HAMSAB diet ameliorates dysfunctional signaling in pancreatic islets in autoimmune diabetes

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Summary

An altered gut microbiota is associated with type 1 diabetes (T1D), affecting the production of short-chain fatty acids (SCFA) and glucose homeostasis. We previously demonstrated that enhancing serum acetate and butyrate using a dietary supplement (HAMSAB) improved glycemia in non-obese diabetic (NOD) mice and patients with established T1D. The effects of SCFA on immune-infiltrated islet cells remain to be clarified. Here, we performed single-cell RNA sequencing on islet cells from NOD mice fed an HAMSAB or control diet. HAMSAB induced a regulatory gene expression profile in pancreas-infiltrated immune cells. Moreover, HAMSAB maintained the expression of β-cell functional genes and decreased cellular stress. HAMSAB-fed mice showed preserved pancreatic endocrine cell identity, evaluated by decreased numbers of poly-hormonal cells. Finally, SCFA increased insulin levels in human β-like cells and improved transplantation outcome in NOD/SCID mice. Our findings support the use of metabolite-based diet as attractive approach to improve glucose control in T1D.

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Highlights

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HAMSAB induces a tolerogenic phenotype of infiltrated immune cells in NOD mice
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HAMSAB reduces the stress response maintaining endocrine cell identity in NOD mice
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SCFAs increase insulin levels and function of human beta-like cells
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HAMSAB is an attractive strategy for clinical studies in early type 1 diabetes

Abstract

Diabetology; Cell biology; Transcriptomics; Model organism

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Most cited references 53

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Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

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Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

Christoph Hafemeister, Rahul Satija (2019)

Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.

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Author and article information

Contributors

Esteban N. Gurzov

Journal

Journal ID (nlm-ta): iScience

Journal ID (iso-abbrev): iScience

Title: iScience

Publisher: Elsevier

ISSN (Electronic): 2589-0042

Publication date PMC-release: 10 December 2023

Publication date Collection: 19 January 2024

Publication date (Electronic): 10 December 2023

Volume: 27

Issue: 1

Electronic Location Identifier: 108694

Affiliations

[1 ]Signal Transduction and Metabolism Laboratory, Université libre de Bruxelles, 1070 Brussels, Belgium

[2 ]IRIBHM, Université libre de Bruxelles, 1070 Brussels, Belgium

[3 ]Clinical and Experimental Endocrinology (CEE), Department of Chronic Diseases, Metabolism and Ageing (CHROMETA), Campus Gasthuisberg O&N 1, KU Leuven, 3000 Leuven, Belgium

[4 ]Inflammatory and Cell Death Signaling in Diabetes group, Signal Transduction and Metabolism Laboratory, Université libre de Bruxelles, 1070 Brussels, Belgium

[5 ]Laboratoire de Physiologie et de Pharmacologie, Université Libre de Bruxelles, 1000 Brussels, Belgium

[6 ]Single Cell and Spatial-Omics Laboratory, Adelaide Centre of Epigenetics, University of Adelaide, Adelaide, SA 5005, Australia

[7 ]Infection and Immunity Program, Biomedicine Discovery Institute, Department of Biochemistry, Monash University, Melbourne, VIC 3800, Australia

[8 ]ImmunoBiota Therapeutics Pty Ltd, Melbourne, VIC 3187, Australia

[9 ]WELBIO Department, WEL Research Institute, Avenue Pasteur 6, 1300 Wavre, Belgium

Author notes

[∗ ]Corresponding author esteban.gurzov@ 123456ulb.be

[10]

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Article

Publisher Item ID: S2589-0042(23)02771-2 Publisher ID: 108694

DOI: 10.1016/j.isci.2023.108694

PMC ID: 10783594

PubMed ID: 38213620

SO-VID: b5ad3078-9c08-4ccf-8ac2-c4296aaa058a

License:

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

History

Date received : 17 April 2023

Date revision received : 23 October 2023

Date accepted : 5 December 2023

HAMSAB diet ameliorates dysfunctional signaling in pancreatic islets in autoimmune diabetes

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Evolutionary Cell Biology

Most cited references 53

Comprehensive Integration of Single-Cell Data

Integrated analysis of multimodal single-cell data

Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

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