This document describes the development of a diagnostic device to rapidly identify bacterial strains responsible for sepsis. Key points: - The device uses droplet digital PCR to amplify and quantify bacterial DNA signatures from patient blood samples. High resolution melt analysis is then used to generate strain-specific melt curves of the 16S rRNA gene. - A microfluidic chip is engineered to generate stable droplets and thermal gradients allowing acquisition of melt curves. Machine learning algorithms classify melt curves to identify bacterial strains. - The goal is to identify bacteria in patient blood samples within clinically relevant timeframes, shortening diagnostic time from 12 days to 3-4 hours for targeted antibiotic treatment. Results show a stable thermal gradient is achieved within the chip

1. 0
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60
80
100
120
140
-20 0 20 40 60 80 100 120 140
Temperature(oC)
Distance (mm)
Temperature Gradient over Aluminum Block with Power
Supply Varied via Pulse Width Modulation (PWM)
Desired Gradient
Increasing
PWM
Fraley, S. I. et. al., 2015, Nucleic Acids Research
Velez, D. O. et. al., 2017, Scientific Reports
• Profile a larger library - the goal is to detect and classify bacteria in
95% of clinical samples
• Modify for point-of-care applica�ons
• Mul�plex universal primers for fungi and viruses
• Has promise to shorten sep�c diagnos�c �me from 12 days to 3-4 hrs
• Flow-based method allows for high-throughput assays while
maximizing dynamic range
• Provides targeted diagnoses of bacterial infec�ons, allowing for
pa�ent-specific an�bio�c treatment regimens
• U-dHRM can be modified to iden�fy any innocuous or infec�ous DNA
with a known melt curve
Results
Methods Figure 4:
Temperature measured at
3 evenly spaced points
along Aluminum block to
test the stability of the
spa�al thermal gradient,
and to measure the �me
to steady state. Steady
state reached in 15-16
minutes.
Figure 5:
Spa�al temperature
gradient. Resistance
thermal detectors
(RTD) were evenly
placed along length of
block. Both pel�er
chips received the
same PWM.
0
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0.8
1
1.2
40 45 50 55 60 65 70 75 80 85
NormalizedFluorescence
Temperature (oC)
Melt Curve for Temperature Calibrator
Figure 6:
A loss-of-fluorescence
curve generated by the
U-dHRM in droplets using
mid temperature
calibrators that melt
around 65C.
Figure 2: (A) 4X Brigh�ield and (B) FITC images of dropets in a channel. Scale
bars are 100 um.
Figure 1:
(A) Brigh�ield and (B) FITC
images of droplets containing
mid temperature thermal
calibrators and EvaGreen
Supermix at 10X objec�ve.
Scale bars are 100 um.
Milestone #3: Engineer a Stable Spa�al Thermal Gradient
Milestone #4: Acquire Melt Curve in Droplet
Milestone #2: Load Droplets into Channel
Milestone #1: Generate Stable Droplets
Ideal Diagnos�c Characteris�cs
High-Throughput Universal Digital High-Resolu�on Melt Pla�orm for Bacterial Iden�fica�on
Noah Elder, Anamik Jhunjhunwala, Benjamin Yang, Idil Eroglu and Dr. Stephanie Fraley
Department of Bioengineering, University of California San Diego, La Jolla, CA 92093
Introduc�on
Sepsis is a life-threatening widespread inflammatory
response to a microbial bloodstream infec�on
• >750,000 cases annually in the US
• Requires rapid and specific diagnosis
• Mortality rate of 28-50%
Current Diagnos�c Method - Blood Culture
Broad-based - Detects and amplifies all bacterial signatures
Sensi�ve - Small sample volumes (1-2000 CFU/mL)
Polymicrobial - Precise composi�on of heterogeneous samples
Rapid - Diagnoses within clinically relevant �meframes
Technical Background
Droplet Digital PCR
• Absolute quan�fica�on without reference dyes
• Poisson distribu�on ensures single genome specificity
• Massively paralleled reac�on with flow-based methods
• Scalable dynamic range and cost-effec�ve
To integrate Droplet Digital PCR, High Resolu�on Melt
technology, and supervised machine learning algorithms
into a high-throughput diagnos�c device to iden�fy bac-
terial strains responsible for sepsis at the point-of-care.
High Resolu�on Melt (HRM)
• Rapid, inexpensive post-PCR sequencing method
• Non-specific intercala�ng fluorescent dye
• Generates sequence-specific loss-of-fluorescence curves
Salmonella enter�dis
confirmed via serotyping
1211
An�bio�c Therapy
543210
Days
Pa�ent DischargedBlood Culture:
Gram (-) bacilli
Pa�ent Admi�ed
BA
BA
References
Future Direc�ons
Discussion
Engineering Design Goal
16S PCR
• 16S rRNA gene present in all bacteria
• Hypervariable strain-specific region
One-versus-one Support Vector Machine (OVO SVM)
• Supervised machine learning algorithm
• Binary classifiers developed for each pair of melt curves
2. Droplet forma�on
3. Droplet loading into
microfluidic chip
6. Melt Curve Analysis
4. Droplet Flow & Fluorescence
Imaging using U-dHRM
Cellular and
bacterial DNA
Bio-Rad
Droplet
Generator
~ 1ml Pa�ent
blood sample
Syringe
Pump
Microfluidic
Chip
6 cm48C 98C
48C 98C
1. DNA Extrac�on from
pa�ent blood sample
5. Loss-of-Fluorescence Analysis
Figure 3:
Picture of experimental
device consis�ng of the
thermoelectric assembly,
the microfluidic chip and
the Arduino system for
power modula�on and
temperature monitoring.
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80
100
120
0 5 10 15 20 25 30 35
Temperature(oC)
Time (min)
Steady State Determination of Spatial Temperature
Gradient on a 57 mm Length of an Aluminum Block
Cold Side (0 mm) Middle (28.5 cm) Hot Side (57 mm)
Quasi Steady
State Reached
Acknowledgements: This research was supported by a Burroughs
Wellcome Fund CASI and UCSD FISP.