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6,040 results for “Single-Cell”
Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data
<p>This repository contains raw and processed data of the original datasets generated for Lee et al. "Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data".</p> <p>The files Dilution_3_ROI02.ome.tiff, Dilution_3_ROI02_mask.ome.tiff, st_reclustered_expr_data.h5ad contain raw data (as ome tiff), a mesmer-generated segmentation mask, and single-cell quantification in anndata format of the cell pellet data generated for the study.</p> <p>The file tonsil-for-zenodo.tar.gz contains all raw and processed data of the IMC data of the 16 human tonsil ROIs:</p> <ul> <li>imc contains raw tiff stacks of the acquisition</li> <li>mask contains mesmer-generated masks</li> <li>channel.yml contains channel info</li> <li>exp_mat/dc/ contains single cell quantifications using mesmer segmentation</li> </ul>
Brain Transcriptome Single-cell (BTS) Atlas: Anndata, Seurat Object, CellTypist model, and Disorder Risk Geneplot
<p>Brain Transcriptome Single-cell Atlas (BTS) Anndata, Seurat object, and Celltypist model for further use of the atlas. The Celltypist model can be utilized to accurately annotate cell types in new datasets based on the atlas. Plots illustrating the expression profile for 3,380 neurological disorder risk genes across the atlas are also uploaded. Further availability for the data can be requested by the corresponding author.<br><br>This dataset is published in Kim, S., Lee, J., Koh, I.G. <em>et al.</em> An integrative single-cell atlas for exploring the cellular and temporal specificity of genes related to neurological disorders during human brain development. <em>Exp Mol Med</em> <strong>56</strong>, 2271–2282 (2024). https://doi.org/10.1038/s12276-024-01328-6</p>
Deprez et al. - 2020 - A Single-Cell Atlas of the Human Healthy Airways
<p>AnnData object of the single-cell atlas</p> <p> </p> <p>Rationale: The respiratory tract constitutes an elaborate line of defense that is based on a unique cellular ecosystem. Objectives: We aimed to investigate cell population distributions and transcriptional changes along the airways by using single-cell RNA profiling. Methods: We have explored the cellular heterogeneity of the human airway epithelium in 10 healthy living volunteers by single-cell RNA profiling. A total of 77,969 cells were collected at 35 distinct locations, from the nose to the 12th division of the airway tree. Measurements and Main Results: The resulting atlas is composed of a high percentage of epithelial cells (89.1%) but also immune (6.2%) and stromal (4.7%) cells with distinct cellular proportions in different regions of the airways. It reveals differential gene expression between identical cell types (suprabasal, secretory, and multiciliated cells) from the nose (MUC4, PI3, SIX3) and tracheobronchial (SCGB1A1, TFF3) airways. By contrast, cell-type–specific gene expression is stable across all tracheobronchial samples. Our atlas improves the description of ionocytes, pulmonary neuroendocrine cells, and brush cells and identifies a related population of NREPpositive cells. We also report the association of KRT13 with dividing cells that are reminiscent of previously described mouse “hillock” cells and with squamous cells expressing SCEL and SPRR1A/B. Conclusions: Robust characterization of a single-cell cohort in healthy airways establishes a valuable resource for future investigations. The precise description of the continuum existing from the nasal epithelium to successive divisions of the airways and the stable gene expression profile of these regions better defines conditions under which relevant tracheobronchial proxies of human respiratory diseases can be developed. Keywords: single-cell RNAseq; epithelium; nose; trachea; bronchus</p>
Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model
<p>This dataset contains the processed scRNA-seq data and code for single-cell unified polarization assessment. Please refer to the article "Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model" for the detailed data and method description.</p> <p>*.rds: the processed scRNA-seq datasets with Universal Cell Embeddings saved as a Seurat object. The Universal Cell Embeddings are saved in the assay 'uce'.</p> <ul> <li>immune_dict_uce.rds: Immune Dictionary.</li> <li>ifnb_treatment_uce.rds: human PBMCs treated with IFN-beta.</li> <li>macrophage_stimulation_uce.rds: human macrophages treated with one or two of IFN-beta, IFN-gamma, TNF-alpha and IL-4.</li> <li>il2_treatment_uce.rds: mouse spleen CD8+ T cells treated with IL-2 or anti-PD-L1.</li> <li>pan_cancer_myeloid_uce.rds: tumor-infiltrating myeloid cells across seven cancer types.</li> </ul> <p>notebooks.zip: Jupyter notebooks containing code for training models and applications to multiple datasets.</p>
Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target
<p>Microwell-based (BD Rhapsody) scRNA-seq of Hairy Cell Leukemia Patients published in </p> <blockquote> <p><strong>Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target, Jan-Paul Bohn et al. Submitted.</strong></p> </blockquote> <p>The files will be made available upon publication. <br></p> <h4><strong>Description of the files</strong></h4> <ul> <li><strong>01_raw_counts: </strong>count matrices as CSV as generated by the BD Rhapsody WTA analysis pipeline</li> <li><strong>10_prepare_adata</strong>: Load BD Rhapsody WTA analysis pipeline outputs into AnnData objects and add metadata.</li> <li><strong>20_scrnaseq_qc</strong>: Use a nextflow pipeline (stored in lib/single-cell-analysis-nf) to perform threshold-based filtering of single-cell data and apply SOLO for doublet detection.</li> <li><strong>30_merge_adata</strong>: Merge samples into a single AnnData object, train a scVI model for batch effect removal, and annotate cell-types based on unsupervised clustering</li> <li><strong>40_cluster_analysis</strong>: Identify and investigate subclusters representing cell-states that go beyond the major cell-types</li> <li><strong>50_de_analysis</strong>: Generate pseudobulk and perform differential gene expression analysis using DESeq2 (based on a wrapper script stored in lib/deseq2_workflow)</li> <li><strong>70_downstream_analysis</strong>: Perform pathway analyses and generate figures for publication based on the data generated in the previous steps</li> <li><strong>containers:</strong> Conda environments used for the analysis packed up as singularity containers. </li> </ul>
Code and data for 'Bacillus subtilis histidine kinase KinC activates biofilm formation by controlling heterogeneity of single-cell responses'
<p>Code and data used in the paper 'Bacillus subtilis histidine kinase KinC activates biofilm formation by controlling heterogeneity of single-cell responses' https://doi.org/10.1128/mBio.01694-21</p>
Additional data for manuscript "Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data"
<p>Additional data for manuscript "Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data".</p> <p>Additional mitochondrial gene sequences for Danio rerio, Homo sapiens, and Mus musculus.</p>
Processing single-cell RNA-seq datasets using SingCellaR
<ul> <li>cellranger_output.zip : Zipped files for cellranger output</li> <li>Code.zip: This contains the code for Step2-7</li> <li>Human_genesets.zip: This includes the geneset signatures (.gmt) files we used in the protocol and original study (Roy et al, 2021)</li> <li>Human_HSPC_All.SingCellaR.rdata: The SingCellaR objects generated in Step 4 on a local computer </li> <li>ABM_1.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>eFL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>PBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>meta.data.txt: meta data includes the donor and batch information </li> </ul>
Data sets used to demonstrate the software MadHitter in the manuscript "The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via a Single-Cell Perspective"
<p>This is a zip archive of nine single-cell RNASeq data sets used in the manuscript entitled:</p> <p>"The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via A Single-Cell Perspective" by Saba Ahmadi, Pattara Sukprasert, Rahulsimham Vegesna, Sanju Sinha, Fiorella Schischlik, Natalie Artzi, Samir Khuller, Alejandro A. Schaffer, Eytan Ruppin,</p> <p>The README.txt describes the data sets in detail.</p> <p>The associated software can be found at https://github.com/ruppinlab/madhitter</p>
Single-Cell Transcriptomic Atlas of Human Cardiac Arteries
<p>This dataset contains the processed single-cell RNA sequencing data and code in article "Single-Cell Transcriptomic Atlas of Different Human Cardiac Arteries Identifies Cell Types Associated With Vascular Physiology". Please refer to the article for the detailed sample information, sequencing and data processing methods.</p> <p>cardiac_arteries_processed_data.zip: Processed single-cell RNA sequencing data from Cellranger output.</p> <p>merged_all_samples: The output of Cellranger aggr, merging processed data of all samples.</p> <p>Abbreviations in samples: AO, aorta; CA, coronary artery; PA, pulmonary artery.</p> <p>notebook.zip: Jupyter notebooks containing code for data analysis.</p>
Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"
<p>Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"</p>
Object for single-cell mouse HSC development data
<p>This file contains SingleCellExperiment object generated from mouse HSC development data, including raw and log-normalized counts, gene regulatory network inferred by PUIC, transcription factor activity matrix estimated by metaRegulon.</p>
Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency
<p class="MsoNormal">Alzheimer's disease (AD) is a progressive neurodegenerative disease observed with aging that represents the most common form of dementia. To date, therapies targeting end-stage disease plaques, tangles, or inflammation have limited efficacy. Therefore, we set out to identify an earlier targetable phenotype. Utilizing a mouse model of AD we found that cell intrinsic neural precursor cell (NPC) dysfunction precedes widespread inflammation and amyloid plaque pathology, making it one of the earlier defects in the evolution of the disease. We demonstrate that reversing impaired NPC self-renewal via genetic reduction of USP16, a histone modifier and critical physiological antagonist of the Polycomb Repressor Complex 1, can prevent downstream cognitive defects and decrease astrogliosis in vivo. To delineate potential self-renewal pathways that might contribute to the defect and rescue of Tg-SwDI NPCs and Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup></em> NPCs, respectively, we performed single-cell RNA-seq and gene set enrichment analysis (GSEA) on lineage depleted primary FACS-sorted CD31<sup><span>-</span></sup>CD45<sup><span>-</span></sup>Ter119<sup><span>-</span></sup>CD24<sup><span>-</span></sup> NPCs from Tg-SwDI, WT, and Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup></em> mice at 3-4 months and 1 year of age. Using the GSEA Hallmark gene sets, we found only three gene sets that were enriched in Tg-SwDI mice over WT mice and rescued in the Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup> </em>mice at both ages: TGF-ß pathway, oxidative phosphorylation, and Myc Targets. The TGF-ß pathway consistently had the highest normalized enrichment score in pairwise comparisons between Tg-SwDI vs WT and Tg-SwDI vs Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup> </em>of the three rescued pathways. These data suggest that USP16 may regulate neural precursor cell function in part through the BMP pathway.</p>
Single-cell datasets for distribution-based sketching
<p>Contains preprocessed single-cell data for sketching single-cell samples. Preprocessed adata objects can be accessed using the 'read_h5ad' function in Scanpy.</p> <ul> <li>The HIV Vaccine Trials Network (HVTN) Flow Cytometry dataset (hvtn_preprocessed.h5ad) was originally downloaded from the Flow Repository under Repository ID FR-FCM-ZZZV (<a href="http://flowrepository.org/id/FR-FCM-ZZZV">http://flowrepository.org/id/FR-FCM-ZZZV</a>).</li> <li>The preeclampsia CyTOF dataset (preeclampsia_preprocessed.h5ad) was originally downloaded from the Flow Repository under Repository ID FR-FCM-ZYRQ (<a href="http://flowrepository.org/id/FR-FCM-ZYRQ">http://flowrepository.org/id/FR-FCM-ZYRQ</a>).</li> <li>The NK-Cell CyTOF dataset (nk_cell_preprocessed.h5ad) from Ref. (<a href="https://www.nature.com/articles/ncomms14825">https://www.nature.com/articles/ncomms14825</a>) was originally downloaded from (<a href="https://github.com/eiriniar/CellCnn">https://github.com/eiriniar/CellCnn</a>).</li> <li>The multiple sclerosis (MS) single-cell RNA sequencing dataset of peripheral blood samples (ms_preprocessed.h5ad) from Ref. (<a href="https://www.nature.com/articles/s41467-019-14118-w">https://www.nature.com/articles/s41467-019-14118-w</a>) was originally accessed from the Gene Expression Omnibus using the accession code GSE138266 (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138266">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138266</a>).</li> </ul>
Single-cell phenotypic characteristics of tolerance under recurring antibiotic exposure in Escherichia coli
