dominant clonal populations. In the present studies we expand on those findings using single-cell RNA sequencing to dissect out differences between fetal, neonatal, and adult hematopoiesis with the aim of identifying cells that are uniquely vulnerable to transformation by childhood leukemia-associated mutations. In addition, we performed single-cell exome sequencing to acquire even higher resolution views of the clonal structures, which revealed evidence for massive population genetic diversity. We have now begun to study the importance of those lower frequency clonal populations in treatment response. Together, these studies highlight the power of single-cell genomics to understand cancer formation and potentially predict treatment response.
72
IMMUNE PROFILING OF BREAST CANCER BY SINGLE CELL GENOME ANALYSIS Woong-Yang Park, Hae-Ock Lee Samsung Medical Center, Samsung Genome Institute, Seoul, South Korea Tumor-infiltrating lymphocytes (TILs) and the immune gene signature correlate with clinical progression in breast cancer. We isolated single cells from four breast cancer patients with different molecular subtypes to analyze the whole transcriptome. Based on copy number alterations (CNAs) in gene expression patterns, tumor cells could be separated from micro-environmental non-tumor cells. Although the pure population of tumor cells from four different subtypes displayed the characteristics of each subtype, heterogeneity was observed in the gene expression of cancer-related pathways. Most non-tumor cells were infiltrated immune cells, which showed the immune-suppressive signature in the triple-negative breast cancer-type sample. Immune cells for the luminal type of breast cancer consisted of activated lymphocytes. In this study, we uncovered molecular characteristics of TILs in breast cancers by single cell transcriptome analysis, especially through CNA-based separation of tumor and non-tumor cells.
73
SINGLE-CELL TRANSCRIPTOMICS OF ONCOGENE-INDUCED SENESCENCE Yee Voan Teo1, Kristina Kirschner4, Anthony Green4, Nicola Neretti1,2, Tamir Chandra3 1Brown University, Molecular Biology, Cell Biology and Biochemistry, Providence, RI, 2Brown University, Center for Computational Molecular Biology, Providence, RI, 3University of Edinburgh, School of Molecular, Genetic and Population Health Sciences, Edinburgh, United Kingdom, 4University of Cambridge, Cambridge Institute for Medical Research, Cambridge, United Kingdom Cells can undergo proliferative arrest known as cellular senescence in response to various stressors including oncogene activation and telomere attrition. Senescent cells have been found to play important roles in several biological processes such as tumor suppression and aging. Here, we used single-cell RNA-seq to perform a time-course and trajectory analysis of oncogene-induced senescence in human diploid fibroblasts. The trajectory analysis revealed an interesting bifurcation event that leads to two senescent endpoints, suggesting that oncogene-induced senescent cells are not homogenous as there is a subpopulation of senescent cells that do not follow the continuous lineage through the time-points into senescence. The results show that both senescent clusters express sets of genes involved in canonical senescent pathways, and interestingly, each subpopulation also exclusively expresses senescent genes that are not in the other subpopulation. The investigation of the two populations of senescent cells may provide insights into different signaling pathways in oncogene-induced senescence that help to maintain growth arrest or possibly drive cells into a cancerous state.
74
EFFECTIVE DETECTION OF VARIATIONS IN SINGLE CELL TRANSCRIPTOME Kuanwei Sheng1, Wenjian Cao1, Qing Deng1, Chenghang Zong1
1Baylor College of Medicine, Molecular and Human Genetics, Houston, TX, 2Baylor College of Medicine, Molecular and Human Genetics, Houston, TX, 3Baylor College of Medicine, Molecular and Human Genetics, Houston, TX, 4Baylor College of Medicine, Molecular and Human Genetics, Houston, TX The development of single-cell RNA-seq methods has allowed the detection of gene expression at the microscopic scale that is not accessible by bulk RNA-seq approaches. While single-cell RNA-seq has been successfully used for the identification of new cell types in complex tissues, recent analyses have also indicated that technical noise still exists in single-cell RNA-seq assays. In contrast to the gene expression differences between different cell types, the next technical challenge for single cell RNA-seq assay is to reliably detect the transcriptional noise among the cells from the same population/type, which is still limited at the current state of art of single cell RNA-seq. To successful detect these transcriptional variations among cells of the same type, we developed a new single-cell RNA-seq assay --- Multiple Annealing and dC-Tailing based Quantitative single-cell RNA-seq (MATQ-seq). This method demonstrates ~90% sensitivity comparing to 50~60% sensitivity of current single-cell RNA-seq assays. More importantly, MATQ-seq provides the highly desired accuracy for detecting transcriptional variations existing in single cells of the same population. The technical noise of MATQ-seq was systematically characterized to warrant the detected variations are biologically genuine. By mapping the reads to the exons and introns respectively, we measured the transcriptional noise in mature RNAs and premature RNAs separately. The observation of large transcriptional noise in premature RNA is consistent with the transcriptional burst dynamics that have been widely observed in biological systems. With the sensitivity and accuracy demonstrated in our study, we believe that MATQ-seq will have broad applications in future biological and clinical research.
