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Single-Nucleus and In Situ RNA–Sequencing Reveal Cell Topographies in the Human Pancreas

  • Luca Tosti
    Affiliations
    Center for Digital Health, Berlin Institute of Health and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
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  • Yan Hang
    Affiliations
    Department of Developmental Biology, Stanford University School of Medicine, Stanford, California

    Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California
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  • Olivia Debnath
    Affiliations
    Center for Digital Health, Berlin Institute of Health and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
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  • Sebastian Tiesmeyer
    Affiliations
    Center for Digital Health, Berlin Institute of Health and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
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  • Timo Trefzer
    Affiliations
    Center for Digital Health, Berlin Institute of Health and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
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  • Katja Steiger
    Affiliations
    Institute of Pathology, Technische Universität München, Munich, Germany
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  • Foo Wei Ten
    Affiliations
    Center for Digital Health, Berlin Institute of Health and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
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  • Sören Lukassen
    Affiliations
    Center for Digital Health, Berlin Institute of Health and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
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  • Simone Ballke
    Affiliations
    Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California
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  • Anja A. Kühl
    Affiliations
    iPATH.Berlin, Berlin Institute of Health and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität, Humboldt-Universität zu Berlin, Berlin, Germany
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  • Simone Spieckermann
    Affiliations
    iPATH.Berlin, Berlin Institute of Health and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität, Humboldt-Universität zu Berlin, Berlin, Germany
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  • Rita Bottino
    Affiliations
    Institute of Cellular Therapeutics, Allegheny Health Network, Pittsburgh, Pennsylvania
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  • Naveed Ishaque
    Affiliations
    Center for Digital Health, Berlin Institute of Health and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
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  • Wilko Weichert
    Affiliations
    Institute of Pathology, Technische Universität München, Munich, Germany
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  • Seung K. Kim
    Correspondence
    Seung K. Kim, MD, PhD, Stanford University School of Medicine, Beckman Center B300 279 Campus Drive Stanford, California 94305-5329.
    Affiliations
    Department of Developmental Biology, Stanford University School of Medicine, Stanford, California

    Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California

    Department of Medicine, Endocrinology Division, Stanford University School of Medicine, Stanford, California
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  • Roland Eils
    Correspondence
    Roland Eils, PhD, Kapelle-Ufer 2, 10117, Berlin, Germany.
    Affiliations
    Center for Digital Health, Berlin Institute of Health and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany

    Health Data Science Unit, Medical Faculty and BioQuant, University of Heidelberg, Heidelberg, Germany
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  • Christian Conrad
    Correspondence
    Correspondence Address correspondence to: Christian Conrad, PhD, Kapelle-Ufer 2, 10117, Berlin, Germany.
    Affiliations
    Center for Digital Health, Berlin Institute of Health and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
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Open AccessPublished:November 16, 2020DOI:https://doi.org/10.1053/j.gastro.2020.11.010

      Background & Aims

      Molecular evidence of cellular heterogeneity in the human exocrine pancreas has not been yet established because of the local concentration and cascade of hydrolytic enzymes that can rapidly degrade cells and RNA upon pancreatic resection. We sought to better understand the heterogeneity and cellular composition of the pancreas in neonates and adults in healthy and diseased conditions using single-cell sequencing approaches.

      Methods

      We innovated single-nucleus RNA-sequencing protocols and profiled more than 120,000 cells from pancreata of adult and neonatal human donors. We validated the single-nucleus findings using RNA fluorescence in situ hybridization, in situ sequencing, and computational approaches.

      Results

      We created the first comprehensive atlas of human pancreas cells including epithelial and nonepithelial constituents, and uncovered 3 distinct acinar cell types, with possible implications for homeostatic and inflammatory processes of the pancreas. The comparison with neonatal single-nucleus sequencing data showed a different cellular composition of the endocrine tissue, highlighting the tissue dynamics occurring during development. By applying spatial cartography, involving cell proximity mapping through in situ sequencing, we found evidence of specific cell type neighborhoods, dynamic topographies in the endocrine and exocrine pancreas, and principles of morphologic organization of the organ. Furthermore, similar analyses in chronic pancreatitis biopsy samples showed the presence of acinar-REG+ cells, a reciprocal association between macrophages and activated stellate cells, and a new potential role of tuft cells in this disease.

      Conclusions

      Our human pancreas cell atlas can be interrogated to understand pancreatic cell biology and provides a crucial reference set for comparisons with diseased tissue samples to map the cellular foundations of pancreatic diseases.

      Graphical abstract

      Keywords

      Abbreviations used in this paper:

      acinar-i (idling acinar cell), acinar-s (secretory acinar cell), CP (chronic pancreatitis), aPSC (activated pancreatic stellate cell), FISH (fluorescence in situ hybridization), ISS (in situ sequencing), mRNA (messenger RNA), PAGA (partition-based graph abstraction), PSC (pancreatic stellate cell), qPSC (quiescent pancreatic stellate cell), scRNA-seq (single-cell RNA sequencing), sNuc-seq (single-nucleus RNA sequencing), SSAM (spot-based spatial cell–type analysis by multidimensional mRNA density estimation), TF (transcription factor), UMI (unique molecular identifier)
      See editorial on page 1014.

       Background and Context

      A comprehensive transcriptomic characterization of human pancreatic cell types has been lacking, mostly due to technical limitations.

       New Findings

      Novel protocols facilitated the generation of single-nucleus RNA-seq data and the identification of unprecedented cell type heterogeneity in the healthy adult, neonatal and in the diseased human pancreas.

       Limitations

      This study made use of snap-frozen biopsies of human pancreata; further perturbation studies are needed using animal or in vitro models.

       Impact

      These data lay the foundation for future studies and comparisons of cell composition, transcriptomic and spatial alterations in pancreatic diseases.

       Lay Summary

      This study generated the first comprehensive human pancreas cell atlas and revealed the existence of novel cell types and states in this organ.
      Pancreatic exocrine tissues contain among the highest levels of hydrolytic enzyme activities in the human body,
      • Farrell R.E.
      Resilient ribonucleases.
      hindering the preparation of intact RNA from this organ. Therefore, previous single-cell RNA–sequencing (scRNA-seq) studies investigating tissue heterogeneity of the human pancreas have been restricted to the islets of Langerhans after removal of the exocrine compartment, constituting the source of digestive enzymes. After their isolation, endocrine islets were cultured in vitro, dissociated, and processed on microfluidics devices before next-generation sequencing.
      • Muraro M.J.
      • Dharmadhikari G.
      • Grün D.
      • et al.
      A single-cell transcriptome atlas of the human pancreas.
      • Baron M.
      • Veres A.
      • Wolock S.L.
      • et al.
      A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure.
      • Segerstolpe Å.
      • Palasantza A.
      • Eliasson P.
      • et al.
      Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes.
      • Lawlor N.
      • George J.
      • Bolisetty M.
      • et al.
      Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes.
      • Wang Y.J.
      • Schug J.
      • Won K.-J.
      • et al.
      Single-cell transcriptomics of the human endocrine pancreas.
      • Enge M.
      • Arda H.E.
      • Mignardi M.
      • et al.
      Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns.

      Camunas-Soler J, Dai X, Hang Y, et al. Patch-seq links single-cell transcriptomes to human islet dysfunction in diabetes. Cell Metab 2020;31:1017-1031.

      Although this strategy proved to be successful in generating a draft of the endocrine human pancreas cell atlas, it has distinct disadvantages. For example, the in vitro culture and dissociation steps are known to introduce technical artifacts in gene expression measurements.
      • van den Brink S.C.
      • Sage F.
      • Vértesy Á.
      • et al.
      Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations.
      Moreover, only a small number of exocrine cells from single-cell studies have been reported, leading to underrepresentation of acinar and ductal cells. As a consequence of this underrepresentation, the presence, extent, or quality of heterogeneity in pancreatic exocrine cells is yet not established.
      Here, we innovated methods for rapidly processing tissue biopsy samples isolated from freshly isolated human donor pancreata, followed by single-nucleus sequencing (sNuc-seq), thereby avoiding in vitro expansion and dissociation procedures. This approach produced an index draft atlas of human pancreatic cells, including epithelial and nonepithelial cells, from both neonatal and adult samples and showed previously undetected heterogeneity within pancreatic exocrine cells. Application of in situ sequencing combined with computational approaches enabled us to elucidate spatial relationships and signaling pathways connecting cell types in the pancreas of both previously healthy and chronic pancreatitis human donors and showed dynamic cellular constitution and spatial arrangements during postnatal pancreas development.

      Materials and Methods

       Human and Pig Pancreas Samples

      Deidentified human pancreata were procured from previously healthy, nondiabetic donors with less than 12-hour cold ischemia time through the Center for Organ Recovery and Education, International Institute for the Advancement of Medicine, and National Diabetes Research Institute, as reported previously
      • Goodyer W.R.
      • Gu X.
      • Liu Y.
      • et al.
      Neonatal β cell development in mice and humans is regulated by calcineurin/NFAT.
      (in accordance with Stanford University institutional review board guidelines). Within minutes of removal from cold transportation media, tissue blocks of 2 cm × 1 cm × 0.2 cm were excised from 3 or 4 anatomic locations (ie, head, body, midbody, and tail) and then immediately transferred into liquid nitrogen to snap freeze. Samples from Technische Universität München were procured from nondiseased pancreatic tissue from patients undergoing partial pancreatectomy (ethics committee number 403/17S). Tissue blocks of 0.5 cm × 0.5 cm × 1 cm were collected immediately after removal of the pancreas, placed into cryotubes, and transferred into liquid nitrogen. Pancreatic tissue from healthy pigs killed for other reasons (approved by the local authorities [AZ .3-8-07, Regierung von Oberbayern, München, Germany]) was sampled under completely standardized conditions. The pancreas was removed after the heartbeat had stopped, and tissue blocks (0.5 cm × 0.5 cm × 1 cm) were sampled at different timepoints (cold ischemia time of 7 minutes and 30 minutes) and transferred into liquid nitrogen.

