Find research datasets worth reusing

opencc-by-4.0Jul 2023View details →

Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells

scRNA sequencing datasets used in the paper titled 'Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells' published in Nature Communications https://www.nature.com/articles/s41467-020-18155-8

opencc-by-4.0Sep 2022View details →

Full-length, single-cell RNA-sequencing of human bone marrow subpopulations reveals hidden complexity

<a href="http://www.biorxiv.org/content/10.1101/2021.07.28.454226v2">Full-length, single-cell RNA-sequencing of human bone marrow subpopulations reveals hidden complexity</a> Bone marrow progenitor cell differentiation has frequently been used as a model for studying cellular plasticity and cell-fate decisions. Recent analysis at the level of single-cells has expanded knowledge of the transcriptional landscape of human hematopoietic cell lineages. Using single-molecule real-time (SMRT) full-length RNA sequencing, we have previously shown that human bone marrow lineage-negative (Lin-neg) cell populations contain a surprisingly diverse set of mRNA isoforms. Here, we report from single cell, full-length RNA sequencing that this diversity is also reflected at the single-cell level. From fresh human bone marrow unselected and lineage-negative progenitor cells were isolated by droplet-based single-cell selection (10xGenomics). The single cell-derived mRNAs were analyzed by full-length SMRT and short-read sequencing. In both samples we detected an average of 8000 different genes using short-read sequencing. Differential expression analysis arranged the single-cells of the total bone marrow into only four clusters whereas the Lin-neg population was much more diverse with nine clusters. mRNA isoform analysis of the single-cell populations using full-length sequencing revealed that Lin-neg cells contain on average 24% more novel splice variants than the total bone marrow cells. Interestingly, among the most frequent genes expressing novel isoforms were members of the spliceosome, e.g. HNRNPs, DEAD box helicases and SRSFs. Mapping the isoforms from all genes to the cell type clusters revealed that total bone marrow cells express novel isoforms only in a small subset of clusters. On the other hand, lineage-negative progenitor cells expressing novel isoforms were present in nearly all subpopulations. In conclusion, on a single-cell level lineage-negative cells express a higher diversity of genes and more alternatively spliced novel isoforms suggesting that cells in this subpopulation are poised for different fates.

opencc-by-4.0Apr 2022View details →

Data files: Single-cell RNA sequencing of Plasmodium vivax sporozoites reveals stage- and species-specific transcriptomic signatures

Scripts, preprocessed count matrices, single-cell data objects, and generated data (tables and .rds files) from the scRNA-seq analyses performed in “Single-cell RNA sequencing of Plasmodium vivax sporozoites reveals stage- and species-specific transcriptomic signatures".

opencc-by-4.0May 2024View details →

Single-cell and single-nucleus RNA-sequencing from paired normal-adenocarcinoma lung samples provides both common and discordant biological insights

The datasets generated by Cellranger for all 24 samples (.h5 format).

opencc-by-4.0Jun 2024View details →

FedscGen: privacy-aware federated batch effect correction of single-cell RNA sequencing data -- Preprocessed datasets

<div> <div> <div> <div> This dataset accompanies the publication "FedscGen: Privacy-Aware Federated Batch Effect Correction of Single-Cell RNA Sequencing Data" and includes eight single-cell RNA sequencing (scRNA-seq) datasets used to benchmark the FedscGen and scGen methods. The datasets are provided in <code>.h5ad</code> format and include comprehensive metadata necessary for replication and further analysis. <h3>Datasets</h3> We analyze various datasets to compare FedscGen against scGen (centralized) in terms of batch correction. For simplicity, we refer to the dataset by abbreviations: <ol> <li> Cell Line (CL): <ul> <li>Derived from the 293t_jurkat experiment with three batches: Zheng et al., 2017.</li> </ul> </li> <li> Human Dendritic Cells (HDC): <ul> <li>scRNA-seq data of human dendritic cells across two batches: Villani et al., 2017.</li> </ul> </li> <li> Human Pancreas (HP): <ul> <li>Consolidated data from five sources with 14,767 cells each: Baron et al., 2016; Muraro et al., 2016; Segerstolpe et al., 2016; Wang et al., 2016; Xin et al., 2016.</li> </ul> </li> <li> Mouse Brain (MB): <ul> <li>Merged datasets with 691,600 and 141,606 cells: Saunders et al., 2018; Rosenberg et al., 2018.</li> </ul> </li> <li> Mouse Cell Atlas (MCA): <ul> <li>Data focusing on 11 cell types from various organs: Han et al., 2018; The Tabula Muris Consortium, 2018.</li> </ul> </li> <li> Mouse Hematopoietic Stem and Progenitor Cells (MHSPC): <ul> <li>Data from SMART-seq2 and MARS-seq protocols: Nestorowa et al., 2016; Paul et al., 2015.</li> </ul> </li> <li> Mouse Retina (MR): <ul> <li>Data from two unassociated laboratories with 26,830 and 44,808 cells: Macosko et al., 2015; Shekhar et al., 2016.</li> </ul> </li> <li> PBMC (human Peripheral Blood Mononuclear Cell): <ul> <li>scRNA-seq data with two batches: Zheng et al., 2017.</li> </ul> </li> </ol> Usage Notes: Each dataset is provided in <code>.h5ad</code> format, compatible with common single-cell analysis tools such as Scanpy. Detailed metadata is included within each file. Keywords: Single-cell RNA sequencing, scRNA-seq, Batch effect correction, Privacy-aware, Federated learning, scGen, FedscGen, Clinical multi-center studies, Genomics, Bioinformatics Contact: For questions or further information, please contact Mohammad Bakhtiari at <a href="mailto:mohammad.bakhtiari@uni-hamburg.de.">mohammad.bakhtiari@uni-hamburg.de.</a> License: Creative Commons Attribution 4.0 International (CC BY 4.0) </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>

opencc-by-4.0Dec 2018View details →

A comparison of automatic cell identification methods for single-cell RNA-sequencing data

Benchmark datasets used to evaluate the performance of 22 classifiers for cell type classification for scRNA-seq data

opencc-by-4.0Jul 2019View details →

Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing

Supplementary data that are needed to rerun the reproducible notebooks from the first steps using Alevin output and configuration files. Intermediate R data object that can be used to rerun the reproducible notebooks after the filtering steps. Validation data for inferring the sample index hopping rate. The 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.

opencc-zeroOct 2023View details →

Single-cell RNA sequencing of sclerotome-derived fibroblasts in zebrafish

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 in vivo, 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.

publicNov 2025View details →

Single-cell RNA sequencing of human salivary gland derived mesenchymal stromal cells under cytokine treatment conditions

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publicOct 2023View details →

Single-cell RNA sequencing of sclerotome-derived fibroblasts in zebrafish

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publicAug 2025View details →

Single-cell RNA sequencing of the testis of drive and standard Teleopsis dalmanni males

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dryad36/100

Global Characterization of Megakaryocytes in Bone Marrow, Peripheral Blood, and Cord Blood by Single-cell RNA Sequencing

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.

opencc-zeroAug 2020View details →

opencc-by-4.0Sep 2020View details →

AnnData files for "Human dermal fibroblast subpopulations are conserved across single-cell RNA sequencing studies"

AnnData files for "Human dermal fibroblast subpopulations are conserved across single-cell RNA sequencing studies".  Includes Joined dataset with all four datasets at once.

opencc-by-4.0Dec 2018View details →

Human bone marrow assessment by single-cell RNA sequencing

Seurat objects and CyTOF data for <a href="https://doi.org/10.1172/jci.insight.124928">10.1172/jci.insight.124928</a> Please run UpdateSeuratObject() after loading. Small object contains annotated metadata with cca and tsne analyses. Large object contains additional reductions (eg umap).

opencc-by-4.0Jun 2024View details →

Cross-disease integration of single-cell RNA sequencing data from lung myeloid cells reveals TAM signature in in vitro model

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.

opencc-by-4.0Dec 2023View details →

Single-Cell RNA-Sequencing Reveals Placental Response under Environmental Stress

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.

opencc-by-4.0Aug 2023View details →

Single-cell RNA sequencing reveals dysregulated cellular programs in the inflamed epithelium of Crohn's disease patients.

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.