A repository for data regarding membrane channels, receptor and neurotransmitters that are expressed in specific types of cells. The database is presently focused on neurons but will eventually include other cell types, such as glia, muscle, and gland cells. This resource is intended to: * Serve as a repository for data on gene products expressed in different brain regions * Support research on cellular properties in the nervous system * Provide a gateway for entering data into the cannonical neuron forms in NeuronDB * Identify receptors across neuron types to aid in drug development * Serve as a first step toward a functional genomics of nerve cells * Serve as a teaching aid

Database characterizing and comparing pluripotent human stem cells. The growth and culture conditions of all 21 human embryonic stem cell lines approved under the August 2001 Presidential Executive Order have been analyzed. Available to the scientific community are the results of our rigorous characterization of these cell lines at a more advanced level.

List of tumor microenvironment scRNA-seq datasets included in TMExplorer.

Single-cell RNA-seq studies profile thousands of cells in developmental processes. Current databases for human single-cell expression atlas only provide search and visualize functions for a selected gene in specific cell types or subpopulations. These databases are limited to technical properties or visualization of single-cell RNA-seq data without considering the biological relations of their collected cell groups. Here, we developed a database to investigate single-cell gene expression profiling during different developmental pathways (SCDevDB). In this database, we collected 10 human single-cell RNA-seq datasets, split these datasets into 176 developmental cell groups, and constructed 24 different developmental pathways. SCDevDB allows users to search the expression profiles of the interested genes across different developmental pathways. It also provides lists of differentially expressed genes during each developmental pathway, T-distributed stochastic neighbor embedding maps showing the relationships between developmental stages based on these differentially expressed genes, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analysis results of these differentially expressed genes. This database is freely available at https://scdevdb.deepomics.org.

The subcellular resource of the Human Protein Atlas provides high-resolution insights into the expression and spatiotemporal distribution of proteins encoded by 13534 genes (67% of the human protein-coding genes), as well as predictions for an additional 3491 secreted- or membrane proteins, covering a total of 17025 genes (84 % of the human protein-coding genes). For each gene, the subcellular distribution of the protein has been investigated by immunofluorescence (ICC-IF) and confocal microscopy in up to three different standard cell lines, selected from a panel of 41 cell lines used in the subcellular resource. For some genes, the protein has also been stained in up to three ciliated cell lines and/or in human sperm cells. Upon image analysis, the subcellular localization of the protein has been classified into one or more of 49 different organelles and subcellular structures. In addition, the resource includes an annotation of genes that display single-cell variation in protein expression levels and/or subcellular distribution, as well as an extended analysis of cell cycle dependency of such variations. The subcellular resource offers a database for detailed exploration of individual genes and proteins of interest, as well as for systematic analysis of proteomes in a broader context. More information about the content of the resouce, as well as the generation and analysis of the data, can be found in the Methods summary. Learn about:

The subcellular distribution of proteins in human cell lines. The subcellular distribution of proteins in human sperm. The proteomes of different organelles and subcellular structures. Single-cell variability in the expression levels and/or localizations of proteins.

