Biology subjects

Bryson, B.

Publications and source records attributed to Bryson, B..

8 recordsLinked to original sources

bioRxiv2026

Restricted MHC-II trafficking in Mycobacterium tuberculosis-infected M2-like macrophages limits CD4+ T cell activation

Recognition of infected macrophages by CD4+ T cells is essential to immune protection against Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB). However, not all infected macrophage subsets successfully elicit T cell activation. We recently discovered that M2-like macrophages fail to efficiently activate memory CD4+ T cells when infected with Mtb, yet successfully elicit T cell activation when loaded with peptides, {gamma}-irradiated bacteria, or Mtb whole cell lysate. Since the mechanisms underlying CD4+ T cell evasion by infected M2 but not M1-like macrophages remain underexplored, we sought to determine the genes and pathways unique to Mtb infection of M2-like cells, including alveolar macrophages. RNA sequencing of human macrophages infected with virulent Mtb identified enrichment of IL-10 and type I interferon (IFN) signaling genes, including IL10RA and HERC5, respectively, in infected M2-like monocyte-derived and alveolar macrophages. However, genes involved in MHC-II trafficking, such as AP1M2, were higher in infected M1-like macrophages. In complementary experiments using fluorescence microscopy and flow cytometry, we observed impaired trafficking of newly synthesized MHC-II to the plasma membrane of Mtb-infected M2-like macrophages despite high total surface MHC-II levels. Neutralization of IL-10 or knockdown of HERC5 restored MHC-II trafficking to the cell surface among infected M2-like macrophages and significantly enhanced activation of memory CD4+ T cells in an MHC-II-dependent manner. These findings identify coordinated IL-10 and type I IFN signaling as key mechanisms that restrict MHC-II trafficking to the plasma membrane in Mtb-infected M2-like macrophages, thereby limiting antigen presentation and CD4+ T cell activation. We propose that host-directed therapies targeting these pathways in infected alveolar macrophages will facilitate T cell recognition for the prevention or treatment of active TB. Author SummaryRecognition of infected macrophages by CD4+ T cells is essential to immune protection against Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB). However, not all infected macrophage subsets successfully elicit T cell activation. We recently discovered that M2-like macrophages fail to efficiently activate memory CD4+ T cells when infected with Mtb, yet they successfully elicit T cell activation when treated with peptides, {gamma}-irradiated bacteria, or Mtb whole cell lysate. In this study, we identified genes and pathways uniquely upregulated in Mtb-infected M2-like macrophages that are linked to inefficient CD4+ T cell activation, including IL-10 signaling and type I interferon (IFN) pathways. These pathways were linked to reduced MHC-II trafficking to the plasma membrane in Mtb-infected M2-like macrophages. Neutralization of IL-10 or knockdown of HERC5 restored MHC-II trafficking and augmented memory CD4+ T cell activation. Our study demonstrates that IL-10 signaling and type I IFN pathways play detrimental roles in macrophages during Mtb infection, impairing MHC-II trafficking and CD4+ T cell activation. Since lung-resident alveolar macrophages express a dominant M2-like phenotype, these findings suggest that targeting IL-10 and type I IFN signaling may offer a strategy to enhance CD4+ T cell-mediated immunity and improve TB outcomes.

immunology↗

bioRxiv2023

Compressed phenotypic screens for complex multicellular models and high-content assays

High-throughput phenotypic screens leveraging biochemical perturbations, high-content readouts, and complex multicellular models could advance therapeutic discovery yet remain constrained by limitations of scale. To address this, we establish a method for compressing screens by pooling perturbations followed by computational deconvolution. Conducting controlled benchmarks with a highly bioactive small molecule library and a high-content imaging readout, we demonstrate increased efficiency for compressed experimental designs compared to conventional approaches. To prove generalizability, we apply compressed screening to examine transcriptional responses of patient-derived pancreatic cancer organoids to a library of tumor-microenvironment (TME)-nominated recombinant protein ligands. Using single-cell RNA-seq as a readout, we uncover reproducible phenotypic shifts induced by ligands that correlate with clinical features in larger datasets and are distinct from reference signatures available in public databases. In sum, our approach enables phenotypic screens that interrogate complex multicellular models with rich phenotypic readouts to advance translatable drug discovery as well as basic biology.

systems biology↗

bioRxiv2021

Artificial neural networks enable genome-scale simulations of intracellular signaling

Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor (TF) activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network constrained by prior knowledge of the signaling network with ligand concentrations as input, TF activity as output and signaling molecules as hidden nodes. Simulations are assumed to reach steady state, and we regularize the parameters to enforce this. Using synthetic data, we train models that generalize to unseen data and predict the effects of gene knockouts. We also fit models to a small experimental data set from literature and confirm the predictions using cross validation. This demonstrates the feasibility of simulating intracellular signaling at the genome-scale.

systems biology↗

bioRxiv2020

Single Cell and Spatial Transcriptomics Defines the Cellular Architecture of the Antimicrobial Response Network in Human Leprosy Granulomas

