Biology subjects

Brusko, T.

Publications and source records attributed to Brusko, T..

3 recordsLinked to original sources

bioRxiv2022

Deletion of CD226 in Foxp3+ T cells Reduces Diabetes Incidence in Non-Obese Diabetic Mice by Improving Regulatory T Cell Stability and Function

Co-stimulation serves as a critical checkpoint for T cell development and activation, and several genetic variants affecting co-stimulatory pathways confer risk for autoimmune diseases. A single nucleotide polymorphism in CD226 (rs763361; G307S) has been shown to increase susceptibility to type 1 diabetes, multiple sclerosis, and rheumatoid arthritis. CD226 competes with the co-inhibitory receptor TIGIT (T cell immunoreceptor with Ig and ITIM domains) to bind CD155 to amplify TCR signaling. We previously found that Cd226 knockout protected non-obese diabetic (NOD) mice from disease, but the impact of CD226 signaling on individual immune subsets remained unclear. We focused on regulatory T cells (Tregs) as a population of interest, as prior reports demonstrated that human CD226+ Tregs exhibit reduced FOXP3+Helios+ purity and suppressive function following expansion. Hence, we hypothesized that global deletion of Cd226 would increase Treg stability and accordingly, Treg-specific Cd226 deletion would inhibit diabetes in NOD mice. Indeed, crossing the NOD.Cd226-/- and NOD.Foxp3-GFP-Cre.R26-loxP-STOP-loxP-YFP Treg-fate tracking strains resulted in increased Treg induction and decreased FoxP3-deficient "ex-Tregs" in the pancreatic lymph nodes. We generated a Treg-conditional knockout (Treg{Delta}Cd226) strain and found that female Treg{Delta}Cd226 mice had decreased insulitis and diabetes incidence compared to TregWT mice. Additionally, we observed increased TIGIT expression on Tregs and conventional CD4+ T cells within the pancreas of Treg{Delta}Cd226 versus TregWT mice. These findings demonstrate that an imbalance of CD226/TIGIT signaling may contribute to Treg destabilization in the NOD mouse and highlight the potential for therapeutic targeting of this pathway to prevent or reverse autoimmunity.

immunology↗

bioRxiv2021

3D-mapping of human lymph node and spleen reveals integrated neuronal, vascular, and ductal cell networks

The spleen and lymph node represent important hubs for both innate and adaptive immunity1,2. Herein, we map immune, endothelial, and neuronal cell networks within these tissues from "normal"/non-diseased organ donors, collected through the NIH Human BioMolecular Atlas Program (HuBMAP)3, using highly multiplexed CODEX (CO-Detection by indEXing) imaging and 3D light sheet microscopy of cleared tissues. Building on prior reports4-6, we observed the lymph node subcapsular sinus expressing podoplanin, smooth muscle actin, and LYVE1. In the spleen, LYVE1 was expressed by littoral cells lining venous sinusoids, whereas podoplanin was restricted to arteries and trabeculae. 3D visualization of perivascular innervation revealed a subset of axonal processes expressing choline acetyl transferase in both tissues, in contrast with prior literature on human spleen7. We further report our novel observations regarding the distinct localization of GAP43 and {beta}3-tubulin within the vascular anatomy of both lymph node and spleen, with Coronin-1A+ cells forming a dense cluster around {beta}3-tubulin positive GAP43 low/negative segments of large vessels in spleen. These data provide an unprecedented 2D and 3D visualization of cellular networks within secondary lymphoid tissues, laying the groundwork for future disease-specific and system-wide studies of neural regulation of immunity in human lymphatics.

immunology↗

bioRxiv2021

geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq.

The problem of selecting targeted gene panels that capture maximum variability encoded in scRNA-sequencing data has become of great practical importance. scRNA-seq datasets are increasingly being used to identify gene panels that can be probed using alternative molecular technologies, such as spatial transcriptomics. In this context, the number of genes that can be probed is an important limiting factor, so choosing the best subset of genes is vital. Existing methods for this task are limited by either a reliance on pre-existing cell type labels or by difficulties in identifying markers of rare cell types. We resolve this by introducing an iterative approach, geneBasis, for selecting an optimal gene panel, where each newly added gene captures the maximum distance between the true manifold and the manifold constructed using the currently selected gene panel. We demonstrate, using a variety of metrics and diverse datasets, that our approach outperforms existing strategies, and can not only resolve cell types but also more subtle cell state differences. Our approach is available as an open source, easy-to-use, documented R package (https://github.com/MarioniLab/geneBasisR).

bioinformatics↗