CoMtb alters the immune landscape following Mtb infection [C3H scRNAseq]
Single-cell RNA-seq · mouse · 46 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
- 46 samples: 12 conditions, mostly with about 4 replicates eachGroups 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
- Early and opposing neutrophil and CD4 T cell responses shape pulmonary tuberculosis pathologyThe Journal of experimental medicine · 2025
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 GSE265843_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 PRJNA1104528 and SRA study SRP503995. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 46 samples.
How we read this: 12 replicated groups read from 46 sample titles; they account for 46 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.
- GSM8229930 C.cotb.Rv.d17.1 C.cotb.Rv.d17
- GSM8229931 C.cotb.Rv.d17.2 C.cotb.Rv.d17
- GSM8229932 C.cotb.Rv.d17.3 C.cotb.Rv.d17
- GSM8229933 C.cotb.Rv.d17.4 C.cotb.Rv.d17
- GSM8229934 C.cotb.Rv.d34.1 C.cotb.Rv.d34
- GSM8229935 C.cotb.Rv.d34.2 C.cotb.Rv.d34
- GSM8229936 C.cotb.Rv.d34.3 C.cotb.Rv.d34
- GSM8229937 C.cotb.Rv.d34.4 C.cotb.Rv.d34
- GSM8229938 C.cotb.SA.d10.1 C.cotb.SA.d10
- GSM8229939 C.cotb.SA.d10.2 C.cotb.SA.d10
- GSM8229940 C.cotb.SA.d10.3 C.cotb.SA.d10
- GSM8229941 C.cotb.SA.d10.4 C.cotb.SA.d10
- GSM8229942 C.cotb.SA.d17.1 C.cotb.SA.d17
- GSM8229943 C.cotb.SA.d17.2 C.cotb.SA.d17
- GSM8229944 C.cotb.SA.d17.3 C.cotb.SA.d17
- GSM8229945 C.cotb.SA.d17.4 C.cotb.SA.d17
- GSM8229946 C.cotb.SA.d34.1 C.cotb.SA.d34
- GSM8229947 C.cotb.SA.d34.2 C.cotb.SA.d34
- GSM8229948 C.cotb.SA.d34.3 C.cotb.SA.d34
- GSM8229949 C.cotb.SA.d34.4 C.cotb.SA.d34
- GSM8229950 C.cotb.none.d0.1 C.cotb.none.d0
- GSM8229951 C.cotb.none.d0.2 C.cotb.none.d0
- GSM8229952 C.cotb.none.d0.3 C.cotb.none.d0
- GSM8229953 C.cotb.none.d0.4 C.cotb.none.d0
- GSM8229954 C.unvax.Rv.d17.1 C.unvax.Rv.d17
- GSM8229955 C.unvax.Rv.d17.2 C.unvax.Rv.d17
- GSM8229956 C.unvax.Rv.d17.3 C.unvax.Rv.d17
- GSM8229957 C.unvax.Rv.d17.4 C.unvax.Rv.d17
- GSM8229958 C.unvax.Rv.d34.1 C.unvax.Rv.d34
- GSM8229959 C.unvax.Rv.d34.2 C.unvax.Rv.d34
- GSM8229960 C.unvax.Rv.d34.3 C.unvax.Rv.d34
- GSM8229961 C.unvax.Rv.d34.4 C.unvax.Rv.d34
- GSM8229962 C.unvax.SA.d10.1 C.unvax.SA.d10
- GSM8229963 C.unvax.SA.d10.3 C.unvax.SA.d10
- GSM8229964 C.unvax.SA.d10.4 C.unvax.SA.d10
- GSM8229965 C.unvax.SA.d17.1 C.unvax.SA.d17
- GSM8229966 C.unvax.SA.d17.2 C.unvax.SA.d17
- GSM8229967 C.unvax.SA.d17.3 C.unvax.SA.d17
- GSM8229968 C.unvax.SA.d34.1 C.unvax.SA.d34
- GSM8229969 C.unvax.SA.d34.2 C.unvax.SA.d34
+ 6 more — browse all 46 samples with per-sample file links →
- GSE271578 Kinetical scRNAseq Analysis Reveals Immune Mechanisms Underlying Hindered Lung Recovery following Influenza Virus Infection in Aged Hosts 12 samples shares following, scrnaseq
- GSE326909 scRNAseq Dataset of lungs from WT and Clec1a KO mice following primary or secondary E. coli lung infection 12 samples shares following, scrnaseq
- GSE278132 Lung structural cell dynamics are altered by influenza virus infection experience leading to rapid immune protection following viral re-challenge 35 samples shares following
- GSE255744 Se Deficiency Exacerbates Hyperoxia-Induced Lung Injury in Newborn C3H/HeN Mice 24 samples shares c3h
- GSE295974 Single-cell multi-omics reveal coordinated neoplastic and immune remodeling following KRAS inhibition 64 samples shares following
- GSE322775 Coordinated immune activation following KRAS inhibition reveals molecular pathways that potentiate and limit anti-tumor immunity 47 samples shares following
- GSE255357 Pharmacological Modulation of BMAL1 Alters Circadian Dynamics and the Macrophage Immune Response 36 samples shares alters
- GSE240727 Gene expression profile at single cell level of immune cells following spinal cord injury 6 samples shares following
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.