Dataset Information

StemMapper: a curated gene expression database for stem cell lineage analysis.

ABSTRACT: Transcriptomic data have become a fundamental resource for stem cell (SC) biologists as well as for a wider research audience studying SC-related processes such as aging, embryonic development and prevalent diseases including cancer, diabetes and neurodegenerative diseases. Access and analysis of the growing amount of freely available transcriptomics datasets for SCs, however, are not trivial tasks. Here, we present StemMapper, a manually curated gene expression database and comprehensive resource for SC research, built on integrated data for different lineages of human and mouse SCs. It is based on careful selection, standardized processing and stringent quality control of relevant transcriptomics datasets to minimize artefacts, and includes currently over 960 transcriptomes covering a broad range of SC types. Each of the integrated datasets was individually inspected and manually curated. StemMapper's user-friendly interface enables fast querying, comparison, and interactive visualization of quality-controlled SC gene expression data in a comprehensive manner. A proof-of-principle analysis discovering novel putative astrocyte/neural SC lineage markers exemplifies the utility of the integrated data resource. We believe that StemMapper can open the way for new insights and advances in SC research by greatly simplifying the access and analysis of SC transcriptomic data. StemMapper is freely accessible at http://stemmapper.sysbiolab.eu.

SUBMITTER: Pinto JP

PROVIDER: S-EPMC5753294 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

StemMapper: a curated gene expression database for stem cell lineage analysis.

Pinto José P JP Machado Rui S R RSR Magno Ramiro R Oliveira Daniel V DV Machado Susana S Andrade Raquel P RP Bragança José J Duarte Isabel I Futschik Matthias E ME

Nucleic acids research 20180101 D1

Transcriptomic data have become a fundamental resource for stem cell (SC) biologists as well as for a wider research audience studying SC-related processes such as aging, embryonic development and prevalent diseases including cancer, diabetes and neurodegenerative diseases. Access and analysis of the growing amount of freely available transcriptomics datasets for SCs, however, are not trivial tasks. Here, we present StemMapper, a manually curated gene expression database and comprehensive resour ...[more]

PMID: 29045725

Similar Datasets

Project description:BackgroundIn mammals, specification of primordial germ cells (PGCs) is established in the early post-implantation embryo. The bone morphogenetic protein (BMP)-SMAD and WNT3-β-catenin signaling initiate the gene regulatory network for PGC specification. The activation of SOX17-BLIMP1 axis is critical for human PGC program. Moreover, EpCAM and INTEGRINα6 were identified as surface markers of human PGC-like cells (PGCLCs) recently. However, the signaling mechanism for PGC specification in non-rodent mammals remains to be clarified.MethodsWe differentiated human induced pluripotent stem cells (hiPSCs) into PGCLCs in vitro in response to Activin A and BMP4. The percentage of EpCAM/INTEGRINα6 double-positive cells (PGCLCs) was analyzed by flow cytometry. The expression of PGC genes was evaluated by qRT-PCR and immunofluorescence. The expression dynamic of multi-lineage genes during the differentiation process was evaluated by qRT-PCR.ResultsUnder the stimulation for PGCLC induction, the embryoids derived from hiPSCs initiated significant upregulation of the early PGC genes (BLIMP1, TFAP2C, and NANOS3), but maintained low or no levels of DPPA3 and late PGC genes (DAZL and DDX4). The percentage of EpCAM/INTEGRINα6 double-positive PGCLCs reached the highest at day 6 of induction. After pre-induction, the incipient mesoderm-like cells (iMeLCs) upregulated most of the mesoderm genes (EOMES, T, MSXI, RUNX2, and MIXL1). The differentiating embryoids showed high levels of key pluripotency genes, OCT4 and NANOG, but became negative for SOX2. In contrast to iMeLCs, the differentiating embryoids downregulated mesoderm genes RUNX2 and EOMES, and ectoderm gene PAX6, but increased the expression of endoderm gene SOX17.ConclusionsDuring PGCLC induction process in vitro, the differentiating embryoids not only activated the PGC-related genes, but also displayed complex regulation of pluripotency genes and multi-lineage genes. These results would be meaningful for future research investigating the regulation of human early germ line development.

Project description:MotivationThe interactions among various types of cells play critical roles in cell functions and the maintenance of the entire organism. While cell-cell interactions are traditionally revealed from experimental studies, recent developments in single-cell technologies combined with data mining methods have enabled computational prediction of cell-cell interactions, which have broadened our understanding of how cells work together, and have important implications in therapeutic interventions targeting cell-cell interactions for cancers and other diseases. Despite the importance, to our knowledge, there is no database for systematic documentation of high-quality cell-cell interactions at the cell type level, which hinders the development of computational approaches to identify cell-cell interactions.ResultsWe develop a publicly accessible database, CITEdb (Cell-cell InTEraction database, https://citedb.cn/), which not only facilitates interactive exploration of cell-cell interactions in specific physiological contexts (e.g. a disease or an organ) but also provides a benchmark dataset to interpret and evaluate computationally derived cell-cell interactions from different tools. CITEdb contains 728 pairs of cell-cell interactions in human that are manually curated. Each interaction is equipped with structured annotations including the physiological context, the ligand-receptor pairs that mediate the interaction, etc. Our database provides a web interface to search, visualize and download cell-cell interactions. Users can search for cell-cell interactions by selecting the physiological context of interest or specific cell types involved. CITEdb is the first attempt to catalogue cell-cell interactions at the cell type level, which is beneficial to both experimental, computational and clinical studies of cell-cell interactions.Availability and implementationCITEdb is freely available at https://citedb.cn/ and the R package implementing benchmark is available at https://github.com/shanny01/benchmark.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

StemMapper: a curated gene expression database for stem cell lineage analysis.

Publications

StemMapper: a curated gene expression database for stem cell lineage analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets