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2,063 results for “single cell transcriptomics”
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>
Other supporting data for our manuscript "Mapping the Single Cell Transcriptomic Response of Murine Diabetic Kidney Disease to Therapies"
<p>Other supplementary data for our paper "Mapping the Single Cell Transcriptomic Response of Murine Diabetic Kidney Disease to Therapies"</p>
Single-cell transcriptome analysis of the in vivo response to viral infection in the cave nectar bat Eonycteris spelaea
<p>Bats are reservoir hosts of many zoonotic viruses with pandemic potential in humans. Here, we<br> utilized single-cell transcriptome sequencing (scRNA-seq) to provide detailed comparative<br> analyses of the immune repertoire and the transcriptional responses in the bat lungs upon in<br> vivo infection with a double-stranded RNA virus, Pteropine orthoreovirus PRV3M. Neutrophils<br> were observed to have basally high IDO1 expression, uniquely amongst mammals currently<br> profiled by scRNA-seq. NK/T cells were the most abundant immune cell type in lung tissue, and<br> included three distinct CD8 + effector T cell populations delineated by the differential expression<br> of KLRB1, GFRA2 and DPP4. We identified NK/T clusters which up-regulated genes involved in<br> T-cell activation and effector function early after viral infection. Alveolar macrophages and<br> classical monocytes were key drivers of antiviral interferon signaling. Infection also resulted in<br> the expansion of a CSF1R + population expressing collagen-like genes, which became the<br> predominant myeloid cell type after infection. This work uncovers novel features relevant to viral<br> disease tolerance in bats, lays a foundation for future in vivo and in vitro experimental<br> investigations, and serves as a key resource for comparative immunology studies across bats<br> and other mammals.</p> <p> </p> <p>This upload is the transcriptome fasta file used for alignment for the dataset.</p>
Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 hours
<p><strong>Aims/hypothesis</strong>: Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycemia, beta cell glucotoxicity, and subsequently type 2 diabetes. In this study, we explored the effects of <em>in vitro</em> hyperglycemic conditions on human pancreatic islet gene expression across 24 hours in six pancreatic cell types: alpha, beta, gamma, delta, ductal, and acinar cells. We hypothesized that genes associated with hyperglycemic conditions may be relevant to the onset and progression of diabetes.</p> <p><strong>Methods</strong>: We exposed human pancreatic islets from two donors to low (2.8 mmol/l) and high (15.0 mmol/l) glucose concentrations over 24 hours <em>in vitro</em>. To assess the transcriptome, we performed single-cell RNA sequencing (scRNA-seq) at seven time points. We modeled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Additionally, we integrated genomic features and genetic summary statistics to nominate candidate effector genes. For three of these genes, we functionally characterized the effect on insulin production and secretion using CRISPR interference to knockdown gene expression in EndoC-βH1 cells, followed by a glucose-stimulated insulin secretion assay.</p> <p><strong>Results</strong>: Across all cell types, we identified 1,447 genes associated with time, 680 genes associated with glucose exposure, and 418 genes associated with interaction effects between time and glucose. By integrating these expression profiles with summary statistics from genetic association studies, we identified 2,449 candidate effector genes for type 2 diabetes, HbA1c, random blood glucose, and fasting blood glucose. Of these candidate effector genes, we showed that three—<em>ERO1B</em>, <em>HNRNPA2B1</em>, and <em>RHOBTB3</em>—exhibited an effect on glucose-stimulated insulin secretion and production in EndoC-βH1 cells.</p> <p><strong>Conclusions/interpretation</strong>: The findings of our study provide an in-depth characterization of the 24-hour transcriptomic response of human pancreatic islets to glucose exposure at a single-cell resolution. By integrating differentially expressed genes with genetic signals for type 2 diabetes and glucose-related traits, we provide insights into the molecular mechanisms underlying glucose homeostasis. Finally, we provide functional evidence to support the role of three candidate effector genes in insulin secretion and production.</p>
Resolving single-cell expression profiles by pseudo-temporal integration of transcriptomic and proteomic datasets.
