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A NAD + Metabolic Checkpoint Regulates Hematopoietic Stem Cell Activation and Aging

GSE268310 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2024/05/28 Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 4 × HSC, young, homeostasis, vs 4 × HSC, young, ex vivo, vs 4 × HSC, old, homeostasis,Groups 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
An NAD(+)-dependent metabolic checkpoint regulates hematopoietic stem cell activation and agingNature aging · 2024
Summary (written by the submitters)
How hematopoietic stem cells (HSCs) maintain metabolic homeostasis to support tissue repair and regeneration throughout the lifespan is elusive. Here we show that CD38, a NAD+ metabolic enzyme, promotes HSC proliferation by inducing mitochondrial Ca2+ influx and mitochondrial metabolism at young age. Conversely, aberrant CD38 upregulation during aging is a driver of HSC deterioration due to compromised mitochondrial stress management.
Read the full summary ↓
Pharmacological inactivation of CD38 reverses HSC aging and the pathophysiological changes of the aging hematopoietic system. Blocking mitochondrial Ca2+ influx inhibits HSC proliferation at young age yet prevents HSC aging. Our study highlights a NAD+ metabolic checkpoint that balances mitochondrial activation to support HSC proliferation and mitochondrial stress to enhance HSC self-renewal throughout the lifespan, and links aberrant Ca2+ signaling to HSC aging.
Published in
An NAD(+)-dependent metabolic checkpoint regulates hematopoietic stem cell activation and aging
Song Z, Park SH, Mu WC et al. · Nature aging 2024 · PMID 39044033 · doi:10.1038/s43587-024-00670-8
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 GSE268310_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 PRJNA1116149 and SRA study SRP509796. Searching any of these in the dataset finder brings you back here.

Study design
4 × HSC, young, homeostasis, vs 4 × HSC, young, ex vivo, vs 4 × HSC, old, homeostasis,

Supports a between-group comparison across 12 samples.

How we read this: 3 replicated groups read from 12 sample titles; they account for 12 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
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