Browse
Submit Data
Databases
API
Help

Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A single-cell transcriptome atlas of the human pancreas

ABSTRACT: To understand organ (dys)function it is important to have a complete inventory of its cell types and the corresponding markers that unambiguously identify these cell types. This is a challenging task, in particular in human tissues, because unique cell-type markers are typically unavailable, necessitating the analysis of complex cell type mixtures. Transcriptome-wide studies on pancreatic tissue are typically done on pooled islet material. To overcome this challenge we sequenced the transcriptome of thousands of single pancreatic cells from deceased organ donors with and without type 2 diabetes (T2D) allowing in silico purification of the different cell types. We identified the major pancreatic cell types resulting in the identification of many new cell-type specific and T2D-specific markers. Additionally we observed several subpopulations within the canonical pancreatic cell types, which we validated in situ. This resource will be useful for developing a deeper understanding of pancreatic biology and diabetes mellitus. Human cadaveric pancreata were used to extract islets of Langerhans, which were kept in culture until single-cell dispersion and FACS sorting. Single-cell transcriptomics was performed on live cells from this mixture using CEL-seq or on cells stained for CD63, CD13, TGFBR3 or CD24 and CD44. The RaceID algorithm was used to identify clusters of cells corresponding to the major pancreatic cell types and to mine for novel cell type-specific genes as well as subpopulations within the known pancreatic cell types.

ORGANISM(S): Homo sapiens

SUBMITTER: Mauro Javier Muraro

PROVIDER: E-GEOD-81076 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Json Xml

Similar Datasets

A single-cell transcriptome atlas of the human pancreas

Project description:To understand organ (dys)function it is important to have a complete inventory of its cell types and the corresponding markers that unambiguously identify these cell types. This is a challenging task, in particular in human tissues, because unique cell-type markers are typically unavailable, necessitating the analysis of complex cell type mixtures. Transcriptome-wide studies on pancreatic tissue are typically done on pooled islet material. To overcome this challenge we sequenced the transcriptome of thousands of single pancreatic cells from deceased organ donors with and without type 2 diabetes (T2D) allowing in silico purification of the different cell types. We identified the major pancreatic cell types resulting in the identification of many new cell-type specific and T2D-specific markers. Additionally we observed several subpopulations within the canonical pancreatic cell types, which we validated in situ. This resource will be useful for developing a deeper understanding of pancreatic biology and diabetes mellitus.

2016-06-24 | GSE81076 | GEO

Single-cell RNA-seq of Ecad positive PyMT bulk tumor cells

Project description:In this study we studied the presence of tumor cells that underwent epithelial-to-mesenchymal transition within polyoma middle T antigen (PyMT) breast tumors. For this we dissociated tumors and isolated Ecad positive tumor cells by FACS sorting. We confirmed that PyMT tumors contain a small set of tumor cells that have undergone EMT in the primary tumor and that E-cadherin can be used as a marker on single cell level for mesenchymal status in this model. We isolated primary tumors from mice, dissociated the tumors and FACS-sorted for single Ecad positive tumor cells, after this we performed single cell sequencing of the cells.

2016-03-09 | E-GEOD-77107 | biostudies-arrayexpress

Single-cell RNA-seq of sorted and micro-dissected mouse bone marrow cells

Project description:We here systematically studied the interaction network of bone marrow cells. To this end, we micro-dissected many small interacting structures (cell doublets, triplets etc.) into single cells, and sequenced their mRNAs, to infer cell identity. After grouping the cells into cell types (based on the single-cell transcriptomes), we identified actual physical interactions that occurred more, or less, than what would be expected by chance. We compared the micro-dissected data to sorted hematopoietic stem cells. After mild dissociation of the bone marrow, we micro-dissected many small interacting structures (cell doublets, triplets etc.) into single cells, and sequenced their mRNAs. In addition, single hematopoietic stem cells were sorted (Lineage- Kit+ Sca1+ CD150+ CD48-) to sequence their transcriptome. In detail, the bone marrow was mildly flushed. Structures composed of about 10 to 20 cells were set apart. With needles and a micro-dissection microscope, we trimmed of smaller structures from these big structure. These smaller structures are mainly two to four cells attached together. These small structures were then further micro-dissected to single cells. The goal of doing these sequential dissections was to keep track of which cells were interacting with which. Each micro-dissected cell sample contains RNA-seq data for 96 single cells. Each single cells received a different barcode that allow us to entangle them, and to produce the processed .coutt.csv file. Each sorted hematopoietic stem cell sample represents a single cell. The processed data file expdata_BMJhscC.csv contains transcript counts for all analyzed cells.

2016-06-24 | E-GEOD-76983 | biostudies-arrayexpress

Single-cell RNA-seq uncovers the pathological alterations of pancreatic islets in type 2 diabetes mice

Project description:Pancreatic islet beta cell heterogeneity has been identified, which plays a pivotal role in the pathological alterations of pancreatic islets in type 2 diabetes (T2D) mice. However, pathological alterations of beta cells in type 2 diabetes (T2D) mice remain to be investigated. We isolated pancreatic islets from the control and T2D mice and conducted scRNA-seq analysis using the 10x Genomics platform. Pathological alterations of beta cells in T2D were also explored.

