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2,598 results for “single cell sequencing”
Single-cell naïve IgM VH:VL sequence data from 22 Kymice
<p>Single-cell VH:VL sequencing data derived from naïve B-cells isolated from 22 Kymice. This dataset is published as part of the review process for the following preprint: https://www.biorxiv.org/content/10.1101/2022.06.27.497709v1. </p>
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>
Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing
<p>Supplementary data that are needed to rerun the reproducible notebooks from the first steps using Alevin output and configuration files.</p> <p>Intermediate R data object that can be used to rerun the reproducible notebooks after the filtering steps.</p> <p>Validation data for inferring the sample index hopping rate. The <em>hiseq4000_joined_datatable_plexed_nonplexed.zip file contains read counts for four samples (two non-multiplexed and two multiplexed) joined by a cell-barcode, UMI, and gene-ID (CUG) key combination. The hiseq4000_inner_joined_with_labels.zip file contains only those CUGs that are observed in both the non-multiplexed and multiplexed samples.</em><em> </em></p>
Single-cell RNA sequencing of sclerotome-derived fibroblasts in zebrafish
<p>Despite their importance in tissue maintenance and repair, fibroblast diversity and plasticity remain poorly understood. Using single-cell RNA sequencing, we uncover distinct sclerotome-derived fibroblast populations in zebrafish, including progenitor-like perivascular/interstitial fibroblasts, and specialized fibroblasts such as tenocytes. To determine fibroblast plasticity <em>in vivo</em>, we develop a laser-induced tendon ablation and regeneration model. Lineage tracing reveals that laser-ablated tenocytes are quickly regenerated by preexisting fibroblasts. By combining single-cell clonal analysis and live imaging, we demonstrate that perivascular/interstitial fibroblasts actively migrate to the injury site, where they proliferate and give rise to new tenocytes. By contrast, perivascular fibroblast-derived pericytes or specialized fibroblasts, including tenocytes, exhibit no regenerative plasticity. Interestingly, active Hedgehog (Hh) signaling is required for the proliferation of activated fibroblasts to ensure efficient tenocyte regeneration. Together, our work highlights the functional diversity of fibroblasts and establishes perivascular/interstitial fibroblasts as tenocyte progenitors that promote tendon regeneration in a Hh signaling-dependent manner.</p>
Single-cell RNA sequencing of human salivary gland derived mesenchymal stromal cells under cytokine treatment conditions
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Single-cell RNA sequencing of sclerotome-derived fibroblasts in zebrafish
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Single-cell RNA sequencing of the testis of drive and standard Teleopsis dalmanni males
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Single and half-cell RNA-sequencing in Stentor coeruleus control and beta-tubulin knockdown cells
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Global Characterization of Megakaryocytes in Bone Marrow, Peripheral Blood, and Cord Blood by Single-cell RNA Sequencing
<p><span><span>Megakaryocytes (MK) are mainly derived from bone marrow (BM) and are mainly involved in platelet production. Recent studies have shown that MK derived from BM may have immune function, and that MK from peripheral blood (PB) are associated with prostate cancer. We analyzed more than 1.2 million single-cell transcriptome data from 132 samples of PB, BM, and cord blood (CB) from healthy individuals and patients, and obtained 4474 MK single cell and 14 MK subtypes. We found that MK were widely distributed and the amount of MK in PB was more than that in BM and there were specificity MK subtypes in PB. We found classical MK1 with typical MK characteristics and non-classical MK2 closely related to immunity which was the most common subtype in BM and CB. Classical MK1 was closely related to Non-Small Cell Lung Cancer (NSCLC) and has diagnostic ability. MK2 may have potential adaptive immune function and play a role in tumor NSCLC and autoimmune diseases Systemic Lupus Erythematosus. This study deepened our understanding of MK and suggested that MK had potential immune functions and was involved in various diseases.</span></span></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>
Single-cell immune repertoire sequencing of two convalescent COVID-19 patients
<p>Single-cell immune repertoire sequencing of two convalescent COVID-19 patients using 10x genomics 5' immune profiling. Resulting output files are from the count and vdj functions from 10x genomic's cellranger v3.1.0. </p>
AnnData files for "Human dermal fibroblast subpopulations are conserved across single-cell RNA sequencing studies"
<p>AnnData files for "Human dermal fibroblast subpopulations are conserved across single-cell RNA sequencing studies". </p> <p>Includes Joined dataset with all four datasets at once.</p>
Sequence data for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - single cells dataset
<p>Sequence data (Illumina MiSeq runs) for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol". dataset of 2300 single cells. File names indicate unique sequencing runs. In the manuscripts, the informations about cell lines are found in the Supplemental Table 1. </p>
