A New Framework for Rapid, Scalable Bacterial Diagnostics with Microfluidics and Compressed Sensing

Date
2022-08-02
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Abstract

Critically ill patients with suspected invasive bacterial infections can be treated effectively once the responsible pathogens are characterized. Rapid and sensitive diagnostic tests rely on a specific sensor for each target pathogen, a paradigm that cannot practically cover the dozens to hundreds of plausible pathogens. As a result, sample culture is a prerequisite for downstream testing but can take days. Clinicians are forced to use broad-spectrum antibiotics in the interim which are ineffective for some patients and contribute to rising drug resistance.

To address these challenges, I present a new approach with compressed sensing (CS) and microfluidics. CS focuses on the efficient recovery of sparse signals. Patient samples from normally sterile sites are sparse because among many pathogens to consider, at most a few are causing any given infection. Microfluidic partitioning technologies split a sample into thousands of compartments, capturing bacterial biomarkers across the partitions according to a Poisson distribution. Leveraging this statistical prior can yield scalable systems that break previous theoretical barriers in CS, enabling the detection of more analytes with arbitrarily few sensors. First, I cover the theory and our newly developed Sparse Poisson Recovery (SPoRe) algorithm. I present SPoRe’s superior performance over baseline CS algorithms, including its high sensitivity and tolerance of measurement noise. Second, I generalize the theoretical results to apply to many common types of biosensors and present the first in vitro realization of SPoRe with droplet digital PCR. We use five DNA probes to barcode the 16S rRNA gene to quantify nine pathogen genera within hours. Given two fluorescent channels, we measure portions of the barcodes in four groups of droplets, pool the data, and infer bacterial quantities with SPoRe. I highlight how the underlying principles of this demonstration enable sensor-constrained systems to scalably cover large panels of analytes, an advance that could unlock new biosensing solutions for multiple industries.

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EMBARGO NOTE: This item is embargoed until 2024-08-01
Degree
Doctor of Philosophy
Type
Thesis
Keywords
biosensing, diagnostics, infections, compressed sensing, microfluidics, signal processing
Citation

Kota, Pavan Kumar. "A New Framework for Rapid, Scalable Bacterial Diagnostics with Microfluidics and Compressed Sensing." (2022) Diss., Rice University. https://hdl.handle.net/1911/113265.

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