2022-04-09 | GSE200531 | GEO

Validation of noise models for single-cell transcriptomics

Project description:Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity amongst single cells. Here we identify two major sources of technical variability, sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this and after validation by single-molecule FISH experiments, we apply these models to demonstrate that growing mES cells in 2i instead of serum/LIF globally reduces gene expression variability. J1 mouse embryonic stem cells (mESCs) were cutured in 2i or in serum medium. Cells were dissociated into a single cell suspension and picked under a stereomicroscope using a 30Î¼m glass capillary and mouth pipette. Picked cells were deposited in the lid of an 0.5ml LoBind eppendorf tube and snap frozen in liquid nitrogen. For the pool and split controls, approximately 1 million cells were lysed, the amount of RNA was quantified on a bioanalyzer (Agilent) using the Eukaryote Total RNA pico kit. 20pg aliquots of total RNA were used for each pool and split control. Single cells and controls were processed using the previously described CEL-seq technique, with a few alterations. A 4bp random barcode as unique molecular identifier (UMI) was added to the primer in between the cell specific barcode and the poly T stretch. Libraries were sequenced on an Illumina HighSeq 2500 using 50bp paired end sequencing. For cells and controls, two libraries were sequenced on two lanes in total for each condition.

2014-04-20 | E-GEOD-54695 | biostudies-arrayexpress

Transcriptional heterogeneity and lineage commitment in myeloid progenitors [single cell RNA-seq]

Project description:Within the bone marrow, hematopoietic stem cells differentiate and give rise to diverse blood cell types and functions. Currently, hematopoietic progenitors are defined using surface markers combined with functional assays that are not directly linked with the in vivo potential or gene regulatory mechanisms. Here we comprehensively identify myeloid progenitor subpopulations by transcriptional sorting of single cells from the bone marrow. We describe multiple progenitor subgroups showing unexpected transcriptional priming towards seven differentiation fates, but no progenitors with a mixed state. Transcriptional differentiation is correlated with combinations of known and previously undefined transcription factors, suggesting the process is tightly regulated. Histone maps and knockout assays are consistent with the transcriptional states while traditional transplantation experiments are only partially overlapping myeloid transcriptional priming. Our analyses uncover the function of the underlying regulatory mechanisms for several sub groups and establishes a general framework for dissecting hematopoiesis. Bone marrow Lin- cKit+ Sca1- myeloid progenitors mRNA profiles from single cells were generated by deep sequencing of thousands of single cells, sequenced in several batches in an Illumina NextSeq Please note that [1] raw data files were processed as single-ended file since second read (mate) files contain only cell/molecule barcodes and therefore, not provided. This information was appended to the fastq entry header [2] The 'experimental_design.txt' file explains the correspondence of each single cell (WXXXX) in the 'umitab.txt' to a sample (ABXXXX).

2015-11-25 | E-GEOD-72857 | biostudies-arrayexpress

Pancreatic progenitor epigenome maps prioritize type 2 diabetes risk genes with roles in development

Project description:Genetic variants associated with type 2 diabetes (T2D) risk affect gene regulation in metabolically relevant tissues, such as pancreatic islets. Here, we investigated contributions of regulatory programs active during pancreatic development to T2D risk. Interrogation of chromatin maps from developmental precursors throughout pancreatic differentiation of human embryonic stem cells (hESCs) identifies enrichment of T2D variants in pancreatic progenitor-specific stretch enhancers that are not active in islets. Genes associated with progenitor-specific stretch enhancers are predicted to regulate developmental processes, most notably tissue morphogenesis. Through gene editing in hESCs, we demonstrate that progenitor-specific enhancers harboring T2D-associated variants regulate cell polarity genes LAMA1 and CRB2. Knockdown of lama1 or crb2 in zebrafish embryos causes a defect in pancreas morphogenesis and impairs islet cell development. Together, our findings reveal that a subset of T2D risk variants specifically affects pancreatic developmental programs, suggesting that dysregulation of developmental processes can predispose to T2D.

2021-01-19 | GSE149148 | GEO

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

Project description: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

2016-11-09 | GSE86469 | GEO

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

2016-11-09 | GSE86468 | GEO

Whole-islet RNA-sequencing analysis of human pancreas from healthy individuals and type 2 diabetes patients

Project description:We performed whole-islet RNA-sequencing of human pancreatic islets from healthy and T2D individuals in order to compare the expression data to the corresponding single-cell RNA-sequencing data from the same donors (related single-cell study: Single-cell analysis of human pancreas and T2D, deposited at ArrayExpress under accession E-MTAB-5061: http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5061/).

2016-09-22 | E-MTAB-5060 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data