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2,917 results for “scRNA”
scRNA data from: Organization of the human Intestine at single cell resolution
<p>The human adult intestinal system is a complex organ that is approximately 9 meters long and performs a variety of complex functions including digestion, nutrient absorption, and immune surveillance. We performed snRNA-seq on 8 regions of of the human intestine (duodenum, proximal-jejunum, mid-jejunum, ileum, ascending colon, transverse colon, descending colon, and sigmoid colon) from 9 donors (B001, B004, B005, B006, B008, B009, B010, B011, and B012). In the corresponding paper, we find cell compositions differ dramatically across regions of the intestine and demonstrate the complexity of epithelial subtypes. We map gene regulatory differences in these cells suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation, and organization in the human intestine, and serve as an important reference map for understanding human biology and disease.</p>
scMARK an 'MNIST' like benchmark to evaluate and optimize models for unifying scRNA data
<p>Here we present a novel benchmark dataset (scMARK.v2), that consists of 11 published cancer scRNA-seq studies, for which we standardized cell-type author labels and gene identifiers. scMARK.v2 can be used to ask how well models integrate data from different scRNA studies. We also provide a 12th standardized study (Wu et al 2021) that we held-out for evaluation of alignment of data "never seen" before, and a 13th study of newly generated in-vitro scRNA-seq data from cancer and fibroblast cells.</p> <ul> <li>Data is provided as aData *h5ad files that can be read with Python's library <a href="https://scanpy.readthedocs.io/en/stable/">Scanpy</a>.</li> <li>Studies inclided in scMARK.v2 were downsampled to 10,000 cells per study.</li> <li>The difference between <a href="https://zenodo.org/record/5765804">scMARK.v1</a> and scMARK.v2, is that in v2, we provide at least two studies for each cancer type and each cell type; whereas in v1 a handfull of cell types were present only in one study.</li> </ul>
PLO(SC)²: Plots and Scripts for scRNA-seq analysis
<p><strong>Availability</strong></p> <p>The PLOSC-project is available from <a href="https://github.com/mjoppich/PLOSC">https://github.com/mjoppich/PLOSC</a> .</p> <p>The sequencing data (h5-files) were taken from:</p> <p>Pekayvaz K, Leunig A, Kaiser R, Joppich M, Brambs S, Janjic A, et al. Protective immune trajectories in early<br> viral containment of non-pneumonic SARS-CoV-2 infection. Nature communications. 2022 Feb;13(1):1018.<br> Available from: <a href="https://www.nature.com/articles/s41467-022-28508-0">https://www.nature.com/articles/s41467-022-28508-0</a>.</p> <p><strong>Background</strong><br> scRNA-seq analysis has become a standard technique to study biological systems.<br> With decreasing costs for scRNA-seq experiments, these also become increasingly complex.<br> While the typical scRNA-seq analysis frameworks provide functionalities for the analysis of even such data sets, the required steps to follow for such experiments become complicated.<br> Moreover, default plots are not suitable to provide specific insight into such complex data sets, and should be enhanced, such that camera-ready fully-interpretable plots are provided.</p> <p><strong>Results</strong><br> We thus describe here a collection of plotting and analysis scripts for use in Seurat-based scRNA-seq data analyses.<br> We first provide a collection of script blocks which allows for an easy basic analysis of scRNA-seq from Seurat object creation, filtering, and over data set integration in less than 10 steps.<br> Subsequently, we provide code blocks for the easy differential analysis of the obtained data sets, including visualizations.<br> Finally, several visualizations enhancing the functionalities of scRNA-seq analysis frameworks are presented, such as the enhanced Heatmap and DotPlot.<br> These, particularly, allow the user to specify how the shown values should be scaled, allowing the creation of condition-wise plots.</p> <p><strong>Conclusions</strong><br> With the PLO(SC)² framework the data analysis of scRNA-seq experiments becomes more stream-lined, and visualizations for interpreting complex datasets are provided.<br> The PLO(SC)² scripts are available from GitHub, including a notebook showing how PLO(SC)² is applied on the use-case data presented here. This way, fellow researchers can directly apply the methods on their data.</p>
Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms - Datasets
<p>Datasets for reproducibility of the results found in Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms. This study utilized data from the following 4 journals:</p> <p>Lake, B.B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun 10, 2832 (2019).</p> <p>Liao, J., Yu, Z., Chen, Y. et al. Single-cell RNA sequencing of human kidney. Sci Data 7, 4 (2020).</p> <p>Menon, R. et al. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight 5, e133267 (2020).</p> <p>Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J Am Soc Nephrol 29: 2069–2080 (2018).</p> <p>Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).</p>
Adult adrenal gland scRNA-seq
<p>Adult adrenal gland scRNA-seq dataset. Gene expression matrices (in dgCMatrix format) are provided as RDS files. Cell annotations (cell types, cortex zonations, and genotypes) are provided in CSV format.</p>
scRNA-seq of CD45+ cells from salivary gland of Aire-knockout rats
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scRNA-seq of thymic epithelial cells from Aire-knockout rats
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scRNA data from: Organization of the human Intestine at single cell resolution
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KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection (LCMV DSM scRNA data and ATAC-seq)
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Transcription start site analysis for heterogenous CD4+ T cells using 5′ scRNA-seq
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scRNA-seq of splenic CD45+ Cells from Aire-knockout rats
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Colorectal cancer scRNA-seq 10xG-format data matrix
<p>Metastatic colorectal cancer (CRC) is a major cause of cancer-related death and incidence is rising in the younger population (<50 years). Current chemotherapies can achieve response rates above 50%, but immunotherapies have limited value for patients with microsatellite-stable (MSS) cancers. The present study investigates the impact of chemotherapy on the tumor immune microenvironment. We treat human liver metastases slices with 5-Fluorouracil (5FU) plus either irinotecan or oxaliplatin, then perform single-cell transcriptome analyses. Results from eight cases reveal two cellular subtypes with divergent responses to chemotherapy. Susceptible tumors are characterized by a stemness signature, an activated interferon pathway, and suppression of PD-1 ligands in response to 5FU+irinotecan. Conversely, immune checkpoint TIM-3 ligands are maintained or up-regulated by chemotherapy in CRC with an enterocyte-like signature, and combining chemotherapy with TIM-3 blockade leads to synergistic tumor killing. Together, our analyses highlight chemo-modulation of the immune microenvironment and provide a framework for combined chemo-immunotherapies. </p>
scRNA-seq revealed the rules for CDR3 length pairing in TCR beta and alpha chains and BCR heavy and light chains
<p>The scRNAseq datasets of CDR3 length pairing in TCR beta and alpha chains which come from human cental and peripheral samples and mouse peripheral samples.</p> <p>The scRNAseq datasets of CDR3 length pairing in BCR heavy and light chainsCDR3 length pairing in TCR beta and alpha chains and BCR heavy and light chains human cental and peripheral samples and mouse cental and peripheral samples.</p> <p> </p>
scRNA-seq dataset "A novel in vitro tubular model to recapitulate features of distal airways: the bronchioid"
<p>We provide a .Rds file of an annotated Seurat Object of scRNA-seq data of two bronchioid models derived from distinct donors after 21days of culture using 10x genomics 3' v3 chemistry. Raw data was processed using CellRanger v7.1.0. Cells were filtered based on detected UMIs (>2000) and fraction of mitochondrial counts (<10%).<br>Metadata annotations contain:<br>- Patient -> patient information for every cell (patient1 or patient2)<br>- nCount_RNA -> UMI counts per cell<br>- nFeature_RNA -> genes detected per cell<br>- percent.mt -> mitochondrial count fraction per cell<br>- seurat_clusters -> unsupervised clustering results using Louvain algorithm with resolution = 0.5<br>- Manual.Annotation -> Cell types annotated based on marker gene expression<br>- Celltypist.prediction -> Cell types predicted with CellTypist Python package<br>- Celltypist.prediction.ari -> Cell types predicted with CellTypist Python package, with harmonized names for comparison with manual annotation</p>
