Analyzing Expression Profiles from Single Stem Cells Using...

Analyzing Expression Profiles from Single Stem Cells Using the Single Cell-to-Ct™ kit

Richard Fekete, Ph.D.Life TechnologiesAustin, Texas

2

Outline

• Background

• Why Single Cell Analysis?

• Single Cell Workflow

• Describe hESC-to-hNSC Differentiation Expt.

• Single Cell Data Analysis/Normalization

• Conclusions

3

Analysis of “populations”• Analyzing gene expression en masse gives an average profile (obscuring

populations)

4


populations)

5


populations)• Small populations are lost• Example finding Cancer Stem cells in a population of normal cells or finding

undifferentiated Embryonic Stem Cells in a population of differentiated (important for transplantation)

6


populations)

• These issues are important for analysis of any analyte• This type of “digital” analysis is not new, flow cytometry looks at individual

cells, but number of analytes per cell is limited and need affinity reagents/dyes

• Small populations are lost• Example finding Cancer Stem cells in a population of normal cells or finding

undifferentiated Embryonic Stem Cells in a population of differentiated (important for transplantation)

7

Key Emerging Trend – Study of Single Cells• Nearly all tools look at populations of cells

• Everyone realizes there are cell-to-cell differences

Rare cell or event• Circulating metastatic cells• Fetal cell in maternal blood• Event within a library

Scarce, precious sample• Archival tissue (FFPE)• Clinical sample (fresh tumor)• Biomarker discovery

Single cell precision in populations

• Drug candidate screening• Cell differentiation (eg stem cell)• Stochastic responses to stimuli

Single cell applications span basic research, clinical research, diagnostics, and therapeutics

8

• 4 functional steps: cell lysis – Cells-to-Ct based, reverse transcription – SuperScriptIII, cDNA pre-amplification – PreAmp MasterMix and Real-Time PCR

• gDNA removal occur at the same time as cell lysis - 5 minutes at room temperature• All done in same tube (no loss due to tube transfers)• Enable use of entire lysate sample in subsequent RT/PreAmp rxns• Performance benefit with smaller volumes and allows use of 384-well plates• Increased sensitivity (SuperScript III)• Optimized volumes in workflow minimize errors in reaction assembly (no water

addition in any step)• Multiple stopping points• microRNA and DNA protocols

Single Cell-to-Ct: a complete workflow to obtain statistically relevant single cell data

9

Single Cell-to-Ct: a complete workflow to obtain statistically relevant single cell data

Dynabeads®

CD3/CD28

Naive orresting T cell

10

•500 μL Single Cell Lysis Solution (store at 4ºC)•50 μL Single Cell Stop Solution (store at -20ºC)•50 μL Single Cell DNase I (store at -20ºC)•150 μL Single Cell VILO™ RT Mix (store at -20ºC)•75 μL Single Cell SuperScript® RT (store at -20ºC)•265 μL Single Cell PreAmp Mix (store at -20ºC)

Kit Components 50 and 400 reaction kits

11

Starting Material

• Samples can be obtained through− FACS− Dilution− Laser capture microdisection (not tested internally)− Physical selection (eg bead based)− Laser Ablation (Cyntellect Leap System; not tested internally)− Mouth pipetting

• Up to 10 cells can be used

• Input volume of cell (s) should be less than 1 ul

• Validated multiple cell lines (including hESC) for this kit and >20 for the parent kit

12

Product Performance

12

14

16

18

20

22

24

26

28

CT

1 Cell Equivalents

Single Cells 100 cells

20.4

21.6

13.6

Single Cell Detection Occurs with expected sensitivity (6.6 Cts difference from 100 cells). Technical reproducibility of the 100 cell samples and single cell equivalents is tight (CV of Cell Equivalents is small). Variability of single cells is due to biological variability in single cells

N=84 single cells

13 | Life Technologies Proprietary & Confidential | 12/3/2012

Ten-cell samples were lysed in the presence of Single Cell Lysis buffer (5 min at 25°C), frozen and thawed five times in PBS, or boiled for 10 min in PBS or RT buffer at 95°C.

Product Performance


Potential applications of hESCs• Pluripotency make embryonic stem cells a means to new cell

replacement strategies• Applications require pure populations of differentiated cells


Neural lineage differentiation markers/pathway

Immature Neuron

Mature Neuron

β3 tubulin+NeuN+

NF160+Synapsin+

Cell death

Quiescence

Maturing stem cells

Neural Stem CellNestin+Sox1+Pax6+CD133+Sox2+

Embryonic Stem Cell

Oct4+Nanog+Sox2+

Neuronal precursor

PSA-NCAM+ β3 tubulin+ CD133-Nestin+ Sox1-, Sox2-

Glial Precursor

A2B5+Olig2+NG2+Nestin+Sox1-

Oligodendrocyte Precursor Late Oligodendrocyte

PrecursorMature

Oligodendrocyte

Olig2+NG2+PDGFRα+Nkx2.2+Sox10+

O4+GalC+Olig2+Nkx2.2+

CNP+PLP+MBP+

Mature Astrocyte

Astrocyte precursor

CD44+GFAP-

GFAP+

UTF1+ZFP42+CD133-

Nestin-Sox1-

• Identifying new markers becomes difficult due to heterogeneity during differentiation


SV25 (Olig2-EGFP), a derivative of BG01

• Platform line maintains expression of hESC markers

17

Grow ESCs on Geltrex plates in CM until 80-90% confluent

Change media to SFM for 24 hours.

