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Multi-omics profiling of living human pancreatic islet donors reveals heterogeneous beta cell trajectories towards type 2 diabetes

Nature Metabolism volume 3, pages 1017–1031 (2021)Cite this article

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Abstract

Most research on human pancreatic islets is conducted on samples obtained from normoglycaemic or diseased brain-dead donors and thus cannot accurately describe the molecular changes of pancreatic islet beta cells as they progress towards a state of deficient insulin secretion in type 2 diabetes (T2D). Here, we conduct a comprehensive multi-omics analysis of pancreatic islets obtained from metabolically profiled pancreatectomized living human donors stratified along the glycemic continuum, from normoglycemia to T2D. We find that islet pools isolated from surgical samples by laser-capture microdissection display remarkably more heterogeneous transcriptomic and proteomic profiles in patients with diabetes than in non-diabetic controls. The differential regulation of islet gene expression is already observed in prediabetic individuals with impaired glucose tolerance. Our findings demonstrate a progressive, but disharmonic, remodelling of mature beta cells, challenging current hypotheses of linear trajectories toward precursor or transdifferentiation stages in T2D. Furthermore, through integration of islet transcriptomics with preoperative blood plasma lipidomics, we define the relative importance of gene coexpression modules and lipids that are positively or negatively associated with HbA1c levels, pointing to potential prognostic markers.

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Fig. 1: Overview of the experimental procedures and cohort characteristics.
Fig. 2: Transcriptional changes among patients without diabetes, with prediabetes and with diabetes.
Fig. 3: Identification of coexpressed gene modules related to diabetes traits.
Fig. 4: Proteomics analysis.
Fig. 5: Lipidomics differential analysis.
Fig. 6: Multiblock data modelling of HbA1c.

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Data availability

RNA-seq data were deposited in the NCBI Gene Expression Omnibus with GEO accession number GSE164416. Human genome reference assembly GRCh38 is publicly available.

The proteomics raw data sets and the MaxQuant output files generated and analysed throughout this study were deposited at the ProteomeXchange Consortium via the PRIDE partner repository with the project accession number PXD022561 (https://www.ebi.ac.uk/pride/archive/). Lipidomics data were deposited in the Zenodo database (zenodo.org, https://doi.org/10.5281/zenodo.4716063). Source data are provided with this paper.

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Acknowledgements

We wish to thank L. Groop, E. Ahlqvist, S. Speier, T. Chavakis, R. Scharfmann and A. Schevchenko for discussion; S. Pradervand for advice on RNA-seq processing and QC; C. Rocher for Bioinformatics analysis support; K. Pfriem for administrative assistance. This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no. 115881 (RHAPSODY). This joint undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA. This work is further supported by the Swiss State Secretariat for Education‚ Research and Innovation (SERI) under contract number 16.0097-2. Work in the Solimena lab is also supported with funds from the German Ministry of Education and Research to the German Center for Diabetes Research (DZD). The opinions expressed and arguments employed herein do not necessarily reflect the official views of these funding bodies.

Author information

Author notes

  1. These authors contributed equally: Leonore Wigger, Marko Barovic, Andreas-David Brunner.

  2. These authors jointly supervised this work: Matthias Mann, Mark Ibberson, Michele Solimena.

Authors and Affiliations

  1. Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Leonore Wigger, Flavia Marzetta, Florence Mehl, Frederic Burdet, Camille Kessler & Mark Ibberson

  2. Department of Molecular Diabetology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany

    Marko Barovic, Eyke Schöniger, Nicole Kipke, Daniela Friedland & Michele Solimena

  3. Paul Langerhans Institute Dresden (PLID), Helmholtz Center Munich, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany

    Marko Barovic, Eyke Schöniger, Nicole Kipke, Daniela Friedland, Ezio Bonifacio & Michele Solimena

  4. German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany

    Marko Barovic, Eyke Schöniger, Nicole Kipke, Daniela Friedland, Ezio Bonifacio & Michele Solimena

  5. Max Planck Institute of Biochemistry, Martinsried, Germany

    Andreas-David Brunner & Matthias Mann

  6. DRESDEN-concept Genome Center, c/o Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany

    Mathias Lesche & Andreas Dahl

  7. Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland

    Bernard Thorens

  8. Center for Regenerative Therapies Dresden, Faculty of Medicine and Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany

    Ezio Bonifacio

  9. Steno Diabetes Center Copenhagen, Gentofte, Denmark

    Cristina Legido-Quigley

  10. King’s College London, London, UK

    Cristina Legido-Quigley

  11. DMPK Center, Technologie Servier, Orléans, France

    Pierre Barbier Saint Hilaire

  12. Institut de Recherches Servier, Pôle d’Innovation Thérapeutique Métabolisme, Suresnes, France

    Philippe Delerive

  13. Lipotype GmbH, Dresden, Germany

    Christian Klose, Mathias J. Gerl & Kai Simons

  14. Department of Pathology, Medical Faculty, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

    Daniela Aust

  15. NCT Biobank Dresden, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

    Daniela Aust

  16. Department of Visceral, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Medical Faculty, Technische Universität Dresden, Dresden, Germany

