Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Single-Cell Transcriptomics Reveals Early Emergence of Liver Parenchymal and Non-parenchymal Cell Lineages.

ABSTRACT: The cellular complexity and scale of the early liver have constrained analyses examining its emergence during organogenesis. To circumvent these issues, we analyzed 45,334 single-cell transcriptomes from embryonic day (E)7.5, when endoderm progenitors are specified, to E10.5 liver, when liver parenchymal and non-parenchymal cell lineages emerge. Our data detail divergence of vascular and sinusoidal endothelia, including a distinct transcriptional profile for sinusoidal endothelial specification by E8.75. We characterize two distinct mesothelial cell types as well as early hepatic stellate cells and reveal distinct spatiotemporal distributions for these populations. We capture transcriptional profiles for hepatoblast specification and migration, including the emergence of a hepatomesenchymal cell type and evidence for hepatoblast collective cell migration. Further, we identify cell-cell interactions during the organization of the primitive sinusoid. This study provides a comprehensive atlas of liver lineage establishment from the endoderm and mesoderm through to the organization of the primitive sinusoid at single-cell resolution.

SUBMITTER: Lotto J

PROVIDER: S-EPMC7643810 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Single-cell transcriptomics reveals early emergence of liver parenchymal and non-parenchymal cell lineages

Project description:To characterize the heterogeneity within endothelial, mesenchymal, and hepatic lineages through formation of the embryonic mouse liver bud, we have performed single-cell RNA sequencing on mouse embryos, spanning days 9.5 to 10.5 of development. This data set includes 12,470 cells with a mean detected gene number of 4,588.

2020-10-29 | E-MTAB-9334 | biostudies-arrayexpress

Single-cell transcriptomics reveals early emergence of liver parenchymal and non-parenchymal cell lineages

Project description:Single-cell transcriptomics reveals early emergence of liver parenchymal and non-parenchymal cell lineages

| PRJEB39325 | ENA

Single cell transcriptomics reveals unanticipated features of early hematopoietic precursors.

Project description:Molecular changes underlying stem cell differentiation are of fundamental interest. scRNA-seq on murine hematopoietic stem cells (HSC) and their progeny MPP1 separated the cells into 3 main clusters with distinct features: active, quiescent, and an un-characterized cluster. Induction of anemia resulted in mobilization of the quiescent to the active cluster and of the early to later stage of cell cycle, with marked increase in expression of certain transcription factors (TFs) while maintaining expression of interferon response genes. Cells with surface markers of long term HSC increased the expression of a group of TFs expressed highly in normal cycling MPP1 cells. However, at least Id1 and Hes1 were significantly activated in both HSC and MPP1 cells in anemic mice. Lineage-specific genes were differently expressed between cells, and correlated with the cell cycle stages with a specific augmentation of erythroid related genes in the G2/M phase. Most lineage specific TFs were stochastically expressed in the early precursor cells, but a few, such as Klf1, were detected only at very low levels in few precursor cells. The activation of these factors may correlate with stages of differentiation. This study reveals effects of cell cycle progression on the expression of lineage specific genes in precursor cells, and suggests that hematopoietic stress changes the balance of renewal and differentiation in these homeostatic cells.

| S-EPMC5388401 | biostudies-literature

Single-cell transcriptomics reveals peripheral immune responses in non-segmental vitiligo

Project description: Not available

| S-EPMC10702986 | biostudies-literature

Single-Cell Transcriptomics of Abedinium Reveals a New Early-Branching Dinoflagellate Lineage.

