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Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes

Nature Metabolism volume 4, pages 284–299 (2022)Cite this article

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Abstract

Type 1 diabetes (T1D) is an autoimmune disease in which immune cells destroy insulin-producing beta cells. The aetiology of this complex disease is dependent on the interplay of multiple heterogeneous cell types in the pancreatic environment. Here, we provide a single-cell atlas of pancreatic islets of 24 T1D, autoantibody-positive and nondiabetic organ donors across multiple quantitative modalities including ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time of flight and ~1,000,000 cells using in situ imaging mass cytometry. We develop an advanced integrative analytical strategy to assess pancreatic islets and identify canonical cell types. We show that a subset of exocrine ductal cells acquires a signature of tolerogenic dendritic cells in an apparent attempt at immune suppression in T1D donors. Our multimodal analyses delineate cell types and processes that may contribute to T1D immunopathogenesis and provide an integrative procedure for exploration and discovery of human pancreatic function.

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Fig. 1: Discernment of human pancreatic cell types using single-cell RNA sequencing.
Fig. 2: AAb+ and T1D donors have both common and distinct transcriptomic changes in endocrine and exocrine cell types.
Fig. 3: The gene signature of Beta-1 cells in GAD+ donors is correlated with donors’ anti-GAD AAb titres.
Fig. 4: Single-cell RNA-seq profiling enables the identification of MHC class II-expressing ductal cells with transcriptional similarities to dendritic cells in T1D.
Fig. 5: Three single-cell resolution protein-based approaches corroborate the existence of MHC class II-expressing ductal cells in T1D.
Fig. 6: Representative examples of IMC measurement corroborate that MHC class II-positive ductal cells are present in pancreatic tissues.

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Data availability

The Gene Expression Omnibus accession number associated with this paper is GSE148073. Additional data are publicly available at https://hpap.pmacs.upenn.edu/. Furthermore, a user-friendly web portal for exploration of the scRNA-seq data is available at https://cellxgene.cziscience.com/e/37b21763-7f0f-41ae-9001-60bad6e2841d.cxg/.

Code availability

Where applicable, scripts used for data processing and analysis are available in the Supplemental Information and Methods and provided on GitHub at https://github.com/GregorySchwartz/multiomics-single-cell-t1d/. TooManyCells is a publicly available suite of tools, algorithms and visualizations (https://github.com/GregorySchwartz/too-many-cells/) that was extensively used in this study, and where applicable, the flags used in TooManyCells to generate specific figures are included in the Methods.

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Acknowledgements

We thank our colleagues for helpful discussions, particularly: A. Chandra, E. L. Prak, B. Stanger, M. Silverman, G. Beatty, K. Z., M. Lazar, R. Vonderheide, A. Minn and E. J. Wherry. Some schematics throughout the paper were created with Biorender.com. We thank A. Georgescu for confocal microscopy and the University of Pennsylvania Diabetes Research Center for the use of the Functional Genomics Core (P30-DK19525). This work was supported by National Institute of Health grants UC4 DK112217 and U01DK112217 (to A.N., K.K., M.B., J.M., M.F., R.B.F. and G.V.), R01CA230800 and Susan G. Komen CCR185472448 (to R.B.F.) and R01HL145754, U01DK127768, U01DA052715, the Burroughs Wellcome Fund, the Chan Zuckerberg Initiative, W. W. Smith Charitable Trust, the Penn Epigenetics Institute and the Sloan Foundation awards to G.V.

Author information

Author notes

  1. These authors contributed equally: Maria Fasolino, Gregory W. Schwartz.

Authors and Affiliations

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

    Maria Fasolino, Abhijeet R. Patil, Maria L. Golson, Yue J. Wang, Ashleigh Morgan, Jonathan Schug, Jinping Liu, Minghui Wu, Daniel Traum, Ayano Kondo, Catherine L. May, Naomi Goldman, Wenliang Wang, Jason H. Moore, Klaus H. Kaestner & Golnaz Vahedi

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

    Maria Fasolino, Gregory W. Schwartz, Abhijeet R. Patil, Aanchal Mongia, Naomi Goldman, Wenliang Wang, Alberto S. Japp, Michael R. Betts, Robert B. Faryabi, Ali Naji & Golnaz Vahedi

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

    Maria Fasolino, Gregory W. Schwartz, Abhijeet R. Patil, Aanchal Mongia, Naomi Goldman, Wenliang Wang, Robert B. Faryabi, Klaus H. Kaestner & Golnaz Vahedi