       Isolation of Nuclei

      A full step-by-step protocol has been deposited in the protocols.io repository.
      • Tosti L.
      • Conrad C.
      Nuclei isolation from snap frozen human pancreatic tissue using a citric acid buffer. protocols.io.
      Briefly, snap-frozen pancreatic tissue samples were cut into pieces of <0.3 cm and homogenized with 1 stroke of loose pestle in 1 mL citric acid–based buffer (sucrose 0.25 mol/L, citric acid 25 mmol/L, Hoechst 33342 1 μg/mL) by using a glass Dounce tissue grinder. The tissue was incubated on ice for 5 minutes and then homogenized with 5–10 more strokes. After 5 minutes of incubation, tissue was homogenized with 3–5 strokes with the loose pestle and 5 more strokes with the tight pestle. Homogenate was filtered through a 35-μm cell strainer and centrifuged for 5 minutes at 500g at 4°C. Supernatant was carefully removed, nuclei were resuspended in 1 mL of citric acid buffer, and the centrifugation step was repeated. Nuclei were then resuspended in 300 μL of cold resuspension buffer (KCl 25 mmol/L, MgCl2 3 mmol/L, Tris buffer 50 mmol/L, Recombinant ribonuclease inhibitor (Takara) 0.4 U/μL, dithiothreitol 1 mmol/L, SUPERaseIn RNase Inhibitor (Thermo Fisher Scientific) 0.4 U/μL, Hoechst 33342 1 μg/mL). Nuclei were counted on a Countess II FL Automated Cell Counter, diluted to the desired concentration, and immediately loaded on the 10x Genomics Chromium controller for a target capture of 10,000 nuclei.

       10x Genomics Sample Processing, Library Preparation, and Sequencing

      Samples were prepared according to the 10x Genomics Single Cell 3′ v2 and 10x Genomics Single Cell 3′ v3 Reagent Kit user guides with small modifications. Final libraries were sequenced with the NextSeq 500 system (Illumina) in high-output mode (paired end, 75 base pairs). Further details regarding downstream RNA-sequencing analyses are included in the supplementary materials.

       Histology and RNA Fluorescence In Situ Hybridization

      Human pancreatic snap-frozen samples (thickness: 2 mm) were fixed in 10% formalin at 4°C for 14–16 hours and paraffin embedded. Sections (4 μm) were processed for RNA in situ detection by using the RNAscope Multiplex Fluorescent Reagent Kit v2 according to the manufacturer’s instructions (Advanced Cell Diagnostics). The RNAscope human probes used were Hs-CPB1 (no. 569891-C3), Hs-RBPJL (no. 581131), Hs-AMY1A (no. 503551-C2, targeting also AMY1B, AMY1C, AMY2A, and AMY2B), and Hs-REG3A (no. 312061). RNA fluorescence in situ hybridization (FISH) images were acquired on a Leica SP8 confocal laser scanning microscope equipped with a 40×/1.30 oil objective (Leica HC APO CS2).

       In Situ Sequencing by CARTANA and H&E Staining

      Sections (4 μm) were cut from formalin-fixed, paraffin-embedded pancreatic tissue prepared as described for RNA-FISH. Four DNA probes for each target gene were designed and supplied by CARTANA, and samples were processed according to the manufacturer instructions (Neurokit 1010-01, CARTANA). To reduce lipofuscin autofluorescence, 1× Lipofuscin Autofluorescence Quencher (Promocell) was applied for 30 seconds before fluorescence labeling. Samples were then shipped to CARTANA for the sequencing step. The resulting table of the spatial coordinates of each molecule for the 98 targets, together with the reference 4′,6-diamidino-2-phenylindole (DAPI) image per sample, were provided by CARTANA. Further details regarding downstream analyses of in situ sequencing (ISS) data are included in the supplementary materials.
      For H&E staining, the slides were unmounted in distilled water for 1 hour, stained with hematoxylin according to Mayer (Morphisto 10231) for 4 minutes, blued in tap water (6 minutes), and then stained with eosin 1% (Morphisto 11503). After rinsing again with distilled water, the slides were mounted with aqua mounter (Bio SB, 0091).

       Data Access

      The interactive exploration tool and data download are available at http://singlecell.charite.de/pancreas. Raw sequencing access-protected data on the European Genome-Phenome Archive are available at https://www.ebi.ac.uk/ega/home under EGAS00001004653. In situ sequencing raw data are available at https://doi.org/10.6084/m9.figshare.12173232.

      Results

       Innovating Single-Nucleus–Sequencing Methods for Pancreas Cells From Previously Healthy Human Donors

      To isolate nuclei from frozen tissue, we applied a common protocol based on the use of dense sucrose solutions and detergents at slightly alkaline pH values.
      • Grindberg R.V.
      • Yee-Greenbaum J.L.
      • McConnell M.J.
      • et al.
      RNA-sequencing from single nuclei.
      However, the RNA extracted from isolated nuclei was highly degraded compared to the RNA in the original bulk tissue (Supplementary Figure 1AC). Several modifications to the original protocol were systematically applied, including the use of dithio-bis(succinimidyl propionate), methanol fixation, or the addition of RNAse inhibitors like ribonucleoside vanadyl complexes, but these failed to improve RNA quality. However, on the basis of protocols first described in the 19th century
      • Carpenter W.B.
      • Smith F.G.
      The microscope and its revelations.
      and subsequently modified,
      • Birnie G.D.
      Isolation of nuclei from animal cells in culture.
      we discovered a citric acid–based buffer that reduced RNA degradation during nuclei isolation and increased complementary DNA yields by 40–50-fold (compared to standard protocols) from human pancreatic samples (Supplementary Figure 1D). We isolated nuclei from flash-frozen human pancreas biopsy samples with short cold ischemia times collected from 3 male and 3 female deceased donors, spanning the age range from 1.5 to 77 years (13 samples in total) (Figure 1A, Supplementary Table 1, and Supplementary Figure 1E), generating—to our knowledge—the largest, most comprehensive extant human pancreas cell transcriptome data set.
      Figure thumbnail gr1
      Figure 1sNuc-Seq identifies cell types in the human healthy pancreas. (A) Overview of the strategy used to perform sNuc-seq and in situ sequencing. (B) Merging of sNuc-seq data generated in this study with previous scRNA-seq data sets
      • Muraro M.J.
      • Dharmadhikari G.
      • Grün D.
      • et al.
      A single-cell transcriptome atlas of the human pancreas.
      • Baron M.
      • Veres A.
      • Wolock S.L.
      • et al.
      A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure.
      • Segerstolpe Å.
      • Palasantza A.
      • Eliasson P.
      • et al.
      Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes.
      • Lawlor N.
      • George J.
      • Bolisetty M.
      • et al.
      Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes.
      ,
      • Grün D.
      • Muraro M.J.
      • Boisset J.-C.
      • et al.
      De novo prediction of stem cell identity using single-cell transcriptome data.
      of the endocrine human pancreas, shown as clusters in a 2-dimensional Uniform Manifold Approximation and Projection (UMAP) embedding. (C) Major cell types identified from sNuc-seq of the human pancreas are shown as clusters in a 2-dimensional UMAP embedding. Enz., enzyme; y/o, years old.
      To aid comprehensive identification of the different pancreatic cell types, we applied canonical correlation analysis. This achieved (1) the reduction of batch effects and (2) the integration of data with previously annotated human pancreas scRNA-seq data sets (Supplementary Figure 1F). Our results confirmed that independent sNuc-seq data sets could be merged and fully integrated with scRNA-seq data sets and that we could capture all previously reported pancreatic cell types (Figure 1B and C). The majority of sNuc-seq data derived from acinar or ductal cell nuclei (Supplementary Figure 2A and B) but also included important nonepithelial cell types (endothelial, stromal, immune) not comprehensively characterized in prior work focused on islet biology.