This dataset contains files reconstructing single-cell data presented in 'Reference transcriptomics of porcine peripheral immune cells created through bulk and single-cell RNA sequencing' by Herrera-Uribe & Wiarda et al. 2021. Samples of peripheral blood mononuclear cells (PBMCs) were collected from seven pigs and processed for single-cell RNA sequencing (scRNA-seq) in order to provide a reference annotation of porcine immune cell transcriptomics at enhanced, single-cell resolution. Analysis of single-cell data allowed identification of 36 cell clusters that were further classified into 13 cell types, including monocytes, dendritic cells, B cells, antibody-secreting cells, numerous populations of T cells, NK cells, and erythrocytes. Files may be used to reconstruct the data as presented in the manuscript, allowing for individual query by other users. Scripts for original data analysis are available at https://github.com/USDA-FSEPRU/PorcinePBMCs_bulkRNAseq_scRNAseq. Raw data are available at https://www.ebi.ac.uk/ena/browser/view/PRJEB43826. Funding for this dataset was also provided by NRSP8: National Animal Genome Research Program (https://www.nimss.org/projects/view/mrp/outline/18464). Resources in this dataset:Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells 10X Format. File Name: PBMC7_AllCells.zipResource Description: Zipped folder containing PBMC counts matrix, gene names, and cell IDs. Files are as follows: matrix of gene counts* (matrix.mtx.gx) gene names (features.tsv.gz) cell IDs (barcodes.tsv.gz) *The ‘raw’ count matrix is actually gene counts obtained following ambient RNA removal. During ambient RNA removal, we specified to calculate non-integer count estimations, so most gene counts are actually non-integer values in this matrix but should still be treated as raw/unnormalized data that requires further normalization/transformation. Data can be read into R using the function Read10X().Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells Metadata. File Name: PBMC7_AllCells_meta.csvResource Description: .csv file containing metadata for cells included in the final dataset. Metadata columns include: nCount_RNA = the number of transcripts detected in a cell nFeature_RNA = the number of genes detected in a cell Loupe = cell barcodes; correspond to the cell IDs found in the .h5Seurat and 10X formatted objects for all cells prcntMito = percent mitochondrial reads in a cell Scrublet = doublet probability score assigned to a cell seurat_clusters = cluster ID assigned to a cell PaperIDs = sample ID for a cell celltypes = cell type ID assigned to a cellResource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells PCA Coordinates. File Name: PBMC7_AllCells_PCAcoord.csvResource Description: .csv file containing first 100 PCA coordinates for cells. Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells t-SNE Coordinates. File Name: PBMC7_AllCells_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for all cells.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells UMAP Coordinates. File Name: PBMC7_AllCells_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for all cells.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells t-SNE Coordinates. File Name: PBMC7_CD4only_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells UMAP Coordinates. File Name: PBMC7_CD4only_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells UMAP Coordinates. File Name: PBMC7_GDonly_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells t-SNE Coordinates. File Name: PBMC7_GDonly_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gene Annotation Information. File Name: UnfilteredGeneInfo.txtResource Description: .txt file containing gene nomenclature information used to assign gene names in the dataset. 'Name' column corresponds to the name assigned to a feature in the dataset.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells H5Seurat. File Name: PBMC7.tarResource Description: .h5Seurat object of all cells in PBMC dataset. File needs to be untarred, then read into R using function LoadH5Seurat().

The SUM human breast cancer cell lines have been used by many labs around the world to develop extensive data sets derived from comparative genomic hybridization analysis, gene expression profiling, whole exome sequencing, and reverse phase protein array analysis. In a previous study, the authors of this paper performed genome-scale shRNA essentiality screens on the entire SUM line panel, as well as on MCF10A cells, MCF-7 cells, and MCF-7LTED cells. In this study, the authors have developed the SUM Breast Cancer Cell Line Knowledge Base, to make all of these omics data sets available to users of the SUM lines, and to allow users to mine the data and analyse them with respect to biological pathways enriched by the data in each cell line.Data access: All the datasets supporting the findings of this study are publicly available in the SLKBase platform here: https://sumlineknowledgebase.com/. RPPA data, drug sensitivity data, apelisib response data, and data on dose response, are also part of this figshare data record (https://doi.org/10.6084/m9.figshare.12497630).Study aims and methodology: This web-based knowledge base provides users with data and information on the derivation of each of the cell lines, provides narrative summaries of the genomics and cell biology of each breast cancer cell line, and provides protocols for the proper maintenance of the cells. The database includes a series of data mining tools that allow rapid identification of the functional oncogene signatures for each line, the enrichment of any KEGG pathway with screen hit and gene expression data for each of the lines, and a rapid analysis of protein and phospho-protein expression for the cell lines. A gene search tool that returns all of the functional genome and functional druggable data for any gene for the entire cell line panel, is included. Additionally, the authors have expanded the database to include functional genomic data for an additional 29 commonly used breast cancer cell lines. The three overarching goals in the original development of the SLKBase are: 1) to provide a rich source of information for anyone working with any of the SUM breast cancer cell lines, 2) to give researchers ready access to the large genomic data sets that have been developed with these cells, and 3) to allow researchers to perform orthogonal analyses of the various genomics data sets that we and others have obtained from the SUM lines. For more information on the development and contents of the database, please read the related article.Datasets supporting the paper:The data mining tools accessed the following datasets to generate the figures and tables, and these datasets are downloadable from the Data Download centre on the SLKBase: Exome sequencing data: SLKBase.exome_.seq_.sum_.xlsxGene amplification and expression data for the SUM cell lines: SUM44amplificationdata.xlsSUM52.xlsSUM149.xlsSUM159.xlsSUM185.xlsSUM190.xlsSUM225.xlsSUM229.xlsSUM1315.xlsCellecta shRNA screen data for the SUM cell lines:SUM44Celectadata.csvSUM52Cellectadata.csvSUM102Cellectadata.csvSUM149Cellectadata.csvSUM159Cellectadata.csvSUM185Cellectadata.csvSUM190Cellectadata.csvSUM225Cellectadata.csvSUM229Cellectadata.csvSUM1315hits.hit.csvMCF10A.hits_.csvBreast cancer cell line data included in this data record (these datasets were used to generate figures 1, 2 and 7 in the article):Proteomics data from the Reverse Phase Protein Array (RPPA) assay analysis: Ethier.SUMline.RPPA.xlsxDrug sensitivity data: NAVITOCLAX.drugsensitivity.Zscores.xlsxApelisib response data: Apelisib all lines (2).xlsxDose response data: 092614 Dose Response CP 52s.11.15.xlsxAll the files are either in .xlsx or .csv file format.

