Pancreas Network

Pancreas Network - Overview

To understand organ function, it is important to have an inventory of its cell types and of their corresponding marker genes. This is a particularly challenging task for human tissues like the pancreas, because reliable markers are limited. Hence, transcriptome-wide studies are typically done on pooled islets of Langerhans, obscuring contributions from rare cell types and of potential subpopulations. To overcome this challenge, we developed an automated platform that uses FACS, robotics, and the CEL-Seq2 protocol to obtain the transcriptomes of thousands of single pancreatic cells from deceased organ donors, allowing in silico purification of all main pancreatic cell types. We identify cell type-specific transcription factors and a subpopulation of REG3A-positive acinar cells. We also show that CD24 and TM4SF4 expression can be used to sort live alpha and beta cells with high purity. This resource will be useful for developing a deeper understanding of pancreatic biology and pathophysiology of diabetes mellitus.

A Single-Cell Transcriptome Atlas of the Human Pancreas.

Although the function of the mammalian pancreas hinges on complex interactions of distinct cell types, gene expression profiles have primarily been described with bulk mixtures. Here we implemented a droplet-based, single-cell RNA-seq method to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two mouse strains. Cells could be divided into 15 clusters that matched previously characterized cell types: all endocrine cell types, including rare epsilon-cells; exocrine cell types; vascular cells; Schwann cells; quiescent and activated stellate cells; and four types of immune cells. We detected subpopulations of ductal cells with distinct expression profiles and validated their existence with immuno-histochemistry stains. Moreover, among human beta- cells, we detected heterogeneity in the regulation of genes relating to functional maturation and levels of ER stress. Finally, we deconvolved bulk gene expression samples using the single-cell data to detect disease-associated differential expression. Our dataset provides a resource for the discovery of novel cell type-specific transcription factors, signaling receptors, and medically relevant genes.

A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure

The gene expression program underlying the specification of human cell types is of fundamental interest. We generated human cell atlases of gene expression and chromatin accessibility in fetal tissues. For gene expression, we applied three-level combinatorial indexing to >110 samples representing 15 organs, ultimately profiling ~4 million single cells. We leveraged the literature and other atlases to identify and annotate hundreds of cell types and subtypes, both within and across tissues. Our analyses focused on organ-specific specializations of broadly distributed cell types (such as blood, endothelial, and epithelial), sites of fetal erythropoiesis (which notably included the adrenal gland), and integration with mouse developmental atlases (such as conserved specification of blood cells). These data represent a rich resource for the exploration of in vivo human gene expression in diverse tissues and cell types.

A human cell atlas of fetal gene expression.

Current catalogs of regulatory sequences in the human genome are still incomplete and lack cell type resolution. To profile the activity of gene regulatory elements in diverse cell types and tissues in the human body, we applied single-cell chromatin accessibility assays to 30 adult human tissue types from multiple donors. We integrated these datasets with single-cell chromatin accessibility data from 15 fetal tissue types to reveal the status of open chromatin for approximately 1.2 million candidate cis-regulatory elements (cCREs) in 222 distinct cell types comprised of >1.3 million nuclei. We used these chromatin accessibility maps to delineate cell type-specificity of fetal and adult human cCREs and to systematically interpret the noncoding variants associated with complex human traits and diseases. This rich resource provides a foundation for the analysis of gene regulatory programs in human cell types across tissues, life stages, and organ systems. Overall design: Applying single cell chromatin accessibility assays to diverse human tissue types from four donors.