<p>Non-heritable drug resistance, such as tolerance and persistence towards antibiotics, is little characterized compared to genetic resistance. Tolerance and persistence allow cells to survive application of antibiotics that are bactericidal to non-tolerant cells. Non-heritable drug resistance challenges antibiotic treatment, particularly of recurrent infections, and have implications towards heritable resistance evolution. Tolerant cells have commonly been characterized as growth arrested cells prior and during antibiotic application that quickly resume growth post-application. Here, we explore characteristic of tolerant and susceptible <em>E. coli</em> single bacteria cells to different levels of recurrent antibiotic exposure and quantify their occurrence. In using a high throughput single-cell microfluidic device, we find that tolerant cells reduce their growth rate by about 50%, but contrary to previous findings do not go into growth arrest or near growth arrest. The growth reduction is induced by antibiotic exposure and not caused by a stochastic switch or predetermined state as previously described. Cells exhibiting constant intermediate growth survived best under antibiotic exposure and selection did not primarily act on fast growing cells, as expected for a β-lactam antibiotic. Control experiments on population cultures confirmed and challenged scaling of single cell findings to population level processes. Our findings suggest a prevalent type of tolerance that differs from previously described tolerance and persister characteristics. Our described characteristics and its high frequency of occurrence supports acclaims of an underappreciated role of tolerant cells towards resistance evolution.</p>
Single-cell datasets for temporal gene expression integration
<p>Contains loom files and preprocessed adata objects to compare methods for temporal gene expression integration. Loom files can be accessed using the 'read' function in Scvelo. Preprocessed adata objects can be accessed using the 'read_h5ad' function in Scanpy.</p> <p>The raw single-cell RNA sequencing datasets can be found under the following accession codes.</p> <ul> <li> <p>Mouse embryonic cell cycle dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/nbt.3102">10.1038/nbt.3102</a>) was originally downloaded from ArrayExpress with the accession code E-MTAB-2805</p> </li> <li> <p>Hematopoiesis differentiation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/10.1182/blood-2016-05-716480</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE81682</p> </li> <li> <p>NKT cell differentiation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/ni.3437">10.1038/ni.3437</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE74596.</p> </li> <li> <p>Hematopoiesis differentiation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org</a>/<a href="https://doi.org/10.1038/nature19348">10.1038/nature19348</a>) was originally downloaded from the Gene Expression Omnibus with the accession codes GSE70236, GSE70240, GSE70244</p> </li> <li> <p>LPS stimulation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1016/j.cels.2017.03.010">10.1016/j.cels.2017.03.010</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE94383.</p> </li> <li> <p>INF-gamma stimulation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41587-020-00803-5">10.1038/s41587-020-00803-5</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE161465.</p> </li> <li> <p>AML chemotherapy dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41591-018-0233-1">10.1038/s41591-018-0233-1</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE116481.</p> </li> <li> <p>AML diagnosis/relapse dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41375-021-01338-7">10.1038/s41375-021-01338-7</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE126068.</p> </li> <li> <p>MS case control PBMC and CSF datasets from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41467-019-14118-w">10.1038/s41467-019-14118-w</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE138266.</p> </li> </ul>
Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data
<p>We proposed DCCA for accurately dissecting the cellular heterogeneity on joint-profiling multi-omics data from the same individual cell by transferring representation between each other.</p>
Single-cell datasets for NK cell
<p>The NK cell mass cytometry dataset total collects 20 patients with 29 features. The attribute is divided into stimulated and unstimulated data. By dealing with data pre-processing in NK cell dataset, the raw data in each feature for every patient were transformed using an arcsinh normalization, which is f(x) = arcsinh(x/5).</p>
Single-cell datasets for preeclampsia
<p>The dataset is preeclampsia with data preprocessing.</p>
SIRV: Spatial inference of RNA velocity at the single-cell resolution
<p>Spatial transcriptomics and scRNA-seq datasets used for integration and prediction of un/spliced expression for spatially measured genes using SIRV, used to infer the RNA velocity in the spatial context</p>
ScienceDex guides
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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.