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S T E P H A N I E I . F R A L E Y , P H . D .A S S I S TA N T P R O F E S S O R
B I O E N G I N E E R I N G , U C S A N D I E G O
ENABLING ACCESSIBLE AND
SENSITIVE SEQUENCE PROFILING FOR
SYSTEMS MEDICINE
Disclosure: The technology presented is patented, and I am an inventor on the patents.
SYSTEMS MEDICINE
Major goals:• Make blood a diagnostic window of health &
disease• Provide deep insights into disease mechanisms in
patient-specific contexts
Mandates:• Develop technologies to explore patient’s data
space in new dimensions• Democratize data-generation & analysis tools
Leroy Hood & Mauricio Flores, New Biotechnology 29, 2012.
TECHNOLOGY DEVELOPMENT
• Need to measure rapidly and often in humans• Reduced cost, effort, & time to profile• Portability for broad access• Time-course readouts to understand disease processes• Low level regulators critical to biological meta-stability &
contribute to diversity1,2
• Higher sensitivity and quantitative power than sequencing & microarrays
• Many applications have no need to re-sequence• As de-novo sequencing discovers & catalogs all sequence
possibilities, profiling can play a larger and larger role
1. M.B. Elowitz, et.al., Science 297, 2002.2. J.L. Spudich & D.E. Koshland. Nature 262, 1976.
BACTERIAL INFECTIONS & SEPSIS
• Hourly mortality risk increase
• Polymicrobial risk higher• Incorrect treatment• Antibiotic resistance• Spread• Biothreat• Host-Pathogen system-
specific responses
IDEAL PROFILING TECHNOLOGY FOR DYNAMIC SYSTEMS MEDICINE
• Broad-based• Identify each individual sequence present
sensitively & specifically • Absolute quantitative power• Rapid• User-independent identification
• Today’s sequencing• Sensitivity-specificity trade-off• Speed• Other tech’s have similar limitations
TYPICAL CLINICAL SCENARIO
Patient admitted
Patient was discharged
Antibiotic Therapy
Blood culture: Gram (-) bacilli
1 2 30 4 5 6 11 12 days
Salmonella enteritidis confirmed via
Sequence ID Technology
Salmonella enteritidis confirmed via serotyping
Salmonella genus identified
OBJECTIVE: PATHOGEN SEQUENCE IDENTIFICATION
• In the context of bacterial blood stream infections…• Broad-based = detect all bacterial pathogens• Specific = which pathogens which drugs• Sensitive = single cell as dictated by sample limitations• Unaffected by contamination• Resolve heterogeneity = resolve polymicrobial infections• Fast• User independent automated analysis
ADVANCING HIGH RESOLUTION MELT
Traditional Bulk HRM Universal Digital HRMFl
uore
scen
ce
Temperature
population average
Fluo
resc
ence
Temperature
Individual sequences
S.I. Fraley, et.al. Nucleic Acids Research, 2013
• Small volume = less reagents, less $$= greater partitioning of molecules= higher sensitivity
• Integrated, automated, disposable – DNA extraction– PCR– Readout
• Portable• Rabid heating/cooling
MICROFLUIDIC DIGITIZATION
~$15 per test
Experimental Variation• Temperature• Buffer• Extraction carry-over
Machine Learning• Generate training data—known
species• Algorithm compares test to training• Automatic ID Training
DataTestingData
?
B. anthracisE. coli
AUTOMATE & IMPROVE IDENTIFICATION WITH MACHINE LEARNING
LONG AMPLICON “READS”
• Long sequences, ~1000 bp• 1-339 nt difference• 100% accuracy inclusivity testing
S.I. Fraley, et.al. in review, 2015
SUPERIORITY OF MACHINE LEARNING ALGORITHM
Reaction Chemistry Variations Mimicking User-to-User Differences
Acc
ura
cy
Athamanolap P, et al., PLoS ONE, 2014
OTHER APPLICATIONS
Cancer Research & Diagnostics100% accuracy in genotyping 6 methylation states of RASSF1
Epidemiology Research 98.9% accuracy in serotyping
92 S. pneumoniae strains
Athamanolap P, et al., PLoS ONE, 2014
Name Sequence Difference
let-7a TGAGGTAGTAGGTTGTATAGTT reference
let-7b TGAGGTAGTAGGTTGTGTGGTT 2 nt
let-7c TGAGGTAGTAGGTTGTATGGTT 1 nt
let-7d AGAGGTAGTAGGTTGCATAGT 2 nt
let-7e TGAGGTAGGAGGTTGTATAGT 1 nt
let-7f TGAGGTAGTAGATTGTATAGTT 1 nt
let-7g TGAGGTAGTAGTTTGTACAGT 2 nt
let-7i TGAGGTAGTAGTTTGTGCTGT 4 nt
miR-98 TGAGGTAGTAAGTTGTATTGTT 2 nt
miR-29 TAGCACCATCTGAAATCGGTTA 17 nt
Lethal-7 MicroRNA Family
S.I. Fraley, et.al. Nucleic Acids Research, 2013
OTHER APPLICATIONS
Automated, scalable, rapid, quantitative, inexpensive sequence identification• Universal PCR• Nucleic Acid Melting• Machine Learning• Microfluidics
Tunable for diverse applications• Bacterial pathogens • MicroRNA biomarkers• Cancer gene methylation signatures
Future Goals• Portability• Integrating automated sample preparation• Expand database, 200 clinically relevant bacterial pathogens • Sequence melt prediction for training in silico and emerging
pathogens
SUMMARY
Contact Information:
Samuel Yang, Emergency Medicine, Johns Hopkins, now at Stanford
Jeff Wang, Biomedical Engineering, Johns Hopkins Richard Rothman, Emergency Medicine, Johns Hopkins Charlotte Gaydos, Emergency Medicine, Johns Hopkins Karen Carroll, Pathology, Johns Hopkins
Pornpat Apthamanolap, Johns Hopkins Justin Hardick, Johns Hopkins Billie Maseck, Johns Hopkins Helena Zec, Johns Hopkins
Fraley Lab Daniel Ortiz-Velez, UC San Diego Sinead Hawker, UC SanDiego
ACKNOWLEDGEMENTS