    Jürgen Weitz & Marius Distler

  17. Sanofi-Aventis Deutschland GmbH, Diabetes Research, Industriepark Höchst, Frankfurt am Main, Germany

    Anke M. Schulte

Authors
  1. Leonore Wigger
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Contributions

J. W. and M. D., participant recruitment and surgery, provision of clinical data; E. S., N. K. and D. F., sample collection and processing, data entry; D. A., pathology; M .B., N. K. and E. S., patient database management and selection; A.-D. B. and M. M., proteomics; M. L., A. D., RNA-seq, C. L. Q., P. B. S. H, P. D., C. Klose, M. G., K. S., lipidomics and sphingolipidomics; L. W., M. B., A.-D. B., F. Marzetta., F. Mehl, F. B. and C. Kessler., analysis and integration of multi-omics data; E. B., autoantibody test; A. S., data in mouse tissue and cell lines; M.B., immunofluorescence stainings and antibody validation; B. T., D. A., J. W., A. S., M. M., M. I. and M. S., conceptual insights and provision of funds; L. W., M. B., A.-D. B., F. Marzetta, F. Mehl, A. S., M. I., M. M. and M. S., writing of the manuscript. All authors read, revised and approved the final version of the manuscript.

Corresponding authors

Correspondence to Matthias Mann, Mark Ibberson or Michele Solimena.

Ethics declarations

Competing interests

K. S. is CEO of Lipotype. K. S. and C. K. are shareholders of Lipotype. M. J. G. is an employee of Lipotype. P. B. S. H. and P. D. are employees of Servier. A. S. is an employee of Sanofi-Aventis Deutschland. The other authors declare no conflict of interest.

Additional information

Peer review information Nature Metabolism thanks Anna Gloyn, Huiyong Yin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Christoph Schmitt.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Deconvolution of cell types based on RNA-Seq data.

a, Cell-type proportions by sample, as estimated with DeconRNASeq. Beta cells, orange; alpha cells, green; delta cells, blue; gamma cells, purple. b, Sample distribution across each cell type proportion. Highlighted are samples presenting a cell type specific gene being the most expressed. Marker genes were GCG and TTR for alpha cells, INS for beta cells, SST for delta cells, and PPY for gamma cells.

Extended Data Fig. 2 Differential gene expression analysis between glycemic groups in the entire cohort.

a-b, Gene expression profile (a) and GSEA analysis (b) of DE genes between IGT, T3cD or T2D and ND PPP. Results are similar to those shown in Fig. 2bc, but obtained from the entire cohort of 133 PPP.

Extended Data Fig. 3 Validation experiments pertaining ALDOB.

a-b, ALDOB expression (RNAseq, Illumina) in (a) islets from 13-week-old male db/db, db/+ mice and C57Bl6 mice (3 animals/strain, 3 independent RNA extractions) or (b) mouse αTC1 clone 6 alpha and Min6s4 beta cell lines (4 independent RNA extractions/cell line). Boxplot spans from 25th until 75th percentile, with centerline at median, whiskers extend to the most extreme data point which is no more than 1.5 times the length of the box away from the box. c, Western blot of MinIN6 single cell-derived clones with antibodies against ALDOB and ALDOA. Framed lanes mark ALDOB knockout clones as verified by site-specific sequencing.

Source data

Extended Data Fig. 4 Extended analysis of proteomic data.

a, Hierarchical clustering of protein expression correlations in all biological replicates highlighting the technical and biological reproducibility of our proteome data set. b, Distribution of differentially expressed proteins in islets of T2D and ND PPP across chromosomes. c, Ranked ABCC8 protein expression levels across T2D and ND subjects. T2D are highlighted in orange, ND are highlighted in blue. Patient 118 was treated with Glimepiride; Patient 87 was treated with Mitiglinide; Patient 197 was treated with Glibenclamide. d, Transcriptome to Proteome abundance correlation (Pearson correlation coefficient = 0.3). e, Differential expression analysis of pancreatic islet transcriptomes of ND vs T2D PPP. (Orange: Significantly upregulated in diseased; Blue: Significantly downregulated in diseased).

Supplementary information

Supplementary Information

Supplementary Tables 1, 3, 8–12

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Supplementary Tables

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Source Data Fig. 1

Statistical source data

Source Data Fig. 4

Statistical source data

Source Data Extended Data Fig. 3

Statistical source data

Source Data Extended Data Fig. 3

Unprocessed western blots

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Wigger, L., Barovic, M., Brunner, AD. et al. Multi-omics profiling of living human pancreatic islet donors reveals heterogeneous beta cell trajectories towards type 2 diabetes. Nat Metab 3, 1017–1031 (2021). https://doi.org/10.1038/s42255-021-00420-9

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