Dataset Information

WcPteSpea

ABSTRACT: Pterobdella sp. o SKG-2022 (leech)

PROVIDER: PRJEB75167 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other
	ERR12945506_1.fastq.gz	Fastqsanger.gz
	ERR12945506_2.fastq.gz	Fastqsanger.gz
	ERR12954108.fastq.gz	Fastqsanger.gz
	ERR13669977_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 6

Similar Datasets

Project description:Purpose: The goals of this study were to study the transcriptome and TCR repertoire of naive CD4 T cells before the induction of arthritis in the SKG and wild-type (WT) mouse with eGFP-Nur77 as a reporter of antigen stimulation (SKGNur and WTNur). Methods: Bulk RNA sequencing was used to generate the mRNA profiles for the top 10% and bottom 10% of GFP expressing cells from three SKGNur and three WTNur BALB/c mice resulting in 12 samples overall (3 WT Low, 3 WT High, 3 SKG Low, 3 SKG High) using the Truseq Stranded mRNA kit followed by sequencing on a HiSeq 2500 sequencer. The sequence reads that passed quality filters were aligned with STAR and then analyzed with DESeq2. Results: We mapped about 38,000,000 million reads per sample to the mouse genome (mm10). The differential expression pairwise comparisons (WT Low v WT High, WT Low v SKG Low, SKG Low v SKG High, and WT High v SKG High) generated a collection of 991 differentially expressed genes with log2FC >1 and Benjamini-Hochberg adjusted p value < 0.05. Hierarchical clustering of differentially expressed genes uncovered six modules of gene expression which highlighted heterogeneity within the naive CD4 T cell compartment and transcriptional signatures associated with the SKG High samples. Methods: Single cell RNA gene expression and TCR sequencing libraries were generated for the top 10% and bottom 10% of GFP expressing cells from two SKGNur and two WTNur BALB/c mice resulting in 8 samples overall (2 WT Low, 2 WT High, 2 SKG Low, 2 SKG High) using the 5' 10x single cell 5"+V(D)J v1 kit and were sequenced on a NovaSeq 6000 sequencer. The reads were aligned using cellranger and analyzed using a combination of single cell tools including scanpy and scvelo. Results: We mapped about 785,000,000 sequence reads and about 379,000,000 sequence reads per sample for the gene expression and TCR libraries to the mouse genome (build mm10 with the addition of the eGFP transcript) and TCR reference (vdj GRCm38 v 3.1.0), respectively, using cellranger. We identified 31,054 transcripts across 101,869 cells and recovered paired TRA and TRB chains from more than 80% of cells. After filtering cells and genes by our quality control metrics, we identified 1119 highly variable genes which were used for PCA analysis to create a nearest neighbors graph for leiden clustering and UMAP projection. We identified 13 clusters that we collapsed into 9 cell sub-types. Within the cluster (T.4_Nr4a1) exclusively expressing our marker gene of interest, Nr4a1, we found that SKG High cells upregulated TCR signaling genes despite their impaired impaired TCR signaling ability due to a Zap70 mutation. Within the T.4Nr4a1 cluster we found two states of acute to chronic TCR signaling, marked by Egr2 and Tnfrsf9, and we used trajectory inference to uncover a continuum of cell states with these acute to chronic TCR signaling states as the endpoints. Interestingly, SKG High cells in the T.4 Nr4a1 cluster seemed arrested in the earlier more acute TCR signaling state suggesting they are unable to appropriately fully upregulate the chronic TCR signaling and more anergic cell state. We also uncovered TRBV restriction within the SKG High samples and specifically within the SKG High cells within the T.4 Nr4a1 cluster that seems to be driven by a failure of negative selection by endogenously expressed super antigens from MMTVs present in the BALB/c mouse. Thus the naive CD4 T cell compartment within the SKG High cells are poised to enter into a reactive immune response via both a unique transcriptomic and TCR repertoire which may underlie the mechanism of high arthritogenic potential of these cells.

Dataset Information

WcPteSpea

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets