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Single-cell simultaneous metabolome and transcriptome profiling revealing metabolite-gene correlation networ

GSE270852 Mus musculus (mouse) Single-cell RNA-seq 35 samples Submitted 2025/09/21 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 35 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 35 samples: 21 distinct titles, 2 of them repeatedGroups 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: Single-Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite-Gene Correlation NetworkAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025

Summary (written by the submitters)

Metabolic studies at single cell level could directly define the cellular phenotype closest to physiological or disease states. However, the current single cell metabolome (SCM) study using mass spectroscopy has the difficulty to give a complete view of the metabolic activity in the cell, while the prediction of metabolism-phenotype relationship is limited by the potential inconsistency between transcriptomic and metabolic levels.

Read the full summary ↓

Here, single-cell simultaneous metabolome and transcriptome profiling method (scMeT-seq) is developed, based on sub-picoliter sampling from the cell for the initial metabolome profiling followed by single cell transcriptome sequencing. This design does not only provide sufficient cytoplasm for SCM, but also nicely keeps the cellular viability for the accurate transcriptomic analysis in the same cell. Diverse relationships between the two omics are revealed in macrophages with the stimulation of lactate and lactate transporter (MCT1) antagonist. Moreover, we mapped metabolite states to the single-cell differentiation trajectory and gene correlation network of macrophages, which allows the unsupervised functional interpretation of metabolome. Thus, the established scMeT-seq should lead to a new perspective in metabolic research by transforming metabolomics from metabolite snapshot to functional approach.

Published in

Single-Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite-Gene Correlation Network

Mao X, Xia D, Xu M et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2025 · PMID 39629980 · doi:10.1002/advs.202411276

This dataset

Open GSE270852 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 GSE270852_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1128632 and SRA study SRP516368. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39629980) ↗

Study design

21 distinct titles, 2 of them repeated

CTL ×8 Lactic ×8

How we read this: Read from 35 sample titles: 21 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

GSM8353460 CTL27
GSM8353461 CTL28
GSM8353462 CTL30
GSM8353463 CTL31
GSM8353464 CTL32
GSM8353465 CTL33
GSM8353466 CTL34
GSM8353467 CTL35
GSM8353468 Lactic27
GSM8353469 Lactic28
GSM8353470 Lactic29
GSM8353471 Lactic30
GSM8353472 Lactic31
GSM8353473 Lactic32
GSM8353474 Lactic33
GSM8353475 Lactic34
GSM8353476 CTL4 SMART2-seq
GSM8353477 CTL8 SMART2-seq
GSM8353478 Lactic5 SMART2-seq
GSM8353479 Lactic6 SMART2-seq
GSM8353480 Lactic7 SMART2-seq
GSM8353481 Lactic13 scMeT-seq
GSM8353482 Lactic14 scMeT-seq
GSM8353483 Lactic16 scMeT-seq
GSM8353484 Lactic17 scMeT-seq
GSM8353485 T10 lactate+AZD
GSM8353486 T11 lactate
GSM8353487 T13 lactate
GSM8353488 W5 lactate
GSM8353489 W10 lactate+AZD
GSM8353490 W14 lactate+AZD
GSM8353491 W20 lactate
GSM8353492 F8 lactate+AZD
GSM8353493 F9 lactate+AZD
GSM8353494 F18 lactate

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