← BioTransfer GEO Dataset Finder
GEO series

Gene expression in human monocyte-derived macrophages: effect of loss of MYC or USF1

GSE240075 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2024/04/10 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 30 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
30 samples: 5 conditions, mostly with about 6 replicates eachGroups 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
Aging-related defects in macrophage function are driven by MYC and USF1 transcriptional programsCell reports · 2024
Summary (written by the submitters)
Macrophage function declines with age, however the underlying mechanisms remain poorly understood. Here, we report that MYC and USF1 are key in driving age-related macrophage functional decline in humans, including reduction in phagocytosis and chemotaxis. We show downregulation with age of MYC and USF1 in human monocyte-derived macrophages (MDMs) and crucially, that knockdown of MYC or USF1 in young MDMs modelled the age-related functional changes.
Read the full summary ↓
Transcriptomic analysis of these young knockdown MDMs revealed alteration in expression of genes crucial to these functions, as well as those linked to adhesion and extracellular matrix formation. Concordant dysregulation of these genes was also seen in older MDMs. Finally, we show that morphology and F-actin content were altered with loss of MYC or USF1, similarly to that seen in ageing. Together, these results define MYC and USF1 as key drivers of MDM age-related functional decline and identify downstream targets to improve macrophage function in ageing.
Published in
Aging-related defects in macrophage function are driven by MYC and USF1 transcriptional programs
Moss CE, Johnston SA, Kimble JV et al. · Cell reports 2024 · PMID 38578825 · doi:10.1016/j.celrep.2024.114073
This dataset
Open GSE240075 in finder → Find more human 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 GSE240075_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 PRJNA1002247 and SRA study SRP453378. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38578825) ↗
Study design
5 conditions, mostly with about 6 replicates each
M0 control, biol ×6 siMYC M0, biol ×6 siUSF1 M0, biol ×6 MLPS+IFNγ control, biol ×6 siUSF1 MLPS+IFNγ control, biol ×6

Supports a between-group comparison across 30 samples.

How we read this: 5 replicated groups read from 30 sample titles; they account for 30 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
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.