Biology subjects

Brown, R. A.

Publications and source records attributed to Brown, R. A..

5 recordsLinked to original sources

bioRxiv2022

Antibiotic action revealed by real-time imaging of the mycobacterial membrane

The current understanding of mycobacterial cell envelope remodeling in response to antibiotics is limited. Chemical tools that report on phenotypic changes with minimal cell wall perturbation are critical to understanding such time-dependent processes. We employed a fluorogenic chemical probe to image how antibiotics perturb mycobacterial cell envelope assembly in real-time. Time-lapse microscopy revealed that differential antibiotic treatment elicited unique cellular phenotypes, providing a platform for simultaneously monitoring cell envelope construction and remodeling responses. Our data show that rifampicin, which does not directly inhibit cell wall biosynthesis, affords a readily detected mycomembrane phenotype. The fluorogenic probe revealed the production of extracellular vesicles in response to antibiotics, and analyses of these vesicles indicate that antibiotic treatment elicits the release of agents that attenuate macrophage activation.

microbiology↗

bioRxiv2021

Injectable Nanoparticle-Based Hydrogels Enable the Safe and Effective Deployment of Immunostimulatory CD40 Agonist Antibodies

When properly deployed, the immune system can eliminate deadly pathogens, eradicate metastatic cancers, and provide long-lasting protection from diverse diseases. Unfortunately, realizing these remarkable capabilities is inherently risky as disruption to immune homeostasis can elicit dangerous complications or autoimmune disorders. While current research is continuously expanding the arsenal of potent immunotherapeutics, there is a technological gap when it comes to controlling when, where, and how long these drugs act on the body. Here, we explore the ability of a slow-releasing injectable hydrogel depot to reduce the problematic dose-limiting toxicities of immunostimulatory CD40 agonist (CD40a) while maintaining their potent anti-cancer efficacy. We leverage a previously described polymer-nanoparticle (PNP) hydrogel system that exhibits shear-thinning and yield-stress properties that we hypothesized would improve locoregional delivery of the CD40a immunotherapy. Using PET imaging, we demonstrate that prolonged hydrogel-based delivery redistributes CD40a exposure to the tumor and the tumor draining lymph node (TdLN), thereby reducing weight loss, hepatotoxicity, and cytokine storm associated with standard treatment. Moreover, CD40a-loaded hydrogels mediate improved local cytokine induction in the TdLN and improve treatment efficacy in the B16F10 melanoma model. PNP hydrogels, therefore, represent a facile, drug-agnostic method to ameliorate immune-related adverse effects and explore locoregional delivery of immunostimulatory drugs.

bioengineering↗

bioRxiv2021

Consistent Tumorigenesis with Self-Assembled Hydrogels Enables High-powered Murine Cancer Studies

Preclinical cancer research is heavily dependent on allograft and xenograft models, but current approaches to tumor inoculation yield inconsistent tumor formation and growth, ultimately wasting valuable resources (e.g., animals, time, and money) and limiting experimental progress. Here we demonstrate a method for tumor inoculation using self-assembled hydrogels to reliably generate tumors with low variance in growth. The observed reduction in model variance enables smaller animal cohorts, improved effect observation and higher powered studies.

bioengineering↗

bioRxiv2019

Clonal replacement of tumor-specific T cells following PD-1 blockade

Immunotherapies that block inhibitory checkpoint receptors on T cells have transformed the clinical care of cancer patients. However, which tumor-specific T cells are mobilized following checkpoint blockade remains unclear. Here, we performed paired single-cell RNA- and T cell receptor (TCR)-sequencing on 79,046 cells from site-matched tumors from patients with basal cell carcinoma (BCC) or squamous cell carcinoma (SCC) pre- and post-anti-PD-1 therapy. Tracking TCR clones and transcriptional phenotypes revealed a coupling of tumor-recognition, clonal expansion, and T cell dysfunction: the T cell response to treatment was accompanied by clonal expansions of CD8+CD39+ T cells, which co-expressed markers of chronic T cell activation and exhaustion. However, this expansion did not derive from pre-existing tumor infiltrating T cell clones; rather, it comprised novel clonotypes, which were not previously observed in the same tumor. Clonal replacement of T cells was preferentially observed in exhausted CD8+ T cells, compared to other distinct T cell phenotypes, and was evident in BCC and SCC patients. These results, enabled by single-cell multi-omic profiling of clinical samples, demonstrate that pre-existing tumor-specific T cells may be limited in their capacity for re-invigoration, and that the T cell response to checkpoint blockade relies on the expansion of a distinct repertoire of T cell clones that may have just recently entered the tumor.

immunology↗

bioRxiv2018

Deep learning for high-throughput quantification of oligodendrocyte ensheathment: a UNet architecture to extract multiple morphological parameters from individual cells

AO_SCPLOWBSTRACTC_SCPLOWHigh-throughput quantification of oligodendrocyte (OL) myelination is a significant challenge that, if addressed, would facilitate the development of therapeutics to promote myelin protection and repair. Here, we established a quantitative high-throughput method to asses OL ensheathment in-vitro, combining nanofiber culture devices and automated imaging with a heuristic approach that informed the development of a deep learning analytic algorithm. The heuristic approach was developed by modeling general characteristics of OL ensheathments, while the deep learning neural network employed a UNet architecture with enhanced capacity to associate ensheathed segments with individual OLs. Reliably extracting multiple morphological parameters from individual cells, without heuristic approximations, mimics the high-level decision-making capacity of human researchers and improves the validity of the neural network. Experimental validation demonstrated that the deep learning approach matched the accuracy of expert-human measurements of the length and number of myelin segments per cell. The combined use of automated imaging and analysis reduces tedious manual labor while eliminating variability. The capacity of this technology to perform multi-parametric analyses at the level of individual cells permits the detection of nuanced cellular differences to accelerate the discovery of new insight into OL physiology.

bioinformatics↗