Additional file 2: Supplementary Table 2–3. This file contains the list of cell markers in each of scTyper.db (Table S2) and CellMarker DB (Table S3) and detailed information such as identifier, study name, species, cell type, gene symbol, and PMID.

Database of neuronal cell types based on multimodal characterization of single cells to enable data-driven approaches to classification. It includes data such as electrophysiology recordings, imaging data, morphological reconstructions, and RNA and DNA sequencing data.

Results of transcript sequencing for AtT-20FlpIn cells. mRNA was isolated from AtT-20FlpIn cells using standard procedures, next generation sequencing was performed by Macrogen (https://dna.macrogen.com/). A report ourtlining the workflow and data analysis methods is available from the Authors by request.

Deposited data is in an Excel file, which includes the gene symbol, transcript ID from the reference mouse genome, protein ID and transcript abundance. The AtT-20FlpIn cells were generated by Dr Santiago, and have been used as the 'wild type' cells for generating cell lines stably expressing GPCR and ion channels for most of the molecular pharmacology projects in the Molecular Pharmacodynamics group.

Database of Immune Cell Expression, Expression quantitative trait loci (eQTLs) and Epigenomics. Collection of identified cis-eQTLs for 12,254 unique genes, which represent 61% of all protein-coding genes expressed in human cell types. Datasets to help reveal effects of disease risk associated genetic polymorphisms on specific immune cell types, providing mechanistic insights into how they might influence pathogenesis.

hPSCreg is a global registry of human pluripotent stem cell (hPSC) lines containing manually validated information, including ethical provenance, procurement, derivation process, genetic and expression data, other biological and molecular characteristics, use, and quality of the line — Current status: 1092 hESC lines, 7212 hiPSC lines, and 182 clinical studies, and 2394 certificates

The C4 DatabaseThis is the official repository for the hdf5 datasets of the cerebellar cell-type classification collaboration (C4), published as a companion to the paper "A deep-learning strategy to identify cell types across species from high-density extracellular recordings" published in Cell (https://doi.org/10.1016/j.cell.2025.01.041).Instructions to use the cell-type classifier, links to download these datasets, and a data explorer can be found at https://www.c4-database.com.The specifications of the fields, data types and data formats stored in the hdf5 binary files can be found at https://www.tinyurl.com/c4database. Hdf5 files can be easily opened with Python, MATLAB and many other programming languages.Using and Citing the C4 DatabaseThe data and visualizations on this website are intended to be freely available for use by the scientific community. The C4 dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, while our classifier is licensed under the GNU General Public License v3.0 as part of NeuroPyxels. If you download and use our data for a publication, and/or if you would like to refer to the database, please cite Beau et al., 2025, Cell together with the NeuroPyxels repository (Beau et al., 2021, Zenodo), and include the link to the C4 online portal https://www.c4-database.com in your methods section. Thank you!

