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1,133 results for “single-cell analysis”
Single-cell transcriptomic analysis of B cells reveals new insights into atypical memory B cells in COVID-19
<p><span>Here, we performed single-cell RNA sequencing of S1 and RBD protein-specific B cells from convalescent COVID-19 patients with different clinical manifestations. This study aimed to evaluate the role and developmental pathway of atypical memory B cells in response to SARS-CoV-2 infection. The results revealed a proinflammatory signature across B cell subsets associated with disease severity, as evidenced by the upregulation of genes such as <em>GADD45B</em>, <em>MAP3K8</em>, and <em>NFKBIA</em> in critical and severe individuals. Furthermore, the analysis of atypical memory B cells suggested a developmental pathway similar to that of conventional memory B cells through germinal centers, as indicated by the expression of several genes involved in germinal center processes, including <em>CXCR4</em>, <em>CXCR5</em>, <em>BCL2</em>, and <em>MYC</em>. Additionally, the upregulation of genes characteristic of the immune response in COVID-19, such as <em>ZFP36</em> and <em>DUSP1</em>, suggested that the differentiation and activation of atypical memory B cells may be influenced by exposure to SARS-CoV-2 and that these genes may contribute to the immune response for COVID-19 recovery. Our study contributes to a better understanding of atypical memory B cells in COVID-19 and the role of other B cell subsets across different clinical manifestations.</span></p>
Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries
<p>Processed data files for manuscript: "Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries" <a href="https://doi.org/10.1093/nar/gky1204">https://doi.org/10.1093/nar/gky1204</a> . Scripts for generating figures are found here: https://github.com/rnabioco/scrna-subsets</p>
Data for 'Comparative Analysis of Single-Cell RNA Sequencing Methods'
<p>Raw sequencing data to "Comparative Analysis of Single-Cell RNA Sequencing Methods". </p> <p>https://www.ncbi.nlm.nih.gov/pubmed/28212749</p> <p> </p> <p>In addition to the GEO submission https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75790, you can find here raw bam files for UMI-methods tagged with cell barcode and UMI sequences.</p> <p>MD5 checksum: f10825509952fffd9c4dc0c1dcb9eb8e</p>
Analysis of single-cell CRISPR perturbations indicates that enhancers predominantly act multiplicatively
<p>This repository contains simulated data generated with GLiMMIRS-sim that was analyzed and presented in Zhou & Guyuvayurappan et al, as well as data pertaining to the <em>NMU</em> RT-qPCR experiment described in the manuscript. </p> <p> </p> <ul> <li><span>sim_base_data.tar.gz: directory containing simulated data for baseline model </span></li> <li> <p><span>sim_data_interactions_pos.tar.gz: directory containing simulated data for interactions model with positive interaction effects </span></p> </li> <li> <p><span>sim_data_interactions_neg.tar.gz: directory containing simulated data for interactions model with negative interaction effects </span></p> </li> <li><span>NMU.xlsx: spreadsheet containing data from the <em>NMU</em> RT-qPCR experiment </span></li> </ul>
Single-cell RNA-Seq and TCR-Seq analysis of PD-1+ CD8+ T-cells responding to anti-PD-1 and anti-PD-1/CTLA-4 immunotherapy in melanoma
<p><strong>This dataset details the scRNASeq and TCR-Seq analysis of sorted PD-1+ CD8+ T cells from patients with melanoma treated with checkpoint therapy (anti-PD-1 monotherapy and anti-PD-1 & anti-CTLA-4 combination therapy) at baseline and after the first cycle of therapy. A major publication using this dataset is accessible here: (reference) </strong></p> <p> </p> <p><strong>*experimental design</strong></p> <p> Single-cell RNA sequencing was performed using 10x Genomics with feature barcoding technology to multiplex cell samples from different patients undergoing mono or dual therapy so that they can be loaded on one well to reduce costs and minimize technical variability. Hashtag oligomers (oligos) were obtained as purified and already oligo-conjugated in TotalSeq-C format from BioLegend. Cells were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.</p> <p> </p> <p><strong>*extract protocol</strong></p> <p> PBMCs were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions.</p> <p> </p> <p><strong>*library construction protocol</strong></p> <p> Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.</p> <p> </p> <p><strong>*library strategy</strong></p> <p> scRNA-seq and scTCR-seq</p> <p> </p> <p><strong>*data processing step</strong></p> <p> Pre-processing of sequencing results to generate count matrices (gene expression and HTO barcode counts) was performed using the 10x genomics Cell Ranger pipeline.</p> <p> Further processing was done with Seurat (cell and gene filtering, hashtag identification, clustering, differential gene expression analysis based on gene expression).</p> <p> </p> <p> <strong>*genome build/assembly</strong></p> <p> Alignment was performed using prebuilt Cell Ranger human reference GRCh38.</p> <p> </p> <p><strong>*processed data files format and content</strong></p> <p> RNA counts and HTO counts are in sparse matrix format and TCR clonotypes are in csv format.</p> <p>Datasets were merged and analyzed by Seurat and the analyzed objects are in rds format.</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>file name</strong></p> </td> <td> <p><strong>file checksum</strong></p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>da2e006d2b39485fd8cf8701742c6d77</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>e125fc5031899bba71e1171888d78205</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_contig_annotations.csv</p> </td> <td> <p>927241805d507204fbe9ef7045d0ccf4</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_contig_annotations.csv</p> </td> <td> <p>8ca544d27f06e66592b567d3ab86551e</p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p><strong>*processed data file </strong></p> </td> <td> <p><strong>antibodies/tags</strong></p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M1_base_monotherapy<br>TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M1_post_monotherapy<br>TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - C1_base_combined_therapy<br>TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - C1_post_combined_therapy<br>TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C2_base_combined_therapy<br>TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C2_post_combined_therapy</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_contig_annotations.csv</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M2_base_monotherapy<br>TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M2_post_monotherapy<br>TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - M3_base_monotherapy<br>TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - M3_post_monotherapy<br>TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C3_base_combined_therapy<br>TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C3_post_combined_therapy</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_contig_annotations.csv</p> </td> <td> <p>none</p> </td> </tr> </tbody> </table> <p> </p>
Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability
<p>Additional files for "Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability".</p> <p><strong>Additional file 1 - list of benchmarks with consolidate survey answers (CSV)</strong></p> <p><strong>Additional file 2 - review form (CSV) </strong></p> <p><strong>Additional file 3 - unedited (anonymized) survey responses (TXT)</strong></p>
Imaging Data for: Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis
<p>This dataset contains the microfluidic microscopy time-lapse data for the publication <a href="https://www.frontiersin.org/articles/10.3389/fmicb.2023.1198170/">"Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis"</a>.</p> <p>The imaging data is recorded as raw 16-bit tif-stacks. The sequences <span>17406 - 17410 and 17411 - </span><span>17415 contain the aerobic and anaerobic conditions, respectively. For details about cultivation conditions and image processing please have a look into our <a href="http://www.frontiersin.org/articles/10.3389/fmicb.2023.1198170/">paper</a> or the public code repository <a href="https://github.com/JuBiotech/Supplement-to-Kasahara-et-al.-2023a">Supplement-to-Kasahara-et-al.-2023a</a>.</span></p>
Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment
<p>These are 3D live-cell imaging datasets of gastric tumor organoids in co-culture with primary human Natural Killer (NK) cells. The datasets were analyzed by a new, deep learning-based 3D image analysis software tool, SiQ-3D, which we developed and presented in the paper titled "Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment". Interested users can download the SiQ-3D software code from GitHub (https://github.com/simonlbd1/SiQ-3D) or Code Ocean (https://codeocean.com/capsule/6676007/tree/v2), analyze the 3D image datasets locally, and cross-check the results with the SiQ-3D quantified results that we provided here.</p>
Data from: Single-cell analysis identifies conserved features of immune dysfunction in simulated microgravity and spaceflight