<p>Raw and processed single cell proteomics (scp-MS) and scRNA-Seq data of HEK293-PIP-FUCCI cells which were challanged with hypoxia. The repository contains data for recreating the pseudo-temporal alignment analysis of transcription-translation profiles.</p>
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>
scDenorm: a denormalisation tool for Integrating Single-cell Transcriptomics Data
<p>Datasets and Jupyter notebooks to reproduce the analyses presented in the manuscript, scDenorm: a denormalisation tool for Integrating Single-cell Transcriptomics Data.</p>
Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples - Time course data
<p>Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.</p> <p><strong>This dataset includes only the 10X Flex time course data and analysis.</strong></p>
Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration
<p>Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.</p>
Multi-Nucleic Acid Interaction Mapping in Single Cell (MUSIC) for simultanouse chromatin, RNA-chromatin and transcriptome mapping at single cell resolution
<p><a href="https://doi.org/10.1101/2023.06.28.546457">MUSIC manuscript:</a> Joint profiling of multiplex chromatin interactions, gene expression, and RNA-chromatin associations in single cells of the human brain.</p> <p>MUSIC-docker is the customized pipeline to process the raw fastq files to bam files: http://sysbiocomp.ucsd.edu/public/wenxingzhao/MUSIC_docker/intro.html.</p> <p>Each bam file records the final output of our single cell mixed species analysis. Read name recods the cell barcode, complex barcode and I7 index. For each DNA/RNA read, read header contains <code>raw read name</code>| <code>BC3</code>_<code>BC2</code>_<code>BC1</code> - <code>10x barcode</code> # <code>UMI</code>. Details of the read name can be found here: http://sysbiocomp.ucsd.edu/public/wenxingzhao/MUSIC_docker/step.html#demultiplexing.</p> <p>merge_DNA [RNA]_human [mouse].sort.bam: bam file of DNA [RNA] reads from the mix species library (H1+E14) that can uniquely mapped to the human [mouse] genome. PCR duplicates have been removed. </p> <p> </p> <p> </p> <p> </p> <p> </p>
In silico spatial transcriptomic editing at single-cell resolution
<p>The data for training the GAN (Inversion) model and reproduce the results reported in the paper </p>
Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios
<p>All total 152 datasets are collected in the benchmarking study.</p> <p>Every dataset contains two parts: the gene expression matrix (or well-established model by dynwrap for trajectory) and the data information including the data id, repository, accession number, URL, technology platform, species, organ (source), cell number, gene number, data type, ERCC spike-in, dilution factor, volume, group condition, treatment, batch information and cluster labels.</p> <p>There are 23 datasets (data79-data101) for evaluating the simulation ability for cell trajectories which are derived from another Zenodo repository (https://zenodo.org/record/1443566).</p>
ALA induced transcriptome and single cell microscopy of M.tuberculosis
<p> <strong>Single cell microscopy of porphyeins in vegetative and dormant <em>Mycobacterium tuberculosis</em>. Confocal fluorescence microscopy, life-time measurements, and microspectrofluorimetry. </strong>Fluorescence lifetime measurements were performed on a PicoQuant MicroTime 200 confocal scanning system (Pico-Quant GmbH, Berlin, Germany) based on an Olympus IX-71 inverted fluorescence microscope (Japan). SymphoTime® software was used for data collection. Fluorescence spectra were recorded in the confocal mode of the MicroTime 200 system using a Shamrock 163 spectrograph with a Newton DU-970 camera (Andor, UK).</p> <p><strong>Transcriptomic analysis of cells of <em>M. tuberculosis </em>in a vegetative state and under transition into dormant state upon administration of exogenous ALA.</strong> The quality of the resulting libraries was checked using the Fragment Analyzer. Quantitative analysis was performed by qPCR. After quality control and assessment of DNA quantity, the pool of libraries was sequenced on an Illumina NovaSeq 6000 instrument (length of reads - 150 bp on both sides of the fragments). FASTQ files were generated using bcl2fastq v2.20 Conversion Software (Illumina). The quality data string record format is Phred 33. As a result, 1,107,614,502 reads were received. </p> <pre>This study was funded by Russian Science Foundation grant 19-15-00324.</pre>
Data used in paper "Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON"
<p>This directory contains the data resources of the following paper:</p> <p>"Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON"</p>
Single-cell and spatially resolved transcriptomic data of mouse regenerative livers under normal and fibrotic conditions
<p>A single-cell spatial-temporal transcriptomic atlas of liver regeneration under normal and fibrotic condition, including a total of 30 mouse liver samples obtained from 15 normal and 15 fibrotic mice at timepoints Day 0, 1, 2, 3, and 7 after a partial hepatectomy (PHx) procedure with three replicates for each time point, followed by the scRNA-seq and SRT sequencing for each sample using the Stereo-seq platform. </p>
Single-cell spatial transcriptomics of an inducible destabilized-domain Cre mouse line to target disease associated microglia
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Integrating single-cell biophysical and transcriptomic features to resolve functional heterogeneity in mantle cell lymphoma
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Gleaning Euglenozoa-specific DNA polymerases in public single-cell transcriptome data
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Data from: Mouse gingival single cell transcriptomic atlas identified a novel fibroblast subpopulation activated to guide oral barrier immunity in periodontitis
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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)
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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.