Single-cell analysis is a valuable tool to dissect cellular heterogeneity in complex systems. Yet, a systematic single-cell atlas has not been achieved for human beings. We used single-cell RNA sequencing to determine cell type composition of all major human organs and construct a basic scheme for the human cell landscape (HCL). We reveal a single-cell hierarchy for many tissues that has not been well characterized previously. We present a ‘‘single-cell HCL analysis’’ pipeline that accurately defines human cell types; and exemplify its utility in stem cell biology. Finally, we perform single-cell comparative analysis for human and mouse cell atlas to reveal the conserved genetic networks in the mammalian system.File Extension SpecificationHCL_Fig1_adata.h5ad: Use scanpy.api.read_h5ad to load AnnData. This AnnData stores the data used for HCL Figure 1.HCL_Fig1_cell_Info: The information, which includes the cell names, samples, clusters, stages, batches, donors and cell types for cells of data used for HCL Figure 1.cluster_markers_HCL&MCA1.1: The cell type annotation and marker genes for 102 HCL clusters of Fig1 and 104 MCA1.1 clusters of SFig. dge_raw_data.tar: The raw digital expression matrix (dge) of more than 720,000 single cells sorted by tissues. The batch genes were not removed.dge_rmbatch_data.tar: The batch gene removed dge of more than 700,000 primary single cells sorted by tissues. Some tissues are not included due to relatively strong batch effects. This dataset can be used to make global tissue tSNE plot and do cross-tissue analysis.annotation_rmbatch_data.tar: The cell annotations, which include cluster ID, belonged tissues, age (gestational age for fetal tissue), clusters and cell types for each rmbatch dge data.annotation_cluster_info: Modified cell type annotation of each cluster in accord with ClusterID in annotation_rmbatch_data.zipMCA1.1_adata.h5ad: Use scanpy.api.read_h5ad to load AnnData. This AnnData stores the MCA1.1 data. MCA1.1_cell_Info: The information, which includes the cell names, samples, clusters, stages, batches, donors and ce;; types for cells of MCA1.1 data.

RNA-seq data for 54 Glioblastoma stem cell (GSC) lines. Fastq files of the strand-specific paired-end RNA-seq data are available.