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

    Maria Fasolino, Abhijeet R. Patil, Maria L. Golson, Yue J. Wang, Ashleigh Morgan, Chengyang Liu, Jinping Liu, Minghui Wu, Daniel Traum, Ayano Kondo, Catherine L. May, Naomi Goldman, Wenliang Wang, Ali Naji, Klaus H. Kaestner & Golnaz Vahedi

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

    Maria Fasolino, Gregory W. Schwartz, Abhijeet R. Patil, Aanchal Mongia, Naomi Goldman, Wenliang Wang, Robert B. Faryabi & Golnaz Vahedi

  6. Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Maria Fasolino, Gregory W. Schwartz, Abhijeet R. Patil, Aanchal Mongia, Naomi Goldman, Wenliang Wang, Michael Feldman, Jason H. Moore, Robert B. Faryabi & Golnaz Vahedi

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

    Gregory W. Schwartz, Aanchal Mongia, Michael Feldman & Robert B. Faryabi

  8. Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Maria L. Golson

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

    Chengyang Liu & Ali Naji

  10. Department of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Alberto S. Japp, Michael R. Betts & Alberto S. Japp

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

    Maria Fasolino, Abhijeet R. Patil, Maria L. Golson, Yue J. Wang, Ashleigh Morgan, Jonathan Schug, Jinping Liu, Minghui Wu, Daniel Traum, Ayano Kondo, Catherine L. May, Naomi Goldman, Wenliang Wang, Jason H. Moore, Klaus H. Kaestner & Golnaz Vahedi

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

    Maria Fasolino, Gregory W. Schwartz, Abhijeet R. Patil, Aanchal Mongia, Naomi Goldman, Wenliang Wang, Alberto S. Japp, Michael R. Betts, Robert B. Faryabi, Ali Naji & Golnaz Vahedi

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

    Maria Fasolino, Gregory W. Schwartz, Abhijeet R. Patil, Aanchal Mongia, Naomi Goldman, Wenliang Wang, Robert B. Faryabi, Klaus H. Kaestner & Golnaz Vahedi

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

    Maria Fasolino, Abhijeet R. Patil, Maria L. Golson, Yue J. Wang, Ashleigh Morgan, Chengyang Liu, Jinping Liu, Minghui Wu, Daniel Traum, Ayano Kondo, Catherine L. May, Naomi Goldman, Wenliang Wang, Ali Naji, Klaus H. Kaestner & Golnaz Vahedi

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

    Maria Fasolino, Gregory W. Schwartz, Abhijeet R. Patil, Aanchal Mongia, Naomi Goldman, Wenliang Wang, Robert B. Faryabi & Golnaz Vahedi

  16. Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Maria Fasolino, Gregory W. Schwartz, Abhijeet R. Patil, Aanchal Mongia, Naomi Goldman, Wenliang Wang, Michael Feldman, Jason H. Moore, Robert B. Faryabi & Golnaz Vahedi

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

    Gregory W. Schwartz, Aanchal Mongia, Michael Feldman & Robert B. Faryabi

  18. Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Maria L. Golson

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

    Chengyang Liu & Ali Naji

  20. Department of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Michael R. Betts

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  1. Maria Fasolino
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Consortia

the HPAP Consortium

  • Maria Fasolino
  • , Gregory W. Schwartz
  • , Abhijeet R. Patil
  • , Aanchal Mongia
  • , Maria L. Golson
  • , Yue J. Wang
  • , Ashleigh Morgan
  • , Chengyang Liu
  • , Jonathan Schug
  • , Jinping Liu
  • , Minghui Wu
  • , Daniel Traum
  • , Ayano Kondo
  • , Catherine L. May
  • , Naomi Goldman
  • , Wenliang Wang
  • , Michael Feldman
  • , Jason H. Moore
  • , Alberto S. Japp
  • , Michael R. Betts
  • , Robert B. Faryabi
  • , Ali Naji
  • , Klaus H. Kaestner
  •  & Golnaz Vahedi

Contributions

C.L. and A.N. procured human pancreatic tissues. A.N. and K.H.K. acquired funding. M.G., Y.J.W. and A.M. generated scRNA-seq libraries. J.S. performed sequencing of scRNA-seq libraries. J. L. performed CyTOF experiments. M.W. and D. T. performed IMC experiments. A.K. assisted in analysis of IMC measurements. C.L.M. performed immunohistochemistry experiments. N.G. assisted in analysis of CyTOF data. W.W. assisted in analysis of scRNA-seq data. A.M. performed annotation of IMC and CyTOF data. M. Feldman supervised the HPAP tissue bank. J.H.M., A.S.J. and M.R.B. are members of the HPAP consortium. K.H.K. supervised M.G., Y.J.W., A.M., J. L., M.W., D.T., A.K. and C.L.M. for generation of scRNA-seq, CyTOF and IMC data. R.B.F. supervised A.M. and G.W.S. in analysis of scRNA-seq, CyTOF and IMC data. G.V. supervised M. Fasolino, A.R.P., N.G. and W.W. M. Fasolino., G.W.S., A.R.P. and G.V. performed computational analysis of scRNA-seq, IMC and CyTOF data. M. Fasolino and G.V. wrote the original draft and revised it with comments from all authors. K.H.K. and R.B.F. edited the original and revised manuscript.