A single-cell atlas of chromatin accessibility in the human genome

ObjectivePancreatic islets of Langerhans secrete hormones to regulate systemic glucose levels. Emerging evidence suggests that islet cells are functionally heterogeneous to allow a fine-tuned and efficient endocrine response to physiological changes. A precise description of the molecular basis of this heterogeneity, in particular linking animal models to human islets, is an important step towards identifying the factors critical for endocrine cell function in physiological and pathophysiological conditions.MethodsIn this study, we used single-cell RNA sequencing to profile more than 50'000 endocrine cells isolated from healthy human, pig and mouse pancreatic islets and characterize transcriptional heterogeneity and evolutionary conservation of those cells across the three species. We systematically delineated endocrine cell types and α- and β-cell heterogeneity through prior knowledge- and data-driven gene sets shared across species, which altogether capture common and differential cellular properties, transcriptional dynamics and putative driving factors of state transitions.ResultsWe showed that global endocrine expression profiles correlate, and that critical identity and functional markers are shared between species, while only approximately 20% of cell type enriched expression is conserved. We resolved distinct human α- and β-cell states that form continuous transcriptional landscapes. These states differentially activate maturation and hormone secretion programs, which are related to regulatory hormone receptor expression, signaling pathways and different types of cellular stress responses. Finally, we mapped mouse and pig cells to the human reference and observed that the spectrum of human α- and β-cell heterogeneity and aspects of such functional gene expression are better recapitulated in the pig than mouse data.ConclusionsHere, we provide a high-resolution transcriptional map of healthy human islet cells and their murine and porcine counterparts, which is easily queryable via an online interface. This comprehensive resource informs future efforts that focus on pancreatic endocrine function, failure and regeneration, and enables to assess molecular conservation in islet biology across species for translational purposes.

A transcriptional cross species map of pancreatic islet cells.

Single cell RNAseq data of human pancreatic islets from 5 non-diabetic donors. Project abstract: Islet-enriched transcription factors (TFs) exert broad control over cellular processes in pancreatic α and β cells and changes in their expression are associated with developmental state and diabetes. However, the implications of heterogeneity in TF expression across islet cell populations are not well understood. To define this TF heterogeneity and its consequences for cellular function, we profiled >40,000 cells from normal human islets by scRNA-seq and stratified α and β cells based on combinatorial TF expression. Subpopulations of islet cells co-expressing ARX/MAFB (α cells) and MAFA/MAFB (β cells) exhibited greater expression of key genes related to glucose sensing and hormone secretion relative to subpopulations expressing only one or neither TF. Moreover, all subpopulations were identified in native pancreatic tissue from multiple donors. By Patch-seq, MAFA/MAFB co-expressing β cells showed enhanced electrophysiological activity. Thus, these results indicate combinatorial TF expression in islet α and β cells predicts highly functional, mature subpopulations.

Combinatorial transcription factor profiles predict mature and functional human islet α and β cells

Single-cell RNA sequencing was used to determine the cell type composition of all major human organs and construct a basic scheme for the human cell landscape (HCL).

Construction of A Human Cell Landscape by Single-cell mRNA-seq

Cancer-associated fibroblasts (CAF) are major players in the progression and drug resistance of pancreatic ductal adenocarcinoma (PDAC). CAFs constitute a diverse cell population consisting of several recently described subtypes, although the extent of CAF heterogeneity has remained undefined. Here we use single-cell RNA sequencing to thoroughly characterize the neoplastic and tumor microenvironment content of human and mouse PDAC tumors. We corroborate the presence of myofibroblastic CAFs and inflammatory CAFs and define their unique gene signatures in vivo. Moreover, we describe a new population of CAFs that express MHC class II and CD74, but do not express classic costimulatory molecules. We term this cell population "antigen-presenting CAFs" and find that they activate CD4+ T cells in an antigen-specific fashion in a model system, confirming their putative immune-modulatory capacity. Our cross-species analysis paves the way for investigating distinct functions of CAF subtypes in PDAC immunity and progression. SIGNIFICANCE: Appreciating the full spectrum of fibroblast heterogeneity in pancreatic ductal adenocarcinoma is crucial to developing therapies that specifically target tumor-promoting CAFs. This work identifies MHC class II-expressing CAFs with a capacity to present antigens to CD4+ T cells, and potentially to modulate the immune response in pancreatic tumors.See related commentary by Belle and DeNardo, p. 1001.This article is highlighted in the In This Issue feature, p. 983.