       Characterization of Adult Human Pancreatic Cell Types

      The comprehensive cell representation in our data aligns well with the known composition of the healthy human pancreas (Figure 2A). Acinar cells (70% of the nuclei) were identified based on the expression levels of digestive enzymes such as CPA1/2 and PRSS1 and hallmark transcription factors (TFs) RBPJL or FOXP2 (a BHLHA15/MIST1 target). Strikingly, our analysis showed unanticipated heterogeneity in this cell type (discussed later). Ductal cells (18.5% of the nuclei) expressed cardinal regulators or markers like CFTR, ANXA4, and SLC4A4 (Figure 2B). Unlike in prior studies,
      • Muraro M.J.
      • Dharmadhikari G.
      • Grün D.
      • et al.
      A single-cell transcriptome atlas of the human pancreas.
      ,
      • Segerstolpe Å.
      • Palasantza A.
      • Eliasson P.
      • et al.
      Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes.
      ,
      • Enge M.
      • Arda H.E.
      • Mignardi M.
      • et al.
      Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns.
      ,
      • Arda H.E.
      • Li L.
      • Tsai J.
      • et al.
      Age-dependent pancreatic gene regulation reveals mechanisms governing human β cell function.
      we identified 2 distinct ductal subtypes (Figure 2A), and visual inspection of the principal component loadings confirmed that they were separated along the third principal component (Figure 2C). The smaller subtype (accounting for 1% of the total ductal cells) was characterized by higher expression levels of genes linked to mucous secretion such as MUC5B (hereafter, MUC5B+ ductal cells); the trefoil factor genes TFF1, TFF2, and TFF3; and the cysteine rich secretory protein 3 CRISP3 (Figure 2C and D). The other ductal subtype, by contrast, showed higher expression levels of classical ductal markers such CFTR, SLC4A4, and SCTR (Figure 2C and D).
      • Baron M.
      • Veres A.
      • Wolock S.L.
      • et al.
      A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure.
      Thus, our study provides evidence for unsuspected molecular, and possible functional, heterogeneity in human pancreatic ductal cells.
      Figure thumbnail gr2
      Figure 2Characterization of ductal cell subtypes. (A) Major cell types identified from sNuc-seq of the human pancreas are shown as clusters in a 2-dimensional UMAP embedding. (B) Dotplot showing the expression of specific markers in ductal (including ductal and MUC5B+ ductal) and acinar (including acinar-i, acinar-s, and acinar-REG+) cells. (C) Scatterplot of ductal and MUC5B+ ductal cells across principal components 2 and 3 (top). Line plot showing the moving average profile of indicated genes across principal component 3 (bottom). (D) Ridge plots showing distinct markers expressed in ductal and MUC5B+ ductal cells. Avg., average; Expr., expression; Norm., normalized.
      One major group of sNuc-seq clusters contained endocrine cells (approximately 6% of the total) characterized by the expression of specific hormone genes—namely, glucagon (GCG, alpha cells), insulin (INS, beta cells), pancreatic polypeptide (PPY, gamma cells), and somatostatin (SST, delta cells) (Figure 3A). Other clusters included endothelial cells (1.9% of total nuclei), characterized by the expression of FLT1, PLVAP, VWF, CD36, and SLCO2A1, and macrophages (0.7% of the total nuclei), expressing CD74, PTPRC, ZEB2, HLA-DRA, HLA-DRB1, and HLA-DPA1 (Figure 3B). We identified pancreatic stellate cells (PSCs) and distinguished 2 states of PSCs called quiescent (qPSCs) and activated pancreatic stellate cells (aPSCs).3 qPSCs expressed higher levels of SPARCL1 messenger RNA (mRNA), similar to hepatic stellate cells, and also PDGFRB and FABP4, which likely regulate retinoid storage (Figure 3B and Supplementary Figure 3). Moreover, qPSCs showed higher levels of desmin (DES) and integrin (ITGA1), both encoding for known regulators of cell structure (Figure 3B and Supplementary Figure 3), and CSPG4 (also known as NG2) and MCAM (also known as CD146), suggesting that qPSCs also constitute pericytes in the pancreas. When PSCs activate, they acquire a myofibroblast-like morphology and are able to migrate and remodel the extracellular matrix. Both qPSCs and aPSCs express COL4A1 and COL4A2, but aPSCs showed higher levels of mRNAs encoding other collagens such as COL5A2, COL6A3, and components of the basement membrane such as laminin proteins LAMA2 and LAMB1 (Figure 3B and Supplementary Figure 3). Furthermore, in aPSCs, we detected higher levels of SLIT2 and LUM, known mediators of fibrogenesis and migration in hepatic stellate cells
      • Bracht T.
      • Schweinsberg V.
      • Trippler M.
      • et al.
      Analysis of disease-associated protein expression using quantitative proteomics—fibulin-5 is expressed in association with hepatic fibrosis.
      ,
      • Chang J.
      • Lan T.
      • Li C.
      • et al.
      Activation of Slit2-Robo1 signaling promotes liver fibrosis.
      (Figure 3B and Supplementary Figure 3). Importantly, fibroblasts are normally present around blood vessels and larger ducts of the pancreas, but the lack of specific markers hinders our ability to distinguish them from cells annotated as aPSCs. We also detected a cluster of Schwann cells (80 nuclei, 0.02% of total) expressing CDH19, S100B, CRYAB, PMP22, and SCN7A (Figure 3B). Overrepresentation analysis showed the enrichment of specific terms such as “axonogenesis,” “synapse organization,” and “synapse assembly” (Figure 3C). Notably, we did not detect transcripts encoding for genes associated with dedifferentiation and reduced myelin sheath formation, which can be up-regulated by cell extraction and culture.3
      Figure thumbnail gr3
      Figure 3Characterization of other pancreatic cell types. (A) UMAP plots showing the expression levels of the endocrine cell markers GCG (alpha cells), INS (beta cells), PPY (gamma cells), and SST (delta cells). (B) Dotplot showing the expression levels of specific markers in Schwann, quiescent stellate, activated stellate, endothelial cells and macrophages. (C) Enrichment map of the gene ontology terms enriched in Schwann cells. adj., adjusted; Neg., negative; Pos., positive.

       Heterogeneity of Acinar Cells in the Adult Human Pancreas

      sNuc-seq data for acinar cells provided an unprecedented opportunity for rigorous assessment of acinar cell heterogeneity. One population of acinar cells (acinar-REG+) expressed higher levels of mRNAs encoding the regenerating (REG) protein family members such as REG3A, REG3G, and REG1B (Figure 4A and Supplementary Figure 4). Acinar-REG+ cells were reported in a previous scRNA-seq study
      • Muraro M.J.
      • Dharmadhikari G.
      • Grün D.
      • et al.
      A single-cell transcriptome atlas of the human pancreas.
      and represent a population of cells likely linked to the development of pancreatic lesions such as acinar-to-ductal metaplasia and pancreatic intraepithelial neoplasia.
      • Liu X.
      • Wang J.
      • Wang H.
      • et al.
      REG3A accelerates pancreatic cancer cell growth under IL-6-associated inflammatory condition: involvement of a REG3A-JAK2/STAT3 positive feedback loop.
      Figure thumbnail gr4
      Figure 4Characterization of acinar cells in the adult human exocrine pancreas. (A) Heatmap of acinar and ductal cell–specific genes. (B) Bar plots showing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched in acinar-s and acinar-i cells. (C) Example image of RNA-FISH for CPB1 and AMY2A. In the magnified views, the horizontal triangles indicate cells with high intensity RNA-FISH signal, and vertical triangles indicate cells with low-intensity RNA-FISH signal. Scale bar = 50 μm. (D) Quantification of low-intensity and high-intensity AMY2A and CPB1 RNA-FISH signal in human pancreas sections. The nuclei (n = 14,788; 20 images) were classified based on k-means clustering applied to the frequency distributions of pixel counts per nucleus. Error bars indicate the standard error of the mean of 2 independent experiments. (E) PAGA abstracted graph showing the most probable subgraph representing the data manifold. Each node corresponds to a cell type, and the size of nodes is proportional to the number of cells in each cluster. (F) Cell density of pancreatic cell types along a pseudotime trajectory reflecting their transcriptomic similarity. Adj. p-val, adjusted P value.
      Strikingly, we detected 2 additional subtypes of acinar cells not previously identified in human scRNA-seq experiments. These 2 clusters had distinct unique molecular identifier (UMI) levels per nucleus but a similar number of expressed genes, denoting a distinct complexity of their transcriptomes (Supplementary Figure 5A). The acinar cell subtype with higher numbers of UMIs was characterized by higher expression levels of 21 genes (Supplementary Table 2) encoding for digestive enzymes and accounting for 50% of their transcriptome (Supplementary Figure 5A), confirming previous reports estimating that the majority of the mRNA molecules in a pancreatic acinar cell encode for fewer than 30 proteins.
      • Harding J.D.
      • MacDonald R.J.
      • Przybyla A.E.
      • et al.
      Changes in the frequency of specific transcripts during development of the pancreas.
      We named this subset secretory acinar cells (hereafter, acinar-s). Gene overrepresentation analysis showed the enrichment of the “ribosome”, “protein processing in the endoplasmic reticulum” and “protein export” terms (Figure 4B), consistent with the view that acinar cells have the highest rate of protein synthesis of any human cell.
      The other acinar cell type also expressed digestive enzyme genes but at markedly lower levels compared to acinar-s cells (<4% vs >50%) (Figure 4A and Supplementary Figure 5A). Gene overrepresentation analysis showed enrichment of terms including “protein processing in the endoplasmic reticulum,” “insulin signaling pathway,” “endocytosis,” and “glucagon signaling pathway” (Figure 4B). Thus, these acinar cells appear less robust in their protein secretion and, instead, enriched for responsiveness to external stimuli, like islet signals, and activation of the endocytic pathway. We named this subset idling acinar cells (hereafter, acinar-i).
      To validate our sNuc-seq findings further, we combined experimental and computational approaches. First, we performed RNA-FISH on the same samples used for nuclei isolation using probes for CPB1 (carboxypeptidase) and AMY2A/B (amylase), and after quantification (Supplementary Figure 5C), we confirmed the presence of acinar cells characterized by differential RNA-FISH signal (Figure 4C and D).
      Second, we applied single-cell regulatory network inference and clustering (SCENIC) to infer TF-target regulatory networks (regulons) from single-cell gene expression.
      • Aibar S.
      • González-Blas C.B.
      • Moerman T.
      • et al.
      SCENIC: single-cell regulatory network inference and clustering.
      Both the acinar-s and acinar-i subtypes showed activation of the regulon CREB3L1, likely involved in the basal secretory activity of the cells (Supplementary Figure 5B). The XBP1 regulon shows high activation in acinar-s cells, in agreement with the role of this TF in the unfolded protein response pathway and consistent with the view that biosynthetic activity and the accompanying increase of endoplasmic reticulum stress are higher in acinar-s cells (Supplementary Figure 5B). Notably, only acinar-s cells showed activation of regulons associated with the maintenance of acinar cell identity such as GATA4, NR5A2, and MECOM (Supplementary Figure 5B).
      Third, we elucidated relationships between acinar-s or acinar-i subtypes and other pancreatic cell types using partition-based graph abstraction (PAGA).
      • Wolf F.A.
      • Hamey F.K.
      • Plass M.
      • et al.
      PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.
      With PAGA representation, nodes represent distinct cell states, whereas edges indicate potential routes of cell transitions between them. Here, we included cell types deriving from a common multipotent progenitor during embryonic development, namely, acinar and ductal cells in the exocrine compartment and alpha, beta, gamma, and delta cells in the endocrine compartment. This unsupervised approach places acinar-i cells in a central position, showing similar connections to the majority of the other cell types such as ductal and endocrine cells (Figure 4E). Fourth, we exploited the principle of pseudotime analysis to order cells on the basis of the similarity of their transcriptome. Interestingly, acinar-i cells occupy an intermediate position between acinar and ductal cells, reflecting the known plasticity of acinar cells and their ability to convert toward the ductal lineage (Figure 4F).
      • Storz P.
      Acinar cell plasticity and development of pancreatic ductal adenocarcinoma.