Corresponding authors

Correspondence to Robert B. Faryabi, Ali Naji, Klaus H. Kaestner or Golnaz Vahedi.

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Competing interests

M.R.B. has a consulting arrangement with Interius BioTherapeutics. The other authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks Tune Pers, Raghavendra Mirmira and the other, anonymous, reviewers for their contribution to the peer review of this work. Primary Handling Editor: Isabella Samuelson.

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

Extended data

Extended Data Fig. 1 Cell numbers and clustering before complete filtering.

a) Pie chart displaying the cell numbers and proportions of each individual donor per donor type. b) Box plot displaying the average gene number per cell per donor type. c) UMAP visualization of cell clusters for all cells. d) Doublets and singlets, as identified using DoubletFinder, across cell clusters visualized by UMAP. e) UMAP visualization of the normalized gene expression counts of each canonical gene marker of each major cell type.

Extended Data Fig. 2 Doublet removal and UMI counts.

a) Doublets and singlets, as identified using Scrublet, across cell clusters visualized by UMAP per individual. b) Venn diagram indicating the number of cells deemed doublets by DoubletFinder and Scrublet, as well as cells that were commonly identified by both approaches. c) Table indicating the number of cells removed and the resulting total cell number for each step of filtering. d) Unique molecular identifier (UMI) counts per cell projected across the dendrogram visualization and clustering of all cells from Fig. 1c. Pie charts at the end of the branches display the breakdown of UMI counts per cell within that terminal cluster. Cells begin at the start pin symbol, and from there are partitioned based on similarities and differences in gene expression. e) UMI counts per cell projected across the dendrogram visualization and clustering of ductal and endocrine cells from Fig. 1d. Pie charts at the end of the branches display the breakdown of UMI counts per cell within that terminal cluster. Cells begin at the start pin symbol, and from there are partitioned based on similarities and differences in gene expression. f) Expression of genes associated with mitochondrial function projected across the dendrogram visualization and clustering of all cells from Fig. 1c. g) Expression of genes associated with mitochondrial function projected across the dendrogram visualization and clustering of ductal and endocrine cells from Fig. 1d.

Extended Data Fig. 3 Cell numbers and clustering after complete filtering.

a) Pie chart displaying the cell numbers/proportions of each individual donor per donor type. b) UMAP visualization of cell clusters for all cells. c) UMAP visualization donor groups across clusters for all cells. d) UMAP visualization of Garnett cellular classifications across clusters for all cells. e) UMAP visualization of the normalized gene expression counts of each canonical gene marker of each major cell type.

Extended Data Fig. 4 Marker gene expression confirms canonical cell types.

a) Dendrograms highlighting the expression of each canonical gene marker of each major cell type across the dendrogram of all cells in Fig. 1c. b) The classification of our scRNA-seq data was confirmed by a label transfer strategy using a previous single-nucleus RNA-seq data set in pancreatic islets10. c) Bar plot demonstrates percentages of agreement between previous annotation and our strategy using a label-transfer strategy. d) Dendrograms highlighting the expression of each canonical gene marker of each major cell type across the dendrogram of ductal and endocrine cells in Fig. 1d. e) To further validate the most closely related cell types to Hybrid cells, we used a label transfer strategy to a previous pancreatic islet scRNA-seq data set19. In concordance with Garnett and canonical gene markers, we corroborated the assignment of beta, alpha, and PP cells to these Hybrid cells. f) Bar plot demonstrates annotation results of label transfer for cells grouped as Hybrid cells. g) Pie chart displaying the cell numbers/proportions of each cell type defined in Fig. 1, c and d. h) Schematic of the human pancreatic islet anatomy and major cell types.

Extended Data Fig. 5 Gene and gene ontology pathways that are shared and different across disease states in Epsilon-1, Epsilon-2, and Immune cells.