Cross-Species Single-Cell Analysis of Pancreatic Ductal Adenocarcinoma Reveals Antigen-Presenting Cancer-Associated Fibroblasts

This study aims to optimize the cryopreservation of dissociated islet cells and obtain functional cells that can be used in single-cell transcriptome studies on the pathology and treatment of diabetes. Using an iterative graded freezing approach we obtained viable cells after cooling in 10% dimethyl sulfoxide and 6% hydroxyethyl starch at 1 °C/min to –40 °C, storage in liquid nitrogen, rapid thaw, and removal of cryoprotectants by serial dilution. The expression of epithelial cell adhesion molecule declined immediately after thaw, but recovered after overnight incubation, while that of an endocrine cell marker (HPi2) remained high after cryopreservation. Patch-clamp electrophysiology revealed differences in channel activities and exocytosis of various islet cell types; however, exocytotic responses, and the biophysical properties of voltage-gated Na+ and Ca2+ channels, are sustained after cryopreservation. Single-cell RNA sequencing indicates that overall transcriptome and crucial exocytosis genes are comparable between fresh and cryopreserved dispersed human islet cells. Thus, we report an optimized procedure for cryopreserving dispersed islet cells that maintained their membrane integrity, along with their molecular and functional phenotypes. Our findings will not only provide a ready source of cells for investigating cellular mechanisms in diabetes but also for bio-engineering pseudo-islets and islet sheets for modeling studies and potential transplant applications.

Cryopreservation and post-thaw characterization of dissociated human islet cells

Generation of surrogate cells with stable functional identities is crucial for developing cell-based therapies. Efforts to produce insulin-secreting replacement cells to treat diabetes require reliable tools to assess islet cellular identity. Here, we conduct a thorough single-cell transcriptomics meta-analysis to identify robustly expressed markers used to build genesets describing the identity of human α-, β-, γ- and δ-cells. These genesets define islet cellular identities better than previously published genesets. We show their efficacy to outline cell identity changes and unravel some of their underlying genetic mechanisms, whether during embryonic pancreas development or in experimental setups aiming at developing glucose-responsive insulin-secreting cells, such as pluripotent stem-cell differentiation or in adult islet cell reprogramming protocols. These islet cell type-specific genesets represent valuable tools that accurately benchmark gain and loss in islet cell identity traits.

Generation of human islet cell type-specific identity genesets.

Massively parallel single-cell RNA-seq technology (Drop-Seq) was applied to analyze the transcriptome of 26,677 pancreatic islets cells from both healthy and type II diabetic (T2D) donors.

Massively parallel single-cell RNA-seq analysis of 26,677 pancreatic islets cells from both healthy and type II diabetic (T2D) donors.

Proinsulin is a misfolding-prone protein, making its biosynthesis in the endoplasmic reticulum (ER) a stressful event. Pancreatic β-cells overcome ER stress by activating the unfolded protein response (UPR) and reducing insulin production. This suggests that β-cells transition between periods of high insulin biosynthesis and UPR-mediated recovery from cellular stress. We now report the pseudotime ordering of single β-cells from humans without diabetes detected by large-scale RNA sequencing. We identified major states with 1) low UPR and low insulin gene expression, 2) low UPR and high insulin gene expression, or 3) high UPR and low insulin gene expression. The latter state was enriched for proliferating cells. Stressed human β-cells do not dedifferentiate and show little propensity for apoptosis. These data suggest that human β-cells transition between states with high rates of biosynthesis to fulfill the body's insulin requirements to maintain normal blood glucose levels and UPR-mediated recovery from ER stress due to high insulin production.

Pseudotime Ordering of Single Human β-Cells Reveals States of Insulin Production and Unfolded Protein Response.

Pancreatic islet cells are critical for maintaining normal blood glucose levels and their malfunction underlies diabetes development and progression. We used single-cell RNA sequencing to determine the transcriptomes of 1,492 human pancreatic α-, β-, δ- and PP cells from non-diabetic and type 2 diabetes organ donors. We identified cell type specific genes and pathways as well as 245 genes with disturbed expression in type 2 diabetes. Importantly, 92% of the genes have not previously been associated with islet cell function or growth. Comparison of gene profiles in mouse and human α- and β-cells revealed species-specific expression. All data are available for online browsing and download and will hopefully serve as a resource for the islet research community.

RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes

We successfully sequenced and annotated more than 400 cells from child, adult control, type 1 diabetes and type 2 diabetes donors. We detect donor-type specific transcript variation. We also report that cells from child donors have less defined gene signature. Cells from type 2 diabetes donors resemble juvenile cells in gene expression.

Single cell RNA-seq of human pancreatic endocrine cells from Juvenile, adult control and type 2 diabetic donors.

As organisms age, cells accumulate genetic and epigenetic changes that eventually lead to impaired organ function or catastrophic failure such as cancer. Here we describe a single-cell transcriptome analysis of 2544 human pancreas cells from donors, spanning six decades of life. We find that islet cells from older donors have increased levels of disorder as measured both by noise in the transcriptome and by the number of cells which display inappropriate hormone expression, revealing a transcriptional instability associated with aging. By analyzing the spectrum of somatic mutations in single cells from previously-healthy donors, we find a specific age-dependent mutational signature characterized by C to A and C to G transversions, indicators of oxidative stress, which is absent in single cells from human brain tissue or in a tumor cell line. Cells carrying a high load of such mutations also express higher levels of stress and senescence markers, including FOS, JUN, and the cytoplasmic superoxide dismutase SOD1, markers previously linked to pancreatic diseases with substantial age-dependent risk, such as type 2 diabetes mellitus and adenocarcinoma. Thus, our single-cell approach unveils gene expression changes and somatic mutations acquired in aging human tissue, and identifies molecular pathways induced by these genetic changes that could influence human disease. Also, our results demonstrate the feasibility of using single-cell RNA-seq data from primary cells to derive meaningful insights into the genetic processes that operate on aging human tissue and to determine which molecular mechanisms are coordinated with these processes. Examination of single cells from primary human pancreas tissue

Single cell transcriptome analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns.

Blood glucose levels are tightly controlled by the coordinated action of at least five cell types constituting pancreatic islets. Changes in the proportion and/or function of these cells are associated with genetic and molecular pathophysiology of monogenic, type 1, and type 2 diabetes (T2D). Cellular heterogeneity impedes precise understanding of the molecular components of each islet cell type that govern islet dysfunction, particularly the less abundant delta and gamma/pancreatic polypeptide (PP) cells. Here, we report single cell transcriptomes for 617 islet cells after profiling ~1000 cells from non-diabetic (ND) and T2D human organ donors. Analyses of non-diabetic single cell transcriptomes identified distinct alpha, beta, delta, and PP/gamma cell-type signatures. Genes linked to rare and common forms of islet dysfunction and diabetes were expressed in the delta and PP/gamma cell types. Moreover, this study revealed that delta cells specifically express receptors that receive and coordinate systemic cues from the leptin, ghrelin, and dopamine signaling pathways implicating them as integrators of central and peripheral metabolic signals into the pancreatic islet. Finally, single cell transcriptome profiling revealed genes differentially regulated between T2D and ND alpha, beta, and delta cells that were undetectable in paired whole islet analyses. This study thus identifies fundamental cell type-specific features of pancreatic islet (dys)function and provides a critical resource for comprehensive understanding of islet biology and diabetes pathogenesis.,Grant ID: Award No. W81XWH-16-1-0130,Grant title: Peer Reviewed Medical Research Program,Funding Source: Assistant Secretary of Defense for Health Affairs,"Affiliation: Jackson Laboratory for Genomic Medicine, Farmington, CT",Name: Michael Stitzel

Single cell transcriptomics defines human islet cell signatures and reveals cell-type-specific expression changes in type 2 diabetes [single cell]

We used single-cell RNA-sequencing to generate transcriptional profiles of endocrine and exocrine cell types of the human pancreas. Pancreatic tissue and islets were obtained from six healthy and four T2D cadaveric donors. Islets were cultured and dissociated into single-cell suspension. Viable individual cells were distributed via fluorescence-activated cell sorted (FACS) into 384-well plates containing lysis buffer. Single-cell cDNA libraries were generated using the Smart-seq2 protocol. Gene expression was quantified as reads per kilobase transcript and per million mapped reads (RPKM) using rpkmforgenes. Bioinformatics analysis was used to classify cells into cell types without knowledge of cell types or prior purification of cell populations. We revealed subpopulations in endocrine and exocrine cell types, identified genes with interesting correlations to body mass index (BMI) in specific cell types and found transcriptional alterations in T2D.  Complementary whole-islet RNA-seq data have also been deposited at ArrayExpress under accession number E-MTAB-5060 (http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5060).

