April Aralar: Improvements in the Quantitative Power of Digital PCR for Microbial DNA Detection in Complex Human Samples
SEMINAR (Zoom)
This UC Next Seminar will be hosted in person as well as online via Zoom. RSVP to receive the zoom link by emailing info@bioengineering.ucsb.edu.
Zoom will open after the host has joined at the start of each seminar. You can ask questions through the chat forum and by raising your "hand" and the speaker will call on you.
Speaker
April Aralar
Ph.D. Candidate
University of California, San Diego
9:00 am via ZOOM
Improvements in the Quantitative Power of Digital PCR for Microbial DNA Detection in Complex Human Samples
Abstract
Digital PCR (dPCR) is an invaluable tool in clinical diagnostics because it enables absolute quantification and rare-target detection; however, a myriad of challenges have prevented widespread adoption of dPCR for clinical applications. Advances such as internal amplification controls (IAC) and high resolution melt (HRM) analysis have been applied in quantitative PCR (qPCR) for more confident detection, identification, and quantification in clinical applications, but have not been applied in dPCR. To address these challenges, we developed a custom dPCR+HRM platform to assess the utility of HRM-based approaches for mitigation of false positives and negatives in dPCR. We also developed an exogenous IAC that could be amplified and detected along with the DNA target. We have shown that the use of our IAC reduces the inclusion of false-negative partitions, changing the calculated DNA concentration by up to 52%. Additionally, traditional dPCR detection and quantification can be hampered by the inclusion of false positive partitions which cannot clearly be labeled as positive or negative. The integration of HRM analysis enabled classification of ambiguous partitions, which accounted for up to 3% of partitions in dPCR. The described IC strategy and HRM methods are in the process of being extensively tested for our specific application of universal, broad-based microbial detection for neonatal sepsis.
We are specifically focused on the challenge of neonatal sepsis diagnostics - a condition that affects about 1 in 1000 babies born worldwide. Neonates are a unique population because of their low blood draw volumes and low bacterial loads - factors that confound the clinical gold standard of blood culture. Our method offers the advantages of single molecule level detection, absolute quantification, and we do not require the relatively high sample volumes needed for blood culture. To test the utility of our platform for clinical diagnostics, we developed a method to generate blood matrices that mimic clinically relevant neonatal sepsis samples. Known concentrations of bacteria were spiked into healthy cord blood to characterize our platform’s utility for clinical diagnostics and compare its performance to blood culture. Preliminary testing shows that we can accurately detect bacteria at clinically relevant concentrations, and additional testing to assess the performance of our method for true clinical blood samples is ongoing. In the future, we hope to continue optimizing and developing this method to become a point of care diagnostic that can be used across clinical settings both in the US and abroad.
BIO
April Aralar received a BS in Bioengineering from George Mason University in Virginia. Her PhD research has been in the field of molecular biology, developing a method for bacterial detection in neonatal blood samples. She has worked closely with a team from Rady Children's Hospital to recruit and characterize blood samples for improved molecular detection in diagnostics. Her research interests also include bacterial community dynamics, acquired antibiotic resistance mechanisms, and interactions between the innate microbiome and invading pathogens. She hopes to pursue a postdoctoral fellowship and eventual academic position to continue to study these phenomena.
