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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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publicOct 2024View details →

Single cell RNA sequencing provides clues for the developmental genetic basis of Syngnathidae’s evolutionary adaptations

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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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publicOct 2024View details →

Single cell RNA sequencing of human tissue along the stomach-intestinal tract

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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 2022View details →

An anti-influenza combined therapy assessed by single cell RNA-sequencing

Figures source data for "An anti-influenza combined therapy assessed by single cell RNA-sequencing"

opencc-by-4.0Apr 2024View details →

The single cell RNA sequencing of Bullous Pemphigoid

The Sparse count tables in 10XGenomics format (barcodes.tsv.gz features.tsv.gz matrix.mtx.gz) of 33 samples as detailed  in Nat Commun 15, 5949 (2024).   Including single-cell RNA sequencing (scRNA-seq) from five lesions of  Bullous Pemphigoid (BP) patients and eight normal skin of healthy donors, eight PBMC of BP patients and eight PBMC of healthy donors, four blister of BP patients. If you utilize this dataset in your research, kindly cite our article:  Liu, T., Wang, Z., Xue, X. et al. Single-cell transcriptomics analysis of bullous pemphigoid unveils immune-stromal crosstalk in type 2 inflammatory disease. Nat Commun 15, 5949 (2024). https://doi.org/10.1038/s41467-024-50283-3.

opencc-by-4.0Sep 2024View details →

Deep Learning Based Models for Preimplantation Mouse and Human Embryos Based on Single Cell RNA Sequencing

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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.