GEO series

CHD7 promotes anti-viral short-lived effector CD8+ T cells [RNA-seq]

GSE274736 Mus musculus (mouse) RNA-seq (gene expression) 17 samples Submitted 2024/09/23 Platform GPL19057

At a glance

RNA-seq (gene expression) · mouse · 17 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Mus musculus (mouse)
Samples: 17 samples: 6 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper: In Vivo CRISPR Screening Reveals CHD7 as a Positive Regulator of Short-lived Effector CellsJournal of immunology (Baltimore, Md. : 1950) · 2024

Summary (written by the submitters)

CD8+ T cells differentiate into two subpopulations in response to acute viral infection: memory precursor effector cells (MPECs) and short-lived effector cells (SLECs). MPECs and SLECs are epigenetically distinct; however, the epigenetic regulators required for formation of these subpopulations are mostly unknown. Here we performed an in vivo CRISPR screen in murine naive CD8+ T cells to identify the epigenetic regulators required for MPEC and SLEC formation, using the acute lymphocytic choriomeningitis virus (LCMV) Armstrong infection model.

Read the full summary ↓

We identified the ATP-dependent chromatin remodeler chromodomain-helicase-DNA-binding-protein-7 (CHD7) as a positive regulator of SLEC formation, as knockout (KO) of Chd7 reduced SLECs numerically. In contrast, KO of Chd7 increased the formation of central memory T cells following pathogen clearance yet attenuated memory cell expansion following a rechallenge. These findings establish CHD7 as a novel positive regulator of SLEC and negative regulator of central memory T cell formation.

Published in

In Vivo CRISPR Screening Reveals CHD7 as a Positive Regulator of Short-lived Effector Cells

LaFleur MW, D'Andrea JM, Patterson DG et al. · Journal of immunology (Baltimore, Md. : 1950) 2024 · PMID 39373572 · doi:10.4049/jimmunol.2400213

This dataset

Open GSE274736 in finder → Find more mouse RNA-seq datasets →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE274736_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1147730 and SRA study SRP526190. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39373572) ↗

Study design

6 conditions, mostly in triplicate

Chd7 KO Day 0 RNA-seq ×3 Chd7 KO MPEC RNA-seq ×3 Control MPEC RNA-seq ×3 Chd7 KO SLEC RNA-seq ×3 Control SLEC RNA-seq ×3 Ctrl Day 0 RNA-seq ×2

Supports a between-group comparison across 17 samples.

How we read this: 6 replicated groups read from 17 sample titles; they account for 17 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

GSM8456504 Chd7 KO Day 0 RNA-seq Replicate 1 Chd7 KO Day 0 RNA-seq
GSM8456505 Chd7 KO Day 0 RNA-seq Replicate 2 Chd7 KO Day 0 RNA-seq
GSM8456507 Chd7 KO Day 0 RNA-seq Replicate 3 Chd7 KO Day 0 RNA-seq
GSM8456508 Ctrl Day 0 RNA-seq Replicate 1 Ctrl Day 0 RNA-seq
GSM8456509 Ctrl Day 0 RNA-seq Replicate 2 Ctrl Day 0 RNA-seq
GSM8456511 Chd7 KO MPEC RNA-seq Replicate 1 Chd7 KO MPEC RNA-seq
GSM8456512 Chd7 KO MPEC RNA-seq Replicate 2 Chd7 KO MPEC RNA-seq
GSM8456514 Chd7 KO MPEC RNA-seq Replicate 3 Chd7 KO MPEC RNA-seq
GSM8456515 Control MPEC RNA-seq Replicate 1 Control MPEC RNA-seq
GSM8456517 Control MPEC RNA-seq Replicate 2 Control MPEC RNA-seq
GSM8456518 Control MPEC RNA-seq Replicate 3 Control MPEC RNA-seq
GSM8456519 Chd7 KO SLEC RNA-seq Replicate 1 Chd7 KO SLEC RNA-seq
GSM8456521 Chd7 KO SLEC RNA-seq Replicate 2 Chd7 KO SLEC RNA-seq
GSM8456522 Chd7 KO SLEC RNA-seq Replicate 3 Chd7 KO SLEC RNA-seq
GSM8456524 Control SLEC RNA-seq Replicate 1 Control SLEC RNA-seq
GSM8456525 Control SLEC RNA-seq Replicate 2 Control SLEC RNA-seq
GSM8456527 Control SLEC RNA-seq Replicate 3 Control SLEC RNA-seq

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