75
SINGLE CELL RNA SEQUENCING REVEALS T CELL PATTERNS IN HEPATOCELLULAR CARCINOMA Chunhong Zheng1, Xinyi Guo1, Liangtao Zheng1, Zemin Zhang1,2
1Peking University, Biomedical Institute for Pioneering Innovation via Convergence, Beijing, China, 2Peking University, Beijing Advanced Innovation Center for Genomics, Beijing, China The interplay between cancer cells and infiltrating lymphocytes holds the secrets to how immune cells are activated or suppressed and how they can play a role in both recognizing and eradicating cancer cells. However, the cell population within any given tumor is highly diverse and heterogeneous, thus prohibiting any conventional experimental methods for studying cellular properties of tumor infiltrating lymphocytes.To reveal the characteristics of immune T cells, we carried out single-cell RNA sequencing on >5000 single T cells isolated from peripheral blood, tumor and adjacent normal tissues from six hepatocellular carcinoma patients. Single cell analyses suggest distinct transcriptome characteristics and T cell receptor repertoires of tumor infiltrating T cells compared with the ones from other tissues. In particular, the T cell receptor repertoires are concordant with the cell characterizations in specific T cell subtypes. In addition, we identified an expanded exhaustion signature by analyzing the exhaustion status of tumor cytotoxic T cells. Potential subtypes of cell within each group are characterized by their expression properties. We also identified genes with expression patterns that resemble known therapeutic targets. Overall, the characteristics and subtypes of tumor infiltrating lymphocytes are provided that enable the deeper understanding of the immune-cancer cell interaction.
76
IMMUNE REPERTOIRE PROFILING BY HIGH-THROUGHPUT SEQUENCING AND SINGLE CELL ANALYSIS Jenny Jiang The University of Texas at Austin, Biomedical Engineering, Austin, TX About 13,000 children under one year old die every day worldwide, and most of these deaths were caused by infection. It has long been recognized that children’s immune systems are immature at birth and require time to mature to provide protection against pathogens or respond to vaccines. However, fewer studies have focused on children’s antibody repertoire development, diversification, and response to an infection. Knowledge in this area holds great interest to vaccine development and vaccination strategy design. This is especially urgent for malaria, as it still kills about 300,000 children under 5 each year, and the most advanced malaria vaccine provides only partial, short-lived protection in African children. However, studying the antibody repertoire in young children is challenging in several regards: 1) lack of analytical tools to exhaustively study the antibody repertoire from small volumes of blood, 2) lack of informatic analysis tools to turn high-throughput data into knowledge, and 3) the rarity of a large set of samples from young children obtained before and at the time of a natural infection. To address these challenges, we developed a highly accurate and high-coverage immune repertoire sequencing tool, MIDCIRS, to analyze antibody repertoire development, diversification, and capacity to respond to a natural infection with high accuracy and coverage in children who were experiencing acute febrile malaria. Unexpectedly, we discovered high levels of somatic hypermuations (SHM) in infants as young as three months old. SHM levels gradually increased with age in infants and stabilized in toddlers. Despite differences in SHM levels between infants and toddlers, SHMs in both age groups were similarly selected, and the degree of repertoire diversification was also similar. Together, these discoveries offer insight into the developing immune system and shed light on vaccine development for the extremely young children.
