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1,353 results for “Single-cell transcriptomics”
Supporting data for "Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche"
<p>This data repository contains several datasets supplementing the paper “Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche” by Fetahu, Esser-Skala, Dnyansagar et al. (2023).</p> <ul> <li>HOMER_Results.zip: detailed results of the HOMER analysis</li> <li>nblast_scopen_gene_activity_normalized_motifs_added.rds: Seurat object with scATAC-seq data</li> <li>snp_array.tgz: SNP array data</li> <li>R_data_generated.tgz: Files generated by the scRNA-seq analysis scripts in the GitHub repository associated with the publication.</li> </ul>
Comparative Analysis of Droplet- vs. Microwell-based Whole Transcriptome Single-Cell Sequencing Technologies in Complex Human Tissues
<p>In the past decade, high-dimensional single-cell omics tools have enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, recent investigations suggest that each technique has its unique strengths but also technology-inherent limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium <em>vs.</em> microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy.</p> <p>Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of detectable cell populations differed. This could in part be ascribed to differences in the performance to recover cells with low-mRNA content. Hence, immune cells such as neutrophils are underrepresented in data generated with the widely used droplet-based scRNA-seq protocol, highlighting the importance of considering platform limitations in low mRNA content cell recovery. In contrast, droplet-based scRNA-seq demonstrated superiority in terms of recovering cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation, affecting the composition of identified tissue profiles and the exploratory value of the generated datasets. Overall, our study emphasizes the importance of carefully selecting the appropriate scRNA-seq platform to improve cell type representation and obtain a more comprehensive and accurate understanding of the TME.</p>
Single-cell repertoire and transcriptome sequencing reveals clonally expanded and transcriptionally distinct lymphocytes in aged CNS
<p>Single-cell repertoire and transcriptome sequencing reveals clonally expanded and transcriptionally distinct lymphocytes in aged CNS. Gene expression and immune receptor repertoire sequencing was performing for both B and T cells. This dataset contains the VDJ sequencing information for the four samples. Each B cell and T cell library was sequenced across four lanes. </p> <p> </p> <p>Files with _WT_ in their name correspond to the young (4-6 week B6 mice) </p> <p>Files with _12_ in their name before the BDJ or VDJ text correspond to the 12-month-old cohort.</p> <p>Files with _18_ in their name before the BDJ or VDJ text correspond to the 18-month-old cohort in which four brains were pooled.</p> <p>Files with 4_18_ in their name before the BDJ or VDJ text correspond to the 18-month-old mouse that was processed and sequenced alone. </p> <p> </p> <p>The L001 - L004 in the file names indicates the sequencing lane. Samples with BDJ correspond to the B cell repertoire library (B cell VDJ). Samples with TDJ correspond to the T cell repertoire library (T cell VDJ). </p>
Gleaning Euglenozoa-specific DNA polymerases in public single-cell transcriptome data
<p><span>Multiple genes encoding family A DNA polymerases (famA DNAPs), which are evolutionary relatives of DNA polymerase </span><span>I</span><span> (Pol</span><span>I</span><span>) in bacteria and phages, have been found in eukaryotic genomes, and many of these proteins are used mainly in organelles. Among members of the phylum Euglenozoa, distinct types of famA DNAP, Pol</span><span>I</span><span>A, Pol</span><span>I</span><span>BCD+, POP, and eugPolA, have been found. It is intriguing how the suite of famA DNAPs had been established during the evolution of Euglenozoa, but the DNAP data have not been sampled from the taxa that sufficiently represent the diversity of this phylum. In particular, little sequence data were available for basal branching species in Euglenozoa until recently. Thanks to the single-cell transcriptome data from symbiontids and phagotrophic euglenids, we have an opportunity to cover the "hole" in the repertory of famA DNAPs in the deep branches in Euglenozoa. The current study identified 16 new famA DNAP sequences in the transcriptome data from 33 phagotrophic euglenids and two symbiontids, respectively. Based on the new famA DNAP sequences, the updated diversity and evolution of famA DNAPs in Euglenozoa are discussed.</span></p>
Spotiphy enables single-cell spatial whole transcriptomics across the entire section
<p><span>Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole tissue sections, but the approach remains dogged by the challenge of achieving single-cell resolution without sacrificing whole genome coverage. Here we present Spotiphy (<u>Spot</u> <u>i</u>mager with <u>p</u>seudo single-cell resolution <u>h</u>istolog<u>y</u>), a novel computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. In evaluations with Alzheimer’s disease (AD) and normal </span><span>mouse brains, </span><span>Spotiphy</span><span> delivers the most precise cellular compositions. For the first time, </span><span>Spotiphy reveals</span><span> novel astrocyte </span><span>regional specification in mouse brains. It distinguishes sub-populations of DAM (Disease-Associated Microglia) located in different AD mouse brain regions. Spotiphy also identifies multiple spatial domains as well as changes in the patterns of tumor-tumor microenvironment interactions using human breast ST data. Spotiphy enables visualization of cell localization and gene expression in tissue sections, offering key insights into the function of complex biological systems.</span></p>
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>
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>
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>
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>
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>
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
Open the record for dataset details and reuse information.
Integrating single-cell biophysical and transcriptomic features to resolve functional heterogeneity in mantle cell lymphoma
Open the record for dataset details and reuse information.
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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.