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Single-cell mRNA-regulation analysis reveals cell type-specific mechanisms of type 2 diabetes

Martínez-López, J. A. ; Lindqvist, A. LU ; Lopez-Pascual, A. LU ; Harder, A. ; Chen, P. ; Ngara, M. LU orcid ; Shcherbina, L. LU ; Siffo, S. LU ; Cowan, E. LU orcid and Baira, S. M. LU , et al. (2025) In Nature Communications 16.
Abstract

Perturbed secretion of insulin and other pancreatic islet hormones is the main cause of type 2 diabetes (T2D). The islets harbor five cell types that are potentially altered differently by T2D. Whole-islet transcriptomics and single-cell RNA-sequencing (scRNAseq) studies have revealed differentially expressed genes without reaching consensus. Here, we demonstrate that further insights into T2D disease mechanisms can be obtained by network-based analysis of scRNAseq data from individual cell types. We developed differential gene coordination network analysis (dGCNA) and analyzed islet SmartSeq2 scRNAseq data from 16 T2D and 16 non-T2D individuals. dGCNA reveals T2D-induced cell type-specific networks of dysregulated genes with remarkable... (More)

Perturbed secretion of insulin and other pancreatic islet hormones is the main cause of type 2 diabetes (T2D). The islets harbor five cell types that are potentially altered differently by T2D. Whole-islet transcriptomics and single-cell RNA-sequencing (scRNAseq) studies have revealed differentially expressed genes without reaching consensus. Here, we demonstrate that further insights into T2D disease mechanisms can be obtained by network-based analysis of scRNAseq data from individual cell types. We developed differential gene coordination network analysis (dGCNA) and analyzed islet SmartSeq2 scRNAseq data from 16 T2D and 16 non-T2D individuals. dGCNA reveals T2D-induced cell type-specific networks of dysregulated genes with remarkable ontological specificity, thus allowing for a comprehensive and unbiased functional classification of genes involved in T2D. In beta cells eleven networks of genes are detected, revealing that mitochondrial electron transport chain, glycolysis, cytoskeleton organization, cell proliferation, unfolded protein response and three networks of beta cell transcription factors are perturbed, whereas exocytosis, lysosomal regulation and insulin translation programs are instead enhanced in T2D. Furthermore, we validated the ability of dGCNA to reveal disease mechanisms and predict the functional context of genes by showing that TMEM176A/B regulates beta cell microfilament organization and that CEPBG is an important regulator of the unfolded protein response. In addition, when comparing beta- and alpha cells, we found substantial differences, reproduced across independent datasets, confirming cell type-specific alterations in T2D. We conclude that analysis of networks of differentially coordinated genes provides detailed insight into cell type-specific gene function and T2D pathophysiology.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Nature Communications
volume
16
article number
9475
publisher
Nature Publishing Group
external identifiers
  • pmid:41145477
  • scopus:105019798745
ISSN
2041-1723
DOI
10.1038/s41467-025-65060-z
language
English
LU publication?
yes
additional info
Publisher Copyright: © The Author(s) 2025.
id
db58518f-8d6f-4fd6-bcce-88ca80fff185
date added to LUP
2025-11-05 13:56:37
date last changed
2025-11-06 03:36:08
@article{db58518f-8d6f-4fd6-bcce-88ca80fff185,
  abstract     = {{<p>Perturbed secretion of insulin and other pancreatic islet hormones is the main cause of type 2 diabetes (T2D). The islets harbor five cell types that are potentially altered differently by T2D. Whole-islet transcriptomics and single-cell RNA-sequencing (scRNAseq) studies have revealed differentially expressed genes without reaching consensus. Here, we demonstrate that further insights into T2D disease mechanisms can be obtained by network-based analysis of scRNAseq data from individual cell types. We developed differential gene coordination network analysis (dGCNA) and analyzed islet SmartSeq2 scRNAseq data from 16 T2D and 16 non-T2D individuals. dGCNA reveals T2D-induced cell type-specific networks of dysregulated genes with remarkable ontological specificity, thus allowing for a comprehensive and unbiased functional classification of genes involved in T2D. In beta cells eleven networks of genes are detected, revealing that mitochondrial electron transport chain, glycolysis, cytoskeleton organization, cell proliferation, unfolded protein response and three networks of beta cell transcription factors are perturbed, whereas exocytosis, lysosomal regulation and insulin translation programs are instead enhanced in T2D. Furthermore, we validated the ability of dGCNA to reveal disease mechanisms and predict the functional context of genes by showing that TMEM176A/B regulates beta cell microfilament organization and that CEPBG is an important regulator of the unfolded protein response. In addition, when comparing beta- and alpha cells, we found substantial differences, reproduced across independent datasets, confirming cell type-specific alterations in T2D. We conclude that analysis of networks of differentially coordinated genes provides detailed insight into cell type-specific gene function and T2D pathophysiology.</p>}},
  author       = {{Martínez-López, J. A. and Lindqvist, A. and Lopez-Pascual, A. and Harder, A. and Chen, P. and Ngara, M. and Shcherbina, L. and Siffo, S. and Cowan, E. and Baira, S. M. and Kryvokhyzha, D. and Karagiannopoulos, A. and Chriett, S. and Skene, N. G. and Prasad, R. B. and Lancien, M. and Johnson, P. F. and Eliasson, P. and Eliasson, L. and Louvet, C. and Spégel, P. and Muñoz-Manchado, A. B. and Sandberg, R. and Hjerling-Leffler, J. and Wierup, N.}},
  issn         = {{2041-1723}},
  language     = {{eng}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Communications}},
  title        = {{Single-cell mRNA-regulation analysis reveals cell type-specific mechanisms of type 2 diabetes}},
  url          = {{http://dx.doi.org/10.1038/s41467-025-65060-z}},
  doi          = {{10.1038/s41467-025-65060-z}},
  volume       = {{16}},
  year         = {{2025}},
}