Culture in NAA media until confluent, changing daily.

Split cells 1:2 using TrypLE, culture on Geltrex plates.

Once confluent use collagenase, create neurospheres 150 – 250 um in Ultra Low Attachment plates.

When cells start to attach to the plate, split 1:2 onto Geltrex plates.

Continue culturing on Geltrex plates, splitting 1 2 2 3 d

Day 0 ESC

Day 1 ESC

Day 2 Differentiating ESC

Day 6 Neurospheres

Day 13 Neurospheres

Day 17 AttachedNeurospheres

Day 20 Rosette formation

Day 22 NSC derived

ESC to NSC Workflow 1575 μm

Day 0 Day 3

Day 7 Day 11

Day 17 Day 18

Day 21 Day 21157 μmDay 24 GFP Overlay

| Life Technologies Proprietary & Confidential | 12/3/2012

18

Single Cell Analysis Workflow• Look at single cells to more closely define NSC profiles, and identify

profiles of cells not differentiating in the NSC pathway (markers not detectable due to low cell numbers)

Day 0 - ESC

Day 7 -Neurospheres

Day 17 –attached Neurospheres Day 18

Day 21 -Rosettes

Day 11 -Neurospheres

Day 3 –diff ESC

0 cells 100 cells

10 genes/cell10 plates/time point

•30 “0” cell samples•30 “100” cell samples•900 “1” cell samples

Cells-to-CT™ TaqMan®PreAmp MMx

Gene Expression Master Mix

VILO RT Superscript®



Analyzed genes• Initial 10 genes for analysis• Genes for ESC and NSC, and for cells differentiating into other types

Gene Symbol

Description / Alternate Name Group Description

POU5F1 OCT4 Stem Cell - slow off

UTF1 UTF1 Stem Cell

ZFP42 REX1 Stem Cell - fast off

SOX1 Homo sapiens SRY (sex determining region Y)-box 1 (SOX1).

Proneural and Neural stem cell - late on

NES NESTIN Proneural and Neural stem cell

PAX6 Proneural and Neural stem cell - early on

TUBB3 Beta 3 Tubulin` Motor Neuron

T BRACHYURY Mesoderm - cardiac

GFAP GLIAL FIBRILLARY ACIDIC PROTEIN

astrocyte

GAPDH Control Kit Control gene

20

100 cells (average CT): 13.7 + 0.2

1 cell low cluster (36 cells): 19.2 + 1.3

1 cell high cluster (48 cells): 27.4 + 1.0

Average CT (84 cells): 21.4 + 4.2

• Analyzing gene expression profiles en masse gives an average profile• Obscures or potentially obliterates any differences in single cells

100 cells 1 cellACTB


• Massive amount of cell-to-cell variation -5,000 fold differences in 84 single cells in an endogenous control gene!

Single cell analysis or Embryonic Stem Cells

21

Single cell analysis or Embryonic Stem Cells• Gene variability - large expression range for one gene; size variations do

not account for this, but cell cycle dependent regulation may

100 cells1 cell

ACTB

100 cells1 cell

OCT4

• Cell-to-cell variability - expression profiles are not the same in every cell• See small sub-populations (OCT4 low expressers)

22

Single cell analysis or Embryonic Stem Cells• Gene variability - large expression range for one gene; size variations do

not account for this, but cell cycle dependent regulation may

100 cells1 cell

ACTB

100 cells1 cell

OCT4

• Cell-to-cell variability - expression profiles are not the same in every cell• See small sub-populations (OCT4 low expressers)

“technical” variability

22.8±0.3 (6.2 from 100 cell samples)

• Technical variability (from method of detection) needs to be identified (low here)


23

10

20

30

40

CT

10

20

30

40

CT

CT

Day 0

Day 14

Day 24

Variation in expression level in single cells

10

20

30

40

GA

PD

GFA

P

NES

PAX6

POU

5F1 T

TUB

B3

UTF

1

ZFP4

2

24

Single cell dynamics (cell-to-cell differences)• 100 cell samples show progression of 0, 7, and 14 days

100 Cell Samples

Day 0 7 14


25

Single cell dynamics (cell-to-cell differences)• 100 cell samples show progression of 0, 7, and 14 days

100 Cell Samples

Day 0 7 14

Single Cell Samples

• Clustering of 1 cell samples shows clustering of 0 and 14 day samples but the 7 day samples show 2 populations – one similar to day 0, the other to day 14