Sequence data for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - Protocol optimization
<p>Sequence data (Illumina MiSeq runs) for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol". Optimization of the protocol. Files names indicate unique run identifiers. In the manuscript, the link between unique run identifiers, cells and purpose of the experiment is found in the Supplemental Table 1. </p>
Human bone marrow assessment by single-cell RNA sequencing
<p>Seurat objects and CyTOF data for <a href="https://doi.org/10.1172/jci.insight.124928">10.1172/jci.insight.124928</a></p> <p>Please run UpdateSeuratObject() after loading. Small object contains annotated metadata with cca and tsne analyses. Large object contains additional reductions (eg umap). </p>
Cross-disease integration of single-cell RNA sequencing data from lung myeloid cells reveals TAM signature in in vitro model
<p>Single cells from a 3D human cell-based model comprising tumor cell line-derived spheroids, cancer-associated fibroblasts and primary monocytes were dissociated and analyzed using scRNAseq. 4 monocyte donors were used in the 3D model, and 3 monocyte donors were used for 2D differentiation of macrophages.</p>
Single-Cell RNA-Sequencing Reveals Placental Response under Environmental Stress
<p>This repository provides scRNA-seq data corresponding to the manuscript "Single-Cell RNA-Sequencing Reveals Placental Response under Environmental Stress" by Van Buren, Azzara, Rangel-Moreno, de la Luz Garcia-Hernandez, Murphy, Cohen, Lin, and Park. The repository includes both count by gene matrices output from CellRanger version 6.0.1 (file names *_filtered_feature_matrix.h5 for each of the eight samples Control_1_M, Control_1_F, Control_2_M, Control_2_F, As_1_M, As_1_F, As_2_M, As_2_F), and a finalized Seurat object including cell type assignments as used for analyses in the manuscript (file name final_Seurat_obj.RData). Accompanying code used in analysis can be found at https://github.com/edvanburen/placenta_code.</p>
Single-cell RNA sequencing reveals dysregulated cellular programs in the inflamed epithelium of Crohn's disease patients.
<p><strong>Crohn’s disease (CD) is a complex inflammatory disorder of incompletely understood molecular aetiology. We generated a large single-cell RNA sequencing dataset from the terminal ileal biopsies of two independent cohorts comprising a total of 50 CD patients and 71 healthy controls. We performed transcriptomic analyses to reveal genes, cell types and mechanisms perturbed in CD, leveraging the power of the two cohorts to confirm our findings and assess replicability. In addition to mapping widespread alterations in cytokine signalling, we provide evidence of pan-epithelial upregulation of MHC class I genes and pathways in CD. Using non-negative matrix factorization we revealed intra- and inter-cellular upregulation of expression programs such as G-protein coupled receptor signalling and interferon signalling, respectively, in CD. We observed an enrichment of CD heritability among marker genes for various activated T cell types and myeloid cells, supporting a causal role for these cell-types in CD aetiology. Comparisons between our discovery and replication cohort revealed significant variation in differential gene-expression replicability across cell types. B, T and myeloid cells showed particularly poor replicability, suggesting caution should be exercised when interpreting unreplicated differential gene-expression result in these cell types. Overall, our results provide a rich resource for identifying cell-type specific biomarkers of Crohn’s disease and identifying genes, cell types and pathways that are causally and replicably associated with disease.</strong></p>
Human breast cancer PDTX models bulk and single cell RNA sequencing
<p>This dataset includes information relevant to the following manuscript from the labs of Prof. Carlos Caldas (University of Cambridge), and Dr. Long V. Nguyen (Princess Margaret Cancer Centre, University Health Network):</p> <p>Nguyen LV et al. Dynamics and plasticity of human breast cancer single cell-derived clones. Under consideration for publication.</p> <p>Bulk RNA sequencing raw count matrices are provided (RawCounts.csv) along with the normalized count matrices (LogCPMNormCounts.csv).</p> <p>Single cell RNA sequencing count matrix processed from R package metacell is provided (mat.pdx_LN_v2_filt.Rda), along with the mc and mc2d files with information on metacell partitions (mc.pdx_LN_v2_filt.Rda and mc2d.pdx_LN_v2_filt.Rda).</p> <p>Single cell RNA sequencing count matrices processed using Seurat are also provided separately for each PDTX model analysed (STG139.rds, STG201.rds, AB040.rds and IC07.rds).</p> <p>Code and information on data analysis is provided for reviewers in our unpublished manuscript and on Github (https://github.com/cclab-brca/clone-dynamics).</p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data : </strong></p> <p>1. Table1_full_version.docx : Marker genes for cell clusters of global Seurat analysis<br> 2. Table2_full_version.docx : List of the Immgen samples used to generate the reference compendium for CMAP signature generation<br> 3. Table5_full_version.docx : Top 20 marker genes for cell clusters of the Seurat analysis on selected cDC1s</p> <p> </p> <p> </p>
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.