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Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes

Pancreatic islet β-cell failure is key to the onset and progression of type 2 diabetes (T2D). The advent of single-cell RNA sequencing (scRNA-seq) has opened the possibility to determine transcriptional signatures specifically relevant for T2D at the β-cell level. Yet, applications of this technique...

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Autores principales: Bosi, Emanuele, Marselli, Lorella, De Luca, Carmela, Suleiman, Mara, Tesi, Marta, Ibberson, Mark, Eizirik, Decio L, Cnop, Miriam, Marchetti, Piero
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7679065/
https://www.ncbi.nlm.nih.gov/pubmed/33575641
http://dx.doi.org/10.1093/nargab/lqaa097
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author Bosi, Emanuele
Marselli, Lorella
De Luca, Carmela
Suleiman, Mara
Tesi, Marta
Ibberson, Mark
Eizirik, Decio L
Cnop, Miriam
Marchetti, Piero
author_facet Bosi, Emanuele
Marselli, Lorella
De Luca, Carmela
Suleiman, Mara
Tesi, Marta
Ibberson, Mark
Eizirik, Decio L
Cnop, Miriam
Marchetti, Piero
author_sort Bosi, Emanuele
collection PubMed
description Pancreatic islet β-cell failure is key to the onset and progression of type 2 diabetes (T2D). The advent of single-cell RNA sequencing (scRNA-seq) has opened the possibility to determine transcriptional signatures specifically relevant for T2D at the β-cell level. Yet, applications of this technique have been underwhelming, as three independent studies failed to show shared differentially expressed genes in T2D β-cells. We performed an integrative analysis of the available datasets from these studies to overcome confounding sources of variability and better highlight common T2D β-cell transcriptomic signatures. After removing low-quality transcriptomes, we retained 3046 single cells expressing 27 931 genes. Cells were integrated to attenuate dataset-specific biases, and clustered into cell type groups. In T2D β-cells (n = 801), we found 210 upregulated and 16 downregulated genes, identifying key pathways for T2D pathogenesis, including defective insulin secretion, SREBP signaling and oxidative stress. We also compared these results with previous data of human T2D β-cells from laser capture microdissection and diabetic rat islets, revealing shared β-cell genes. Overall, the present study encourages the pursuit of single β-cell RNA-seq analysis, preventing presently identified sources of variability, to identify transcriptomic changes associated with human T2D and underscores specific traits of dysfunctional β-cells across different models and techniques.
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spelling pubmed-76790652021-02-10 Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes Bosi, Emanuele Marselli, Lorella De Luca, Carmela Suleiman, Mara Tesi, Marta Ibberson, Mark Eizirik, Decio L Cnop, Miriam Marchetti, Piero NAR Genom Bioinform Standard Article Pancreatic islet β-cell failure is key to the onset and progression of type 2 diabetes (T2D). The advent of single-cell RNA sequencing (scRNA-seq) has opened the possibility to determine transcriptional signatures specifically relevant for T2D at the β-cell level. Yet, applications of this technique have been underwhelming, as three independent studies failed to show shared differentially expressed genes in T2D β-cells. We performed an integrative analysis of the available datasets from these studies to overcome confounding sources of variability and better highlight common T2D β-cell transcriptomic signatures. After removing low-quality transcriptomes, we retained 3046 single cells expressing 27 931 genes. Cells were integrated to attenuate dataset-specific biases, and clustered into cell type groups. In T2D β-cells (n = 801), we found 210 upregulated and 16 downregulated genes, identifying key pathways for T2D pathogenesis, including defective insulin secretion, SREBP signaling and oxidative stress. We also compared these results with previous data of human T2D β-cells from laser capture microdissection and diabetic rat islets, revealing shared β-cell genes. Overall, the present study encourages the pursuit of single β-cell RNA-seq analysis, preventing presently identified sources of variability, to identify transcriptomic changes associated with human T2D and underscores specific traits of dysfunctional β-cells across different models and techniques. Oxford University Press 2020-11-20 /pmc/articles/PMC7679065/ /pubmed/33575641 http://dx.doi.org/10.1093/nargab/lqaa097 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Standard Article
Bosi, Emanuele
Marselli, Lorella
De Luca, Carmela
Suleiman, Mara
Tesi, Marta
Ibberson, Mark
Eizirik, Decio L
Cnop, Miriam
Marchetti, Piero
Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes
title Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes
title_full Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes
title_fullStr Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes
title_full_unstemmed Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes
title_short Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes
title_sort integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes
topic Standard Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7679065/
https://www.ncbi.nlm.nih.gov/pubmed/33575641
http://dx.doi.org/10.1093/nargab/lqaa097
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