Single-cell RNA-seq analysis of human pancreas from healthy individuals and type 2 diabetes patients

Chronic pancreatitis (CP) is considered an irreversible fibroinflammatory pancreatic disease with no active FDA approved therapy. Due to difficulty in accessing pancreas tissues, little is known about local immune responses in human CP. Here we attempted to uncover the disease-specific immune responses in pancreata from two different etiologies of CP (hereditary and idiopathic CP) compared with those from non-diseased controls by using CITE-seq and scTCR-seq. Methods: We performed cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq) and T cell receptor sequencing of pancreatic immune cells isolated from organ donors (n=3) and CP patients (Hereditary CP, n=5; Idiopathic CP, n=4) who underwent total pancreatectomy. Results: Deep single-cell sequencing revealed distinct immune characteristics and a significantly enriched CCR6+ CD4+ T cells in hereditary compared with idiopathic CP. In hereditary CP, a reduction in T cell clonality was observed due to the increased CD4+ T (Th) cells that replaced tissue resident CD8+ T cells. Lineage tracing analysis with scRNA/TCR-seq data also unveiled unique interactions between CCR6+ Th and Th1 subsets, and TCR clustering analysis showed unique common antigen binding motifs in hereditary CP. In addition, we observed a significant upregulation of the CCR6 ligand (CCL20) among monocytes in hereditary CP as compared with those in idiopathic CP. The functional significance of the CCR6 expression in CD4+ T cells was confirmed by flow cytometry and migration assay. Conclusions: Our approaches with integrative single-cell analyses unveiled distinct pancreatic immune signatures and pathways between different etiologies of CP. Our study specifically unveiled pancreas-specific immune crosstalks through a CCR6-CCL20 axis that might be leveraged as a potential future target in human hereditary CP Overall design: Simultaneous single-cell profiles of 13 feature protein expressions, transcriptomes, and TCR repertoires in pancreatic immune cells from humam donors and CP patients.

Single-cell sequencing unveils distinct immune microenvironment in human chronic pancreatitis

Single-cell RNA-seq (scRNA-seq) of pancreatic islets have reported on α- and β-cell gene expression in mice and subjects of predominantly European ancestry. We aimed to assess these findings in East-Asian islet-cells. 448 islet-cells were captured from three East-Asian non-diabetic subjects for scRNA-seq. Hierarchical clustering using pancreatic cell lineage genes was used to assign cells into cell-types. Differentially expressed transcripts between α- and β-cells were detected using ANOVA and in silico replications of mouse and human islet cell genes were performed. We identified 118 α, 105 β, 6 δ endocrine cells and 47 exocrine cells. Besides INS and GCG, 26 genes showed differential expression between α- and β-cells. 10 genes showed concordant expression as reported in rodents, while FAM46A was significantly discordant. Comparing our East-Asian data with data from primarily European subjects, we replicated several genes implicated in nuclear receptor activations, acute phase response pathway, glutaryl-CoA/tryptophan degradations and EIF2/AMPK/mTOR signaling. Additionally, we identified protein ubiquitination to be associated among East-Asian β-cells. We report on East-Asian α- and β-cell gene signatures and substantiate several genes/pathways. We identify expression signatures in East-Asian β-cells that perhaps reflects increased susceptibility to cell-death and warrants future validations to fully appreciate their role in East-Asian diabetes pathogenesis. Overall design: 448 islet-cells were captured from three East-Asian non-diabetic subjects for scRNA-seq. 223 islet-cells remained after samples QC, and these cells were used for subsequent analyses. Hierarchical clustering using pancreatic cell lineage genes was used to assign cells into cell-types. We identified 118 α and 105 β endocrine cells in our dataset.