77
TRACING HAEMATOPOIETIC STEM CELL FORMATION AT SINGLE-CELL RESOLUTION Fan Zhou1, Xianlong Li2, Weili Wang3, Ping Zhu2,4, Jie Zhou1, Wenyan He1, Meng Ding1, Fuyin Xiong1, Xiaona Zheng1, Zhuan Li1, Yanli Ni1, Xiaohuan Mu3, Lu Wen2,5, Tao Cheng3,6, Yu Lan7, Weiping Yuan3, Fuchou Tang2,4,5,8, Bing Liu1,3,9
1Academy of Military Medical Sciences, Affiliated Hospital, Beijing, China, 2Peking University, College of Life Sciences, Beijing, China, 3Chinese Academy of Medical Sciences, Institute of Hematology and Blood Diseases Hospital, Tianjin, China, 4Peking University, Peking-Tsinghua Center for Life Sciences, Beijing, China, 5Peking University, Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China, 6National Institute of Biological Sciences, Collaborative Innovation Center for Cancer Medicine, Tianjin , China, 7Institute of Biotechnology, Genetic Laboratory of Development and Diseases, Beijing, China, 8Peking University, Center for Molecular and Translational Medicine, Beijing, China, 9Medical College of Jinan University, Institute of Hematology, Guangzhou , China Haematopoietic stem cells (HSCs) are derived early from embryonic precursors, such as haemogenic endothelial cells and pre-haematopoietic stem cells (pre-HSCs), the molecular identity of which still remains elusive. Here we use potent surface markers to capture the nascent pre-HSCs at high purity, as rigorously validated by single-cell-initiated serial transplantation. Then we apply single-cell RNA sequencing to analyse endothelial cells, CD45− and CD45+ pre-HSCs in the aorta-gonad-mesonephros region, and HSCs in fetal liver. Pre-HSCs show unique features in transcriptional machinery, arterial signature, metabolism state, signalling pathway, and transcription factor network. Functionally, activation of mechanistic targets of rapamycin (mTOR) is shown to be indispensable for the emergence of HSCs but not haematopoietic progenitors. Transcriptome data-based functional analysis reveals remarkable heterogeneity in cell-cycle status of pre-HSCs. Finally, the core molecular signature of pre-HSCs is identified. Collectively, our work paves the way for dissection of complex molecular mechanisms regulating stepwise generation of HSCs in vivo, informing future efforts to engineer HSCs for clinical applications. Key Words: Haematopoietic stem cell; Single-cell RNA sequencing; Molecular signature
78
INTERROGATING STEM CELL HETEROGENEITY BY SINGLE CELL TRANSCRIPTOMICS Ana Martin-Villalba German Cancer Research Center (DKFZ), Molecular Neurobiology, Heidelberg, Germany Adult mammalian neurogenesis is best characterized in the neurogenic niche of the subventricular zone of the lateral ventricles and the subgranular zone of the dentate gyrus. The process of neurogenesis includes activation of neural stem cell to generate neurogenic progenitors that differentiate in preset glial and neuronal lineages. This process can be influenced by signals present within the microenvironment of the niche that are regulated by environmental factors such as physical exercise, mood disorders, injury, and others. The study of heterogeneity in the response of neural stem cells to these events has not fully been addressed yet. In my talk I will discuss what we have learned about neural stem cell heterogeneity in the adult brain during homeostasis in the young and aged brain and following injury and how they compare to stem cells in other organs.
79
SINGLE-CELL TRAJECTORY ANALYSIS WITH REVERSE GRAPH EMBEDDING Xiaojie Qiu1, Hannah Pliner1, Qi Mao2, Cole Trapnell1 1University of Washington, Genome Sciences, Seattle, WA, 2HERE Company, Chicago, IL Single-cell trajectory analysis has emerged as a powerful technique for analyzing the sequence of regulatory changes that accompany cell differentiation and other complex processes. However, current algorithms for so-called “pseudotemporal analysis” are limited in their ability to reconstruct trajectories with complex branching structure. Many also require some degree of supervision to select genes that characterize progression through the biological process of interest. The large-scale, technically noisy nature of single-cell genomics experiments also demands robustness in the face of high-dimensionality and variability in the input. Here, we apply a recently developed technique called “reverse graph embedding” to address these computational challenges. Reverse graph embedding learns a map from the original high-dimensional input space to latent points in a low-dimensional space as well as a graph on the latent points that characterize the intrinsic structure of the data. The approach thus simultaneously reduces data dimensionality and learns the underlying trajectory. By limiting the number of latent points in the graph, our implementation can scale to very large single-cell experiments, learning the structure of complex biological processes in just seconds on a common PC. We will illustrate the effectiveness of reverse graph embedding through examples from several biological settings, including myoblast differentiation, myogenic reprogramming, and embryonic stem cell differentiation.
80
SINGLE-CELL ANALYSES OF X CHROMOSOME INACTIVATION DYNAMICS AND PLURIPOTENCY DURING DIFFERENTIATION Qiaolin Deng Karolinska Institutet, Cell and Molecular Biology, Stockholm, Sweden Pluripotency, differentiation, and X Chromosome inactivation (XCI) are key aspects of embryonic development. However, the underlying relationship and mechanisms among these processes remain unclear. Here, we systematically dissected these features along developmental progression using mouse embryonic stem cells (mESCs) and single-cell RNA sequencing with allelic resolution. We “digitalized” XCI progression using allelic expression of active and inactive X Chromosomes and surprisingly found that XCI states exhibited profound variability in each developmental state, including the 2i condition. XCI progression was not tightly synchronized with loss of pluripotency and increase of differentiation at the single-cell level, although these processes were globally correlated. In addition, highly expressed genes, including core pluripotency factors, were in general biallelically expressed. Taken together, our study sheds light on the dynamics of XCI progression and the asynchronicity between pluripotency, differentiation, and XCI.
81
SEQUENCING IS BELIEVING: PROBING MUTAGENESIS ONE CELL AT A TIME Cheng-Zhong Zhang1,2,3,4, Neil T Umbreit2, Alexander Spektor2, Yingying Zhang3, Matthew Meyerson3,4, David Pellman2,4 1Dana-Farber Cancer Institute, Bioinformatics and Computational and Biology, Boston, MA, 2Dana-Farber Cancer Institute, Pediatric Oncology, Boston, MA, 3Dana-Farber Cancer Institute, Medical Oncology, Boston, MA, 4Broad Institute of Harvard and MIT, Cancer Program, Cambridge, MA Single-cell sequencing can directly reveal cell-to-cell variation at both genomic and transcriptomic levels. The digital nature and base-pair resolution of single-cell sequencing also makes it a high-resolution, high-throughput assay for studying molecular biology at the single-cell level. We have recently developed an approach (‘Look-Seq’) combining DNA sequencing and live-cell imaging to characterize DNA damage due to cell division errors. The Look-Seq analysis directly links genetic mutations detected by DNA sequencing to aberrant chromosomes detected by live-cell imaging by the chromosomal haplotype. Importantly, comparison of the genetic variants phased to each homologous chromosome distinguishes biological variation on a single chromosome (but not its homolog) from single-cell sequencing artifacts affecting both homologs. I discuss how DNA damage and chromosome missegregation can be measured by single-cell sequencing and how we apply the LookSeq analysis to characterize mutagenesis on lagging or bridging chromosomes.
82
SCALABLE WHOLE-GENOME SINGLE-CELL LIBRARY PREPARATION WITHOUT PRE-AMPLIFICATION Hans Zahn*1, Adi Steif*2, Emma Laks2, Peter Eirew2, Michael VanInsberghe1, Sohrab P Shah2, Samuel Aparicio2, Carl L Hansen1
1University of British Columbia, Michael Smith Laboratories, Vancouver, Canada, 2BC Cancer Agency, Department of Molecular Oncology, Vancouver, Canada *authors contributed equally Genomic heterogeneity is a central feature in cancer and plays a critical role in disease initiation, progression, and response to treatment. Studying this heterogeneity requires robust, scalable, and high-fidelity single-cell genomics technologies. To date, the predominant strategy for single-cell genomics relies on whole genome amplification (WGA) prior to library construction. While several approaches have been developed to minimize these effects, all amplification methods introduce representational bias that obscures structural variants and limits genome coverage. Here we describe a new microfluidic device and protocol that uses nano-litre volume transposition reactions to enable the streamlined preparation of single-cell next-generation sequencing libraries without the need for prior amplification. The 192-chamber device integrates the entire library preparation workflow at a cost of <$0.5 per cell, including single-cell isolation, lysis, tagmentation, and PCR amplification for index barcode and adapter incorporation. We apply our direct library preparation (DLP) method to examine the whole genomes of 782 cells from two immortalized cell lines and two murine xenografts derived from a primary triple-negative human breast cancer tumor. Low-coverage multiplexed sequencing (about 0.07X per cell) and analysis reveals that lack of pre-amplification results in genomes with more uniform coverage than existing WGA methods, permitting inference of high-resolution copy number profiles. By pooling our low-depth single-cell genomes, we generate a “bulk-equivalent” genome in silico, and demonstrate that this achieves equivalent coverage breadth and uniformity to a standard bulk genome at the same depth. We also show that LOH, SNVs, and breakpoints can be reliably detected in “bulk-equivalent” genomes. Finally, we apply the method to examine 514 single-cell copy number profiles from two polyclonal xenograft passages. Phylogenetic analysis reveals striking diversity, with multiple sub-populations that are undetectable in the bulk, as well as dynamic expansion and diversification of rare clones between the two xenograft passages. We contend that low-depth sequencing and pooling of single-cell libraries constructed without pre-amplification presents an attractive replacement for conventional bulk sequencing strategies, permitting detailed reconstruction of copy number clonal lineage as well as standard bulk inference of other variants, without significant increase in total sequencing effort.
83
MAPPING HUMAN HEMATOPOIETIC HIERARCHY AT SINGLE CELL RESOLUTION BY MICROWELL-SEQ Shujing Lai, Yang Xu, Guoji Guo Zhejiang University, Center for Stem Cell and Regenerative Medicine, Hangzhou, China Stem cell differentiation pathways are most often studied at the population level, whereas critical decisions are executed at the level of single cells. Here we present a simple approach for high throughput analysis of single cells. Using agarose microwells and magnetic index beads, we can label thousands of single cells and analyze their transcriptome at the same time. We show that Microwell-seq can be used to dissect cellular heterogeneity and define cell types in a cost effective way. Systematic analysis of human hematopoietic stem and progenitor cells by Microwell-seq reveals that few oligopotent progenitor intermediates were present in the adult human hematopoietic hierarchy. Instead, we present a revised differentiation pathway with mainly unipotent progenitors downstream of human hematopoietic stem cells. Our method is widely applicable to other stem cell systems.