<p>3-dimensional super-resolution microscopy volumes of human PBMCs recorded on a Zeiss LSM980 Airyscan2 laser scanning confocal microscope.</p> <p>Sample preparation and image capture:</p> <p>Live PBMCs were stained with 60 nM MitoTracker Red-CMX-Ros (ThermoFisher, Waltham, MA) either in 6-well plates or in the microgravity chambers for the last 2 hr of the microgravity simulation. At the end of the microgravity simulation cells were immediately fixed by 1:1 mixing the cell suspensions with 2× concentrated fixative (10% Sucrose (w/v) 120 mM KCl, 1% (w/v) glutaraldehyde, 8% (w/v) PFA pH 7.4) and incubated for 15 minutes at room temperature followed by 15 minutes on ice. Fixed cells were washed and stored in PBS until further staining for up to a week at 4 °C. 1 million fixed cells were resuspended in 1 mL of permeabilization solution (0.1% TritonX-100 in PBS) for 5 minutes. After twice washing in PBS, pellets were resuspended in 0.5 mL 1% BSA PBS containing Phalloidin-iFluor-488 (cat# ab176753, Abcam plc., Cambridge, UK) at the manufacturer’s recommended dilution, and were incubated for 90 minutes with gentle agitation. After washing in PBS, cells were stained with Hoechst 33342 (1 µg/mL in PBS) for 10 minutes. The fixed-stained cells were immobilized at 3 × 10<sup>5</sup> cells per well density in glass-bottom 96-well microplates (Greiner Bio-One, Monroe, NC), which were pre-coated with polyethyleneimine (1:15,000 (w/v)) for 16 hours in a 37 °C incubator, and washed twice with PBS. Microplates with the cell suspensions were centrifuged in a swing plate rotor centrifuge (Eppendorf 5810 R) at 400 × <em>g</em> and for 10 min and then fixed on the surface by adding an equal volume of 8% (w/v) PFA for 5 min. Finally, the fixative was replaced with 100 µL of antifade reagent (Vector Prolong Gold (ThermoFisher)). Samples were imaged immediately after this procedure on a Zeiss LSM980 Airyscan2 laser scanning confocal microscope (Carl Zeiss Microscopy, White Plains, NY). Single PBMCs were manually selected for recording based on low-resolution preview scans showing only nuclei. All singlet cells were selected in a small neighborhood to avoid biases. In each microscopy session, 24-40 cells were selected for recording in one well for each condition. This was performed in an interleaved manner, capturing 6-8 cells at a time, and then moving to the next well and then repeating this multiple times using the Experiment Designer module for automation. Super-resolution volumes of (358 × 358 × 70 pixels, 0.035 × 0.035 × 0.13 µm/voxel resolution) were recorded in the above-determined positions using Definite Focus autofocusing. A Plan-Apochromat 63 × 1.40 Oil lens, Airyscan2 SR (super-resolution) mode with optimal sampling and frame switching between 3 fluorescence channels to minimize spectral cross-bleed were used. MitoTracker Red, iFluor488, and Hoechs33342 were excited with 561, 488, and 405 nm solid-state lasers, respectively, using the optimal emission filter for each channel. 3D Airyscan2 processing was performed with standard filtering settings.</p> <p>File naming:</p> <p>Four zip files were deposited named as <Donor#id>.zip, where id goes from 1 to 4.</p> <p>Each zip file contains the following Zeiss Microscopy format image files: <Condition>_<Donor#id>_<Stain#batch>_<Cell>.czi</p> <p><Condition>:</p> <ul> <li>1G – Control culturing in 6-well plates for 25h</li> <li>uG – simulated microgravity culturing for 25h in NASA Rotating Wall Vessels</li> <li>1G+TLR – as above, with TLR 7/8 agonist (1 μM R848)</li> <li>uG+TLR– as above, with TLR 7/8 agonist (1 μM R848)</li> <li>1G+CyD– as above, with cytochalasin D</li> <li>uG+CyD– as above, with cytochalasin D</li> <li>1G+Q – as above, with quercetin 50µM</li> <li>uG+Q – as above, with quercetin 50µM</li> </ul> <p><Donor#id>: 1-4 indicates biological replicates</p> <p><Stain#batch>: 1-2 indicates experimental replicates of phalloidin staining and imaging session</p> <p><Cell>: arbitrary number to distinguish images within the same condition/donor/stain set.</p> <p>See image analysis pipelines used with these data at: https://github.com/gerencserlab/Superresolution-actin-and-mitochondria-analysis</p>
Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data
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Single-cell multi-modal analysis of tumor microenvironment in human non-small cell lung cancer tissues
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Data from: Single-cell transcriptomic analysis of tumor-derived fibroblasts and normal tissue-resident fibroblasts reveals fibroblast heterogeneity in breast cancer
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Single-cell Transcriptomic Analysis Identifies Extensive Heterogeneity in the Cellular Composition of Mouse Achilles Tendons
<p>Tendon is a dense connective tissue that stores and transmits forces between muscles and bones. Cellular heterogeneity is increasingly recognized as an important factor in the biological basis of tissue homeostasis and disease, yet little is known about the diversity of cell types that populate tendon. To address this, we determined the heterogeneity of cell populations within mouse Achilles tendons using single-cell RNA sequencing. In assembling a transcriptomic atlas of Achilles tendons, we identified 11 distinct types of cells, including 3 previously undescribed populations of tendon fibroblasts. This table contains differential gene expression for specific genes identified in distinct populations of cells within tendon tissue. </p>
Single-cell Roadmap dataset "Cardiac differentiation roadmap for analysis of plasticity and balanced lineage commitment" (Snabel et al.)
