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


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.

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 ( Lipidomics data were deposited in the Zenodo database (, Source data are provided with this paper.


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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




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 or 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

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Supplementary Tables 2, 4–7, 13–16

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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).

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