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Transcriptional response in RAW 264.7 macrophages infected with zinc-replete and zinc-limited Mycobacterium tuberculosis

GSE255565 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2025/01/17 Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 24 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
24 samples: 8 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
Zinc-limited Mycobacterium tuberculosis stimulate distinct responses in macrophages compared with standard zinc-replete bacteriaInfection and immunity · 2025
Summary (written by the submitters)
The goal of this experiment was to determine if macrophages can distinguish between M. tuberculosis grown in standard, zinc-replete media and those grown in zinc-limited media. In order to determine this, we performed RNA-sequencing (RNA-seq) of RAW 264.7 macrophages infected with zinc-limited Mtb and zinc-replete Mtb at 4 hours post-infection (hpi) and 24hpi. Transcriptional response in the infected macrophages was compared to uninfected controls (macrophages exposed to growth media alone) and between macrophages infected with zinc-limited Mtb and zinc-replete Mtb at each time point.
Published in
Zinc-limited Mycobacterium tuberculosis stimulate distinct responses in macrophages compared with standard zinc-replete bacteria
Marcantonio E, Burger AD, Chang KH et al. · Infection and immunity 2025 · PMID 39903447 · doi:10.1128/iai.00578-24
This dataset
Open GSE255565 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 GSE255565_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 PRJNA1075454 and SRA study SRP494134. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39903447) ↗
Study design
8 conditions, mostly in triplicate
macrophage, uninfected with ZRM at 4h, ×3 macrophage, uninfected with ZLM at 4h, ×3 macrophage, infected with Mtb-ZRM for 4h, ×3 macrophage, infected with Mtb-ZLM for 4h, ×3 macrophage, uninfected with ZRM at 24h, ×3 macrophage, uninfected with ZLM at 24h, ×3 macrophage, infected with Mtb-ZRM for 24h, ×3 macrophage, infected with Mtb-ZLM for 24h, ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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

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