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

Flu

ore

scen

ce

Universal Bacterial PCR

High Resolution Melt

METHODS

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

POLYMICROBIAL DETECTION

S.I. Fraley, et.al. Nucleic Acids Research, 2013

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

Sifraley@ucsd.edu

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