Single-cell transcriptome analysis revealed cellular reprogramming following Tetramethylpyrazine treatment in LPS-induced acute lung injury
Single-cell RNA-seq · mouse · 12 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
- 12 samples: 3 × Con vs 3 × LPS vs 3 × LPS+Dex vs 3 × LPS+TMPGroups 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.
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE276682_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1158678 and SRA study SRP531500. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 12 samples.
How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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.
- GSM8504078 Con-1 Con
- GSM8504079 Con-2 Con
- GSM8504080 Con-3 Con
- GSM8504081 LPS-1 LPS
- GSM8504082 LPS-2 LPS
- GSM8504083 LPS-3 LPS
- GSM8504084 LPS+Dex-1 LPS+Dex
- GSM8504085 LPS+Dex-2 LPS+Dex
- GSM8504086 LPS+Dex-3 LPS+Dex
- GSM8504087 LPS+TMP-1 LPS+TMP
- GSM8504088 LPS+TMP-2 LPS+TMP
- GSM8504089 LPS+TMP-3 LPS+TMP
- GSE327176 Single-cell transcriptomic profiling unveils tumor-mediated reprogramming of neutrophils and their unique vulnerability that inhibits metastasis 25 samples shares reprogramming, single-cell
- GSE241318 Asynchronous female reproductive organ ageing as revealed by single-cell transcriptomic atlas 21 samples shares single-cell, revealed
- GSE264278 TIme-cource single-cell RNA-seq analysis of bleomycin and LPS-induced lung injury and fibrosis 18 samples shares single-cell, lps-induced
- GSE264607 Molecular targets of colorectal cancer transformation revealed by single-cell sequencing 14 samples shares single-cell, revealed
- GSE215963 Subclonal evolution revealed by single-cell profiles of expressed mutations identifies adaptation mechanisms to immunotherapy in melanoma. 1497 samples shares single-cell, revealed
- GSE294258 Intracellular iron homeostasis-regulated epigenetic reprogramming contributes to -7/del(7q) leukemia 71 samples shares reprogramming, cellular
- GSE267452 Loss of Nuclear Receptor Nr4a3 Alleviates Cellular Senescence to Promote Adult Direct Cardiac Reprogramming 54 samples shares reprogramming, cellular
- GSE301501 Extracellular matrix reprogramming by the YAP/TAZ/TGF-ß2 axis drives immune exclusion in cholangiocarcinoma models 45 samples shares reprogramming, cellular
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.