Granulomas are complex cellular structures comprised predominantly of macrophages and lymphocytes that function to contain and kill invading pathogens. Here, we investigated single cell phenotypes associated with antimicrobial responses in human leprosy granulomas by applying single cell and spatial sequencing to leprosy biopsy specimens. We focused on reversal reactions (RR), a dynamic process in which some patients with disseminated lepromatous leprosy (L-lep) transition towards self-limiting tuberculoid leprosy (T-lep), mounting effective antimicrobial responses. We identified a set of genes encoding proteins involved in antimicrobial responses that are differentially expressed in RR versus L-lep lesions, and regulated by IFN-{gamma} and IL-1{beta}. By integrating the spatial coordinates of the key cell types and antimicrobial gene expression in RR and T-lep lesions, we constructed a map revealing the organized architecture of granulomas depicting compositional and functional layers by which macrophages, T cells, keratinocytes and fibroblasts contribute to the antimicrobial response.

immunology↗

bioRxiv2019

Coexpression uncovers a unified single-cell transcriptomic landscape

Single-cell transcriptomic studies of diverse and complex systems are becoming ubiquitous. Algorithms now attempt to integrate patterns across these studies by removing all study-specific information, without distinguishing unwanted technical bias from relevant biological variation. Integration remains difficult when capturing biological variation that is distributed across studies, as when combining disparate temporal snapshots into a panoramic, multi-study trajectory of cellular development. Here, we show that a fundamental analytic shift to gene coexpression within clusters of cells, rather than gene expression within individual cells, balances robustness to bias with preservation of meaningful inter-study differences. We leverage this insight in Trajectorama, an algorithm which we use to unify trajectories of neuronal development and hematopoiesis across studies that each profile separate developmental stages, a highly challenging task for existing methods. Trajectorama also reveals systems-level processes relevant to disease pathogenesis within the microglial response to myelin injury. Trajectorama benefits from efficiency and scalability, processing nearly one million cells in around an hour.

bioinformatics↗

bioRxiv2019

Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape

Large-scale single-cell RNA-sequencing (scRNA-seq) studies that profile hundreds of thousands of cells are becoming increasingly common, overwhelming existing analysis pipelines. Here, we describe how to enhance and accelerate single-cell data analysis by summarizing the transcriptomic heterogeneity within a data set using a small subset of cells, which we refer to as a geometric sketch. Our sketches provide more comprehensive visualization of transcriptional diversity, capture rare cell types with high sensitivity, and accurately reveal biological cell types via clustering. Our sketch of umbilical cord blood cells uncovers a rare subpopulation of inflammatory macrophages, which we experimentally validated in vitro. The construction of our sketches is extremely fast, which enabled us to accelerate other crucial resource-intensive tasks such as scRNA-seq data integration. We anticipate that our algorithm will become an increasingly essential step when sharing and analyzing the rapidly-growing volume of scRNA-seq data and help enable the democratization of single-cell omics.

bioinformatics↗

bioRxiv2018

Panoramic stitching of heterogeneous single-cell transcriptomic data

Researchers are generating single-cell RNA sequencing (scRNA-seq) profiles of diverse biological systems1-4 and every cell type in the human body.5 Leveraging this data to gain unprecedented insight into biology and disease will require assembling heterogeneous cell populations across multiple experiments, laboratories, and technologies. Although methods for scRNA-seq data integration exist6,7, they often naively merge data sets together even when the data sets have no cell types in common, leading to results that do not correspond to real biological patterns. Here we present Scanorama, inspired by algorithms for panorama stitching, that overcomes the limitations of existing methods to enable accurate, heterogeneous scRNA-seq data set integration. Our strategy identifies and merges the shared cell types among all pairs of data sets and is orders of magnitude faster than existing techniques. We use Scanorama to combine 105,476 cells from 26 diverse scRNA-seq experiments across 9 different technologies into a single comprehensive reference, demonstrating how Scanorama can be used to obtain a more complete picture of cellular function across a wide range of scRNA-seq experiments.

bioinformatics↗

bioRxiv2018

Dual RNAseq of human leprosy lesions identifies bacterial determinants linked to host immune response

To understand how the interaction between an intracellular bacterium and the host immune system contributes to outcome at the site of infection, we studied leprosy, a disease that forms a clinical spectrum, in which progressive infection by the intracellular bacterium Mycobacterium leprae is characterized by the production of type | IFNs and antibody production. We performed dual RNAseq on patient lesions, identifying a continuum of distinct bacterial states that are linked to the host immune response. The bacterial burden, represented by the fraction of bacterial transcripts, correlates with a host type | IFN gene signature, known to inhibit antimicrobial responses. Second, the bacterial transcriptional activity, defined by the bacterial mRNA/rRNA ratio, links bacterial heat shock proteins with the BAFF-BCMA host antibody response pathway. Our findings provide a platform for interrogation of host and pathogen transcriptomes at the site of infection, allowing insight into mechanisms of inflammation in human disease.

microbiology↗