Project description:Dinoflagellates possess many cellular characteristics with unresolved evolutionary histories. These include nuclei with greatly expanded genomes and chromatin packaged using histone-like proteins and dinoflagellate-viral nucleoproteins instead of histones, highly reduced mitochondrial genomes with extensive RNA editing, a mix of photosynthetic and cryptic secondary plastids, and tertiary plastids. Resolving the evolutionary origin of these traits requires understanding their ancestral states and early intermediates. Several early-branching dinoflagellate lineages are good candidates for such reconstruction, however these cells tend to be delicate and environmentally sparse, complicating such analyses. Here, we employ transcriptome sequencing from manually isolated and microscopically documented cells to resolve the placement of two cells of one such genus, Abedinium, collected by remotely operated vehicle in deep waters off the coast of Monterey Bay, CA. One cell corresponds to the only described species, Abedinium dasypus, whereas the second cell is distinct and formally described as Abedinium folium, sp. nov. Abedinium has classically been assigned to the early-branching dinoflagellate subgroup Noctilucales, which is weakly supported by phylogenetic analyses of small subunit ribosomal RNA, the single characterized gene from any member of the order. However, an analysis based on 221 proteins from the transcriptome places Abedinium as a distinct lineage, separate from and basal to Noctilucales and the rest of the core dinoflagellates. The transcriptome also contains evidence of a cryptic plastid functioning in the biosynthesis of isoprenoids, iron-sulfur clusters, and heme, a mitochondrial genome with all three expected protein-coding genes (cob, cox1, and cox3), and the presence of some but not all dinoflagellate-specific chromatin packaging proteins.

| S-EPMC7846120 | biostudies-literature

Single-cell transcriptomics reveals distinct inflammation-induced microglia signatures

Project description:Microglia are specialized parenchymal-resident phagocytes of the central nervous system (CNS) that actively support, defend and modulate the neural environment. Dysfunctional microglia responses are thought to worsen CNS diseases, nevertheless their impact during neuroinflammatory processes remains largely obscure. Here, using a combination of single-cell RNA sequencing and multicolour flow cytometry, we comprehensively profile microglia in the brain of lipopolysaccharide (LPS)-injected mice. By excluding the contribution of other immune CNS-resident and peripheral cells, we show that microglia isolated from LPS-injected mice display a global downregulation of their homeostatic signature together with an upregulation of inflammatory genes. Notably, we identify distinct microglia activated profiles under inflammatory conditions, which greatly differ from neurodegenerative disease-associated profiles. These results provide insights into microglia heterogeneity and establish a resource for the identification of specific phenotypes in CNS disorders, such as neuroinflammatory and neurodegenerative diseases.

| S-SCDT-EMBOR-2018-46171-T | biostudies-other

Single-cell transcriptomics reveals conserved cell identities and fibrogenic phenotypes in zebrafish and human liver.

Project description:The mechanisms underlying liver fibrosis are multifaceted and remain elusive with no approved antifibrotic treatments available. The adult zebrafish has been an underutilized tool to study liver fibrosis. We aimed to characterize the single-cell transcriptome of the adult zebrafish liver to determine its utility as a model for studying liver fibrosis. We used single-cell RNA sequencing (scRNA-seq) of adult zebrafish liver to study the molecular and cellular dynamics at a single-cell level. We performed a comparative analysis to scRNA-seq of human liver with a focus on hepatic stellate cells (HSCs), the driver cells in liver fibrosis. scRNA-seq reveals transcriptionally unique populations of hepatic cell types that comprise the zebrafish liver. Joint clustering with human liver scRNA-seq data demonstrates high conservation of transcriptional profiles and human marker genes in zebrafish. Human and zebrafish HSCs show conservation of transcriptional profiles, and we uncover collectin subfamily member 11 (colec11) as a novel, conserved marker for zebrafish HSCs. To demonstrate the power of scRNA-seq to study liver fibrosis using zebrafish, we performed scRNA-seq on our zebrafish model of a pediatric liver disease with mutation in mannose phosphate isomerase (MPI) and characteristic early liver fibrosis. We found fibrosis signaling pathways and upstream regulators conserved across MPI-depleted zebrafish and human HSCs. CellPhoneDB analysis of zebrafish transcriptome identified neuropilin 1 as a potential driver of liver fibrosis. Conclusion: This study establishes the first scRNA-seq atlas of the adult zebrafish liver, highlights the high degree of similarity to human liver, and strengthens its value as a model to study liver fibrosis.

| S-EPMC9234649 | biostudies-literature

Single-cell transcriptomics reveals a mechanosensitive injury signaling pathway in early diabetic nephropathy.

