GEO series

Static Magnetic Fields Exposure Allievates Liver Injury via Promoting Ca 2+ Influx and Cellular Respiration [scRNA-Seq]

GSE314515 Mus musculus (mouse) Single-cell RNA-seq 8 samples Submitted 2026/06/30 Platform GPL19057

At a glance

Single-cell RNA-seq · mouse · 8 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: 8 samples: 3 × mice treated with CCl₄, control vs 3 × mice treated with CCl₄, smf vs 2 × mice treated with CCl₄，smfGroups 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.

Summary (written by the submitters)

Magnetic fields influence a wide range of biological processes, from animal navigation to human cellular metabolism. Despite these diverse effects, the therapeutic mechanisms of static magnetic fields (SMFs) remain poorly understood. Here we show that SMFs exposure markedly ameliorates drug-induced liver injury in mice, including both acetaminophen- and CCl4-induced models.

Read the full summary ↓

SMFs treatment reduced serum aminotransferases, limited collagen deposition, and improved hepatocyte survival in vitro and in vivo. Multi-omics analyses revealed pronounced metabolic reprogramming, characterized by enhanced ATP production, increased oxygen consumption, and elevated mitochondrial membrane potential. These findings indicate that SMFs promote cellular respiration and stabilize hepatic energy homeostasis under injury conditions. At the mechanistic level, SMFs increased intracellular Ca2+ influx via activation of the Piezo1 channel and enhanced malate–aspartate shuttle activity through malate dehydrogenase 1 (MDH1). Together, our study establishes SMFs as a potent modulator of hepatocyte metabolism and highlights their promise as a non-invasive therapeutic strategy for liver disease.

This dataset

Open GSE314515 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 GSE314515_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 PRJNA1391441 and SRA study SRP656763. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

3 × mice treated with CCl₄, control vs 3 × mice treated with CCl₄, smf vs 2 × mice treated with CCl₄，smf

Supports a between-group comparison across 8 samples.

How we read this: 3 replicated groups read from 8 sample titles; they account for 8 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

GSM9400178 mice treated with CCl₄, control-1 mice treated with CCl₄,…
GSM9400179 mice treated with CCl₄, control-2 mice treated with CCl₄,…
GSM9400180 mice treated with CCl₄, control-3 mice treated with CCl₄,…
GSM9400181 mice treated with CCl₄, smf-1 mice treated with CCl₄,…
GSM9400182 mice treated with CCl₄, smf-2 mice treated with CCl₄,…
GSM9400183 mice treated with CCl₄, smf-3 mice treated with CCl₄,…
GSM9400184 mice treated with CCl₄，smf-4 mice treated with CCl₄，…
GSM9400185 mice treated with CCl₄，smf-5 mice treated with CCl₄，…

Datasets on a similar topic

GSE269347 Daunorubicin-induced T cell remodeling facilitates a high antitumor response to PD-1 blockade therapy by promoting the expansion and effector function of CD8+ prolifeating T cells [scRNA-Seq] 16 samples shares promoting, scrna-seq
GSE299996 Transcriptional control of Sulf1 by WT1 primes epicardial signalling and fate by modifying extracellular heparan sulfate status [scRNA-Seq] 1 samples shares scrna-seq, cellular
GSE241770 ACSS2 mediates an epigenetic pathway to regulate maternal Beta-cell adaptation and progression of gestational diabetes (scRNA-Seq) 7899 samples shares scrna-seq
GSE264105 Plate-based scRNA-seq of plasma-positive microglia and plasma-negative microglia in young mice 1111 samples shares scrna-seq
GSE271100 Stem cells tightly regulate dead cell clearance to maintain tissue fitness [scRNA-seq] 1037 samples shares scrna-seq
GSE326100 scRNA-seq of mouse cochlea organoid cells 768 samples shares scrna-seq
GSE265909 yHSC and oHSC scRNAseq with oHSC AThi ATlo Index Sorting. Assessing the transcriptional changes associated with autophagy engagement in old hematopoietic stem cells at single cell resolution. [scRNA-Seq] 563 samples shares scrna-seq
GSE217662 Decoding Trp53 regulatory program with knockout cell transcriptomic landscape analyses [scRNA-seq] 247 samples shares scrna-seq

Search all mouse 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.