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opencc-zeroApr 2022View details →

Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency

Alzheimer's disease (AD) is a progressive neurodegenerative disease observed with aging that represents the most common form of dementia. To date, therapies targeting end-stage disease plaques, tangles, or inflammation have limited efficacy. Therefore, we set out to identify an earlier targetable phenotype. Utilizing a mouse model of AD we found that cell intrinsic neural precursor cell (NPC) dysfunction precedes widespread inflammation and amyloid plaque pathology, making it one of the earlier defects in the evolution of the disease. We demonstrate that reversing impaired NPC self-renewal via genetic reduction of USP16, a histone modifier and critical physiological antagonist of the Polycomb Repressor Complex 1, can prevent downstream cognitive defects and decrease astrogliosis in vivo. To delineate potential self-renewal pathways that might contribute to the defect and rescue of Tg-SwDI NPCs and Tg-SwDI/Usp16+/- NPCs, respectively, we performed single-cell RNA-seq and gene set enrichment analysis (GSEA) on lineage depleted primary FACS-sorted CD31-CD45-Ter119-CD24- NPCs from Tg-SwDI, WT, and Tg-SwDI/Usp16+/- mice at 3-4 months and 1 year of age. Using the GSEA Hallmark gene sets, we found only three gene sets that were enriched in Tg-SwDI mice over WT mice and rescued in the Tg-SwDI/Usp16+/- mice at both ages: TGF-ß pathway, oxidative phosphorylation, and Myc Targets. The TGF-ß pathway consistently had the highest normalized enrichment score in pairwise comparisons between Tg-SwDI vs WT and Tg-SwDI vs Tg-SwDI/Usp16+/- of the three rescued pathways. These data suggest that USP16 may regulate neural precursor cell function in part through the BMP pathway.

opencc-by-4.0Aug 2022View details →

Supplementary Data for "The shaky foundations of simulating single-cell RNA sequencing data"

Supplementary Data for "The shaky foundations of simulating single-cell RNA sequencing data" See description.txt, Supplementary Text, Methods and https://github.com/HelenaLC/simulation-comparison for further description of the files available here.

opencc-by-4.0May 2024View details →

Datasets accompanying "Deciphering the heterogeneity of differentiating hPSC-derived corneal limbal stem cells through single-cell RNA-sequencing"

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).

opencc-by-nc-sa-4.0Feb 2017View details →

Data for 'Comparative Analysis of Single-Cell RNA Sequencing Methods'

Raw sequencing data to "Comparative Analysis of Single-Cell RNA Sequencing Methods".  https://www.ncbi.nlm.nih.gov/pubmed/28212749   In addition to the GEO submission https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75790, you can find here raw bam files for UMI-methods tagged with cell barcode and UMI sequences. MD5 checksum: f10825509952fffd9c4dc0c1dcb9eb8e

opencc-by-4.0Jan 2023View details →

Single-cell RNA sequencing of mouse embryonic cells from the oocyte, 2-cell, 4-cell, 8-cell, blastocyst, and morula stages

STRT-N is a newly optimized single-cell RNA sequencing method for studies of early genome activation in mammalian preimplantation development. Single embryos from the oocyte, 2-cell, 4-cell, 8-cell, blastocyst, and morula stages were sampled for experiments and were sequenced using STRT-N method. Here is the raw data from STRTN-seq. FASTQ files are available in <a href="https://www.ebi.ac.uk/biostudies/studies/S-BSST976">BioStudies database</a>.

opencc-by-4.0Jul 2023View details →

Characterising neutrophil subtypes in cancer using human and murine single-cell RNA sequencing datasets

Single cell RNA sequencing data generated by 10xGenomics for Neutrophils derived from colorectal cancer (CRC) KPN tumours (CRC_KPN_counts.csv) and normalised counts (CRC_KPN_NormalisedCounts.csv) as well as from other mouse models of CRC carrying AKPT, BPN, BP and KP mutations (CRC_other_counts.csv and CRC_other_NormalisedCounts.csv), together with the relevant metadata (CRC_KPN_metadata.csv and CRC_other_metadata.csv).

publicFeb 2025View details →

Single-cell and spatial RNA sequencing identify divergent microenvironments and progression signatures in early- versus late-onset prostate cancer

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publicApr 2022View details →

Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency

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publicAug 2020View details →

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

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opencc-by-4.0Nov 2023View details →

Integrated single-cell RNA-sequencing data of unwounded and wounded mouse skin and fibroblasts.

This repository contains the .h5ad files that store the integrated scRNA-seq data we generated for the work, Almet et al. (2023), "Fibroblasts evolve in single-cell state to drive extracellular matrix and signaling changes across wound healing", to be published in the Journal of Investigative Dermatology.The integrated* files contain both raw counts, normalized counts, as well as unspliced and spliced count estimates that were obtained using kallisto|bustools and velocyto. We integrated the data from the following published datasets:<ol><li><a href=" https://doi.org/10.7554/eLife.60066">Phan et al. (2021)</a>: Unwounded P21 mice and small wound P21 + 7 mice</li><li><a href="https://doi.org/10.1016/j.celrep.2020.02.091">Haensel et al. (2020)</a>: Unwounded P49 mice and small wound P49 + 4 mice</li><li><a href="https://doi.org/10.1038/s41467-018-08247-x)">Guerrero-Juarez et al. (2019)</a>: Large wound day 12 mice</li><li><a href="https://doi.org/10.1016/j.stem.2020.07.008">Abbasi et al. (2020)</a>: Large wound day 14 mice</li><li><a href="https://doi.org/10.1126/sciadv.aay3704">Gay et al. (2020)</a>: Large wound fibrotic (hairless) and regenerative (hair follicle neogenesis) day 18 mice</li></ol>The unwounded_* files were used to briefly integrated unwounded skin scRNA-seq from mouse models of different ages that have been used to analyze wound healing in <a href="https://doi.org/10.1016/j.celrep.2020.02.091">Haensel et al. (2020),</a> <a href=" https://doi.org/10.7554/eLife.60066">Phan et al. (2021)</a>, and <a href="https://doi.org/10.1016/j.celrep.2022.111155">Vu et al. (2022)</a>, which generated scRNA-seq for unwounded skin from mice aged P21, P49, and P616, respectively. The data can be loaded using the Python package Scanpy or AnnData, but you can also load it in R if you use zellkonverter.

Single-cell RNA sequencing of CNS-infiltrating HSC-derived phagocytes of Ms4a3Ai14, BM chimeric mice (CD45.2 Csf2rb-/-: CD45.1 Csf2rb+/+ and CD45.2 Ifngr1-/-: CD45.1 Ifngr1+/+) using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.

Single-cell RNA sequencing of CNS-infiltrating HSC-derived phagocytes of Ms4a3Ai14 at onset and peak EAE,  BM chimeric mice (CD45.2 Csf2rb-/-: CD45.1 Csf2rb+/+ and CD45.2 Ifngr1-/-: CD45.1 Ifngr1+/+) using 10X Genomics platform. The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell. Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from Ms4a3Ai14 mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.

Single-cell RNA sequencing of Lymph node-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.

Single-cell RNA sequencing of Lymph node-infiltrating HSC-derived phagocytes of Ms4a3Ai14 at onset and peak EAE using 10X Genomics platform. The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell. Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from Ms4a3Ai14 mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.

Single-cell RNA sequencing of Bone Marrow-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.

Single-cell RNA sequencing of Bone Marrow-infiltrating HSC-derived phagocytes of Ms4a3Ai14 at onset and peak EAE using 10X Genomics platform. The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell. Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from Ms4a3Ai14 mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.

Single-cell RNA sequencing of Blood-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.

Single-cell RNA sequencing of Blood-infiltrating HSC-derived phagocytes of Ms4a3Ai14 at onset and peak EAE using 10X Genomics platform. The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell. Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from Ms4a3Ai14 mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.

opencc-by-4.0Jun 2022View details →

Integration of single-cell RNA-sequencing data across tissues and cancer types towards immune cell characterization

To better understand dendritic cell states and subtypes, we collected individual single-cell RNAseq datasets from various studies and further integrated, batch corrected, and reprocessed the data using Besca (https://github.com/bedapub/besca). The following files are included: 1) study_table_integrated_DCs.xlsx - contains a list of studies from where the datasets were gathered. 2)  int_dcs.raw.h5ad - An anndata object file containing the combined raw single-cell counts for DCs from individual studies. The datasets were joined based on the union of variables. 3) intersection_genes_integrated_dcs.tsv - List of genes if the datasets were joined based on the intersection of variables. These variables were used in the subsequent analyses. 4) int_dcs.annotated.h5ad - An anndata object file containing single-cell logarithmized counts for DCs data that have been integrated and reprocessed. The rows of the file contain cells, and the columns contain highly variable genes. A sparse matrix containing the logarithmized counts from all the genes (from the intersection genes integrated dcs.tsv file) can also be found (adata.raw.X) in the object. In the observations, cell-type annotation is available at three different hierarchal levels. This data was further used to produce results for the publication (https://jitc.bmj.com/content/10/6/e004268) on the effects of Toll-like receptor 8 agonists on conventional DCs.

opencc-by-4.0Sep 2024View details →

Single-cell RNA sequencing reveals immunosuppressive pathways associated with metastatic breast cancer

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dryad32/100

Spatial reconstruction of the early hepatic transcriptomic landscape after an acetaminophen overdose using single-cell RNA sequencing

We leveraged single-cell RNA sequencing to understand the early molecular events that define the hepatocyte response to acetaminophen exposure at a subpopulation level. We spatially assigned hepatocytes along the portol-central vein axis by using established landmark genes. By spatially assigning the hepatocytes were were able to account for innate differences in gene expression that existed along this gradient. The excel files herein provide the full list of differentially expressed genes between key subpopulations of interest. Additionally, we classified genes as either pericentral zonated, periportal zonated, or non-zonated, the full list of genes and their spatial assignments are included in the appropriate excel file.

opencc-zeroAug 2021View details →

opencc-by-4.0Jan 2023View details →

Benchmarking the Autoencoder Design for Imputing Single-Cell RNA Sequencing Data

This repository contains the real and synthetic datasets used in the paper "Benchmarking the Autoencoder Design for Imputing Single-Cell RNA Sequencing Data". The zip file includes three folders: 1. overall imputation accuracy: the 12 real scRNA-seq datasets used in the evaluation of overall imputation accuracy. 2. cell clustering: the 20 real scRNA-seq datasets with cell type labels used in the evaluation of cell clustering. 3. DE gene: the 20 scRNA-seq syntehtic datasets with ground-truth DE genes used in the evaluation of DE gene analysis. These datasets are simulated by simulator scDesign and 20 real datasets.

opencc-by-4.0Dec 2022View details →

Single-cell RNA sequencing of Sox17-expressing lineages reveals distinct gene regulatory networks and dynamic developmental trajectories

Two seurat objects contains single-cell RNA sequencing data that captures Sox17-expressing lineages during embryogenesis. sox17_integrated_Figure2B.rds : This is a seurat object that contains single-cell RNA sequencing data from integration of GFP+ cells produced from Sox17GFPCre allele marking cells that currently express Sox17 or short-term progeny of Sox17-expressing progenitors and TdTomato+ cells produced from R26LSL.TdTomato reporter allele in the presence of Sox17GFPCre marking long-term progeny of Sox17-expressing progenitors. Inferred cell types in this seurat object reflects Figure 2B in the article. sox17_Prox1_endoderm_Figure5A.rds : This is a seurat object that contain single-cell RNA sequencing data from integration of Sox17- and Prox1-expressing endoderm dataset. Prox-1 expressing endoderm data is from the Willnow et al. Nature(2021). Inferred cell types in this seurat object reflects Figure 5A in the article.