Project description:BackgroundDiabetic nephropathy (DN) is the leading cause of end-stage renal disease, and histopathologic glomerular lesions are among the earliest structural alterations of DN. However, the signaling pathways that initiate these glomerular alterations are incompletely understood.MethodsTo delineate the cellular and molecular basis for DN initiation, we performed single-cell and bulk RNA sequencing of renal cells from type 2 diabetes mice (BTBR ob/ob) at the early stage of DN.ResultsAnalysis of differentially expressed genes revealed glucose-independent responses in glomerular cell types. The gene regulatory network upstream of glomerular cell programs suggested the activation of mechanosensitive transcriptional pathway MRTF-SRF predominantly taking place in mesangial cells. Importantly, activation of MRTF-SRF transcriptional pathway was also identified in DN glomeruli in independent patient cohort datasets. Furthermore, ex vivo kidney perfusion suggested that the regulation of MRTF-SRF is a common mechanism in response to glomerular hyperfiltration.ConclusionsOverall, our study presents a comprehensive single-cell transcriptomic landscape of early DN, highlighting mechanosensitive signaling pathways as novel targets of diabetic glomerulopathy.

| S-EPMC9830686 | biostudies-literature

Single-cell RNA transcriptome landscape of hepatocytes and non-parenchymal cells in healthy and NAFLD mouse liver.

Project description:Nonalcoholic fatty liver disease (NAFLD) is a global health-care problem with limited therapeutic options. To obtain a cellular resolution of pathogenesis, 82,168 single-cell transcriptomes (scRNA-seq) across different NAFLD stages were profiled, identifying hepatocytes and 12 other non-parenchymal cell (NPC) types. scRNA-seq revealed insights into the cellular and molecular mechanisms of the disease. We discovered a dual role for hepatic stellate cells in gene expression regulation and in the potential to trans-differentiate into myofibroblasts. We uncovered distinct expression profiles of Kupffer cells versus monocyte-derived macrophages during NAFLD progression. Kupffer cells showed stronger immune responses, while monocyte-derived macrophages demonstrated a capability for differentiation. Three chimeric NPCs were identified including endothelial-chimeric stellate cells, hepatocyte-chimeric endothelial cells, and endothelial-chimeric Kupffer cells. Our work identified unanticipated aspects of mouse with NAFLD at the single-cell level and advanced the understanding of cellular heterogeneity in NAFLD livers.

| S-EPMC8560975 | biostudies-literature

Single-Cell Transcriptomics Reveals Zone-Specific Alterations of Liver Sinusoidal Endothelial Cells in Cirrhosis.

Project description:BackgroundDysfunction of liver sinusoidal endothelial cells (LSECs) is permissive for the progression of liver fibrosis and cirrhosis and responsible for its clinical complications. Here, we have mapped the spatial distribution of heterogeneous liver ECs in normal vs cirrhotic mouse livers and identified zone-specific transcriptomic changes of LSECs associated with liver cirrhosis using scRNA-seq technology.Approach & resultsCirrhosis was generated in endothelial specific green fluorescent protein (GFP) reporter mice through carbon tetrachloride inhalation for 12 weeks. GFP-positive liver EC populations were isolated from control and cirrhotic mice by FACS. We identified 6 clusters of liver EC populations including 3 clusters of LSECs, 2 clusters of vascular ECs and 1 cluster of lymphatic ECs. Based on previously reported LSEC-landmarks, we mapped the 3 clusters of LSECs in zones 1, 2, and 3, and determined phenotypic changes in each zone between control and cirrhotic mice. We found genes representing capillarization of LSECs (eg, CD34) as well as extracellular matrix genes were most upregulated in LSECs of zone 3 in cirrhotic mice, which may contribute to the development of basement membranes. LSECs in cirrhotic mice also demonstrated decreased expression of endocytic receptors, most remarkably in zone 3. Transcription factors (Klf2 [Kruppel-like factor-2], Klf4 [Kruppel-like factor-4], and AP-1) that induce nitric oxide production in response to shear stress were downregulated in LSECs of all zones in cirrhotic mice, implying increased intrahepatic vascular resistance.ConclusionThis study deepens our knowledge of the pathogenesis of liver cirrhosis at a spatial, cell-specific level, which is indispensable for the development of novel therapeutic strategies to target the most dysfunctional liver ECs.

| S-EPMC7903131 | biostudies-literature

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