       Single-Nucleus Sequencing of the Human Neonatal Pancreas

      A wealth of data are available about the embryonic development of pancreas in mammals,
      • Larsen H.L.
      • Grapin-Botton A.
      The molecular and morphogenetic basis of pancreas organogenesis.
      ,
      • Kim S.
      • Whitener R.L.
      • Peiris H.
      • et al.
      Molecular and genetic regulation of pig pancreatic islet cell development.
      but less is known about the postnatal development of this organ. We procured 2 1-day-old samples (Supplementary Table 3) and performed sNuc-seq (10,528 nuclei) (Figure 5A and B). The exocrine neonatal compartment accounts for approximately 50% of the organ, whereas it constitutes approximately 90% of the adult pancreas, in agreement with early studies of postnatal growth in rodents (Figure 5C).
      • Kachar B.
      • Taga R.
      • Kniebel G.A.
      • et al.
      Morphometric evaluation of the number of exocrine pancreatic cells during early postnatal growth in the rat.
      Moreover, pancreatic endocrine cells account for 22% of detected cells in neonates compared to 6% in the adult (Figure 5C). Endocrine cells showed changes in cellular composition, with neonatal delta cells, the third major cell type of endocrine islets, accounting for 29% of the endocrine cells compared to 15% in adults (Figure 5D). Alpha and beta cells accounted for 21% and 48% of the neonatal endocrine cells, in line with findings in humans and pigs (Figure 5D).
      • Kim S.
      • Whitener R.L.
      • Peiris H.
      • et al.
      Molecular and genetic regulation of pig pancreatic islet cell development.
      We also captured 42 GHRL+ (Ghrelin) epsilon cells in the endocrine compartment
      • Wierup N.
      • Svensson H.
      • Mulder H.
      • et al.
      The ghrelin cell: a novel developmentally regulated islet cell in the human pancreas.
      (Figure 5B), characterized by high mRNA levels of ACSL1, encoding for an Acyl-CoA synthetase enzyme that catalyzes the addition of an octanoyl group to ghrelin, a modification that is essential for its optimal biological activity
      • Hougland J.L.
      Ghrelin octanoylation by ghrelin O-acyltransferase: unique protein biochemistry underlying metabolic signaling.
      (Figure 5E).
      Figure thumbnail gr5
      Figure 5Characterization of the cellular composition of neonatal healthy pancreas. (A) On the left, the boxplots show the distribution of UMIs per nucleus for each neonatal sample processed in this study. On the right, the boxplots show the distribution of genes per nucleus for each neonatal sample. (B) Major cell types identified from sNuc-seq of the human neonatal pancreas shown as clusters in a 2-dimensional UMAP embedding. (C) Frequency of different cell types in adult and neonatal pancreas. (D) Frequency of different endocrine cells in the adult and neonatal pancreas. (E) Dot plot of distinct genes expressed in neonatal endocrine cells.
      In exocrine cells from neonates, we detected acinar-i and acinar-s cells (Figure 6A). Approximately 4% of the transcripts in acinar-i cells encode for digestive enzyme genes compared to 33% in acinar-s, similar to the levels found in cognate adult cells (Figure 6A). However, we did not detect AMY2A, AMY2B, and PNLIP in neonatal acinar-s cells, consistent with prior findings that little to no pancreatic amylase or lipase enzyme activity is detectable in newborns (Figure 6B).
      • Zoppi G.
      • Andreotti G.
      • Pajno-Ferrara F.
      • et al.
      Exocrine pancreas function in premature and full term neonates.
      Moreover, we did not detect a population of acinar-REG+ cells, suggesting a further layer of REG protein regulation during postnatal to adult development (Figure 5B). In the ductal compartment, we did not detect the MUC5B+ population, but this may reflect low duct cell yields from human neonatal pancreas (Figure 5B).
      Figure thumbnail gr6
      Figure 6Characterization of the acinar, endothelial, and immune constituents of the neonatal healthy pancreas. (A) Quantification of UMIs per nucleus (left) and genes per nucleus (center) for the different acinar cell states. On the right, the percentage of transcriptome encoding for digestive enzymes (Supplementary Table 2) is represented. (B) Violin plot showing the expression levels of selected digestive enzyme genes in adult and neonatal pancreas. (C) Heat map showing different genes expressed in 3 different endothelial cell types. (D) Heat map depicting the number of all possible interactions among the analyzed cell types of the neonatal pancreas as calculated by CellPhoneDB. (E) Enrichment map of gene ontology terms enriched in angiogenetic endothelial cells. (B) Dot plot of specific lymphatic endothelial cell markers. (F) Dot plot showing the expression of specific genes in macrophages (MΦ), B cells, and T cells. (G) Dot plot of specific lymphatic endothelial cell markers. (H) Dot plot depicting selected MΦ-angiogenetic endothelial interactions enriched in healthy neonatal pancreas. (I) Scatterplot in diffusion map basis (components 1 and 2) of combined neonatal and adult beta cells, colored by pseudotime (left) or by age of the donor (right). Act., activated; Ang. end., angiogenetic endothelial; Lymp. end., lymphatic endothelial; Qui., quiescent.
      Unlike in the adult pancreas, we observed evidence of endothelial cell heterogeneity in neonatal samples (Figure 6C). In addition to an adult-like endothelial signature, we observed an angiogenetic endothelial type enriched for mRNA previously linked to programs of blood vessel morphogenesis and extracellular matrix remodeling (Figure 6E). Furthermore, on the basis of the expression of canonical markers such as LYVE1, PDPN, FLT4, and PROX1, we identified a population of lymphatic endothelial cells (Figure 6G) that most likely function to collect interstitial fluid containing cell debris.
      Few immune cells were detected in the human adult healthy pancreas, but in the neonatal pancreas, we identified at least 3 immune populations, including follicular B cells (CD19, CR2, CD22, FCER2), T lymphocytes (CD247/CD3Z, CD3G, IL7R), and macrophages (CD14, CD86, CSF2RA) (Figures 5B and 6F). To clarify the potential interactions of macrophages with other cell types in the neonatal pancreas, we applied CellPhoneDB to predict ligand-receptor interactions from single-cell transcriptomics data.
      • Vento-Tormo R.
      • Efremova M.
      • Botting R.A.
      • et al.
      Single-cell reconstruction of the early maternal-fetal interface in humans.
      This analysis showed that macrophages have a higher number of interactions with angiogenic endothelial cells (Figure 6D), supporting their role in vascular development and remodeling.
      • Nucera S.
      • Biziato D.
      • De Palma M.
      The interplay between macrophages and angiogenesis in development, tissue injury and regeneration.
      We also noted specific sets of ligands and receptors, such as the TAM receptors (AXL, MERKT, and the ligand GAS6), usually active in tissues subject to remodeling and involved in the phagocytosis of apoptotic cells, and members of the Notch pathway, including the NOTCH2 receptor and the antagonistic ligands DLL4, JAG1, and JAG2. Moreover, a strong interaction was predicted between CD74 and APP, suggesting an important role for APP in pancreatic neonatal angiogenesis (Figure 6H).
      Beta cells are not fully functional at the perinatal stage, but their functions, including glucose-regulated insulin secretion, mature with age.
      • Arda H.E.
      • Li L.
      • Tsai J.
      • et al.
      Age-dependent pancreatic gene regulation reveals mechanisms governing human β cell function.
      To investigate changes across the 2 different age groups (neonatal and adult), we combined the data sets and performed diffusion pseudotime analyses to investigate age-dependent development. Pseudotime ordering recapitulated donor age (Figure 6I), thereby permitting us to model gene expression using a generalized additive model and to identify highly dynamic genes. We identified groups of genes showing increasing or decreasing expression levels across pseudotime (Supplementary Figure 6A). Some of the genes expressed at lower levels in adult samples are involved in beta cell proliferation such as PDZD2 and IGFBP5
      • Suen P.M.
      • Zou C.
      • Zhang Y.A.
      • et al.
      PDZ-domain containing-2 (PDZD2) is a novel factor that affects the growth and differentiation of human fetal pancreatic progenitor cells.
      ,
      • Gleason C.E.
      • Ning Y.
      • Cominski T.P.
      • et al.
      Role of insulin-like growth factor-binding protein 5 (IGFBP5) in organismal and pancreatic beta-cell growth.
      (Supplementary Figure 6A and B). PLAG1, a protein known to decline within a few days after birth and known to inhibit insulin secretion in neonatal murine islets,
      • Hoffmann A.
      • Spengler D.
      Transient neonatal diabetes mellitus gene Zac1 impairs insulin secretion in mice through Rasgrf1.
      was also detected in neonatal samples (Supplementary Figure 6A and B). By contrast, CD99, the expression level of which increases in adult mouse islets,
      • Aguayo-Mazzucato C.
      • Haaren M van
      • Mruk M.
      • et al.
      β cell aging markers have heterogeneous distribution and are induced by insulin resistance.
      also had increased mRNA levels in adult human beta cells (Supplementary Figure 6A and B). In human adults, we also detected higher levels of RASD1 and SYT16, genes previously reported to be up-regulated in human islets exposed to relatively high glucose.
      • Hall E.
      • Dekker Nitert M.
      • Volkov P.
      • et al.
      The effects of high glucose exposure on global gene expression and DNA methylation in human pancreatic islets.
      ,
      • Huang C.
      • Walker E.M.
      • Dadi P.K.
      • et al.
      Synaptotagmin 4 regulates pancreatic β cell maturation by modulating the Ca2+ sensitivity of insulin secretion vesicles.
      Genes encoding members of the secretogranin-chromogranin family like SCG2, SCG5, and CHGB, known for their essential role within the insulin secretory granule, were also more highly expressed in adult beta cells (Supplementary Figure 6A and B). Thus, by applying sNuc-seq to tissues procured at specific developmental stages, our work reveals markers and unrecognized possible regulators of human beta cell functional maturation.

