Giant magnetoresistive biosensing platforms for point-of-care genetic analysis
Abstract/Contents
- Abstract
- The rapidly growing personalized medicine field promises to dramatically improve health outcomes by providing individually tailored medical care. Under this paradigm, clinicians leverage insights into an individual's genetic profile to better understand their disease susceptibility, disease prognosis, and treatment responsiveness. Despite these potential benefits, integration of personalized medicine techniques into routine clinical practice remains limited due to the expense, complexity, and slow turnaround-times associated with conventional genetic analysis methods. To translate meaningful genetic insights into timely and actionable clinical practice, cost-effective and widely accessible genetic testing technologies are needed at the point-of-care (POC). In this work, we present a rapid, portable, and highly automated giant magnetoresistive (GMR) biosensing platform for point-of-care genetic analysis. We begin by describing the development of a platform that couples isothermal recombinase polymerase amplification (RPA) with GMR biosensors to perform non-invasive genotyping of four single nucleotide polymorphisms (SNPs) related to pain sensitivity and opioid use outcomes (catechol-O-methyltransferase gene (COMT) alleles rs4633, rs4680, rs4818, rs6269). We then bolster the POC compatibility of the platform by showing that we were able to develop a rapid nucleic acid extraction protocol that can be directly integrated onto the device to facilitate sample-to-answer SNP analysis. Through a series of validation experiments, we demonstrate that this platform can successfully amplify, detect, and genotype all four SNPs of interest, with experimental results demonstrating 100% accordance with results obtained using a formerly validated PCR genotyping assay. While RPA-based assays are useful in many settings, they are limited in their quantitative capabilities. Accordingly, we also present our efforts towards adapting our initial platform to accommodate polymerase chain reaction (PCR)-based amplification: a method that is more readily amenable to absolute or relative quantification. As proof-of-concept, we employ this adapted platform to perform a gene expression analysis assay wherein we relatively quantify host-based expression of four key genes related to influenza viral infections: HERC5, HERC6, IFI27, and IFIH1. Through a series of validation experiments, we show that this platform is not only capable of accurately measuring gene expression, but also discriminating between symptomatic individuals with influenza viral infections and other respiratory viral infections in under 35 minutes. Equipping healthcare providers with highly versatile platforms, like those presented in this work, promises to open new pathways to improve diagnostics, prognostics, and timely treatment selection at the point-of-care. We anticipate that the impact of such devices will continue to grow with the exploration of new application spaces beyond SNP detection and gene expression analysis, heralding a new era of personalized medicine and enhancements to patient outcomes.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | de Olazarra, Ana Sofia |
|---|---|
| Degree supervisor | Wang, Shan X |
| Thesis advisor | Wang, Shan X |
| Thesis advisor | Howe, Roger Thomas |
| Thesis advisor | Utz, PJ |
| Degree committee member | Howe, Roger Thomas |
| Degree committee member | Utz, PJ |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Ana Sofia de Olazarra. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/qz201rd9902 |
Access conditions
- Copyright
- © 2023 by Ana Sofia de Olazarra
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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