<p>View the temporal single-cell transcriptomics data (UMAP, PCA, Heatmaps and Violin plots) using the Shiny App interface of iSEE (<a href="https://doi.org/10.12688/f1000research.14966.1">doi:10.12688/f1000research.14966.1</a>) for easy visualization of the single-cell data described in "Single-cell roadmap of cardiac differentiation identifies roles for ZNF711 and retinoic acid in balanced epicardial and cardiomyocyte lineage commitment" (Snabel et al., bioRXiv).</p> <p>For instructions on how to use this data, please visit https://github.com/Rebecza/scRoadmap_CardiacDiffs/.</p>
Material for Single-cell RNA analysis (I) BIO463 Week 9
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Teaching Datasets for Single-Cell RNA-seq Analysis Course
<p>This repository contains teaching datasets used in the Single-Cell RNA-seq Analysis Course, which is part of the SeuratExtend project (<a href="https://github.com/huayc09/SeuratExtend">https://github.com/huayc09/SeuratExtend</a>). The course materials are available at <a href="https://huayc09.github.io/SeuratExtend/#single-cell-rna-seq-analysis-course-new-in-v110-1">https://huayc09.github.io/SeuratExtend/#single-cell-rna-seq-analysis-course-new-in-v110-1</a>, with code and scripts hosted at <a href="https://github.com/huayc09/single-cell-course">https://github.com/huayc09/single-cell-course</a>.</p> <p>The datasets include:</p> <ol> <li>3k Peripheral Blood Mononuclear Cells (PBMCs)</li> <li>Paired PBMC samples processed with 10x Genomics' 3' kit</li> <li>Paired PBMC samples processed with 10x Genomics' 5' kit</li> </ol> <p>All original data were obtained from 10x Genomics (<a href="https://www.10xgenomics.com/resources/datasets">https://www.10xgenomics.com/resources/datasets</a>) and processed for educational purposes.</p>
Unveiling genetic signatures of immune response in immune-related diseases through single-cell eQTL analysis across diverse conditions
<p><em><strong>Unveiling genetic signatures of immune response in immune-related diseases through single-cell eQTL analysis across diverse conditions</strong></em></p> <p> </p> <p>Tools and scripts were used to generate results in Zhang et al 2024.</p> <p>Supplementary files that were not included in the initial submission.</p> <p>Full summary statistics of eQTLs including top eQTLs and all SNP-gene pairs of each cell type, consistent.tar.gz for consistent eQTLs per cell and response.tar.gz for response eQTLs, respectively.</p>
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis
<p>A detailed list of the selected scRNA-seq datasets used in the paper, also provided in Additional file <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1898-6#MOESM1">1</a>: Table S1-S2.</p>
scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis
<p>Analysis code for the paper "scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis"</p>
Seurat objects for "Single-cell multi-omic analysis of the vestibular schwannoma ecosystem uncovers a nerve injury-like state" (https://doi.org/10.1038/s41467-023-42762-w)
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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.