       In Situ Sequencing of the Human Pancreas Localizes Messenger RNA

      To elucidate the spatial relation of the cell types identified in our study, we performed ISS
      • Ke R.
      • Mignardi M.
      • Pacureanu A.
      • et al.
      In situ sequencing for RNA analysis in preserved tissue and cells.
      using matching tissues processed for nucleus isolation (Figure 1A and Supplementary Figure 7A). We selected 98 marker genes (Supplementary Table 4) identified from sNuc-seq that distinguish pancreatic cell types, then applied ISS to tissue from 1 juvenile (1.5 years old) and 2 adult (30 and 53 years old) donors. ISS-based mRNA localization was used to generate spatial cell maps by applying spot-based spatial cell–type analysis by multidimensional mRNA density estimation (SSAM), a segmentation-free algorithm that identifies cell type signatures from spatially resolved in situ transcriptomics data (Figure 7A and Supplementary Figure 7BD).
      • Park J.
      • Choi W.
      • Tiesmeyer S.
      • et al.
      Segmentation-free inference of cell types from in situ transcriptomics data. bioRχiv.
      Figure thumbnail gr7
      Figure 7In situ sequencing of the human healthy pancreas. (A) On the left, the cell map generated by SSAM from a tissue section of an adult donor (AFHE365-head). On the right, magnified views showing macroscopic features such as (1) quiescent and activated stellate cells in the connective tissue, (2) interlobular duct enriched in MUC5B+ ductal cells, and (3) and (4) endocrine islets enriched in alpha and beta cells but also including delta cells, endothelial cells, and macrophages. Scale bar: 1 mm. (B) Bar plot comparing the numbers of cells identified via sNuc-seq with the normalized surface area calculated by SSAM for each cell type. (C) Line plot showing the results of the spatial modeling analysis for the alpha cells. (D) Line plot showing the results of the spatial modeling analysis for the beta cells. (E) Line plot showing the results of the spatial modeling of islet cells (combination of alpha, beta, gamma, and delta cells). Stellate includes activated and quiescent stellate cells; ductal includes ductal and MUC5B+ ductal cells; and acinar includes acinar-i, acinar-s, and acinar-REG+ cells.
      SSAM cell maps contained all the cell types identified by sNuc-seq and permitted ready recognition of multicellular tissue features (Figure 7A, magnified views). The proportion of cell types detected by SSAM cell maps also corresponded well with those detected with sNuc-seq (Figure 7B). To probe spatial relationships among pancreatic cell types, we performed empirical statistical modeling of cell type proximity. To confirm the validity of this approach, we first quantified spatial relations within unambiguous multicellular structures, confirming the mutual proximity of alpha and beta cells (Figure 7C and D). We then looked at nonepithelial cell types in close proximity to the endocrine islets and found that endothelial cells are the closest ones (Figure 7E) because they support high oxygen demand, and the glucose-sensing and endocrine functions of islets.
      • Bonner-Weir S.
      • Orci L.
      New perspectives on the microvasculature of the islets of Langerhans in the rat.
      Analysis of ISS provided further insights into the intraislet and interislet architecture. Quantification of islet size showed a higher frequency of small islets (radius smaller than 40 μm) in juvenile tissue compared to adult tissue (Supplementary Figure 8A). We then performed proximity analysis on cells located outside of the islets and discovered that, in the juvenile sample (1.5 years), alpha cells are the most proximal (in the first 40 μm), followed by beta cells, suggesting enrichment of alpha cells in the mantle of the islets, whereas beta cells preferentially locate in the core (Supplementary Figure 8B).
      • Bonner-Weir S.
      • Sullivan B.A.
      • Weir G.C.
      Human islet morphology revisited: human and rodent islets are not so different after all.
      Importantly, this trend is diminished in adult samples (30 and 53 years), reflecting an age-dependent increase of architectural heterogeneity in adult islets (Supplementary Figure 8B).
      • Dybala M.P.
      • Hara M.
      Heterogeneity of the human pancreatic islet.
      We then calculated the distance between the centroids of manually annotated endocrine islets; in both neonatal and adult samples, we found a minimum distance of 400 μm, providing quantification of pancreatic islet dispersion during pancreas morphogenesis (Supplementary Figure 8D).
      • Pauerstein P.T.
      • Tellez K.
      • Willmarth K.B.
      • et al.
      A radial axis defined by semaphorin-to-neuropilin signaling controls pancreatic islet morphogenesis.
      Finally, we investigated the spatial relations of the new acinar cell states identified in this work. The acinar-s and acinar-i cells did not show a specific cell neighborhood (Supplementary Figure 9), in agreement with their vast abundance (approximately 80%) in the tissue. By contrast, acinar-REG+ cells appeared to localize significantly closer to islet cells,
      • Muraro M.J.
      • Dharmadhikari G.
      • Grün D.
      • et al.
      A single-cell transcriptome atlas of the human pancreas.
      like delta and gamma cells (Supplementary Figure 8C), and to macrophages. This latter finding is consistent with prior reports that REG3A/PAP protein modulates chemoattraction and activation of macrophages in pancreatic diseases.
      • Gironella M.
      • Calvo C.
      • Fernández A.
      • et al.
      Reg3β deficiency impairs pancreatic tumor growth by skewing macrophage polarization.
      Together, these results highlight how mRNA localization combined with sNuc-seq can be used to identify cell types and reconstruct known and unrecognized morphologic patterns in the pancreas.

