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  • Correspondence
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Single-cell expression profiling of islets generated by the Human Pancreas Analysis Program

Nature Metabolism volume 5, pages 713–715 (2023)Cite this article

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In this Correspondence, we introduce the Human Pancreas Analysis Program and discuss ongoing efforts with a specific focus on the continuous release of integrated single-cell RNA sequencing (scRNA-seq) measurements across pancreatic tissues. Although it has been over 50 years since Willy Gepts described the pancreatic pathology in type 1 diabetes (T1D), detailed understanding of the initial molecular perturbations that occur during disease pathogenesis is still incomplete. Two nontrivial constraints hamper insights into these processes: (1) the inability to safely biopsy the human pancreas of living donors; and (2) the substantial disease progression and beta cell destruction that has occurred by the time patients are clinically diagnosed with T1D. These limitations have meant that the majority of T1D studies have been performed on peripheral leukocytes from the blood, which is not the site of pathogenesis. A revolution in single-cell transcriptomic technologies during the past decade has influenced the molecular profiling of islets. Several primary single-cell pancreatic islet atlases for mouse and humans have become available1,2,3,4,5,6,7,8. However, the limited numbers of cells and relatively small numbers of human donors used for the abovementioned studies could not provide comprehensive coverage of the human pancreatic islet transcriptome. In 2016, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) charged a group of investigators with forming the Human Pancreas Analysis Program (HPAP)9. The goal of this initiative is to use cutting-edge but robust and tested technologies in pancreatic tissues and to release high-quality datasets for the T1D community. A central mission of HPAP is to organize and share data resulting from the study of these tissues through an open-access resource database called PANC-DB. Owing to the early success of HPAP-T1D, in 2020 HPAP-T2D was also funded by NIDDK to define the molecular pathogenesis of islet dysfunction by studying human pancreatic tissue samples from organ donors with T2D10.

Currently, the acceptance criteria for HPAP relates to donors who are younger than 40 years of age and have had T1D for 7 years or less, or donors without diabetes who are 40 years or younger with one or more T1D-associated autoantibody (AAb). These criteria have evolved over time from data demonstrating that residual insulin-positive islets and insulitis are more likely to be found using these acceptance criteria. Our goal is to process tissue from at least nine donors with recent-onset T1D per year, although the unpredictable nature of organ donor recovery does not allow us to provide a precise timeline for new donors. Multiple data modalities from molecular to functional measurements are being generated by HPAP, where raw measurements from individual donors become available on PANC-DB in real time. Among these data modalities, single-cell transcriptomic measurements are distinct, considering that the high-throughput nature of this technology and the maturity of computational techniques for these assays have been under active development for robust data integration across donors. Although the raw FASTQ files for scRNA-seq data are uploaded to PANC-DB once scRNA-seq libraries from each donor have been sequenced, HPAP periodically releases fully processed integrated scRNA-seq data on PANC-DB’s ‘Interactive Analysis’ section once new batches of data from 10 donors have been sequenced and passed various quality control metrics (Supplementary Fig. 1). In this Correspondence, we highlight various aspects of the release of scRNA-seq data integration across HPAP donors.

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Fig. 1: scRNA-seq reveals the cell populations of human pancreatic islets.

Data availability

All pancreatic islet sequencing data have been uploaded to PANC-DB (https://hpap.pmacs.upenn.edu/) and the interactive session can be accessed on the cellxgene database hosted on PANC-DB (https://faryabi16.pmacs.upenn.edu/view/T1D_T2D_public.h5ad/). The raw islet sequencing data in the form of FASTQ files for each HPAP sample can be obtained from the PANC-DB database. The processed data can be downloaded in two formats, RDS or h5ad, which can be loaded for analysis using Seurat12 and Scanpy19 pipelines.

Code availability

The scripts used for data processing and analysis of the scRNA-seq data are available on GitHub (https://github.com/faryabiLab/HPAP-scRNA-seq-Workflow-2022). The cellxgene interactive tool with HPAP data can be accessed via the cellxgene database (https://faryabi16.pmacs.upenn.edu/view/T1D_T2D_public.h5ad/)

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Acknowledgements

We thank the Vahedi, Faryabi and Kaestner lab members for discussions. This work was supported by National Institute of Health grants UC4 DK112217, U01DK112217 (A.N., K.H.K., R.B.F. and G.V.), R01HL145754, U01DK127768 and U01DA052715 and awards from the Burroughs Wellcome Fund, the Chan Zuckerberg Initiative, W. W. Smith Charitable Trust, the Penn Epigenetics Institute and the Sloan Foundation (G.V.).

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Authors and Affiliations

  1. Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Abhijeet R. Patil, Jonathan Schug, Klaus H. Kaestner & Golnaz Vahedi

  2. Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Abhijeet R. Patil, Ali Naji, Robert B. Faryabi & Golnaz Vahedi

  3. Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Abhijeet R. Patil, Klaus H. Kaestner, Robert B. Faryabi & Golnaz Vahedi

  4. Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Abhijeet R. Patil, Jonathan Schug, Klaus H. Kaestner & Golnaz Vahedi

  5. Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Ali Naji & Robert B. Faryabi

  6. Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Robert B. Faryabi

  7. Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Robert B. Faryabi & Golnaz Vahedi

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  1. Abhijeet R. Patil
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Correspondence to Golnaz Vahedi.

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Nature Metabolism thanks Yan Li and Benoit Gauthier for their contribution to the peer review of this work.

Supplementary information

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Supplementary Figs. 1-2, Supplementary Notes and Discussion

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Supplementary Tables 1 to S2

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Patil, A.R., Schug, J., Naji, A. et al. Single-cell expression profiling of islets generated by the Human Pancreas Analysis Program. Nat Metab 5, 713–715 (2023). https://doi.org/10.1038/s42255-023-00806-x

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