84
DIVERSE EVOLUTIONARY DYNAMICS IN TUMOR AND EARLY NEURODEVELOPMENT INFERRED BY SINGLE CELL SEQUENCING Guibo Li1,2, Kui Wu1,2, Shiping Liu1,3, Zhouchun Shang1, Liang Wu1, Luting Song1, Bo Li1, Yong Hou1,2, Xun Xu1 1BGI-Research, BGI, Shenzhen, China, 2Department of Biology, University of Copenhagen, Copenhagen, Denmark, 3School of Life Science, Sun Yat-sun University, Guangzhou, China Cell evolution accompanies whole process of embryonic development, and also promotes development of tumor. However, it remains a big challenge how individual cells respond to microenvironmental perturbations and evolve during biological processes such as tumorigenesis and development. Single cell omics technologies which is advancing rapidly provide unprecedented opportunities to study cell evolution. Here, we firstly developed a high throughput single cell sequencing system which can generate thousands of single cells omics data. Then single cell sequencing was applied on tumor evolution and early neurodevelopmental research. We reconstructed evolution process and gene expression pattern on a CLL patient over the last 18 years of her 29-year disease course. Also we carried out multi-regional single cell sequencing on 448 cells derived from two primary GBMs, characterized the evolutionary dynamics in both GBM tumors by pinpointing alteration events in distinct cell populations. In an advanced prostate cancer who had serial biopsies over the course of his clinical history, we isolated CTCs from his peripheral blood and identified the shared genomic alterations well as some heterogeneity between CTCs and tumor tissues. Single cell RNA sequencing on iPSCs-derived neural progenitor cells reveals the heterogeneity and molecular signatures of Rosettes in early neural development and immediate progeny during that stage. Our analysis further delineated molecular cascades underlying early development of neural lineage. Single cell omics open a new and broad window for cellular heterogeneity and evolution studies. Back to the basic unit of life - individual cell, we can apply classic population evolution research strategy on cell evolution, and reconstruct more precise evolution path, which provide valuable information for disease diagnosis and intervention.
85
INVESTIGATION OF EPITHELIAL TISSUE HETEROGENEITY USING MICROFLUIDIC SINGLE-CELL TRANSCRIPTOMICS Angela R Wu1,2, Norma F Neff2, Barbara Treutlein 2,3, Winston Koh2, Michael E Rothenberg4,5, Yair J Blumenfeld7, Yasser Y El-Sayed7, Michael F Clarke4,5, Stephen R Quake1,6 1The Hong Kong University of Science and Technology, Div. of Life Science, Hong Kong, China, 2Stanford University, Dept. of Bioengineering, Stanford, CA, 3Max Planck Institute for Evolutionary Anthropology, Dept. of Evolutionary Genetics, Leipzig, Germany, 4Stanford University Medical Center, Dept. of Internal Medicine, Div. of Oncology, Stanford, CA, 5Stanford University Medical Center, Institute for Stem Cell Biology and Regenerative Medicine and the Ludwig Cancer Center, Stanford, CA, 6Howard Hughes Medical Institute, Chevy Chase, MD, 7Stanford University Medical Center, Div. of Maternal-Fetal Medicine, Dept. of Ob-Gyn, Stanford, CA Interest in single-cell whole transcriptome analysis is growing rapidly, especially in cases where one wants to profile rare or heterogeneous populations of cells. We compared the performance of various commercially available single-cell RNA amplification methods in both microliter and nanoliter volumes. We benchmarked each method to conventional RNA-seq of the same sample using bulk total RNA, as well as to multiplexed qPCR, which is the current gold standard for quantitative single-cell gene expression analysis. In doing so, we were able to systematically evaluate the sensitivity, precision, and accuracy of various approaches to single-cell RNA-seq. Our results show that it is possible to use single-cell RNA-seq to perform quantitative transcriptome measurements of single cells, that it is possible to obtain useful gene expression measurements with a relatively small number of sequencing reads, and that when such measurements are performed on large numbers of cells, one can recapitulate both the bulk transcriptome complexity as well as the distributions of gene expression levels found by single-cell qPCR. We subsequently used microfluidic-based RNA-seq to profile single cells of the human colon and human cervix, in order to identify potentially interesting biomarkers and novel cellular subpopulations, demonstrating the powerful applications single cell transcriptomics in various biological samples.