Single-cell transcriptomics of East-Asian pancreatic islets cells

Background: Single-cell RNA sequencing (scRNA-seq) contributes to discovering intrinsic heterogeneity of pancreatic adenosquamous carcinoma (PASC), a lethal human malignancy. However, the gene-pattern variations within ductal and stromal cells remain unclear. Methods: We fulfilled scRNA-seq profiling with one healthy donor pancreas, one intraductal papillary mucinous neoplasm (IPMN), and one PASC sample tissue. Multiple bioinformatics analyses were used to investigate the transcriptomics abnormalities from normal stage to tumor formation. Results: We identified ten subpopulations from 10,346 single cells, including myeloid, immune, ductal, fibroblast, acinar, stellate, endothelial, and malignant cells. Within our analysis, pancreatic cancer cells were further divided into five clusters; notably, cluster 1 exhibited stem-like phenotypes, such as UBE2C, ASPM, and TOP2A. We also found that S100A2 could be a specific biomarker for tumor cells, relating to PASC progression. LGALS1, NPM1, RACK1, and PERP were upregulated gradually from ductal to malignant cells. Furthermore, the level of copy number variation in ductal and cancer cells was significantly greater than reference cells. The expression of EREG, FCGR2A, FCGR2A, and CCL4L2 increased in myeloid cells along with tumor development, while cancer-associated fibroblast could induce the immunosuppressive tumor microenvironment, promoting pancreatic cancerogenesis. Finally, we demonstrated that EGFR-associated ligand-receptor pairs were activated in stromal to malignant cell communication. Conclusion: This study revealed the variations of ductal and stromal cells, defined cancer-associated signaling pathways, and deciphered cellular interactions following PASC progression. Thus, we provided valuable information to further understand the crucial regulatory mechanisms that embrace the potential therapeutic targets for PASC. Overall design: mRNA profiles of normal pancreas, intraductal papillary mucinous neoplasm, and pancreatic adenosquamous carcinoma

Single-cell transcriptomics of normal pancreas, intraductal papillary mucinous neoplasm, and pancreatic adenosquamous carcinoma reveals the heterogeneous progression of pancreatic ductal and stromal cells

We report the single-cell RNA-seq based identification of 6 known human islet cell types (alpha cells, beta cells, delta cells, pp cells, acinar cells and duct cells) based on the expression of known marker genes. We further assess cell type specific gene expression and suggest novel marker genes for several cell types.

Single-cell transcriptomics reveals unique features of human pancreatic islet cell subtypes

BackgroundSingle-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts.MethodsWe obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library.ResultsFirst, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (β-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three β-cell sub-clusters: an INS pre-mRNA cluster (β3), an intermediate INS mRNA cluster (β2), and an INS mRNA-rich cluster (β1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo.ConclusionsIn summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo.

Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles.

We have created a compendium of single cell transcriptome data from the model
organism Mus musculus comprising more than 100,000 cells from 20 organs and
tissues. These data represent a new resource for cell biology, revealing gene
expression in poorly characterized cell populations and allowing for direct and
controlled comparison of gene expression in cell types shared between tissues, such
as T-lymphocytes and endothelial cells from distinct anatomical locations. Two
distinct technical approaches were used for most tissues: one approach, microfluidic
droplet-based 3’-end counting, enabled the survey of thousands of cells at relatively
low coverage, while the other, FACS-based full length transcript analysis, enabled
characterization of cell types with high sensitivity and coverage. The cumulative
data provide the foundation for an atlas of transcriptomic cell biology.

Tabula Muris: Transcriptomic characterization of 20 organs and tissues from Mus musculus at single cell resolution

In recent years there has been tremendous progress towards deep molecular characterization of cell types using single cell transcriptome sequencing. Here we report a single cell transcriptomic atlas comprising nearly 500,000 cells from 24 different human tissues and organs. In several instances multiple organs were analyzed from the same donor. Analyzing organs from the same individual controls for genetic background, age, environment, and epigenetic effects, and enables a detailed comparison of cell types that are shared between tissues. This resource provides a rich molecular characterization of more than 400 cell types, their distribution across tissues, and detailed information about tissue specific variation in gene expression. We have used the fact that multiple tissues came from the same donor to study the clonal distribution of T cells between tissues, to understand the tissue specific mutation rate in B cells, and to analyze the cell cycle state and proliferative potential of shared cell types across tissues. Finally, we have also used this data to characterize cell type specific RNA splicing and how such splicing varies across tissues within an individual.