       Single-Nucleus and In Situ Sequencing of Human Pancreatitis Samples

      Chronic pancreatitis (CP) is a progressive inflammatory syndrome in which damage of the pancreatic tissue leads to acinar atrophy, fibrosis, and chronic inflammation.
      • Kleeff J.
      • Whitcomb D.C.
      • Shimosegawa T.
      • et al.
      Chronic pancreatitis.
      Here, snap-frozen samples from 2 CP donors (Supplementary Table 5) were processed for sNuc-seq, and despite the extensive fibrosis and reduced cellular content (Supplementary Figure 10A), we successfully profiled 2726 nuclei. Cell types were identified by using gene markers established in the healthy tissue; moreover, we detected a chemosensory (tuft cells) and 2 immune (mast cells and monocytes) CP-specific cell types (Supplementary Figure 10B and C). The cellular composition of the pancreas is altered in CP, with a reduction of both endocrine and exocrine cells and simultaneous increase of activated stellate and immune cells (Supplementary Figure 10D). Importantly, the acinar cells detected by sNuc-seq belonged to the REG+ subtype, suggesting a previously unexplored role of these cells in the development of the disease.
      Macrophages and T lymphocytes constitute the predominant immune cell infiltrates in the CP samples. Macrophages were CD163+ (Supplementary Figure 11A), indicating an alternatively activated or M2 status. Generally, M2 macrophages dampen inflammation and promote healing of tissues partially through the recruitment and activation of fibroblastic cells. Therefore, we investigated ligand-receptor pairs involved in the communication between M2 macrophages and aPSCs. CellPhoneDB analysis showed the involvement of different signaling axes, including transforming growth factor β, platelet-derived growth factor, vascular endothelial growth factor, and granulin. In turn, aPSCs stimulate macrophage proliferation and M2 polarization via the complement system and the CXCL12-CXCR4 axis (Supplementary Figure 11B). Importantly, we applied ISS to 1 CP sample and confirmed the presence of inflammatory infiltrates consisting of macrophages and aPSCs in close spatial relation (Supplementary Figure 10A and Supplementary Figure 11C, magnified views), as validated by unbiased ISS cell proximity analysis (Supplementary Figure 11D).
      Among the other cell types, T cells were characterized by the expression of PRF1 and GZMH, supporting their involvement in cell-mediated cytotoxicity (Supplementary Figure 11E). Tuft cells, identified for the first time in human pancreatitis samples, were characterized by high expression of cytosolic phospholipase A2 (PLA2G4A) and prostaglandin D2 synthase (HPGDS), encoding for 2 enzymes involved in the generation of prostaglandin D2, a molecule responsible for anti-inflammatory signaling in pancreatic diseases
      • DelGiorno K.E.
      • Chung C.-Y.
      • Vavinskaya V.
      • et al.
      Tuft cells inhibit pancreatic tumorigenesis in mice by producing prostaglandin D2.
      (Supplementary Figure 11E).
      Together, these results highlight how the pancreas cell atlas can be extended to define the cellular makeup of pancreatic diseases.

      Discussion

      The heterogeneity of the exocrine pancreas has been previously investigated by using immunohistology-based assays,
      • Uchida E.
      • Steplewski Z.
      • Mroczek E.
      • et al.
      Presence of two distinct acinar cell populations in human pancreas based on their antigenicity.
      but recent single-cell analyses
      • Muraro M.J.
      • Dharmadhikari G.
      • Grün D.
      • et al.
      A single-cell transcriptome atlas of the human pancreas.
      ,
      • Segerstolpe Å.
      • Palasantza A.
      • Eliasson P.
      • et al.
      Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes.
      ,
      • Wollny D.
      • Zhao S.
      • Everlien I.
      • et al.
      Single-cell analysis uncovers clonal acinar cell heterogeneity in the adult pancreas.
      suggested that high-throughput sequencing could reveal undetected singularities in pancreatic exocrine cells. From the analysis of more than 120,000 pancreatic cells, we found 3 distinct acinar populations (acinar-i, acinar-s, and acinar-REG+), distinguished by differential expression levels of digestive enzyme genes, distinct activation of pancreatic gene regulatory networks, expression of specific protein family genes, and cell neighborhoods. Because in vitro systems that reconstitute mature human acinar cells in their native architecture have not yet been achieved, we used orthogonal approaches to validate our sNuc-seq results, including RNA-FISH, in situ sequencing, and computational approaches. Notably, the new methods were also applied and validated in pancreatic disease samples.
      Our studies showed that acinar-REG+ cells were absent in the neonatal pancreas, suggesting that their function might be specific for the adult tissue. Previous scRNA-seq studies described a subset of REG3A+ acinar cells,
      • Muraro M.J.
      • Dharmadhikari G.
      • Grün D.
      • et al.
      A single-cell transcriptome atlas of the human pancreas.
      but the application of in situ sequencing analyses—for the first time, to our knowledge—to the human pancreas, showed their localization near macrophages, nominating acinar-REG+ cells as possible regulators of pancreatic inflammatory processes. Importantly, we detected acinar-REG+ cells in chronic pancreatitis samples, and these cells were also recently described in a single-cell study of pancreatic ductal adenocarcinoma.
      • Hwang W.L.
      • Jagadeesh K.A.
      • Guo J.A.
      • et al.
      Single-nucleus and spatial transcriptomics of archival pancreatic cancer reveals multi-compartment reprogramming after neoadjuvant treatment. bioRχiv.
      In acinar-i cells, we find evidence suggesting that hydrolytic enzyme production may be reduced compared to acinar-s cells and speculate that this acinar cell state could be a protective adaptation to periods of intense zymogen production and increased endoplasmic reticulum stress. If so, acinar-i cells might be analogous to a subset of postulated metabolically stressed islet beta cells.
      • Baron M.
      • Veres A.
      • Wolock S.L.
      • et al.
      A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure.
      ,
      • Szabat M.
      • Page M.M.
      • Panzhinskiy E.
      • et al.
      Reduced insulin production relieves endoplasmic reticulum stress and induces β cell proliferation.
      Acinar-i cells showed a decreased activation of acinar cell gene regulatory networks and occupied a central position in a PAGA lineage relation graph; hence, these cells might have the capacity to convert into other pancreatic cell types, including both ductal and endocrine cells. A previous single-cell study of the mouse pancreas identified acinar cell subtypes characterized by differential expression of TFs, partially recapitulating our findings in the human pancreas and suggesting that acinar cell heterogeneity is a conserved feature in the 2 species.47
      In this work, we did not identify centroacinar cells, which have been postulated to include pancreatic progenitor cells.
      • Rovira M.
      • Scott S.-G.
      • Liss A.S.
      • et al.
      Isolation and characterization of centroacinar/terminal ductal progenitor cells in adult mouse pancreas.
      Future studies could clarify the transcriptome of centroacinar cells in the human pancreas and their possible lineage or spatial relation with the acinar cell and ductal cell types captured in our data sets.
      sNuc-seq data generated from neonatal tissue uncovered developmental dynamics in the pancreas, unique immune interactions (involving B cells, T cells, and macrophages), and different cellular composition compared to the adult pancreas. We identified distinct endothelial cell states, including angiogenetic endothelial cells in neonatal pancreata, that may reflect the increased supply of nutrients required by the rapidly replicating cells at this developmental stage.
      • Cleaver O.
      • Dor Y.
      Vascular instruction of pancreas development.
      Furthermore, we identified a previously uncharted landscape of cell crosstalk, including macrophages and endothelial cells, and possible ligand-receptor interactions involved in organ remodeling during growth. We also revealed aspects of human endocrine pancreas development and regulation that constitute the basis of future comparisons with human
      • Arda H.E.
      • Li L.
      • Tsai J.
      • et al.
      Age-dependent pancreatic gene regulation reveals mechanisms governing human β cell function.
      and murine data sets.
      • Byrnes L.E.
      • Wong D.M.
      • Subramaniam M.
      • et al.
      Lineage dynamics of murine pancreatic development at single-cell resolution.
      The combination of adult and neonatal data sets allowed pseudotime analyses and nominated candidate regulators and effectors of beta cell maturation, including age-dependent restriction of beta cell proliferation and development of secretory activities.
      • Arda H.E.
      • Li L.
      • Tsai J.
      • et al.
      Age-dependent pancreatic gene regulation reveals mechanisms governing human β cell function.
      ISS analyses confirmed differences in islet size and intraislet architecture between juvenile and adult pancreatic tissue and provided evidence for possibly stereotyped dispersion of islets throughout the human pancreas, like in rodents.
      • Pauerstein P.T.
      • Tellez K.
      • Willmarth K.B.
      • et al.
      A radial axis defined by semaphorin-to-neuropilin signaling controls pancreatic islet morphogenesis.
      In summary, our studies combine technical innovations to produce a human pancreas cell atlas that provides conceptual advances and reveals cellular, genetic, signaling, and physiologic mechanisms regulating pancreatic cells in health and disease.

      Acknowledgments

      The authors would like to thank the organ donors and their families; David Ibberson (Heidelberg University), Ulrike Krüger ( Berlin Institute of Health [BIH], Berlin), and Marten Jager (BIH, Berlin) for next-generation sequencing services; the Biomaterial Bank (MTBIO) of the Technical University Munich for support; Katharina Jechow (BIH, Berlin), Lorenz Chua (BIH, Berlin), and Alison McGarvey (MDC, Berlin) for critically revising the manuscript; Jeongbin Park (BIH, Berlin) for his help with the analysis of ISS data; and all members of the Conrad laboratory for the constructive discussions. The graphical abstract was created with BioRender.com. This publication is part of the Human Cell Atlas (www.humancellatlas.org/publications).