86
CONVENTIONAL AND ‘VIRTUAL’ MICROFLUIDICS FOR LOW-INPUT & SINGLE-CELL GENOMICS Liyi Xu1,2, Soohong Kim1, Dwayne Vickers1, Navpreet Ranu1,2, Paul Blainey1,2 1The Broad Institute of MIT and Harvard, Cambridge, MA, 2Massachusetts Institute of Technology, Department of Biological Engineering, Cambridge, MA We report advancements in microfluidic sample preparation systems for ultra-sensitive sequencing assays. First, a multilayer lab-on-chip platform that reduces DNA input requirements 100-fold for microbial isolate sequencing that integrates the key steps in cells-to-sequence library sample preparation for up to 96 samples. This platform has a general-purpose micro-architecture able to run workflows with arbitrary numbers of reaction and cleanup steps, and we are exploring its application in analysis of DNA, RNA, and chromatin. Alongside lab-on-chip systems, we have developed hydrogel-based “virtual microfluidics” as a simple and robust alternative to complex engineered microfluidic systems for the compartmentalization of nucleic acid amplification reactions without micro-engineered devices or consumables. We applied in-gel digital multiple displacement amplification (dMDA) to purified DNA templates, cultured bacterial cells and human microbiome samples in the virtual microfluidics system, and demonstrated whole-genome sequencing of single-cell MDA products with excellent coverage uniformity and markedly reduced chimeric sequences. We expect the ease-of-use and improved data quality to help drive the adoption of single-cell sequencing in many life science laboratories.
NOTES
NOTES
NOTES
NOTES
NOTES
NOTES
Participant ListDr. Ophelie Arnaud
Riken Institute
Dr. Li BAO
KU
Ms. Shuhui Bian
Peking University
Ms. Chen Cao
Peking University
Dr. Yun-Juan Janet Chang
Rutgers University
Dr. Xiaowei Chen
Novartis
Dr. Chongyi Chen
Harvard University
Dr. Wei-Ting Chen
VIB, KU Leuven
Ms. Chuan Chen
Tongji University
Dr. Chun-Liang Chen
University of Texas Health at San Antonio
Mr. Kai Chen
Shanghai Institutes for Biological Sciences
Mr. He Chen
Peking University
Mr. Zeyu Chen
University of Pennsylvania
Dr. Xuewen Cheng
Institute of Neuroscience
Dr. Li-Fang Chu
Morgridge Institute for Research-UW Madison
Ms. Chu Chu
YunnanKeyLaboratory of Primate Biomedical Research
Dr. Pin Cui
Peking University
Ms. Yueli Cui
Peking University
Dr. Ya Cui
Chinese Academy of Sciences
Dr. Yupeng Cun
University Colonge
Dr. Dai Dai
Rengji Hospital
Dr. Haiqiang Dai
Chinese Academy of Sciences
Mr. Alexander Davis
University of Texas MD Anderson Cancer Center
Ms. Qiaolin Deng
Karolinska Institutet
Mr. Ji Dong
Peking University
Dr. James H. Eberwine
University of Pennsylvania Medical School
Prof. Richard Epstein
The Kinghorn Cancer Centre
Dr. Xiaoying Fan
Peking university
Dr. Yue Fan
China Novartis Institutes for BioMedical Research
Dr. Andrew Farmer
Takara Bio. USA
Ms. Yusi Fu
Peking University
Mr. Cheuk Wang Fung
Hong Kong University of Science and Technology
Dr. Suvarna M Gandlur
Takara Bio USA
Ms. Yun Gao
Peking University
Dr. Shouguo Gao
National Institutes of Health
Ms. Rui Gao
Tongji University
Dr. Yawei Gao
Tongji University
Dr. Shuai Gao
Peking University
Dr. Lana Garmire
University of Hawaii Cancer Center
Dr. Charles Gawad
St. Jude Children's Research Hospital
Dr. Hui Ge
Novartis
Ms. Chenyang Geng
Peking University
Dr. Shuang Geng
Peking University
Mr. Arsham Ghahramani
Francis Crick Institute
Dr. Haibiao Gong
Fluidigm Corporation
Dr. Veronica Gonzalez-Pena
St. Jude Children's Research Hospital
Prof Guoji Guo
Zhejiang University School of Medicine
Dr. Hongshan Guo
Peking University
Prof. Yong Guo
Tsinghua University
Dr. Xiaojie Guo
Amgen Asia R
Dr. Fan Guo
Peking university
Ms. Xinyi Guo
Peking University
Dr. Yanmei Han
Second Military Medical University
Dr. Kyung Yeon Han
Samsung Medical Center
Dr. Xiaoping Han
Zhejiang University School of Medicine
Dr. Chunyan He
IU Richard M. Fairbanks School of Public Health
Mr. Zhisong He
CAS-MPG PICB, SIBS, CAS
Dr. Ying He
Amgen Asia R
Dr. Yu Hou
Peking University
Dr. Ganlu Hu
Shanghai Institutes for Biological Sciences
Dr. Yuqiong Hu
Peking university
Dr. Linping Hu
State Key Laboratory of Experimental Hematology
Dr. Boqiang Hu
Peking university
Prof. Yanyi Huang
peking universtiy
Ms. Dongqing Jiang
Peking University
Dr. Di Jiang
Cell Research
Dr. Ning Jenny Jiang
University of Texas Austin
Mr. Haoxuan Jin
BGI
Prof. Naihe Jing
Shanghai Institutes for Biological Sciences
Ms. YU KANG
Kunming University of Science and Technology
Dr. Hui Kang
OrigiMed Inc.
