Engineered biocircuits designed with biological components have the capacity to expand and augment living functions. Here we demonstrate that proteases can be integrated into digital or analog biocircuits to process biological information. We first construct peptide-caged liposomes that treat protease activity as two-valued (i.e., signal is 0 or 1) operations to construct the biological equivalent of Boolean logic gates, comparators and analog-to-digital converters. We use these modules to assemble a cell-free biocircuit that can combine with bacteria-containing blood, quantify bacteria burden, and then calculate and unlock a selective drug dose. By contrast, we treat protease activity as multi-valued (i.e., signal is between 0 and 1) by controlling the degree to which a pool of enzymes is shared between two target substrates. We perform operations on these analog values by manipulating substrate concentrations and combine these operations to solve the mathematical problem Learning Parity with Noise (LPN). These results show that protease activity can be used to process biological information by binary Boolean logic, or as multi-valued analog signals under conditions where substrate resources are shared.

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.

CRISPR-associated proteins (Cas) are enabling powerful new approaches to control mammalian cell functions, yet the lack of spatially defined, noninvasive modalities limits their use as biological tools. Here, we integrate thermal gene switches with dCas9 complexes to confer remote control of gene activation and suppression with short pulses of heat. Using a thermal switch constructed from the heat shock protein A6 (HSPA6) locus, we show that a single heat pulse 3-5 °C above basal temperature is sufficient to trigger expression of dCas9 complexes. We demonstrate that dCas9 fused to the transcriptional activator VP64 is functional after heat activation, and, depending on the number of heat pulses, drives transcription of endogenous genes GzmB and CCL21 to levels equivalent to that achieved by a constitutive viral promoter. Across a range of input temperatures, we find that downstream protein expression of GzmB closely correlates with transcript levels (R2 = 0.99). Using dCas9 fused with the transcriptional suppressor KRAB, we show that longitudinal suppression of the reporter d2GFP depends on key thermal input parameters including pulse magnitude, number of pulses, and dose fractionation. In living mice, we extend our study using photothermal heating to spatially target implanted cells to suppress d2GFP in vivo. Our study establishes a noninvasive and targeted approach to harness Cas-based proteins for modulation of gene expression to complement current methods for remote control of cell function.

Cell-based immunotherapies, such as T cells engineered with chimeric antigen receptors (CARs), have the potential to cure patients of disease otherwise refractory to conventional treatments. Early-on-treatment and long-term durability of patient responses depend critically on the ability to control the potency of adoptively transferred T cells, as overactivation can lead to complications like cytokine release syndrome, and immunosuppression can result in ineffective responses to therapy. Drugs or biologics (e.g., cytokines) that modulate immune activity are limited by mass transport barriers that reduce the local effective drug concentration, and lack site or target cell specificity that results in toxicity. Emerging technologies that enable site-targeted, remote control of key T cell functions - including proliferation, antigen-sensing, and target-cell killing - have the potential to increase treatment precision and safety profile. These technologies are broadly applicable to other immune cells to expand immune cell therapies across many cancers and diseases. In this review, we highlight the opportunities, challenges and the current state-of-the-art for remote control of synthetic immunity.

The development of protease-activatable drugs and diagnostics requires identifying substrates specific to individual proteases. However, this process becomes increasingly difficult as the number of target proteases increases because most substrates are promiscuously cleaved by multiple proteases. We introduce a method—substrate libraries for compressed sensing of enzymes (SLICE)—for selecting libraries of promiscuous substrates that classify protease mixtures (1) without deconvolution of compressed signals and (2) without highly specific substrates. SLICE ranks substrate libraries using a compression score (C), which quantifies substrate orthogonality and protease coverage. This metric is predictive of classification accuracy across 140 in silico (Pearson r = 0.71) and 55 in vitro libraries (r = 0.55). Using SLICE, we select a two-substrate library to classify 28 samples containing 11 enzymes in plasma (area under the receiver operating characteristic curve [AUROC] = 0.93). We envision that SLICE will enable the selection of libraries that capture information from hundreds of enzymes using fewer substrates for applications like activity-based sensors for imaging and diagnostics.

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.

Immune checkpoint blockade (ICB) therapy does not benefit the majority of treated patients, and those who respond to the therapy can become resistant to it. Here we report the design and performance of systemically administered protease activity sensors conjugated to anti-programmed cell death protein 1 (αPD1) antibodies for the monitoring of antitumour responses to ICB therapy. The sensors consist of a library of mass-barcoded protease substrates that, when cleaved by tumour proteases and immune proteases, are released into urine, where they can be detected by mass spectrometry. By using syngeneic mouse models of colorectal cancer, we show that random forest classifiers trained on mass spectrometry signatures from a library of αPD1-conjugated mass-barcoded activity sensors for differentially expressed tumour proteases and immune proteases can be used to detect early antitumour responses and discriminate resistance to ICB therapy driven by loss-of-function mutations in either the B2m or Jak1 genes. Biomarkers of protease activity may facilitate the assessment of early responses to ICB therapy and the classification of refractory tumours based on resistance mechanisms.

Understanding mechanisms of antibiotic failure is foundational to combating the growing threat of multidrug-resistant bacteria. Prodrugs—which are converted into a pharmacologically active compound after administration—represent a growing class of therapeutics for treating bacterial infections but are understudied in the context of antibiotic failure. We hypothesize that strategies that rely on pathogen-specific pathways for prodrug conversion are susceptible to competing rates of prodrug activation and bacterial replication, which could lead to treatment escape and failure. Here, we construct a mathematical model of prodrug kinetics to predict rate-dependent conditions under which bacteria escape prodrug treatment. From this model, we derive a dimensionless parameter we call the Bacterial Advantage Heuristic (BAH) that predicts the transition between prodrug escape and successful treatment across a range of time scales (1–104 h), bacterial carrying capacities (5 × 104–105 CFU/µl), and Michaelis constants (KM = 0.747–7.47 mM). To verify these predictions in vitro, we use two models of bacteria-prodrug competition: (i) an antimicrobial peptide hairpin that is enzymatically activated by bacterial surface proteases and (ii) a thiomaltose-conjugated trimethoprim that is internalized by bacterial maltodextrin transporters and hydrolyzed by free thiols. We observe that prodrug failure occurs at BAH values above the same critical threshold predicted by the model. Furthermore, we demonstrate two examples of how failing prodrugs can be rescued by decreasing the BAH below the critical threshold via (i) substrate design and (ii) nutrient control. We envision such dimensionless parameters serving as supportive pharmacokinetic quantities that guide the design and administration of prodrug therapeutics.

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.