7 14 7 14 0 7


26

qPCR results from 1 and 100 cell samples1 Cell 100 Cell

Day 0

Day 14

Day 24

1014182226303438

CT

1014182226303438

CT

1014182226303438

CT

GAPD NESOCT4

TUBB3ZFP42

1014182226303438

1014182226303438

1014182226303438

GAPD NESOCT4

TUBB3ZFP42

CT

CT

CT

27

Normalization - 100 Cell Data From Day 0

5

10

15

20

25

30

35

GAPDH NES POU5F1 TUBB3 ZFP42

CT

Thirty 100 cell samples show similar expression levels as demonstrated by small center quantiles (left). Normalized expression levels of each gene to GAPDH expression levels remove some of the sample to sample variability as shown by smaller box and whisker (right) and show that the gene “profiles” of each sample are very similar.

•100 cell samples have similar expression levels which tighten when normalized

28

Normalization - 100 Cell Data From Day 0

5

10

15

20

25

30

35


CT

Thirty 100 cell samples show similar expression levels as demonstrated by small center quantiles (left). Normalized expression levels of each gene to GAPDH expression levels remove some of the sample to sample variability as shown by smaller box and whisker (right) and show that the gene “profiles” of each sample are very similar.

•100 cell samples have similar expression levels which tighten when normalized

-10

-5

0

5

10

15

20

ΔC

TNES-

GAPDHPOU5F1-GAPDH

TUBB3-GAPDH

ZFP42-GAPDH

29

Normalization - Single Cell Data From Day 0•Single cell samples give wide range of expression levels which spreads out further when normalized

900 single cell samples show a wide range of expression levels shown by the large box and whiskers (left). After normalization (right), box and whisker sizes increase as does the number of outliers

15

20

25

30

35

40

45


CT

30

Normalization - Single Cell Data From Day 0•Single cell samples give wide range of expression levels which spreads out further when normalized

900 single cell samples show a wide range of expression levels shown by the large box and whiskers (left). After normalization (right), box and whisker sizes increase as does the number of outliers

15

20

25

30

35

40

45


CT

-10

-5

0

5

10

15

20

NES-GAPDH

POU5F1-GAPDH

TUBB3-GAPDH

ZFP42-GAPDH

ΔC

T

31

15

20

25

30

35

40

45

GAPD GFAP NES PAX6 POU5F1 T TUBB3 UTF1 ZFP42

Avg.

Ct V

alue

Three cells shows different expression patterns

32

Conclusions and Impacts

• There are significant differences from cell-to-cell

• Single cells have a large range of gene expression levels.

• Analyzing gene expression en masse gives an average profile and masks differences and variability (gene-to-gene and cell-to-cell)

• Small populations are lost when large populations are averaged

• Average expression patterns of single cell populations are similar to 100 cells.



Normalization Conclusions

•When analyzed en masse, variation in expression level and sample size is reduced when results are normalized to reference genes.

•In a single cell expression levels of each gene vary independently

•In single cells normalization increases the variation in calculated expression level.

•These normalized values are not the same within each cell and vary depending on the genes compared.

•These results suggest that normalizing single cell data is not an accurate method of analysis.

34

The TaqMan Single Cell-to-Ct kit


• Optimized reagents provide a simplified workflow for expression analysis of single cells by qRT-PCR

• Enables transfer of entire cell into each step

• No sample is lost during the reaction which occurs in a single tube

• Sensitivity is maximized by use of reagents such as SuperScript III and PreAmp Master Mix

• Variation introduced by the protocol is very low

• Enables the acquisition of statistically significant data sets

35

Thank you


100 cells1 cell

Life Technologies, Applied Biosystems and Ambion products are for Research Use Only. Not for use in diagnostic procedures.

Life Technologies, Applied Biosystems, and Ambion are registered trademarks of Life Technologies Corporation or its subsidiaries in the US and/or certain other countries.

Cells-to-Ct is a trademark of Ambion, Inc. in the U.S. and/or certain other countries. TaqMan is registered trademark of Roche Molecular Systems, Inc.

Practice of the patented 5’ Nuclease Process requires a license from Applied Biosystems. The purchase of the TaqMan® Gene Expression Cells-to-Ct™ Kit includes an immunity from suit under patents specified in the product insert to use only the amount purchased for the purchaser's own internal research when used with the separate purchase of a Licensed Probe. No other patent rights are conveyed expressly, by implication, or by estoppel.

Purchase of the TaqMan® Gene Expression Cells-to-Ct™ Kit is accompanied by a limited license under U.S. Patent 5,035,996 and foreign equivalents to use for research.

© 2011 Life Technologies Corporation. All rights reserved.


Questions?

Date post:	05-Jun-2020
Category:	Documents
Upload:	others
View:	0 times
Download:	0 times

Analyzing Expression Profiles from Single Stem Cells Using...

Documents