Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,040
datasets available to search
ShareScore release 0.9.0
Dataset results
6,040 results for “Single-Cell”
Single-cell expression and TCR data from CD19-specific CAR T cells in a phase I/II clinical trial
<p><span>By leveraging single-cell transcriptome and T cell receptor (TCR) sequencing, we aimed to track the transcriptional signatures of CAR T cell clonotypes throughout the course of treatment and furthermore identify molecular patterns leading to potent CAR T cell cytotoxicity. The data presented in this study encompass blood and bone marrow samples from patients ≤ 21 years of age with relapsed or refractory B-cell acute lymphoblastic leukemia (B-ALL) participating in the SJCAR19 phase I/II clinical trial (<a href="https://clinicaltrials.gov/ct2/show/NCT03573700">NCT03573700</a>). In brief, patients enrolled in the clinical trial received either 1 x 10^6 (dose level 1) or 3 x 10^6 (dose level 2) per kilogram of body weight following successful generation of autologous CAR T cell products and lymphodepleting chemotherapy. Peripheral blood was drawn from each participant every week until week 4 post-infusion, at week 6 or 8, and month 3 or 6 if feasible. At week 4 post-infusion, blood marrow was also collected from participants. Total T cells (CD3+) were sorted from each post-infusion sample, as well as the pre-infusion CAR T cell products, and processed through 10x Genomics' single-cell gene expression and V(D)J sequencing platform using the standard protocol. We identified a unique and unexpected transcriptional signature in a subset of pre-infusion CAR T cells that shared TCRs with post-infusion cytotoxic effector CAR T cells. Functional validation of cells with even a subset of these pre-effector markers demonstrated their immediate cytotoxic potential and resistance to exhaustion.</span></p>
Supplementary Data for "The shaky foundations of simulating single-cell RNA sequencing data"
<p>Supplementary Data for "The shaky foundations of simulating single-cell RNA sequencing data"</p> <p>See description.txt, Supplementary Text, Methods and https://github.com/HelenaLC/simulation-comparison for further description of the files available here.</p>
Sequence tracks of Ago2 Neural stem cells and differentiated neurons from single-cells- Related to Fig. 5
<p>Single neural stem cells were isolated from the Hippocampus of newborn mice generated from a hybrid cross. Some of these cells were differentiated In vitro and either the NSC or differentiated neurons were lysed and underwent a reverse transcription. The newly formed cDNA was used as a template to amplify expressed Ago2 transcript which was then sent off for Sanger sequencing. A SNP located within the exon was used to determine whether the transcript from that cell was generated from the maternal or paternal allele.</p>
Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19
<p>The biological determinants underlying the range of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multivariate model classifying COVID-19 severity (multi-class AUC<sub>training</sub> = 0.799, p-value = 4.2e-6; multi-class AUC<sub>validation</sub> = 0.773, p-value = 7.7e-6). Examination of informative model features reveals novel biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.</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>
Datasets accompanying "Deciphering the heterogeneity of differentiating hPSC-derived corneal limbal stem cells through single-cell RNA-sequencing"
<p>Datasets include two seurat objects: one with all unfiltered cells and raw expression data (seu_unfiltered.rds) and one dataset with normalised expression and latest annotations (differentiation_object_latest.rds). Additionally, a single-cell object containing post-(py)SCENIC analysis is added (ipsc_scenic.h5ad).</p>
Identifying cell states in single-cell RNA-seq data at statistically maximal resolution
<p>In this repository we provide the datasets for the results of the Cellstates method as shown in the article: “Identifying cell states in single-cell RNA-seq data at statistically maximal resolution” by Pascal Grobecker, Thomas Sakoparnig, and Erik van Nimwegen.</p> <p>A preprint is available under the following DOI: https://doi.org/10.1101/2023.10.31.564980</p> <p>There is a README file that describes the contents and formats of each of the tab-separated values files. There is one subdirectory containing files with Cellstates' results on the dataset of Zeisel et al. (DOI: 10.1016/j.cell.2018.06.021) which is accompanied by another README file describing the formats and contents of these files. </p>
Analysis of public single-cell sequencing database of COVID lung samples
<p>Lung endothelial cells from three published scRNA-seq datasets (GSE122960, GSE149878, GSE171668) of healthy subjects and COVID-19 patients were collected for further integrative analyses. The endothelial cells were classified into three sub-groups according to their distinguished expression of IL7R, DKK2, and EDNRB. For differential analysis of gene expression, counts per million of aggregated UMIs in each group were adopted in Wilcoxon rank-sum test.</p>
STING OPS: BJ1 Secondary Screen Single-Cell Features
<p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for BJ1 fibroblast secondary screen.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_BJ1.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</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>
STING OPS: HeLa cGAMP (6 hours) Secondary Screen Single-Cell Features
<div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa secondary screen, 6 hours post-cGAMP, with or without rolling ball background subtraction.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_HeLa.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> <p> </p> </div>
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>
STING OPS: HeLa Unstimulated Secondary Screen Single-Cell Features
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa secondary screen, unstimulated, without rolling ball background subtraction.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_HeLa.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> <p> </p> </div> <p> </p> </div>
STING OPS: HeLa Unstimulated Secondary Screen Single-Cell Features (Background-Subtracted)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa secondary screen, unstimulated, with rolling ball background subtraction.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_HeLa.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> <p> </p> </div> <p> </p> </div>
STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 2/5)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 2/5.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 5/5)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 5/5.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
STING OPS: HeLa cGAMP (4 hours) Secondary Screen Single-Cell Features
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa secondary screen, 4 hours post-cGAMP, with or without rolling ball background subtraction.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_HeLa.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 1/5)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 1/5.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 3/5)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 3/5.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
ScienceDex guides
Understand access before you commit
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.