Dr. Peter Kharchenko
Harvard Medical School
Mr. Jinhyun Kim
Seoul National University
Dr. Kyu-Tae Kim
Samsung Medical Center
Dr. Sungsik Kim
Seoul National University
Dr. Hae-Ock Lee
Samsung Medical Center
Mr. YONGJU LEE
Seoul National University
Mr. Sangmoon Lee
Seoul National University College of Medicine
Dr. Qingqing Li
Peking university
Prof. Jing Li
Nanjing Medical University
Ms. Congru Li
Beijing Institute of Genomics, CAS
Dr. Yanjun Li
Peking University
Dr. Jingyi Li
Peking university
Ms. Chunmei Li
Peking University
Dr. Jiesheng Li
Peking University
Dr. Lin Li
Peking university
Dr. Jingyun Li
Peking university
Ms. Li Li
Peking University
Dr. Xian-long LI
Academy of Military Medical Sciences
Ms. Xiangyu Li
Tsinghua
Dr. Zong-cheng Li
Academy of Military Medical Sciences
Dr. Kwangil Lim
Sookmyung Women's University
Dr. Yunfu Lin
University of Texas MD Anderson Cancer Center
Dr. Veronique Lisi
UCSB
Dr. BENJAMIN Liu
Fluidigm
Mr. Chengqian LIU
University of Turku
Dr. Kai Liu
HKUST
Dr. Xiaomeng Liu
Peking university
Prof. Bing Liu
Academy of Military Medical Sciences
Ms. Zhe Liu
National Institute of Biological Sciences, Beijing
Dr. Zhenbo Liu
Beijing Institute of Genomics
Dr. Longqi Liu
BGI-Shenzhen
Mr. Xiang Liu
Peking University
Mr. Xiaotian liu
Hong Kong University of Science and Technology
Dr. Guifen Liu
Tongji University
Dr. Mingyang Lu
Jackson Laboratory
Dr. Limin Lu
Amgen Asia R
Dr. Sijia Lu
Yikon Genomics
Dr. Chongjin Lu
Peking University
Dr. Wenji Ma
Columbia University
Dr. John Marioni
European Molecular BiologyLaboratory
Dr. Ana Martin-Villalba
German Cancer Research Center
Mr. Zhun Miao
Tsinghua University
Dr. Peggy S Myung
Yale University
Mr. Nicholas E Navin
The University of Texas MD Anderson Cancer Center
Dr. chao ning
Beijing institue of genomics, CAS
Dr. Yabo Ouyang
Beijing Institute of Hepatology
Dr. Xinghua PAN
Southern Medical University
Dr. Yuhong Pang
Peking University
Prof. Woong-Yang Park
Samsung Medical Center
Dr. Donghyun Park
Samsung Medical Center
Dr. Guangdun Peng
Shanghai Institutes for Biological Sciences
Dr. Joseph E Powell
University of Queensland
Dr. Joseph Powell
University of Queensland
Dr. Sebastian A Preissl
Ludwig Institute For Cancer Research
Prof. Kun Qu
University of Science and Technology of China
Dr. Stephen Quake
Stanford and HHMI
Dr. John Rasko
The University of Sydney
Dr. Wolf Reik
The Babraham Institute
Dr. Jie Ren
Peking University
Dr. Paul Robson
The Jackson Laboratory for Genomic Medicine
Dr. Nick Sampas
Agilent Technologies
Dr. Rickard Sandberg
Karolinska Institutet
Dr. Jonathan Schimmel
10x Genomics
Dr. zhouchun shang
BGI-Shenzhen
Mr. Kuanwei Sheng
Baylor College of Medicine
Dr. Bo SiTu
Southern Medical University
Dr. Liane S Slaughter
Hong Kong University of Science and Technology
Ms. Adi Steif
BC Cancer Agency / University of British Columbia
Dr. Xianbin Su
Shanghai Jiaotong University
Prof. Xiaodong Su
Peking university
Dr. Hao Sun
Fuzhou University
Mr. Yaoyu Sun
Beijing Institute of Genomics
Dr. Shengbao Suo
CAS-MPG Partner institute for Computational Biolog
Dr. Fuchou Tang
Peking University
Ms. Yee Voan Teo
Brown University
Ms. Eszter Toth
University of Tsukuba
Mr. Tam M Tran
Celgene Corporation
Mr. Cole Trapnell
University of Washington
Dr. Barbara Treutlein
Max Planck Institute for Evolutionary Anthropology
Dr. Cheuk Ho TSANG
Chinese University of Hong Kong
Dr. Alexander van Oudenaarden
Hubrecht Institute
Mr. Michael A VanInsberghe
University of British Columbia
Dr. lei wang
BGI
Dr. Jiaxu Wang
Genome Institute of Singapore
