From Sample to Answer: Innovations in sample processing and CRISPR-based diagnostics for enhanced clinical translation and field deployment

The recent (re)emergence and rapid spread of infectious disease agents underscore the urgent need for effective disease prevention and control strategies. Accurate and timely diagnostics serve as the base for effective disease management, enabling the rapid identification of disease outbreaks, guiding treatment decisions, and informing public health interventions. However, the global diagnostic testing capacity is currently insufficient to effectively respond to emerging infectious disease threats, which has fueled the spread of Lassa, Ebola, SARS-CoV-2, and Zika virus in recent years. Globally, this diagnostic gap is particularly pronounced at the primary care or community level, an essential site for swift and effective response. Widespread, rapid, and user-friendly diagnostic tests are vital components of effective outbreak containment and response strategies, as they enable the rapid identification of new cases, thereby facilitating timely intervention and preventing further pathogen spread. Therefore, addressing the critical need in the global diagnostic testing infrastructure requires the development and deployment of diagnostic tools that are accurate, affordable, and accessible in decentralized settings. Existing diagnostics fall short in bridging the current diagnostic gap, but recent advances in nucleic acid-based technologies, and CRISPR-based diagnostics (CRISPR-Dx) in particular, have shown significant promise in transforming infectious disease detection. CRISPR-Dx are easily programmable, robust, sensitive, isothermal, and highly specific, but further advances will be required to facilitate their use outside of centralized laboratories. This thesis aims to address this critical gap in global diagnostic testing capacity, focusing on the innovation, validation, and deployment of CRISPR-Dx for infectious diseases. We first developed SHINE, a rapid and sensitive Cas13-based nucleic acid detection platform without the need for nucleic acid extraction. In this first version (SHINEv1), we simplified the CRISPR-Dx workflow, reducing user manipulations and assay time, and enabling automated interpretation of assay results using a companion smartphone application. Next, we made further improvements and thoroughly validated this platform to create SHINEv2, a further streamlined, equipment-free, and easily deployable technology with the ability to discriminate SARS-CoV-2 variants of concern (VOCs). Given the excellent programmability of CRISPR-Dx, we expanded the use of SHINE beyond SARS-CoV-2 to other clinically relevant pathogens. We developed and validated SHINE assays to detect and discriminate species, subtypes, and variants of influenza virus, with important implications for public health and clinical care. We also designed and tested multiplexed diagnostic assays for the detection and differentiation of three tick-borne pathogens in clinical samples. Finally, given the inadequacy of existing sample processing methods – and their importance to nucleic acid test deployment – we developed a high throughput experimental workflow to analyze the effects of chemical reagents on diagnostic assay performance and nuclease activity in patient samples using a commercially available microfluidic platform. Together, the research presented in this thesis contributes to the development of more effective, accessible, and field-deployable diagnostic solutions, thereby enhancing our ability to respond to the global burden of infectious diseases.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Harvard-MIT Program in Health Sciences and Technology

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Arizti Sanz, Jon

Advisor dc:contributor.advisor

• Sabeti, Pardis

Statement dc:rights

• Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-sa/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/157575

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/157575