PGK1 moonlights as an NF-kB/p65 kinase to orchestrate M2-like macrophage-mediated immunosuppression in triple-negative breast cancer
Single-cell RNA-seq · mouse · 6 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Mus musculus (mouse)
- Samples
- 6 samples: 3 × shNC vs 3 × shPgk1Groups 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.
Read the full summary ↓
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 GSE341921_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 PRJNA1504824 and SRA study SRP722719. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 6 samples.
How we read this: 2 replicated groups read from 6 sample titles; they account for 6 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.
- GSM9920061 shNC_1 shNC
- GSM9920062 shNC_2 shNC
- GSM9920063 shNC_3 shNC
- GSM9920064 shPgk1_1 shPgk1
- GSM9920065 shPgk1_2 shPgk1
- GSM9920066 shPgk1_3 shPgk1
- GSE268241 HPV16-expressing tumors release multiple IL-1 ligands to orchestrate systemic immunosuppression whose disruption enables efficacy of a therapeutic vaccine. 63 samples shares immunosuppression, orchestrate
- GSE294999 Obesity-driven inflammation enables triple-negative breast cancer progression through immunosuppression [scRNA-Seq] 16 samples shares immunosuppression, triple-negative
- GSE254384 Senescence defines a distinct subset of myofibroblasts that orchestrates immunosuppression in pancreatic cancer 2 samples shares immunosuppression, orchestrate
- GSE250166 KEAP1-mutant lung cancers weaken anti-tumor immunity and promote an M2-like macrophage phenotype. 15 samples shares m2-like
- GSE298836 Loss of Histone Methyltransferase Ezh2 Exacerbates Polarization of Macrophages toward M2-Like Phenotypes by Hepatocellular Carcinoma [RNA-Seq] 14 samples shares m2-like
- GSE283034 Matrix stiffness enhances cancer-macrophage interactions and M2-like macrophage accumulation in the breast tumor microenvironment 6 samples shares m2-like
- GSE318688 Single-cell RNA sequencing reveals M2-like macrophage signatures associated with mesenchymal stem cell treatment in a murine sepsis model 6 samples shares m2-like
- GSE310152 Ablation of CD22 Induces a Pro-Inflammatory Transcriptome Shift and Endocytic Deficits in M2-like Macrophages 6 samples shares m2-like
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.