Biology subjects

Wasserfall, C.

Publications and source records attributed to Wasserfall, C..

8 recordsLinked to original sources

bioRxiv2025

Proinflammatory circulating extracellular vesicles from type 1 diabetes patients contribute to beta cell cytotoxicity and disease pathogenicity

In Type 1 diabetes (T1D), {beta}-cell loss and autoimmunity initiate years before disease diagnosis. However, the mechanisms remain unknown. Here, we investigated the role and mechanisms of circulating extracellular vesicles (cEVs) on T1D pathogenesis and {beta}-cell cytotoxicity. We used cEVs isolated from healthy donors (HD), T1D, multiple autoantibody-positive (AAb+), pre- and post-islet transplantation T1D subjects, NOD-T1D mice, and T1D PBMCs. Patient T1D-cEVs significantly induced apoptosis in vitro in human {beta}-cells but not -cells compared to HD-cEVs. cEVs from AAb+ subjects and pre-diabetic mice were cytotoxic, demonstrating that cEV-induced {beta}-cell cytotoxicity precedes T1D onset and diagnosis. cEV reduction in prediabetic NOD-T1D mice improved {beta}-cell health implying cEV contribution to T1D development. Proteomic analysis of patient T1D-cEVs found several proinflammatory proteins, including interferon gamma, which induced {beta}-cell cytotoxicity. Our data using cEVs from pre- and post-islet transplant and PBMC-EVs from T1D patients implicated immune cells as a potential cellular source of cytotoxic cEVs in T1D. Collectively, our data using T1D patient samples coupled with studies in mice demonstrate that cEVs contribute to {beta}-cell cytotoxicity and to the progression of T1D pathogenicity. Our study is one of the first to demonstrate a functional role of the proinflammatory cEV protein cargo on glucose homeostasis, {beta}-cell health, and insulitis. Our studies on T1D-cEVs may lead to discovery of novel biomarkers and therapeutic targets for T1D.

cell biology↗

bioRxiv2023

Robust collection and processing for label-free single voxel proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk tissues. However, such bulk measurement lacks spatial resolution and obscures important tissue heterogeneity, which make it impossible for proteome mapping of tissue microenvironment. Here we report an integrated wet collection of single tissue voxel and Surfactant-assisted One-Pot voxel processing method termed wcSOP for robust label-free single voxel proteomics. wcSOP capitalizes on buffer droplet-assisted wet collection of single tissue voxel dissected by LCM into the PCR tube cap and MS-compatible surfactant-assisted one-pot voxel processing in the collection cap. This convenient method allows reproducible label-free quantification of [~]900 and [~]4,600 proteins for single voxel from fresh frozen human spleen tissue at 20 m x 20 m x 10 m (close to single cells) and 200 m x 200 m x 10 m ([~]100 cells), respectively. 100s-1000s of protein signatures with differential expression levels were identified to be spatially resolved between spleen red and white pulp regions depending on the voxel size. Region-specific signaling pathways were enriched from single voxel proteomics data. Antibody-based CODEX imaging was used to validate label-free MS quantitation for single voxel analysis. The wcSOP-MS method paves the way for routine robust single voxel proteomics and spatial proteomics.

molecular biology↗

bioRxiv2021

Optimizing multiplexed imaging experimental design through tissue spatial segregation estimation

Recent advances in multiplexed imaging methods allow simultaneous detection of dozens of proteins and hundreds of RNAs enabling deep spatial characterization of both healthy and diseased tissues. Parameters for design of optimal sequencing-based experiments have been established, but such parameters, especially those estimating how much area has to be imaged to capture all cell phenotype clusters, are lacking for multiplex imaging studies. Here, using a spatial transcriptomic atlas of healthy and tumor human tissues, we developed a new statistical framework that determines the number and area of fields of view necessary to accurately identify all cell types that are part of a tissue. Using this strategy on imaging mass cytometry data, we identified a measurement of tissue spatial segregation that enables optimal experimental design. This strategy will enable significantly improved design of multiplexed imaging studies.