(a-c) (Left) For each cell type, Venn diagrams indicate the numbers of upregulated and downregulated genes, as well as overlapping genes, across the two disease states. Circles indicate the numbers of genes that are ‘T1D enriched’ or ‘AAb enriched’. p-values presented are the results of hypergeometric CDF tests (one-tailed test for overrepresentation). (Middle) For each cell type, displayed are gene ontology pathways that are shared across T1D and AAb+ cells when compared to Control cells (top) or pathways that are differently enriched in T1D cells vs AAb+ cells (bottom). The top 20 clusters are displayed and a stringent cut-off of 1e-6 was applied to determine significant gene ontology pathways. (Right) Heatmaps displaying the degree of gene expression changes of genes (rows) that are shared (top) or differential (bottom) across AAb+ and T1D disease states. (d) GSEA analysis plots of FDR q-value vs Normalized Enrichment Score. For both ductal populations, Ductal-1 and Ductal-2, T1D cells were compared to AAb+ or Control cells to determine differentially enriched gene sets. Demarcated in red and labeled are signatures of interest.

Extended Data Fig. 6 Corroboration of HLA-DR+ Ductal cells.

(a-b) Dendrograms highlighting the expression of the MHC class II complex (a) or MHC class II activity (b) across the dendrogram of all cells in Fig. 1C. Scale bars represent normalized transcript numbers (mean across all MHC class II complex genes (a) or MHC class II activity genes (b)). (c-d) Dendrograms highlighting the expression of the MHC class II complex (c) or MHC class II activity (d) across the dendrogram of ductal and endocrine cells in Fig. 1D. Scale bars represent normalized transcript numbers (mean across all MHC class II complex genes (c) or MHC class II activity genes (d)). (e-f) Dendrograms highlighting the expression of the HLA-DPB1 (E) or KRT19 (f) across the dendrogram of ductal and endocrine cells in Fig. 1D. Scale bars represent normalized transcript numbers. (g) Dendrograms highlighting the expression of the immune-related genes across the dendrogram of ductal and endocrine cells in Fig. 1D. Scale bars represent normalized transcript numbers. (h) Dendrograms highlighting the expression of the BMPR1A across the dendrogram of ductal and endocrine cells in Fig. 1D. Scale bars represent normalized transcript numbers.

Extended Data Fig. 7 GSEA analysis across annotated cells types for dendritic cells gene sets.

a) DC1 gene signature is significantly enriched within Ductal-2 cells of T1D donors. Integrated GSEA analysis for dendritic cells gene sets from Villani et al47 across ranked lists of differentially expressed genes between T1D and control donors. b) Expression analysis of the inhibitory marker VSIR in dendritic cells demonstrates the high level of this gene in T1D ductal cells compared with control ductal cells.

Extended Data Fig. 8 CyTOF validation of canonical cell types.

a) Bar graph displaying the proportion of cells for all major pancreatic cell types from each donor group where cell annotations were obtained by our new machine-learning based strategy using CyTOF measurements across 12 donors. b) Dendrogram visualization of the immune cell cluster, CD45 positive (+) cells, as determined by the analysis of the flow cytometry by time-of-flight (CyTOF) data. c) Dendrogram visualization of the beta cell cluster, C-peptide positive (+) cells, as determined by the analysis of the CyTOF data. d) Dendrogram visualization of the alpha cell cluster, Glucagon positive (+) cells, as determined by the analysis of the CyTOF data. e) Major cell types projected on TooManyCells tree based on our machine-learning based annotation using CyTOF data (n=6,945,575 cells). f) Two-parameter CyTOF analysis of HLA-DR and cytokeratin protein expression in single cells from T1D donor #3 (HPAP023). g) Two parameter CyTOF analysis of HLA-DR and cytokeratin protein expression in single cells from Control donor #3 (HPAP034), a donor with a very low percentage of HLA-DR+ ductal cells as determined by unbiased analysis of CyTOF data with TooManyCells.

Extended Data Fig. 9 IMC validation of HLA-DR+ ductal cells.

a) Bar graph displaying the proportion of cells for all major pancreatic cell types from each donor group where cell annotations were obtained by our machine-learning-based strategy using IMC measurements. Further manual inspection of CD19 and FOXP3 staining used for annotating B and Tregs indicated low quality of these markers across tissue slides. b) Dendrogram visualization of the immune cell cluster, CD45 positive (+) cells, as determined by the analysis of the imaging mass cytometry (IMC) data analysis. c) Dendrogram visualization of the beta cell cluster, C-peptide positive (+) cells, as determined by the analysis of the IMC data analysis. d) Dendrogram visualization of the alpha cell cluster, Glucagon positive (+) cells, as determined by the analysis of the IMC data analysis. e) Major cell types projected on TooManyCells tree as they were annotated by our machine-learning based strategy using IMC data (n=1,170,001 cells).