Dr. Ran Wang
Chinese Academy of Sciences
Dr. Xi Wang
German Cancer Research Center
Ms. Rui Wang
Peking University
Dr. Larry Wang
Fluidigm
Mr. Ping Wang
Peking University
Dr. Jianbin Wang
Tsinghua University
Ms. Shuyu Wang
Tsinghua University
Dr. Han Wang
Tsinghua University
Dr. Gang Wei
Fudan University
Dr. Lu Wen
Peking University
Mr. Pao-Shu Wu
MacKay Memorial Hospital
Prof. Ligang Wu
Shanghai Institute for Biological Sciences
Ms. Liang Wu
BGI-Shenzhen
Dr. Angela Wu
The Hong Kong University of Science and Technology
Ms. You Wu
Tongji University
Ms. Xiaoxuan Wu
Peking University
Dr. Peng Wu
Janssen
Dr. Xinglong Wu
Peking University
Mr. Weikun Xia
Tsinghua University
Prof. Xiaoliang Sunney Xie
Harvard University
Dr. Lili Xu
Massachsetts Institute of Technology
Dr. Xun Xu
BGI
Ms. Xiaochan Xu
Peking University
Dr. Lu Yang
Peking University
Dr. Yong Yao
National Institutes of Health
Dr. Lizhi Yi
Beijing Institute of Genomics,UCAS
Ms. PEIPEI YIN
Peking University
Mr. Lei YU
Hong Kong University of Science and Technology
Dr. Kai Yu
Peking University
Mr. Peijia Yu
Peking University
Ms. Qianhui Yu
Partner Institute for Computational Biology
Dr. Guohong Yuan
Tsinghua University
Mr. Hans zAHN
UBC
Ms. Ruge Zang
Tongji University
Dr. Weiwei Zhai
Genome Institute of Singapore
Ms. Lu Zhang
center for quantitative biology
Dr. Cheng-Zhong Zhang
Dana-Farber Cancer Institute
Mr. Xiannian Zhang
Peking University
Dr. Yi Zhang
Peking University
Dr. Kunshan Zhang
Tongji hospital, Tongji University School of Medic
Prof. Yong Zhang
Tongji University
Dr. Yanni Zhang
Amgen Asia R
Dr. Yun Zhang
Peking University
Dr. Zemin Zhang
Peking University
[email protected];[email protected]
Dr. Lin-lin Zhang
Academy of Military Medical Sciences
Dr. Lei Zhang
Peking University
Dr. Yao-zhong Zhang
The University of Tokyo
Dr. Gang Zhang
University of Oxford
Ms. Shu Zhang
Peking University
Ms. Ling Zhang
Peking University
Dr. Li-Feng Zhang
Nanyang Technological University
Mr. Wencao Zhao
Shanghai Institute for Biological Sciences
Dr. Shanrong Zhao
Pfizer Inc
Ms. Zhikun Zhao
BGI-shenzhen
Dr. Yuxuan Zheng
Peking University
Dr. Jingjing Zheng
Cell Research
Dr. Xiao-na Zheng
Academy of Military Medical Sciences
Prof Lei Zheng
Southern Medical University
Dr. Chunhong Zheng
Peking University
Dr. Chaofang Zhong
Huazhong University of Science and Technology
Dr. Fan Zhou
Peking University
Dr. Yuan Zhou
Peking University
Dr. Jie Zhou
Academy of Military Medical Sciences
Ms. Wen Zhou
Caltech
Dr. Ping Zhu
Peking University
Mr. Xiurui Zhu
Tsinghua University
Prof. Chenghang Zong
Baylor College of Medicine
Mr. Jun Zou
Sichuan University
Dr. Yang Zou
Jiangxi Provincial Maternal and Child Health Hospi
Dr. Xin Zou
Key Laboratory of Systems Biomedicine
GENERAL INFORMATION
Issues Contacts Number
Reception 86-512-69568888
Security Duty Manager 86-18913579302
Clinic Front Office
Duty Manager
86-18913579300 Engineering IT Front Office
Dining, Bar Dining Rooms: Octagon – Floor 1 Suz Garden – Floor 1 Bar: D Lounge: First Floor Opening Hours: 09:00 – 00:00
Internet Access Public Area – Wireless Room – Wireless / Plug-in
Business Center Location: First Floor Opening Hours: 08:00 – 20:00
Gymnasium & Aerobic Room Location: Mezzanine Floor Opening Hours: 06:00 – 23:00 Indoor Swimming Pool Location: Mezzanine Floor Opening Hours: 06:00 – 23:00 Taxi Local Taxi: Hotline: 86-512-67776777 Out of city - eHi Car Rental: Tel: 4008886608 Email: [email protected]
You may contact Cold Spring Harbor Asia Office at 86-512-62729029 for any conference inquiries.