      CRediT Authorship Contributions

      Luca Tosti, PhD (Data curation: Lead; Formal analysis: Lead; Investigation: Lead; Methodology: Lead; Software: Equal; Validation: Lead; Visualization: Equal; Writing – original draft: Lead; Writing – review & editing: Equal);
      Yan Hang, PhD (Resources: Equal; Writing – review & editing: Equal);
      Olivia Debnath, MSc (Formal analysis: Equal; Software: Equal; Visualization: Equal);
      Sebastian Tiesmeyer, MSc (Formal analysis: Equal; Software: Equal; Visualization: Equal);
      Timo Trefzer, MSc (Investigation: Supporting);
      Katja Steiger, Dr med vet (Resources: Equal);
      Foo Wei Ten, MSc (Formal analysis: Supporting; Software: Supporting; Visualization: Supporting);
      Sören Lukassen, PhD (Formal analysis: Supporting);
      Simone Ballke, Dr med vet (Resources: Supporting); Anja A. Kuehl, PhD (Resources: Supporting);
      Simone Spieckermann, MSc (Resources: Supporting);
      Rita Bottino, PhD (Resources: Supporting);
      Naveed Ishaque, PhD (Formal analysis: Supporting; Software: Supporting; Supervision: Equal);
      Wilko Weichert, MD (Conceptualization: Equal; Funding acquisition: Equal; Resources: Supporting);
      Seung K. Kim, MD, PhD (Conceptualization: Equal; Resources: Equal; Writing – review & editing: Equal);
      Roland Eils, PhD (Conceptualization: Equal; Funding acquisition: Equal; Project administration: Equal; Supervision: Equal; Writing – original draft: Supporting; Writing – review & editing: Equal);
      Christian Conrad, PhD (Conceptualization: Equal; Funding acquisition: Equal; Project administration: Equal; Supervision: Equal; Writing – original draft: Supporting; Writing – review & editing: Equal).

      Supplementary Material

      Figure thumbnail fx2
      Supplementary Figure 1sNuc-seq library generation and integration with scRNA-seq datasets. (A) Electropherogram of bulk RNA extracted from snap-frozen pig pancreatic tissue subject to either 7 or 30 minutes of total cold ischemia (B) Electropherograms of bulk RNA extracted from snap-frozen human pancreatic tissue (bulk tissue). RNA was extracted from nuclei that were isolated from the same tissue as in lane 2 by using either a citric acid buffer or the standard buffer (lanes 3 and 4). (C) Gel view of the same samples as in (B). (D) Yield of cDNA from a sample processed with either the standard or the citric acid-based protocol. The same number of nuclei and PCR cycles were used for both conditions. (E) On the left, the boxplots show the distribution of Unique Molecular Identifiers (UMIs) per nucleus for each sample processed in this study. On the right, the boxplots show the distribution of genes per nucleus for each sample. The red dashed lines represent mean values (1287 for UMIs and 692 for the genes). (F) Merged sNuc-Seq and previously published scRNA-seq datasets shown in a two-dimensional UMAP embedding before batch effect removal. (G) Following batch-effect removal, sNuc-seq data were split by sample of origin and shown in a two-dimensional UMAP embedding.
      Figure thumbnail fx3
      Supplementary Figure 2Different proportion of cells detected by sNuc-seq and scRNA-seq. (A) Barplots showing the proportion of cell types identified in each sNuc-seq sample. (B) Gaussian kernel density estimation was used to calculate the density of cells and was represented in the UMAP embedding for the 2 distinct technologies, namely scRNA-seq and sNuc-seq. High density values indicate strong contribution of the cells to the overall dataset (ie. exocrine cells have higher contribution in sNuc-seq and endocrine cells in scRNA-seq).
      Figure thumbnail fx4
      Supplementary Figure 3Differential gene expression between stellate cells. Volcano plot showing differentially expressed genes between activated and quiescent stellate cells. Red dots represent genes with average log expression >0.5 and an adjusted P value <.05.
      Figure thumbnail fx5
      Supplementary Figure 4RNA-FISH in the healthy human pancreas. Example image of RNA-FISH for CPB1 and REG3A in the human adult healthy pancreas. Acinar-REG+ cells constitute a subset of CPB1+ acinar cells.
      Figure thumbnail fx6
      Supplementary Figure 5Characterization of acinar-i and acinar-s cells. (A) Quantification of UMI per nucleus (left) and genes per nucleus (center) for the different acinar cell states. On the right, the percentage of transcriptome encoding for digestive enzymes (Supplementary Table 2) is represented. (B) SCENIC regulons specifically active in the acinar-i and acinar-s cells. Above each plot, the transcription factor is indicated. (C) Pipeline applied for the quantification of differential RNA-FISH signal. Raw RNA-FISH images were thresholded to remove the background signal and a nuclear segmentation mask was generated by applying a deep-learning algorithm to the DAPI channel of the same image. Signal was quantified for each nucleus and the signal distribution was used to identify acinar cells expressing different level of digestive enzyme genes as explained in Materials and Methods.
      Figure thumbnail fx7
      Supplementary Figure 6Gene expression changes in beta cell from neonatal to adult stage. (A) Heat map showing gene expression changes across pseudotime reflecting beta cell maturation, from neonatal to adult. (B) The dots indicate the expression levels of individual cells colored by age type in the β-cell cluster. The blue lines approximate expression along the inferred trajectory by polynomial regression fits.
      Figure thumbnail fx8
      Supplementary Figure 7In situ sequencing signal and cell maps. (A) On the left, localization of different marker genes in an endocrine islet (SST for delta cells, INS for beta cells, GCG for alpha cells, B2M for endothelial cells) as captured by ISS. On the right, markers for ductal (BICC1, CFTR, MUC6) and acinar cells (PRSS1, REG3A) as captured by ISS. (B) Cell map generated by SSAM from a tissue section of the sample AFES448-midbody. (C) Cell map generated by SSAM from a tissue section of the sample AGBR024-head. (D) Cell map generated by SSAM from a tissue section of the sample AGBR024-body. For the metadata, see Supplementary Table 1. Scale bar = 1 mm.
      Figure thumbnail fx9
      Supplementary Figure 8Characterization of intra and inter-islet architecture. (A) On the left, histogram and density line showing the distribution of juvenile islet radii. On the right, histogram and density line showing the distribution of adult islet radii. (B) Line plot showing the results of the spatial modelling analysis for any cell surrounding the endocrine islets. (C) Line plot showing the results of the spatial modelling analysis for the acinar-REG+ cells. (D) Each row of the heatmaps represent a single islet in each sample. The distances between the centroids of each islet and all the other islets were calculated and the scaled intensity of the frequency is represented in each row. High (red) and low (blue) values indicate higher or lower presence of other islets at the specific distance, respectively.
      Figure thumbnail fx10
      Supplementary Figure 9Statistical modeling of spatial relationship for different pancreatic cell types. Line plot showing the results of the modeling analysis for 11 different cell types.
      Figure thumbnail fx11
      Supplementary Figure 10sNuc-seq analysis of human chronic pancreatitis samples. (A) H&E staining of a chronic pancreatitis section used for ISS; scale bar = 500 μm. In the black inlet, a magnification showing the extensive fibrosis present in the tissue; scale bar = 50 μm. In the blue inlet, a magnification showing lymphocytic infiltration in the tissue; scale bar = 50 μm. (B) Dotplot showing the expression of specific markers in chronic pancreatitis samples. (C) Major cell types identified from sNuc-Seq of the human chronic pancreatitis samples shown as clusters in a two-dimensional UMAP embedding. (D) Bar plot comparing the proportion of cell types identified via sNuc-seq in healthy and chronic pancreatitis samples.
      Figure thumbnail fx12
      Supplementary Figure 11Characterization of immune and stellate cells in the chronic pancreatitis samples. (A) UMAP plot showing the expression of CD163 in the macrophage cluster. (B) Dot plot depicting selected interaction between MΦ and activated stellate cells in chronic pancreatitis tissue. (C) On the left, cell map generated by SSAM from a tissue section of a chronic pancreatitis sample. The magnified views show clusters of cells enriched in macrophages and activated stellate cells. Scale bar = 1 mm. (D) Line plot showing the results of the spatial modelling analysis for macrophages and activated stellate cells. (E) UMAP plot showing the expression of PRF1 and GZMH in T cells, HPGDS and PLA2G4A in tuft cells.

      References

        • Farrell R.E.
        Resilient ribonucleases.
        in: Farrell R.E. RNA methodologies. 4th ed. Academic Press, San Diego2010: 155-172
        • Muraro M.J.
        • Dharmadhikari G.
        • Grün D.
        • et al.
        A single-cell transcriptome atlas of the human pancreas.
        Cell Syst. 2016; 3: 385-394
        • Baron M.
        • Veres A.
        • Wolock S.L.
        • et al.
        A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure.
        Cell Syst. 2016; 3: 346-360
        • Segerstolpe Å.
        • Palasantza A.
        • Eliasson P.
        • et al.
        Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes.
        Cell Metab. 2016; 24: 593-607
        • Lawlor N.
        • George J.
        • Bolisetty M.
        • et al.
        Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes.
        Genome Res. 2017; 27: 208-222
        • Wang Y.J.
        • Schug J.
        • Won K.-J.
        • et al.
        Single-cell transcriptomics of the human endocrine pancreas.
        Diabetes. 2016; 65: 3028-3038
        • Enge M.
        • Arda H.E.
        • Mignardi M.
        • et al.
        Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns.
        Cell. 2017; 171: 321-330
      1. Camunas-Soler J, Dai X, Hang Y, et al. Patch-seq links single-cell transcriptomes to human islet dysfunction in diabetes. Cell Metab 2020;31:1017-1031.

