GEO series

The microbiota and T cells non-genetically modulate inherited phenotypes transgenerationally

GSE240797 Mus musculus (mouse) Non-coding RNA profiling 208 samples Submitted 2024/03/24 Platform GPL24247Platform GPL32159Platform GPL21626

At a glance

Non-coding RNA profiling · mouse · 208 samples

What it is: Non-coding RNA profilingLevels of RNAs that do not code for proteins, such as miRNA or lncRNA.
Organism: Mus musculus (mouse)
Samples: 208 samples: 208 samples, each with a distinct title — no replicated groupsGroups 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: The microbiota and T cells non-genetically modulate inherited phenotypes transgenerationallyCell reports · 2024

Summary (written by the submitters)

The host-microbiota relationship has evolved to shape mammalian physiology, including immunity, metabolism, and development. Germ-free models are widely used to study microbial effects on host processes such as immunity. Here, we find that both germ-free and T cell-deficient mice exhibit a robust sebum secretion defect persisting across multiple generations despite microbial colonization and T cell repletion.

Read the full summary ↓

These phenotypes are inherited by progeny conceived during in vitro fertilization using germ-free sperm and eggs, demonstrating that non-genetic information in the gametes is required for microbial-dependent phenotypic transmission. Accordingly, gene expression in early embryos derived from gametes from germ-free or T-cell deficient mice are strikingly and similarly altered. Our findings demonstrate that microbial and immune-dependent regulation of non-genetic information in the gametes can transmit inherited phenotypes transgenerationally in mice. This mechanism could rapidly generate phenotypic diversity to enhance host adaptation to environmental perturbations.

Published in

The microbiota and T cells non-genetically modulate inherited phenotypes transgenerationally

Harris JC, Trigg NA, Goshu B et al. · Cell reports 2024 · PMID 38573852 · doi:10.1016/j.celrep.2024.114029

This dataset

Open GSE240797 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 GSE240797_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 ↗

Also filed as BioProject PRJNA1005268 and SRA study SRP454949. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38573852) ↗

Study design

208 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 208 sample titles: 208 distinct titles with little repetition. 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

GSM7709423 SG_F0_CR_1, mRNA-seq
GSM7709424 SG_F0_CR_2, mRNA-seq
GSM7709425 SG_F0_CR_3, mRNA-seq
GSM7709426 SG_F0_GF_1, mRNA-seq
GSM7709427 SG_F0_GF_2, mRNA-seq
GSM7709428 SG_F0_GF_3, mRNA-seq
GSM7709429 SG_F1_CR_1, mRNA-seq
GSM7709430 SG_F1_CR_2, mRNA-seq
GSM7709431 SG_F1_GF_1, mRNA-seq
GSM7709432 SG_F1_GF_2, mRNA-seq
GSM7709433 SG_F1_GF_3, mRNA-seq
GSM7709434 skin_CRxCR_F1_1, mRNA-seq
GSM7709435 skin_CRxCR_F1_2, mRNA-seq
GSM7709436 skin_CRxCR_F1_3, mRNA-seq
GSM7709437 skin_CRxCR_F1_4, mRNA-seq
GSM7709438 skin_CRxCR_F1_5, mRNA-seq
GSM7709439 skin_GFxGF_F1_1, mRNA-seq
GSM7709440 skin_GFxGF_F1_2, mRNA-seq
GSM7709441 skin_GFxGF_F1_3, mRNA-seq
GSM7709442 skin_GFmxCRf_F1_1, mRNA-seq
GSM7709443 skin_GFmxCRf_F1_2, mRNA-seq
GSM7709444 skin_GFmxCRf_F1_3, mRNA-seq
GSM7709445 skin_CRxCR_F2_1, mRNA-seq
GSM7709446 skin_CRxCR_F2_2, mRNA-seq
GSM7709447 skin_CRxCR_F2_3, mRNA-seq
GSM7709448 skin_CRxCR_F2_4, mRNA-seq
GSM7709449 skin_CRxCR_F2_5, mRNA-seq
GSM7709450 skin_GFxGF_F2_1, mRNA-seq
GSM7709451 skin_GFxGF_F2_2, mRNA-seq
GSM7709452 skin_GFxGF_F2_3, mRNA-seq
GSM7709453 skin_GFxCR_F2_1, mRNA-seq
GSM7709454 skin_GFxCR_F2_2, mRNA-seq
GSM7709455 skin_GFxCR_F2_3, mRNA-seq
GSM7709456 skin_GFxCR_F2_4, mRNA-seq
GSM7709457 skin_GFxCR_F2_5, mRNA-seq
GSM7709458 skin_GFxCR_F2_6, mRNA-seq
GSM7709459 intestine_GFxCR_F1_1, mRNA-seq
GSM7709460 intestine_GFxCR_F1_2, mRNA-seq
GSM7709461 intestine_GFxCR_F1_3, mRNA-seq
GSM7709462 intestine_CRxCR_F1_1, mRNA-seq

+ 168 more — browse all 208 samples with per-sample file links →

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Search all mouse RNA-seq datasets in GEO →

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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.