Noninvasive disease diagnostics using engineered synthetic urinary biomarkers

Accurate, timely, and effective diagnosis is the first step in appropriately treating disease. Many diseases have confusing symptoms, nonspecific biomarkers, or require invasive biopsy; these factors and others contribute to the low rates of early diagnosis for noncommunicable diseases like cancer, clotting disorders, or fibrotic diseases. A promising approach is the introduction of pro-diagnostic agents that interact with pathologic processes to produce a readout. In this vein, our group has developed responsive nanomaterials that, upon cleavage by disease-associated proteases, release reporters into the urine. This thesis sought to improve these tools by enabling the noninvasive quantification of disease-associated protease activity, deskilling complex diagnostic procedures, and developing a pipeline for extending these tools to additional diseases. Drawing inspiration from existing diagnostics, we modified our protease nanosensors to release ligand-encoded reporters compatible with clinical ELISA and paper-based lateral flow assays. These detection techniques enable simple and inexpensive quantification of our synthetic disease reporters by ensuring compatibility with existing diagnostic resources and infrastructure. To demonstrate our platform's versatility, we adapted it to a highly sensitive single molecule array (SiMoA) assay and validated disease detection in mice using 1000-fold lower doses of nanosensors. We next used disease-specific protease expression data to develop an inhalable formulation of our protease nanosensors and investigated direct tissue delivery. Finally, we built a pipeline to improve protease substrate sensitivity and specificity. Using liver fibrosis as a model, we identified target proteases, designed a peptide-screening assay, and nominated peptide candidates that efficiently classify diseased tissue. The protease nanosensors developed here provide a noninvasive, quantitative, and otherwise unavailable glimpse of the complex proteolytic milieu of disease and health. These tools form a framework for developing new diagnostics that simply, rapidly, and inexpensively identify protease-driven diseases without complex equipment or specialized personnel.

Department dc:contributor.department

Harvard--MIT Program in Health Sciences and Technology.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Warren, Andrew David

Advisor dc:contributor.advisor

• Sangeeta N. Bhatia.

• Harvard--MIT Program in Health Sciences and Technology.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/104609

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/104609