A New Framework for Rapid, Scalable Bacterial Diagnostics with Microfluidics and Compressed Sensing
| dc.contributor.advisor | Drezek, Rebekah A | en_US |
| dc.contributor.advisor | Baraniuk, Richard G | en_US |
| dc.creator | Kota, Pavan Kumar | en_US |
| dc.date.accessioned | 2022-09-23T18:15:19Z | en_US |
| dc.date.created | 2022-08 | en_US |
| dc.date.issued | 2022-08-02 | en_US |
| dc.date.submitted | August 2022 | en_US |
| dc.date.updated | 2022-09-23T18:15:19Z | en_US |
| dc.description | EMBARGO NOTE: This item is embargoed until 2024-08-01 | en_US |
| dc.description.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. | en_US |
| dc.embargo.lift | 2024-08-01 | en_US |
| dc.embargo.terms | 2024-08-01 | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Kota, Pavan Kumar. "A New Framework for Rapid, Scalable Bacterial Diagnostics with Microfluidics and Compressed Sensing." (2022) Diss., Rice University. <a href="https://hdl.handle.net/1911/113265">https://hdl.handle.net/1911/113265</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/113265 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | biosensing | en_US |
| dc.subject | diagnostics | en_US |
| dc.subject | infections | en_US |
| dc.subject | compressed sensing | en_US |
| dc.subject | microfluidics | en_US |
| dc.subject | signal processing | en_US |
| dc.title | A New Framework for Rapid, Scalable Bacterial Diagnostics with Microfluidics and Compressed Sensing | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Bioengineering | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |
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