        • van den Brink S.C.
        • Sage F.
        • Vértesy Á.
        • et al.
        Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations.
        Nat Methods. 2017; 14: 935-936
        • Goodyer W.R.
        • Gu X.
        • Liu Y.
        • et al.
        Neonatal β cell development in mice and humans is regulated by calcineurin/NFAT.
        Dev Cell. 2012; 23: 21-34
        • Tosti L.
        • Conrad C.
        Nuclei isolation from snap frozen human pancreatic tissue using a citric acid buffer. protocols.io.
        (Accessed January 2021)
        • Grindberg R.V.
        • Yee-Greenbaum J.L.
        • McConnell M.J.
        • et al.
        RNA-sequencing from single nuclei.
        Proc Natl Acad Sci U S A. 2013; 110: 19802-19807
        • Carpenter W.B.
        • Smith F.G.
        The microscope and its revelations.
        Blanchard and Lea, Philadelphia1856
        • Birnie G.D.
        Isolation of nuclei from animal cells in culture.
        in: Stein G. Stein J. Kleinsmith L.J. Methods in cell biology. 17. Academic Press, New York1978: 13-26
        • Arda H.E.
        • Li L.
        • Tsai J.
        • et al.
        Age-dependent pancreatic gene regulation reveals mechanisms governing human β cell function.
        Cell Metab. 2016; 23: 909-920
        • Bracht T.
        • Schweinsberg V.
        • Trippler M.
        • et al.
        Analysis of disease-associated protein expression using quantitative proteomics—fibulin-5 is expressed in association with hepatic fibrosis.
        J Proteome Res. 2015; 14: 2278-2286
        • Chang J.
        • Lan T.
        • Li C.
        • et al.
        Activation of Slit2-Robo1 signaling promotes liver fibrosis.
        J Hepatol. 2015; 63: 1413-1420
        • Liu X.
        • Wang J.
        • Wang H.
        • et al.
        REG3A accelerates pancreatic cancer cell growth under IL-6-associated inflammatory condition: involvement of a REG3A-JAK2/STAT3 positive feedback loop.
        Cancer Lett. 2015; 362: 45-60
        • Harding J.D.
        • MacDonald R.J.
        • Przybyla A.E.
        • et al.
        Changes in the frequency of specific transcripts during development of the pancreas.
        J Biol Chem. 1977; 252: 7391-7397
        • Aibar S.
        • González-Blas C.B.
        • Moerman T.
        • et al.
        SCENIC: single-cell regulatory network inference and clustering.
        Nat Methods. 2017; 14: 1083-1086
        • Wolf F.A.
        • Hamey F.K.
        • Plass M.
        • et al.
        PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.
        Genome Biol. 2019; 20: 59
        • Storz P.
        Acinar cell plasticity and development of pancreatic ductal adenocarcinoma.
        Nat Rev Gastroenterol Hepatol. 2017; 14: 296-304
        • Larsen H.L.
        • Grapin-Botton A.
        The molecular and morphogenetic basis of pancreas organogenesis.
        Semin Cell Dev Biol. 2017; 66: 51-68
        • Kim S.
        • Whitener R.L.
        • Peiris H.
        • et al.
        Molecular and genetic regulation of pig pancreatic islet cell development.
        Development. 2020; 147: dev186213
        • Kachar B.
        • Taga R.
        • Kniebel G.A.
        • et al.
        Morphometric evaluation of the number of exocrine pancreatic cells during early postnatal growth in the rat.
        Acta Anat. 1979; 103: 11-15
        • Wierup N.
        • Svensson H.
        • Mulder H.
        • et al.
        The ghrelin cell: a novel developmentally regulated islet cell in the human pancreas.
        Regul Pept. 2002; 107: 63-69
        • Hougland J.L.
        Ghrelin octanoylation by ghrelin O-acyltransferase: unique protein biochemistry underlying metabolic signaling.
        Biochem Soc Trans. 2019; 47: 169-178
        • Zoppi G.
        • Andreotti G.
        • Pajno-Ferrara F.
        • et al.
        Exocrine pancreas function in premature and full term neonates.
        Pediatr Res. 1972; 6: 880-886
        • Vento-Tormo R.
        • Efremova M.
        • Botting R.A.
        • et al.
        Single-cell reconstruction of the early maternal-fetal interface in humans.
        Nature. 2018; 563: 347-353
        • Nucera S.
        • Biziato D.
        • De Palma M.
        The interplay between macrophages and angiogenesis in development, tissue injury and regeneration.
        Int J Dev Biol. 2011; 55: 495-503
        • Suen P.M.
        • Zou C.
        • Zhang Y.A.
        • et al.
        PDZ-domain containing-2 (PDZD2) is a novel factor that affects the growth and differentiation of human fetal pancreatic progenitor cells.
        Int J Biochem Cell Biol. 2008; 40: 789-803
        • Gleason C.E.
        • Ning Y.
        • Cominski T.P.
        • et al.
        Role of insulin-like growth factor-binding protein 5 (IGFBP5) in organismal and pancreatic beta-cell growth.
        Mol Endocrinol. 2010; 24: 178-192
        • Hoffmann A.
        • Spengler D.
        Transient neonatal diabetes mellitus gene Zac1 impairs insulin secretion in mice through Rasgrf1.
        Mol Cell Biol. 2012; 32: 2549-2560
        • Aguayo-Mazzucato C.
        • Haaren M van
        • Mruk M.
        • et al.
        β cell aging markers have heterogeneous distribution and are induced by insulin resistance.
        Cell Metab. 2017; 25: 898-910
        • Hall E.
        • Dekker Nitert M.
        • Volkov P.
        • et al.
        The effects of high glucose exposure on global gene expression and DNA methylation in human pancreatic islets.
        Mol Cell Endocrinol. 2018; 472: 57-67
        • Huang C.
        • Walker E.M.
        • Dadi P.K.
        • et al.
        Synaptotagmin 4 regulates pancreatic β cell maturation by modulating the Ca2+ sensitivity of insulin secretion vesicles.
        Dev Cell. 2018; 45: 347-361
        • Ke R.
        • Mignardi M.
        • Pacureanu A.
        • et al.
        In situ sequencing for RNA analysis in preserved tissue and cells.
        Nat Methods. 2013; 10: 857-860
        • Park J.
        • Choi W.
        • Tiesmeyer S.
        • et al.
        Segmentation-free inference of cell types from in situ transcriptomics data. bioRχiv.
        (Available at:) (Published October 13, 2019. Accessed March 29, 2020)
        • Bonner-Weir S.
        • Orci L.
        New perspectives on the microvasculature of the islets of Langerhans in the rat.
        Diabetes. 1982; 31: 883-889
        • Bonner-Weir S.
        • Sullivan B.A.
        • Weir G.C.
        Human islet morphology revisited: human and rodent islets are not so different after all.
        J Histochem Cytochem. 2015; 63: 604-612
        • Dybala M.P.
        • Hara M.
        Heterogeneity of the human pancreatic islet.
        Diabetes. 2019; 68: 1230-1239
        • Pauerstein P.T.
        • Tellez K.
        • Willmarth K.B.
        • et al.
        A radial axis defined by semaphorin-to-neuropilin signaling controls pancreatic islet morphogenesis.
        Development. 2017; 144: 3744-3754
        • Gironella M.
        • Calvo C.
        • Fernández A.
        • et al.
        Reg3β deficiency impairs pancreatic tumor growth by skewing macrophage polarization.
        Cancer Res. 2013; 73: 5682-5694
        • Kleeff J.
        • Whitcomb D.C.
        • Shimosegawa T.
        • et al.
        Chronic pancreatitis.
        Nat Rev Dis Primers. 2017; 3: 17060
        • DelGiorno K.E.
        • Chung C.-Y.
        • Vavinskaya V.
        • et al.
        Tuft cells inhibit pancreatic tumorigenesis in mice by producing prostaglandin D2.
        Gastroenterology. 2020; 159: 1866-1881
        • Uchida E.
        • Steplewski Z.
        • Mroczek E.
        • et al.
        Presence of two distinct acinar cell populations in human pancreas based on their antigenicity.
        Int J Pancreatol. 1986; 1: 213-225
        • Wollny D.
        • Zhao S.
        • Everlien I.
        • et al.
        Single-cell analysis uncovers clonal acinar cell heterogeneity in the adult pancreas.
        Dev Cell. 2016; 39: 289-301
        • Hwang W.L.
        • Jagadeesh K.A.
        • Guo J.A.
        • et al.
        Single-nucleus and spatial transcriptomics of archival pancreatic cancer reveals multi-compartment reprogramming after neoadjuvant treatment. bioRχiv.
        (Published August 25, 2020. Accessed August 28, 2020)
        • Szabat M.
        • Page M.M.
        • Panzhinskiy E.
        • et al.
        Reduced insulin production relieves endoplasmic reticulum stress and induces β cell proliferation.
        Cell Metab. 2016; 23: 179-193
        • Rovira M.
        • Scott S.-G.
        • Liss A.S.
        • et al.
        Isolation and characterization of centroacinar/terminal ductal progenitor cells in adult mouse pancreas.
        Proc Natl Acad Sci U S A. 2010; 107: 75-80
        • Cleaver O.
        • Dor Y.
        Vascular instruction of pancreas development.
        Development. 2012; 139: 2833-2843
        • Byrnes L.E.
        • Wong D.M.
        • Subramaniam M.
        • et al.
        Lineage dynamics of murine pancreatic development at single-cell resolution.
        Nat Commun. 2018; 9: 3922
        • Grün D.
        • Muraro M.J.
        • Boisset J.-C.
        • et al.
        De novo prediction of stem cell identity using single-cell transcriptome data.
        Cell Stem Cell. 2016; 19: 266-277