Proteases are multifunctional, promiscuous enzymes that degrade proteins as well as peptides and drive important processes in health and disease. Current technology has enabled the construction of libraries of peptide substrates that detect protease activity, which provides valuable biological information. An ideal library would be orthogonal, such that each protease only hydrolyzes one unique substrate, however this is impractical due to off-target promiscuity (i.e., one protease targets multiple different substrates). Therefore, when a library of probes is exposed to a cocktail of proteases, each protease activates multiple probes, producing a convoluted signature. Computational methods for parsing these signatures to estimate individual protease activities primarily use an extensive collection of all possible protease-substrate combinations, which require impractical amounts of training data when expanding to search for more candidate substrates. Here we provide a computational method for estimating protease activities efficiently by reducing the number of substrates and clustering proteases with similar cleavage activities into families. We envision that this method will be used to extract meaningful diagnostic information from biological samples.

Proteases are multi-functional enzymes that specialize in the hydrolysis of peptide-bonds and control broad biological processes including homeostasis and allostasis. Moreover, dysregulated protease activity drives pathogenesis and is a functional biomarker of diseases such as cancer; therefore, the ability to detect protease activity in vivo may provide clinically relevant information for biomedical diagnostics. The goal of this protocol is to create nanosensors that probe for protease activity in vivo by producing a quantifiable signal in urine. These protease nanosensors consist of two components: a nanoparticle and substrate. The nanoparticle functions to increase circulation half-life and substrate delivery to target disease sites. The substrate is a short peptide sequence (6-8 AA), which is designed to be specific to a target protease or group of proteases. The substrate is conjugated to the surface of the nanoparticle and is terminated by a reporter, such as a fluorescent marker, for detection. As dysregulated proteases cleave the peptide substrate, the reporter is filtered into urine for quantification as a biomarker of protease activity. Herein we describe construction of a nanosensor for matrix metalloproteinase 9 (MMP9), which is associated with tumor progression and metastasis, for detection of colorectal cancer in a mouse model.

The ability to analyze and isolate cells based on the expression of specific surface markers has increased our understanding of cell biology and produced numerous applications for biomedicine. However, established cell-sorting platforms rely on labels that are limited in number due to biophysical constraints, such as overlapping emission spectra of fluorophores in FACS. Here, we establish a framework built on a system of orthogonal and extensible DNA gates for multiplexed cell sorting. These DNA gates label target cell populations by antibodies to allow magnetic bead isolation en masse and then selectively unlock by strand displacement to sort cells. We show that DNA gated sorting (DGS) is triggered to completion within minutes on the surface of cells and achieves target cell purity, viability, and yield equivalent to that of commercial magnetic sorting kits. We demonstrate multiplexed sorting of three distinct immune cell populations (CD8+, CD4+, and CD19+) from mouse splenocytes to high purity and show that recovered CD8+T cells retain proliferative potential and target cell-killing activity. To broaden the utility of this platform, we implement a double positive sorting scheme using DNA gates on peptide-MHC tetramers to isolate antigen-specific CD8+T cells from mice infected with lymphocytic choriomeningitis virus (LCMV). DGS can potentially be expanded with fewer biophysical constraints to large families of DNA gates for applications that require analysis of complex cell populations, such as host immune responses to disease.

Antigen-specific T cells are found at low frequencies in circulation but carry important diagnostic information as liquid biomarkers in numerous biomedical settings, such as monitoring the efficacy of vaccines and cancer immunotherapies. To enable detection of antigen-specific T cells with high sensitivity, we develop peptide-MHC (pMHC) tetramers labeled with DNA barcodes to detect single T cells by droplet digital PCR (ddPCR). We show that site-specific conjugation of DNA via photocleavable linkers allows barcoded tetramers to stain T cells with similar avidity compared to conventional fluorescent tetramers and efficient recovery of barcodes by light with no loss in cell viability. We design an orthogonal panel of DNA-barcoded tetramers to simultaneously detect multiple antigen-specific T cell populations, including from a mouse model of viral infection, and discriminate single cancer-specific T cells with high diagnostic sensitivity and specificity. This approach of DNA-barcoding can be broadened to encompass additional rare cells for monitoring immunological health at the single cell level.