bioinformatics↗

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↗

bioRxiv2021

Anatomical Structures, Cell Types, and Biomarkers Tables Plus 3D Reference Organs in Support of a Human Reference Atlas

1.This paper reviews efforts across 16 international consortia to construct human anatomical structures, cell types, and biomarkers (ASCT+B) tables and three-dimensional reference organs in support of a Human Reference Atlas. We detail the ontological descriptions and spatial three-dimensional anatomical representations together with user interfaces that support the registration and exploration of human tissue data. Four use cases are presented to demonstrate the utility of ASCT+B tables for advancing biomedical research and improving health.

cell biology↗

bioRxiv2020

Development of early-stage type 1 diabetes in germ-free interleukin-10 deficient mice

Several experimental models demonstrate a role for gut microbiota in the progression of type 1 diabetes (T1D) in genetically prone hosts. While the association between disturbances in gut microbiota, or microbial dysbiosis, and complex immune diseases such as inflammatory bowel diseases (IBD) are well established, less is known about its role in T1D pathogenesis. In IBD-prone interleukin-10 deficient (IL-10 KO) mice, the absence of gut microbiota under germ-free (GF) conditions prevents IBD development. However, in aged GF IL-10 KO mice (>6-months of age), polyuria and pancreatic lymphocytic infiltration resembling T1D lesions was observed. Approximately 50% of male and female mice above 6-months of age develop pancreatic immune cell infiltration, as compared to none in conventionally-raised and fecal microbiota transplanted (FMT) IL-10 KO counterparts. Immunofluorescence staining of islet infiltrates was positive for adaptive and innate immunological markers, including lymphoid and myeloid cell markers, which typically characterize autoimmune T1D lesions. A subset of GF IL-10 KO mice was also positive for insulin autoantibodies (IAA), but the majority of mice did not become diabetic. Our findings of early stage lymphocytic infiltrates in the pancreas and IAA in the absence of overt diabetes in GF IL-10 KO mice embody the early stages of T1D pathogenesis. As such, we propose that the presence of gut microbiota play a protective role against immune infiltration in the pancreas of genetically prone hosts. Moreover, our model provides an opportunity to better understand the role of the microbiota in the early stages of immune pathogenesis and perhaps conceive the development of microbe-mediated prophylactic strategies to treat or even prevent T1D.

physiology↗

bioRxiv2017

Untargeted Metabolomic Analysis In Naturally Occurring Canine Diabetes Mellitus Identifies Similarities To Human Type 1 Diabetes

While predominant as a disease entity, knowledge voids exist regarding the pathogenesis of canine diabetes. To test the hypothesis that diabetic dogs have similar metabolomic perturbations to humans with type 1 diabetes (T1D), we analyzed serum metabolomic profiles of breed- and body weight-matched, diabetic (n=6) and healthy (n=6) dogs by liquid chromatography-mass spectrometry (LC-MS) profiling. We report distinct clustering of diabetic and control groups based on heat map analysis of known and unknown metabolites. Random forest classification identified 5/6 dogs per group correctly with overall out of bag error rate=16.7%. Diabetic dogs demonstrated significant upregulation of glycolysis/gluconeogenesis intermediates (e.g., glucose/fructose, C6H12O6, keto-hexose, deoxy-hexose, (P<0.01)), with significant downregulation of tryptophan metabolism metabolites (e.g., picolinic acid, indoxyl sulfate, anthranilate, (P<0.01)). Multiple amino acids (AA), AA metabolites, and bile acids were also significantly lower in diabetic versus healthy dogs (P<0.05) with the exception of the branched chain AA valine, which was elevated in diabetic animals (P<0.05). Metabolomic profiles in diabetic versus healthy dogs shared similarities with those reported in human T1D (e.g., alterations in glycolysis/gluconeogensis metabolites, bile acids, and elevated branched chain AA). Further studies are warranted to evaluate the utility of canine diabetes to provide novel mechanistic insights to the human disorder.\n\nAbbreviations

systems biology↗