Dataset Information

Single-cell transcriptomic analyses reveal distinct dorsal/ventral pancreatic programs.

ABSTRACT: The pancreas of vertebrates is separately derived from both the dorsal and ventral endodermal domains. However, the difference between these two programs has been unclear. Here, using a pancreatic determination gene, Pdx1, driven GFP transgenic mouse strain, we identified Pdx1-GFP highly expressing cells (Pdx1high) and Pdx1-GFP lowly expressing cells (Pdx1low) in both embryonic dorsal Pdx1-expressing region (DPR) and ventral Pdx1-expressing region (VPR). We analyzed the transcriptomes of single Pdx1low and Pdx1high cells from the DPR and VPR. In the VPR, Pdx1low cells have an intermediate progenitor identity and can generate hepatoblasts, extrahepatobiliary cells, and Pdx1high pancreatic progenitor cells. In the DPR, Pdx1high cells are directly specified as pancreatic progenitors, whereas Pdx1low cells are precocious endocrine cells. Therefore, our study defines distinct road maps for dorsal and ventral pancreatic progenitor specification. The findings provide guidance for optimization of current ?-cell induction protocols by following the in vivo dorsal pancreatic specification program.

SUBMITTER: Li LC

PROVIDER: S-EPMC6172462 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Single-cell transcriptomic analyses reveal distinct dorsal/ventral pancreatic programs.

Li Lin-Chen LC Qiu Wei-Lin WL Zhang Yu-Wei YW Xu Zi-Ran ZR Xiao Yi-Ni YN Hou Caiying C Lamaoqiezhong Yu Peng P Cheng Xin X Xu Cheng-Ran CR

EMBO reports 20180731 10

The pancreas of vertebrates is separately derived from both the dorsal and ventral endodermal domains. However, the difference between these two programs has been unclear. Here, using a pancreatic determination gene, Pdx1, driven GFP transgenic mouse strain, we identified Pdx1-GFP highly expressing cells (Pdx1high) and Pdx1-GFP lowly expressing cells (Pdx1low) in both embryonic dorsal Pdx1-expressing region (DPR) and ventral Pdx1-expressing region (VPR). We analyzed ...[more]

PMID: 30065074

Similar Datasets

Project description:Separation of B cells into different subsets has been useful to understand their different functions in various immune scenarios. In some instances, the subsets defined by phenotypic FACS separation are relatively homogeneous and so establishing the functions associated with them is straightforward. Other subsets, such as the "Double negative" (DN, CD19+CD27-IgD-) population, are more complex with reports of differing functionality which could indicate a heterogeneous population. Recent advances in single-cell techniques enable an alternative route to characterize cells based on their transcriptome. To maximize immunological insight, we need to match prior data from phenotype-based studies with the finer granularity of the single-cell transcriptomic signatures. We also need to be able to define meaningful B cell subsets from single cell analyses performed on PBMCs, where the relative paucity of a B cell signature means that defining B cell subsets within the whole is challenging. Here we provide a reference single-cell dataset based on phenotypically sorted B cells and an unbiased procedure to better classify functional B cell subsets in the peripheral blood, particularly useful in establishing a baseline cellular landscape and in extracting significant changes with respect to this baseline from single-cell datasets. We find 10 different clusters of B cells and applied a novel, geometry-inspired, method to RNA velocity estimates in order to evaluate the dynamic transitions between B cell clusters. This indicated the presence of two main developmental branches of memory B cells. A T-independent branch that involves IgM memory cells and two DN subpopulations, culminating in a population thought to be associated with Age related B cells and the extrafollicular response. The other, T-dependent, branch involves a third DN cluster which appears to be a precursor of classical memory cells. In addition, we identify a novel DN4 population, which is IgE rich and closely linked to the classical/precursor memory branch suggesting an IgE specific T-dependent cell population.

Project description:BackgroundThere has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC.MethodsMultiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses were conducted to construct a TF-mRNA-miRNA coregulatory network based on identified core targets. Furthermore, immunohistochemistry staining (IHC) and real-time quantitative PCR (RT-qPCR) were employed for the validation of the expression and biological functions of core proteins, including HSPAA1, HSPA8, SOD1, and transcription factors SREBF2 and GTAT2, in ovarian tumors.ResultsThe immune cell infiltration analysis based on mRNA expression data for both DM and OC, as well as analysis using single-cell datasets, reveals significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Simultaneously, IHC and RT-qPCR reveal differential expression of core targets in ovarian tumors after the onset of diabetes. RT-qPCR further revealed that SREBF2 and GATA2 may influence the expression of core proteins, including HSP90AA1, HSPA8, and SOD1.ConclusionThis study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.

Project description:Deep understanding the differentiation process of human embryonic stem cells (hESCs) is essential for developing cell-based therapeutic strategy. Substantial efforts have been made to investigate protein-coding genes, yet it remains lacking comprehensive characterization of long non-coding RNAs (lncRNAs) during this process. hESCs were passaged every 5-6 days and had maintained stable karyotype even until the 50th generation. Pancreatic progenitor specification of in vitro differentiation from hESCs was performed and modified. The nuclei were stained with 4,6-Diamidino-2-phenylindole (DAPI). Droplet-based platform (10X Genomics) was applied to generate the single-cell RNA sequencing (scRNA-seq) data. The quality of the filtered read pairs was evaluated by using FastQC. Batch effects were removed using the size factor method. Dimension reduction and unsupervised clustering analyses were performed using Seurat R package. The Monocle 2 and MetaCell algorithms were used to order single cells on a pseudotime course and partition the scRNA-seq data into metacells, respectively. Co-expression network was constructed using WGCNA. Module- and hub-based methods were adopted to predict the functions of lncRNAs. A total of 77,382 cells during the differentiation process of hESCs toward pancreatic progenitors were sequenced. According to the single-cell map, the cells from different time points were authenticated to constitute a relatively homogeneous population, in which a total of 7382 lncRNAs could be detected. Through further analyzing the time course data, conserved and specific expression features of lncRNAs during hESC differentiation were revealed. Based upon pseudotime analysis, 52 pseudotime-associated lncRNAs that grouped into three distinct expression patterns were identified. We also implemented MetaCell algorithm and network-based methods to explore the functional mechanisms of these lncRNAs. Totally, 464 lncRNAs, including 49 pseudotime-associated lncRNAs were functionally annotated by either module-based or hub-based methods. Most importantly, we demonstrated that the lncRNA HOTAIRM1, which co-localized and co-expressed with several HOX genes, may play crucial role in the generation of pancreatic progenitors through regulation of exocytosis and retinoic acid receptor signaling pathway. Our single-cell analyses provide valuable data resources for biological researchers and novel insights into hESC differentiation processes, which will guide future endeavors to further elucidate the roles of lncRNAs.

Dataset Information

Single-cell transcriptomic analyses reveal distinct dorsal/ventral pancreatic programs.

Publications

Single-cell transcriptomic analyses reveal distinct dorsal/ventral pancreatic programs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets