Data from: Single cell RNA-seq data of E18 fetal thymocytes from HEB Vav-iCre and Id3-KO mice and their wild type littermate counterparts
Data files
Dec 08, 2025 version files 179.15 MB
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Final_for_HEBcKO.Rmd
19.91 KB
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Final_for_Id3KO.Rmd
15.18 KB
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HEBcKO_cell_identity_barcodes.tsv.gz
28.15 KB
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HEBcKO_gene_expression_levels_matrix.mtx.gz
39.59 MB
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HEBcKO_gene_name_features.tsv.gz
279.36 KB
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HEBcKO_rmd.html
20.75 MB
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HEBWT_cell_identity_barcodes.tsv.gz
17.42 KB
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HEBWT_gene_expression_levels_matrix.mtx.gz
27.28 MB
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HEBWT_gene_name_features.tsv.gz
279.36 KB
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Id3KO_cell_identity_barcodes.tsv.gz
21.41 KB
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Id3KO_gene_expression_levels_matrix.mtx.gz
51.46 MB
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Id3KO_gene_name_features.tsv.gz
290.90 KB
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Id3KO_rmd.html
10.35 MB
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Id3WT_cell_identity_barcodes.tsv.gz
20.07 KB
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Id3WT_gene_expression_levels_matrix.mtx.gz
28.44 MB
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Id3WT_gene_name_features.tsv.gz
290.90 KB
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README.md
8.10 KB
Abstract
γδ T cells that produce IL-17 (γδT17) play essential roles in barrier immunity and autoimmunity, but the gene networks that install their functions are not well understood. To understand how HEB and Id3 regulate γδT17 cell development, we conducted single-cell RNA-sequencing on fetal thymic γδ T cells from Tcf12-deficient (HEB cKO) and Id3 knockout (Id3-KO) E18 embryos. Four datasets were generated. The first two consist of WT and HEB cKO datasets derived from sorted gamma-delta T cells. The second two consist of WT and Id3-KO datasets derived from magnetically enriched for CD4/CD8-negative cells. HEB datasets were generated with 10X Genomics 5' chemistry, and Id3 datasets were generated with 10X Genomics 3' chemistry. Raw sequence files were processed using Cell Ranger to generate matrix files, which have been uploaded here. We also include R-markdown files (RMD) and R-markdown HTML output files to provide code and programs used for the data analysis shown in the associated paper.
Dataset DOI: 10.5061/dryad.08kprr5cq
Description of the data and file structure
Title of dataset: scRNA-seq analysis of E18 fetal thymocytes lacking HEB or Id3 transcriptional regulators
Author/Principal Investigator Information
Name: Michele K. Anderson
ORCID: 0000-0002-8820-5910
Institution: Sunnybrook Research Institute, University of Toronto
Address: 2075 Bayview Ave, M7-615, Toronto, ON Canada M4N 3M5
Email: manderso@sri.utoronto.ca
Date of data collection: December 2020 (HEB cKO), June 2022 (Id3-KO)
Geographic location of data collection: Toronto, Canada
Funding Sources: Canadian Institutes for Health Research, National Institutes of Health
Description of the Project
γδ T cells that produce IL-17 (γδT17) play essential roles in barrier immunity and autoimmunity, but the gene networks that install their functions are not well understood. To understand how the transcriptional regulators HEB and Id3 regulate γδT17 cell development, we conducted single cell RNA-sequencing on 1) fetal thymocytes from mice with a germline deletion of Id3 in parallel with their WT littermates, and 2) fetal thymic γδ T cells from mice lacking HEB in all hematopoietic cells in parallel with their WT littermates. To obtain WT and Id3-KO cells, timed matings were performed by placing male and female Id3+/- mice together overnight. The pair was separated the next day to give a known day of conception for those mice that were pregnant. Embryos were harvested eighteen days later, and tail tissue was taken used for genotyping by PCR. HEB cKO embryos were obtained from timed matings of HEB cKO Vav-Cre+ and HEB cKO Vav-Cre- mice. Thymic lobes were harvested, and single cell suspensions were obtained by pushing the cells through wire mesh into ice cold 1XHBSS buffer. For HEB cKO experiments, cells of like genotypes were pooled and stained with antibodies that detect CD3 and TCRγδ, followed by isolation of CD3+γδTCR+ cells by fluorescence activated cells sorting. For Id3-KO experiments, cells of the same genotype were pooled and stained with antibody-labeled magnetic beads against CD4 and CD8. Double negative (DN; CD4-CD8-) cells, which are enriched for gd T cells, were isolated by magnetic sorting using MACS columns (Miltenyi). HEB cKO gd T cells and Id3 DN cells were subjected to single cell RNA sequencing using the 10X Genomics platform, and data was analyzed using R-Seurat.
Description of scRNA-sequencing technology and processing platform
The HEB cKO dataset was generated with the 10X Genomics platform using the Chromium Next GEM Single Cell 5' Kit v2 (Dual Index). Next generation Illumina sequencing was performed to an approximate depth of 25,000 reads. The count matrix was generated from raw FASTQ files.
The Id3-KO dataset was generated using the 10X Genomics platform using the Chromium Next GEM Single Cell 3' Kit v2 (Dual Index) (10X Genomics). Next generation Illumina sequencing was performed to an approximate depth of 100,000 reads. The count matrix was generated from raw FASTQ files.
