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Prediction and validation of a human immunodeficiency with CRISPR

GSE245463 Homo sapiens (human) RNA-seq (gene expression) 14 samples Submitted 2024/03/11 Platform GPL24247Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 14 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
Homo sapiens (human)
Samples
14 samples
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
MEF2C regulates NK cell effector functions through control of lipid metabolismNature immunology · 2024
Summary (written by the submitters)
Inborn errors of immunity (IEI) result in increased morbidity and mortality from infections. However, discovering new IEIs is limited by the clinical identification of rare patient cohorts. To predict immunodeficiency-associated genes in a patient-independent approach, we performed a targeted CRISPR-Cas9 knockout screen in healthy human donor-derived PBMCs and identified myocyte enhancer factor 2C (MEF2C) as essential for primary human natural killer (NK) cell functionality ex vivo.
Read the full summary ↓
MEF2C haploinsufficient (MCHS) patients and mice displayed impaired NK cell development, ex vivo function, and increased susceptibility to viral infection. MEF2C was required for cytokine-induced changes in NK cell metabolism and SREBP-mediated lipid homeostasis, and oleic acid supplementation restored MCHS patient NK cell cytotoxic function. Thus, we demonstrate a CRISPR-based methodology in primary human immune cells to accelerate the identification of new IEIs, and apply this approach to predict and validate a new NK cell immunodeficiency associated with metabolic and functional defects.
Published in
MEF2C regulates NK cell effector functions through control of lipid metabolism
Li JH, Zhou A, Lee CD et al. · Nature immunology 2024 · PMID 38589619 · doi:10.1038/s41590-024-01811-2
This dataset
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.

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 GSE245463_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1028553 and SRA study SRP466535. Searching any of these in the dataset finder brings you back here.

Samples in this study

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