LungMAP Azimuth Reference - Human Adult Lung scRNA-Seq
<p>The LungMAP scRNA-Seq reference associated with the Lung CellCards resource. The initial reference integrated 259k cells from 72 donors from five published (PMIDs: 32726565, 32427931, 30554520, 32832599, 32832598) and one unpublished single cell RNA-seq cohort. Non-diseased adult and pediatric healthy lung single-cell 10x Genomics captures (3’ v2 and v3). Cells from different donors were integrated using Batchlor. Preliminary cell types were called based on Leiden clustering analysis and expression patterns of LungMAP cell card markers. UMAP embeddings were generated using monocle3. This reference, along with a corresponding single-nucleus specific version of this atlas, is under active construction. We expect to release the beta version of the reference in November 2021. Conforms to<strong> </strong>Azimuth reference data structure described at <a href="https://github.com/satijalab/azimuth/wiki/Azimuth-Reference-Format">https://github.com/satijalab/azimuth/wiki/Azimuth-Reference-Format</a>.</p>
moFluMemB - Dataset : scRNA-seq from Lymph node, Spleen and Lung
<p><strong>Title</strong></p> <p>Viral infection engenders bona fide and bystander subsets of lung-resident memory B cells through a permissive mechanism<br><br><strong>Authors</strong><br>Claude Gregoire,1 Lionel Spinelli,1 Sergio Villazala-Merino,1 Laurine Gil,1 María Pía Holgado,1 Myriam Moussa,1 Chuang Dong,1 Ana Zarubica,2 Mathieu Fallet,1 Jean-Marc Navarro,1 Bernard Malissen,1,2 Pierre Milpied,1,* and Mauro Gaya1,*<br><br><strong>Affiliations</strong><br>1 Centre d'Immunologie de Marseille-Luminy (CIML), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>2 Centre d'Immunophénomique (CIPHE), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>* Correspondence: milpied@ciml.univ-mrs.fr (P.M.), gaya@ciml.univ-mrs.fr (M.G.)<br><br><strong>Summary</strong><br>Lung-resident memory B cells (MBCs) provide localized protection against reinfection in the respiratory airways. Currently, the biology of these cells remains largely unexplored. Here, we combined influenza and SARS-CoV-2 infection with fluorescent-reporter mice to identify MBCs regardless of antigen specificity. We found that two main transcriptionally distinct subsets of MBCs colonized the lung peribronchial niche after infection. These subsets arose from different progenitors and were both class-switched, somatically mutated and intrinsically biased in their differentiation fate towards plasma cells. Combined analysis of antigen-specificity and B cell receptor repertoire segregated these subsets into “bona fide” virus-specific MBCs and “bystander” MBCs with no apparent specificity for eliciting viruses and generated through an alternative permissive mechanism. Thus, diverse transcriptional programs in MBCs are not linked to specific effector fates but rather to divergent strategies of the immune system to simultaneously provide rapid protection from reinfection while diversifying the initial B cell repertoire.</p> <p><strong>Data</strong></p> <ul> <li>custom_201216_m_moFluMemB_processedData.tar.gz : pre-processed data of FB5P-seq protocol (Attaf et al., 2020) on memory B cells sorted from single-cell suspensions of lungs with enzymatic digestion of lung tissue at 37°C, with index sorting information for a panel of antibodies identifying subsets of memory B cells.</li> <li>moFluMemB_DockerImages.tar.gz: Docker images used by the analysis</li> <li>moFluMemB_SingularityImages.tar.gz: Singularity images used by the analysis (conversion of the docker images)<br> </li> </ul> <p>See the three other Zenodo deposit for the rest of the data:</p> <p><strong>10.5281/zenodo.5565863</strong></p> <p><strong>10.5281/zenodo.5564624</strong></p> <p><strong>10.5281/zenodo.10559312</strong></p>
Colorectal cancer interleukin-10 blockade scRNA-seq
<p><em>Objective:</em> PD-1 checkpoint inhibition and adoptive cellular therapy have limited success in patients with microsatellite stable colorectal cancer liver metastases (CRLM). We demonstrate that interleukin-10 (IL-10) blockade enhances endogenous T cell and chimeric antigen receptor T (CAR-T) cell anti-tumor function in CRLM slice cultures.<br><br><em>Design:</em> We created organotypic slice cultures from human CRLM (n = 38) and tested the anti-tumor effects of a neutralizing antibody against IL-10 (αIL-10). We evaluated slice cultures with single and multiplex immunohistochemistry, in situ hybridization, single cell RNA sequencing, and time-lapse fluorescent microscopy. In addition, we studied the effects of αIL-10 on carcinoembryonic antigen (CEA)-specific CAR-T cells exogenously administered to both human CRLM slice cultures and a CRLM murine model. <br><br><em>Results: </em>There was little effect of PD-1 blockade in CRLM slice cultures. In contrast, αIL-10 generated 1.8-fold increase in T cell-mediated carcinoma cell death, and increased proportion of CD8+ T cells and inflammatory polarization of macrophages. In addition to effects on endogenous immune cells in human CRLM, αIL-10 also rescued murine CAR-T cell proliferation and cytotoxicity from myeloid cell-mediated immunosuppression. In human CRLM slices, αIL-10 dramatically improved CEA-specific CAR-T cell cytotoxicity, generating nearly 70% carcinoma apoptosis across multiple human tumors. We saw a less dramatic, but similar effect of pretreatment of CAR-T cells with an IL-10 receptor blocking antibody, demonstrating that IL-10 inhibits CAR-T function in the CRLM tumor microenvironment.</p> <p><em>Conclusion:</em> Neutralizing the effects of IL-10 in human CRLM has therapeutic potential as a stand-alone treatment and to augment the function of adoptively transferred CAR-T cells.</p>
Supplementary table of PRIDE datasets analyzed for "FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data"
<p>Our proteomics dataset comes from The PRoteomics IDEntifications (PRIDE) database, the world’s largest data repository of mass spectrometry-based proteomics data. Specifically, we used 633 human proteomics project experiments with a total of 32,546 runs and reanalyzed them using ionbot with an FDR threshold of 0.01 [16], resulting in a total of 154,885,151 peptide spectrum matches for 18,846 proteins. Here is the full list of projects, runs, and general statistics.</p>
EMBL-EBI scRNA Bioinformatics T cell course 2022 (RESULTS)
<p>Repository: Final results for the projects developed during the 'Bioinformatics for T-Cell immunology' course (11-15/07/2022) at EMBL-EBI: [https://www.ebi.ac.uk/training/events/bioinformatics-t-cell-immunology-2022](https://www.ebi.ac.uk/training/events/bioinformatics-t-cell-immunology-2022)</p> <p>Official website: [https://elolab.github.io/Bioinfo_Tcell_projects_22](https://elolab.github.io/Bioinfo_Tcell_projects_22)</p> <p>Maintainer: [Elo lab](https://elolab.utu.fi)</p> <p>Contact: **António Sousa** ([ENLIGHT-TEN+](http://www.enlight-ten.eu) PhD student at the [Medical Bioinformatics Centre](https://elolab.utu.fi), TBC, University of Turku & Åbo Akademi)</p> <p>Last update: 07/07/2022</p> <p> </p> <p><br></p> <p> </p> <p>---</p> <p> </p> <p><br></p> <p> </p> <p>### Projects</p> <p> </p> <p>This repository hosts the results related with three standalone/independent data analysis projects examples using publicly available data generated and published elsewhere properly referenced below:</p> <p> 1. _Integration of single-cell data from patients developing arthritis arAE under ICI_<br> <br> + _publication_: [Kim et al., 2022](https://www.nature.com/articles/s41467-022-29539-3)<br> <br> + _data_: GEO [GSE173303](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE173303)<br> <br> + _R markdown notebook_: `01_integration_arthritis_arAE_ICI.Rmd`<br> <br> + _vignette_: [01_integration_arthritis_arAE_ICI.html](https://elolab.github.io/Bioinfo_Tcell_projects_22/pages/01_integration_arthritis_arAE_ICI.html)<br> <br> + _results_ (_in this repository_): GSE173303.tar.gz<br> <br> 2. _Fine-grained clustering of single-cell data of melanoma immune/stroma cells_<br> <br> + _publication_: [Jerby-Arnon et al., 2018](https://www.sciencedirect.com/science/article/pii/S0092867418311784?via%3Dihub)<br> <br> + _data_: GEO [GSE115978](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115978)<br> <br> + _R markdown notebook_: `02_clustering_seurat_vs_iloreg.Rmd`<br> <br> + _vignette_: [02_clustering_seurat_vs_iloreg.html](https://elolab.github.io/Bioinfo_Tcell_projects_22/pages/02_clustering_seurat_vs_iloreg.html)</p> <p> + _results_ (_in this repository_): GSE115978.tar.gz</p> <p> 3. _Differential gene expression of stimulated CD4+ T single-cell data with single-cell and pseudobulk methods_<br> <br> + _publication_: [Cano-Gamez et al., 2020](https://www.nature.com/articles/s41467-020-15543-y)<br> <br> + _data_: [www.opentargets.org](https://www.opentargets.org/projects/effectorness)<br> <br> + _R markdown notebook_: `03_pseudobulks_dge_rots_cd4_act.Rmd`<br> <br> + _vignette_: [03_pseudobulks_dge_rots_cd4_act.html](https://elolab.github.io/Bioinfo_Tcell_projects_22/pages/03_pseudobulks_dge_rots_cd4_act.html)</p> <p> + _results_ (_in this repository_): CanoGamez_et_al_2020.tar.gz</p> <p> </p> <p><br></p> <p> </p> <p>---</p> <p> </p> <p><br></p> <p> </p> <p>### Disclaimer</p> <p> </p> <p>>All the data used along each project notebook was made public elsewhere by the respective authors and it has been properly referenced in each project (proper links were provided along each project notebook). The data and tools chosen to address the topic(s) of each project notebook reflect only my personal experience/knowledge and they were chosen to highlight particular aspects that I consider important. The results generated and explored within each project notebook have just the general purpose of give a brief introduction to the topics addressed in each project and do not aim, at any point, to reproduce or question neither the approaches taken nor the main findings published along with the data sets used herein.</p> <p> </p>
moFluMemB - Dataset : scRNA-seq from Lymph node, Spleen and Lung - 10x_191105_m_moFluMemB
<p><strong>Title</strong></p> <p>Viral infection engenders bona fide and bystander subsets of lung-resident memory B cells through a permissive mechanism<br><br><strong>Authors</strong><br>Claude Gregoire,1 Lionel Spinelli,1 Sergio Villazala-Merino,1 Laurine Gil,1 María Pía Holgado,1 Myriam Moussa,1 Chuang Dong,1 Ana Zarubica,2 Mathieu Fallet,1 Jean-Marc Navarro,1 Bernard Malissen,1,2 Pierre Milpied,1,* and Mauro Gaya1,*<br><br><strong>Affiliations</strong><br>1 Centre d'Immunologie de Marseille-Luminy (CIML), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>2 Centre d'Immunophénomique (CIPHE), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>* Correspondence: milpied@ciml.univ-mrs.fr (P.M.), gaya@ciml.univ-mrs.fr (M.G.)<br><br><strong>Summary</strong><br>Lung-resident memory B cells (MBCs) provide localized protection against reinfection in the respiratory airways. Currently, the biology of these cells remains largely unexplored. Here, we combined influenza and SARS-CoV-2 infection with fluorescent-reporter mice to identify MBCs regardless of antigen specificity. We found that two main transcriptionally distinct subsets of MBCs colonized the lung peribronchial niche after infection. These subsets arose from different progenitors and were both class-switched, somatically mutated and intrinsically biased in their differentiation fate towards plasma cells. Combined analysis of antigen-specificity and B cell receptor repertoire segregated these subsets into “bona fide” virus-specific MBCs and “bystander” MBCs with no apparent specificity for eliciting viruses and generated through an alternative permissive mechanism. Thus, diverse transcriptional programs in MBCs are not linked to specific effector fates but rather to divergent strategies of the immune system to simultaneously provide rapid protection from reinfection while diversifying the initial B cell repertoire.</p> <p><strong>Data:</strong> 10x_191105_m_moFluMemB_processedData.tar.gz : pre-processed data of 10x 5’ scRNA-Seq on memory B cells sorted from single-cell suspensions of spleen, lymph nodes and lungs with mechanical dissociation of lung tissue at 4°C.</p> <p>See the three other Zenodo deposit for the rest of the data:</p> <p><strong>10.5281/zenodo.5566674</strong></p> <p><strong>10.5281/zenodo.5564624</strong></p> <p><strong>10.5281/zenodo.10559312</strong></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.