Files and variables
Description of Samples:
HEB cKO samples:
Species: Mouse
Strain: C57Bl/6
Allele 1 (HEB fl/fl): HEB flox/flox (normal WT), loxP sites surrounding the helix-loop-helix domain in the Tcf12gene locus abbreviated as HEB fl/fl
Allele 2: Vav-Cre, deletes regions between loxP sites in all hematopoietic cells, including hematopoietic stem cells
Control mice (“WT”): HEB fl/fl, without Vav-Cre, HEB intact
Experimental mice(“HEB cKO”): HEB fl/fl, with Vav-Cre, HEB deleted from all hematopoietic cells
Cells: sorted gamma delta (gd) T cells (gdT)
Tissue: fetal thymus, embryonic day 18
Id3-KO samples:
Species: Mouse
Strain: C57Bl6
Allele (Id3-KO): red fluorescence protein (RFP) was inserted into the Id3 gene locus to create an Id3 RFP knock-in knock-out in mice (KI/KO) with homozygous mutant alleles. We bred these mice to obtain them on the B6 background.
Control mice (“WT”): no RFP KI/KO allele
Experimental mice (“Id3-KO”): Homozygous Id3 RFP KI/KO alleles
Cells: Magnetic bead enriched double negative (CD4-CD8-) thymocytes
Tissue: fetal thymus, embryonic day 18
#Description of the Files
HEBcKO_E18gd_scRNAseq_matrix/
-gdT_WT_matrix/
- HEBWT_cell_identity_barcodes.tsv.gz
- HEBWT_gene_name_features.tsv.gz
- HEBWT_gene_expression_levels_matrix.mtx.gz
- gdT_HEBcKO_matrix/
- HEBcKO_gene_expression_levels_matrix.mtx.gz
- HEBcKO_cell_identity_barcodes.tsv.gz
- HEBcKO_gene_name_features.tsv.gz
Id3KO_E18_DN_scRNAseq_matrix/
-Id3KO_matrix/
-Id3KO_gene_expression_levels_matrix.mtx.gz
-Id3KO_gene_name_features.tsv.gz
-Id3KO_cell_identity_barcodes.tsv.gz
-Id3WT_matrix/
-Id3WT_gene_expression_levels_matrix.mtx.gz
-Id3WT_gene_name_features.tsv.gz
-Id3WT_cell_identity_barcodes.tsv.gz
Final_for_HEBcKO.Rmd is the R-markdown file for the Seurat analysis code used to generate HEBcKO scRNA-seq figures, and HEBcKO_rmd.html file is the html output of the analysis run with that code.
Final_for_Id3KO.Rmd is the R-markdown file for the Seurat analysis code used to generate Id3-KO scRNA-seq figures, and Id3KO_rmd.html file is the html output of the analysis run with that code.
Description of the Matrix Files
This directory contains the gene expression count matrix and associated metadata files generated from single-cell RNA sequencing data.
The data is provided as a set of three files in the standard 10x Genomics output format (compressed Matrix Market format).
`matrix.mtx.gz`: The core count data as a sparse matrix.
`features.tsv.gz`: Corresponds to the rows of the matrix, containing gene identifiers and symbols.
`barcodes.tsv.gz`: Corresponds to the columns of the matrix, containing cell barcodes.
Matrix Structure and Content
Dimensions: The matrix is a gene-by-cell matrix, where rows represent individual genes and columns represent individual cells.
Data Type: The entries are raw unique molecular identifier (UMI) counts.
Matrix values: Each value represents the number of UMIs mapped to a specific gene in a given cell.
Code/software
For both the HEB cKO and Id3-KO datasets, the count matrix was generated from raw FASTQ files using the following Cell Ranger software and the Alignment/Quantification tool STAR.
HEB cKO sequences were aligned to the Reference Genome: Mouse, mm10.
Id3-KO sequences were aligned to the Reference Genome: Mouse, mm39.
Usage Example
To load and begin working with this data in a common bioinformatics environment, you might use a tool like Seurat in R.
R (Seurat): Use the Read10X() function to load the data directory.
Code and software
scRNA-seq matrix files were analyzed using programs in R-Seurat. All code and programs are included in R-markdown files (RMD and html output)
Timed matings were set up with HEBfl/fl Vav-iCre (HEB cKO) mice and HEB fl/fl (HEB WT) mice, and for Id3-KO and WT (Id3-WT) mice. Embryos were harvested 18 days later (E18). Fetal thymuses were dissected and genotyped, and cells were pooled into four groups by genotype. Single cell suspensions were made by pushing the cells through wire mesh into 1XHBSS. For HEB mice, cells were stained with antibodies for CD3 and TCRγδ, and CD3+γδTCR+ cells were sorted and subjected to scRNA-seq using the Chromium Next GEM Single Cell 5' Kit v2 (Dual Index) (10X Genomics). For Id3 mice, CD4-CD8- cells were enriched by magnetic sorting and subjected to scRNA-seq using the Chromium Next GEM Single Cell 3' Kit v2 (Dual Index) (10X Genomics). For HEB cKO samples, next-generation Illumina sequencing was performed to a depth of ~20,000 reads, whereas Id3-KO samples were sequenced to a depth of approximately 100,000 reads. HEB cKO fastq files were aligned to the mouse mm10 genome, and Id3-KO fastq files were aligned to the mouse mm39 genome. These alignments were used to generate matrix files, which can be analyzed using R-Seurat or other scRNA-seq analysis packages.