Files pertaining to data analyses performed and presented in the preprint, 'Porcine intestinal innate lymphoid cells and lymphocyte spatial context revealed through single-cell RNA sequencing' by Wiarda et al. 2022 are provided in this dataset. Single cell suspensions enriched for lymphocytes were obtained from ileum of two seven-week-old pigs and subjected to single-cell RNA sequencing (scRNA-seq). Peripheral blood mononuclear cells (PBMCs) were collected and processed for scRNA-seq in parallel. scRNA-seq was performed to provide transcriptomic profiles of lymphocytes in porcine ileum, with 31,983 cells annotated into 26 cell types. Deeper interrogation of data revealed previously undescribed cells in porcine intestine, including SELLhi γδ T cells, group 1 and group 3 innate lymphoid cells (ILCs), and four subsets of B cells. Single-cell transcriptomes in ileum were compared to those in porcine blood, and subsets of activated lymphocytes were detected in ileum but not periphery. Comparison to scRNA-seq human and murine ileum data revealed a general consensus of ileal lymphocytes across species. Lymphocyte spatial context in porcine ileum was conferred through differential tissue dissection prior to scRNA-seq. Antibody-secreting cells, B cells, follicular CD4 αβ T cells, and cycling T/ILCs were enriched in ileum with Peyer’s patches, while non-cycling γδ T, CD8 αβ T, and group 1 ILCs were enriched in ileum without Peyer’s patches. Data files included herein are .h5seurat files of the various cell subsets included in analyses of the manuscript. Files may be used to reconstruct different analyses and perform further data query. Scripts for original data analyses are found at https://github.com/USDA-FSEPRU/scRNAseq_Porcine_Ileum_PBMC. Raw data are available at GEO accession GSE196388. Data are available for online query at https://singlecell.broadinstitute.org/single_cell/study/SCP1921/intestinal-single-cell-atlas-reveals-novel-lymphocytes-in-pigs-with-similarities-to-human-cells. Resources in this dataset:Resource Title: Ileum_AllCells. File Name: Ileum_AllCells.tarResource Description: .h5seurat object of all the cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: GutBlood_IntegratedILCs. File Name: GutBlood_IntegratedILCs.tarResource Description: .h5seurat object of ILCs derived from both ileum and PBMC samples. Untar into .h5seurat file before use.Resource Title: Ileum_Bonly. File Name: Ileum_Bonly.tarResource Description: .h5seurat object of B cells and antibody-secreting cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_CD4Tonly. File Name: Ileum_CD4Tonly.tarResource Description: .h5seurat object of non-naive CD4 ab T cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_gdCD8Tonly. File Name: Ileum_gdCD8Tonly.tarResource Description: .h5seurat object of gd and CD8 ab T cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_ILConly. File Name: Ileum_ILConly.tarResource Description: .h5seurat object of innate lymphoid cells (ILCs) derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_MyeloidOnly. File Name: Ileum_MyeloidOnly.tarResource Description: .h5seurat object of myeloid lineage leukocytes derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_NonImmuneOnly. File Name: Ileum_NonImmuneOnly.tarResource Description: .h5seurat object of non-immune cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_TILConly. File Name: Ileum_TILConly.tarResource Description: .h5seurat object of all T cells and innate lymphoid cells (ILCs) derived from ileum samples. Untar into .h5seurat file before use.Resource Title: PBMC_AllCells. File Name: PBMC_AllCells.tarResource Description: .h5seurat object of all cells derived from PBMC samples. Untar into .h5seurat file before use.

A Dye-sensitized Solar-Cell Device Database Auto-generated Using ChemDataExtractor

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. This database provides information on the components of cellular signaling pathways and their relations to one another, which are organized into pathways called Connections Maps, which serve as the graphical interface into the database. Access to the database is free. Scientists with expertise in a given field, designated as Pathway Authorities, provide the information. With canonical or general data about cell signaling, as well as specific data about particular signaling processes in specific organisms and cells, there is information for both novices to cell signaling and experts. The Connections Maps are dynamically generated graphical interface to a database of information on the components of cellular signaling pathways and their relations to one another. Information is provided by pathway authorities with expertise in a given field. These Maps provide information on Canonical Pathways -- idealized or generalized pathways that represent common properties of a particular signaling module or pathway. Sponsors: This database is supported by AAAS.

Lung endothelial cells from three published scRNA-seq datasets (GSE122960, GSE149878, GSE171668) of healthy subjects and COVID-19 patients were collected for further integrative analyses. The endothelial cells were classified into three sub-groups according to their distinguished expression of IL7R, DKK2, and EDNRB. For differential analysis of gene expression, counts per million of aggregated UMIs in each group were adopted in Wilcoxon rank-sum test.

This linked data API allows online access to all the AusPIX cells as a database. All DGGS cells, at all common resolutions, are mapped on individual landing pages, along with descriptors for spatial …Show full descriptionThis linked data API allows online access to all the AusPIX cells as a database. All DGGS cells, at all common resolutions, are mapped on individual landing pages, along with descriptors for spatial extent, centroid, neighbours, parent cells and child cells. Includes alternate views in a variety of formats, and can be manually or machine read. This is an online resource for the "AusPIX Data Integration by Locality Framework". It is built as a virtual database where the AusPIX DGGS Engine calculates information on demand. Location of this Linked data API is: http://ec2-52-63-73-113.ap-southeast-2.compute.amazonaws.com/AusPIX-DGGS-dataset/ausPIX/