Extended Data Fig. 10 Cellular neighborhood analysis in IMC data demonstrates the enrichment of CD4+ T cells surrounding HLA-DR+ ductal cells.

a) Bar plot displaying the proportion of HLA-DR+ cytokeratin+ cells from each pancreatic region determined by IMC. b-c) HLA-DR+ cytokeratin+ cells versus percentage of myeloid cells. For each donor group, the median of percentage of each annotated immune subtype and the median HLA-DR+ ductal cell percentage of total cells across all individual donors per donor group was computed. Only myeloid cells demonstrated significant correlation with respect to the number of HLA-DR+ cytokeratin+ cells across donor groups. d) Dendrogram visualization of the clusters of HLA-DR+ cytokeratin+ cells (red), cells neighboring HLA-DR+ cytokeratin+ (blue), and cells distant from HLA-DR+ cytokeratin+ cells (grey) as determined by leveraging the spatial architecture provided by IMC data. e) Boxplots showing the normalized protein expression of different canonical markers in cells neighboring HLA-DR+ cytokeratin+ cells (blue) versus cells neighboring random cells (grey). The number of random cells evaluated was equal to the number of HLA-DR+ cytokeratin+ cells. Differential marker expression significance for neighbors in the IMC analysis was determined using permutation tests. For each marker, the distribution of that marker value for each of the designated n neighbors was compared against 100 distributions derived from n random cells across the entire IMC tree. * indicates p-value < 0.01. Total number of cells in both blue and gray groups is 195,633. Box-and-whisker plots (centre, median; box limits, upper (75th) and lower (25th) percentiles; whiskers, 1.5 × interquartile range; points, outliers). f) CD4+ T cells are the number one immune subtypes enriched at the neighborhood of HLA-DR+ cytokeratin+ cells. Annotation of neighbors of HLA-DR+ cytokeratin+ cells was performed our machine-learning based strategy.

Supplementary information

Supplementary Information

Supplementary Figs. 1–10 and legends and Supplementary Table legends

Reporting Summary

Supplementary Table 1

Donor clinical information.

Supplementary Table 2

Donor HLA information.

Supplementary Table 3

Summary statistics of scRNA-seq data.

Supplementary Table 4

Differential gene list between Beta-1 cells (positive FC) and Beta-2 cells (negative FC).

Supplementary Table 5

Differential gene list between Ductal-1 cells (positive FC) and Ductal-2 cells (negative FC).

Supplementary Table 6

Differential gene list between Ductal-2 cells (positive FC) and Acinar-2 cells (negative FC).

Supplementary Table 7

Differential gene list between Epsilon-1 cells (positive FC) and Epsilon-2 cells (negative FC).

Supplementary Table 8

Differential gene list between AAb+ cells (positive FC)) and control cells (negative FC) using individual cells strategy.

Supplementary Table 9

Differential gene list between T1D cells (positive FC) and AAb+ cells (negative FC) using individual cells strategy.

Supplementary Table 10

Differential gene list between T1D cells (positive FC) control cells (negative FC) using individual cells strategy.

Supplementary Table 11

Differential gene list between AAb+ cells (positive FC) and control cells (negative FC) using a pseudo-bulk strategy. Column gene represents the NCBI official gene symbol. P value represents the P-value uncorrected estimate. FDR is Benjamini–Hochberg FDR (that is, P-value corrected).

Supplementary Table 12

Differential gene list between T1D cells (positive FC) and AAb+ cells (negative FC) using a pseudo-bulk strategy. CColumn gene represents the NCBI official gene symbol. P value represents the P-value uncorrected estimate. FDR is Benjamini–Hochberg FDR (that is, P-value corrected).

Supplementary Table 13

Differential gene list between T1D cells (positive FC) and control cells (negative FC) using a pseudo-bulk strategy. Column gene represents the NCBI official gene symbol. P value represents the P-value uncorrected estimate. FDR is Benjamini–Hochberg FDR (that is, P-value corrected).

Supplementary Table 14

1,473 genes whose expression significantly correlates with the GAD titre in single AAb+ GAD+ donors. Column gene represents the NCBI official gene symbol. P value represents the P-value uncorrected estimate. FDR is Benjamini–Hochberg FDR (that is, P-value corrected).

Supplementary Table 15

CyTOF panel.

Supplementary Table 16

IMC panel.

Supplementary Table 17

Garnett cell-type marker file.

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Fasolino, M., Schwartz, G.W., Patil, A.R. et al. Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes. Nat Metab 4, 284–299 (2022). https://doi.org/10.1038/s42255-022-00531-x

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