The Tabula Sapiens: a single cell transcriptomic atlas of multiple organs from individual human donors

Human pancreatic islets, including insulin secreting beta-cells are a major focus of transplantation strategies aimed at identifying new therapeutic approaches to counteract hyperglycemia in patients with diabetes. Identifying the transcriptomic signature of human islet cells provides insights into regulatory pathways that can be harnessed for planning therapeutic strategies. In this context, single-cell RNA-sequencing (scRNA-seq) has been used mostly in vitro. However, in experimental human islet transplantation models the small amount of tissue is principally used for immunostaining and poses a challenge in performing ‘omics’ studies that provide unbiased information. To circumvent this limitation, we report the use of single nucleus RNA-sequencing (snRNA-seq) on frozen/archived human islet grafts, to define the transcriptomic signature of islet cells preserved after in vivo studies. Interrogating nuclear RNA, we were able to successfully identify all islet endocrine cells, obtain adequate coverage of genes and define molecular pathways that are important for studying human islet cell biology (e.g. cell cycle, apoptosis, insulin secretion). Intersecting our nuclear transcriptomic output with publicly available single-cell RNA-seq datasets, revealed ~90% overlap of the detected genes. In conclusion, we propose that snRNA-seq represents a reliable strategy to probe transcriptomic profiles of fresh or archived transplanted human islets. Overall design: A total of 1000 hand-picked human islets equivalents (IEQs) were transplanted under the kidney capsule of 8-to-12-week-old male Non-obese diabetic (NOD)/SCID-gamma (NSG) mice and animals were followed for 4 weeks. At the end of the follow-up period, mice were sacrificed by cervical dislocation. Human islet grafts were rapidly dissected under the microscope, snap-frozen and stored at -80C. Frozen grafts were transferred to dounce tissue grinder tubes (D8938; Sigma) containing 0.5 ml ice-cold Nuclei EZ lysis buffer (NUC-101; Sigma) and homogenized with pestles A and B on ice each for one minute. Samples were transferred to clean 15 ml tubes and homogenizers were rinsed with 1.5 ml buffer followed by 2 ml buffer and transferred to the same 15 ml tubes to obtain a final volume of 4 ml. The tubes were vortexed briefly at moderate speed and set on ice for 5 minutes for cell lysis. To separate nucleus and cytoplasm, the tubes were centrifuged at 500x g for 5 minutes at 4C. The pellet containing nuclei was resuspended in 0.5 ml cold buffer by vortexing briefly at moderate speed and 3.5 ml cold buffer was added. The nuclear suspension was mixed by vortexing briefly and set on ice for 5 minutes. The tubes were centrifuged at 500x g for 5 minutes and the pellet was resuspended in suspension buffer (0.5 ml PBS containing 0.01% non-acetylated BSA; Sigma and 0.1% RNase inhibitor; 2313A from Clontech). The nuclear suspension was pipetted ten times with a 1 ml tip, filtered through a 30 um pre-separation filter (130-041-407; Miltenyi Biotech); cell number and cell viability were determined by cell counter using 0.4% trypan blue stain. The average number of total nuclei obtained from one-half graft was approximately 8.5x10e5 nuclei (1.7x10e6 cells/ml) with 5-10 um size and 93.3 +/-1.1% dead cell rate (n=32 samples across three independent experiments). The number of isolated nuclei was adjusted to 1000 nuclei/ul with suspension buffer and 10,000 nuclei were immediately used for generation of Gel Beads In-Emulsion (GEMs) and barcoding. Transcriptomic analysis was performed using the 10x Genomics Chromium Single Cell Gene Expression Assay.

Pancreas Network

Network Description

Pancreas Network atlases are still under development.

Network Coordinators