Biology subjects

This, S.

Publications and source records attributed to This, S..

3 recordsLinked to original sources

bioRxiv2026

Type 1 and type 2 dendritic cell subsets cooperate to maintain intestinal immune tolerance via integrin αvβ8-mediated TGF-β activation

The gastrointestinal tract is a unique immunological environment where the host must balance tolerance to commensal microbes with defense against pathogens. A critical mechanism for maintaining this balance is the peripheral conversion of naive T cells into regulatory T cells (pTregs), a process that depends on the TGF-{beta} cytokine, which is produced in a latent form and must be activated. While the activation of latent TGF-{beta} relies on the membrane-bound v{beta}8 integrin, the precise cellular subset(s) responsible for this essential process have yet to be clearly defined. Conventional dendritic cells (cDCs), which migrate from the intestinal lamina propria to the gut-draining mesenteric lymph nodes (MLN), have long been considered the primary antigen-presenting cells (APCs) responsible for v{beta}8-mediated TGF-{beta} activation and pTreg induction. However, recent studies have challenged this paradigm by highlighting a new family of rare ROR{gamma}t-expressing APCs, able to induce pTreg via v integrins, raising questions about the in vivo role of cDCs in the maintenance of mucosal immune homeostasis. Using a {beta}8 integrin gene reporter mouse model (Itgb8-IRES-tdTomato) combined with single-cell profiling, we comprehensively mapped Itgb8-expressing APCs in the MLN. We show that cDCs, in particular migratory type 1 (cDC1) and type 2 (cDC2), constitute the predominant Itgb8TdTomato+ cells, both in neonatal and adult mice. Through cDC subset-specific {beta}8 knockout models, we demonstrate that both cDC1 and cDC2 are required for optimal pTreg generation. Loss of {beta}8 integrin in either subset led to a partial reduction in pTreg, while combined deletion resulted in profound pTreg loss and spontaneous colitis. Importantly, these effects were independent of ROR{gamma}t APC populations, including ILC3s and Thetis cells. These findings resolve longstanding questions about the identity of key APCs driving pTreg induction in the MLNs. They demonstrate that cDC1 and cDC2 are non-redundant, essential mediators of pTreg induction and intestinal immune tolerance. Although different populations of ROR{gamma}t APCs may contribute in specific contexts, such as early development, infection, or in the prevention of allergic disease, cDCs remain one of the primary guardians of intestinal immune homeostasis in response to microbiota.

immunology↗

bioRxiv2023

Predicting T cell activation based on intracellular calcium fluctuations

Adoptive T cell therapies rely on the transduction of T cells with a predetermined antigen receptor which redirects their specificity towards tumor-specific antigens. Despite the development of multiple platforms for tumor-specific T cell receptor (TCR) discovery, this process remains time consuming and skewed toward high-affinity TCRs. Specifically, the methods for identifying therapeutically-relevant TCR sequences, predominantly achieved through the enrichment of antigen-specific T cells, represents a major bottleneck for the broader application of TCR-engineered cell therapies. Fluctuation of intracellular calcium levels in T cells is a well described, proximal readout of TCR signaling. Hence, it is an attractive candidate marker for identifying antigen-specific T cells that does not require in vitro antigen-specific T cell expansion. However, calcium fluctuations downstream of TCR engagement with antigen are highly variable; we propose that appropriately-trained machine learning algorithms may allow for T cell classification from complex datasets such as those related to polyclonal T cell signaling events. Using deep learning tools, we demonstrate efficient and accurate prediction of antigen-specificity based on intracellular Ca2+ fluctuations of in vitro-stimulated CD8+ T cells. Using a simple co-culture assay to activate monoclonal TCR transgenic T cells of known specificity, we trained a convolutional neural network to predict T cell reactivity, and we test the algorithm against T cells bearing a distinct TCR transgene as well as a polyclonal T cell response. This approach provides the foundation for a new pipeline to fast-track antigen specific TCR sequence identification for use in adoptive T cell therapy. Significance StatementWhile T cells engineered to express a cancer-specific T cell receptor (TCR) are emerging as a viable approach for personalized therapies, the platforms for identifying clinically-relevant TCR sequences are often limited in the breadth of antigen receptors they identify or are cumbersome to implement on a personalized basis. Here, we show that imaging of intracellular calcium fluctuations downstream of TCR engagement with antigen can be used, in combination with artificial intelligence approaches, to accurately and efficiently predict T cell specificity. The development of cancer-specific T cell isolation methods based on early calcium fluctuations may avoid the biases of current methodologies for the isolation of patient-specific TCR sequences in the context of adoptive T cell therapy.

immunology↗

bioRxiv2020

Open Source ImmGen: network perspective on metabolic diversity among mononuclear phagocytes

We dissect metabolic variability of mononuclear phagocyte (MNP) subpopulations across different tissues through integrative analysis of three large scale datasets. Specifically, we introduce ImmGen MNP Open Source dataset that profiled 337 samples and extended previous ImmGen effort which included 202 samples of mononuclear phagocytes and their progenitors. Next, we analysed Tabula Muris Senis dataset to extract data for 51,364 myeloid cells from 18 tissues. Taken together, a compendium of data assembled in this work covers phagocytic populations found across 38 different tissues. To analyse common metabolic features, we developed novel network-based computational approach for unbiased identification of key metabolic subnetworks based on cellular transcriptional profiles in large-scale datasets. Using ImmGen MNP Open Source dataset as baseline, we define 9 metabolic subnetworks that encapsulate the metabolic differences within mononuclear phagocytes, and demonstrate that these features are robustly found across all three datasets, including lipid metabolism, cholesterol biosynthesis, glycolysis, and a set of fatty acid related metabolic pathways, as well as nucleotide and folate metabolism. We systematically define major features specific to macrophage and dendritic cell subpopulations. Among other things, we find that cholesterol synthesis appears particularly active within the migratory dendritic cells. We demonstrate that interference with this pathway through statins administration diminishes migratory capacity of the dendritic cells in vivo. This result demonstrates the power of our approach and highlights importance of metabolic diversity among mononuclear phagocytes.

immunology↗