Mast cells trap and cannibalize swarming neutrophils for metabolic independence
RNA-seq (gene expression) · mouse · 28 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Mus musculus (mouse)
- Samples
- 28 samples: 28 samples, each with a distinct title — no replicated groupsGroups 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
- Neutrophil trapping and nexocytosis, mast cell-mediated processes for inflammatory signal relayCell · 2024
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 GSE168801_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA714063 and SRA study SRP310407. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 28 sample titles: 28 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.
- GSM5170211 MIT, no Ribotag, 12hrs, anti-HA [E1_M1_25_IP]
- GSM5170212 MIT, Ribotag, 12hrs, anti-HA [E1_M1_30_IP]
- GSM5170213 MIT, Ribotag, 12hrs, Isotype control [E1_M2_30_IP]
- GSM5170214 MIT, no Ribotag, 24hrs, anti-HA [E1_M3_25_IP]
- GSM5170215 MIT, Ribotag, 24hrs, anti-HA [E1_M3_30_IP]
- GSM5170216 MIT, Ribotag, 24hrs, Isotype control [E1_M4_30_IP]
- GSM5170217 PMC, no Ribotag, 12hrs, anti-HA [E1_P1_25_IP]
- GSM5170218 PMC, Ribotag, 12hrs, anti-HA [E1_P1_30_IP]
- GSM5170219 PMC, Ribotag, 12hrs, Isotype control [E1_P2_30_IP]
- GSM5170220 PMC, no Ribotag, 24hrs, anti-HA [E1_P3_25_IP]
- GSM5170221 PMC, Ribotag, 24hrs, anti-HA [E1_P3_30_IP]
- GSM5170222 PMC, Ribotag, 24hrs, Isotype control [E1_P4_30_IP]
- GSM5170223 MIT, no Ribotag, 12hrs, anti-HA [E2_M1_25_IP]
- GSM5170224 MIT, Ribotag, 12hrs, anti-HA [E2_M1_30_IP]
- GSM5170225 MIT, Ribotag, 12hrs, Isotype control [E2_M2_30_IP]
- GSM5170226 MIT, no Ribotag, 24hrs, anti-HA [E2_M3_25_IP]
- GSM5170227 MIT, Ribotag, 24hrs, anti-HA [E2_M3_30_IP]
- GSM5170228 MIT, Ribotag, 24hrs, Isotype control [E2_M4_30_IP]
- GSM5170229 MIT, Ribotag, 24hrs, anti-HA [E2_M5_30_IP]
- GSM5170230 MIT, Ribotag, 24hrs, Isotype control [E2_M6_30_IP]
- GSM5170231 PMC, no Ribotag, 12hrs, anti-HA [E2_P1_25_IP]
- GSM5170232 PMC, Ribotag, 12hrs, anti-HA [E2_P1_30_IP]
- GSM5170233 PMC, Ribotag, 12hrs, Isotype control [E2_P2_30_IP]
- GSM5170234 PMC, no Ribotag, 24hrs, anti-HA [E2_P3_25_IP]
- GSM5170235 PMC, Ribotag, 24hrs, anti-HA [E2_P3_30_IP]
- GSM5170236 PMC, Ribotag, 24hrs, Isotype control [E2_P4_30_IP]
- GSM5170237 PMC, Ribotag, 24hrs, anti-HA [E2_P5_30_IP]
- GSM5170238 PMC, Ribotag, 24hrs, Isotype control [E2_P6_30_IP]
- GSE278570 Neutrophils physically interact with tumor cells to form a signaling niche promoting breast cancer aggressiveness 309 samples shares neutrophils
- GSE272476 Neutrophils exposed to a cholesterol metabolite secrete extracellular vesicles that promote epithelial-mesenchymal transition and stemness in breast cancer cells [RNA-seq] 30 samples shares neutrophils
- GSE271038 Natural killer cells cooperate with neutrophils to suppress pathological angiogenesis in neovascular age-related macular degeneration 22 samples shares neutrophils
- GSE278430 Tumor-Initiating Stem Cells Fine-tune the Cell States of Neutrophils to Drive Cancer Relapse from Immunotherapy [neutrophil bulk RNAseq] 10 samples shares neutrophils
- GSE284277 Symbiotic Adhesion of Neutrophils and Epithelial-like Cancer Cells Shapes the Tumor Microenvironment and Promotes Metastasis [1] [coculture] 9 samples shares neutrophils
- GSE248113 Innate lymphoid cells activated by the cytokine TL1A link colitis to emergency granulopoiesis and the recruitment of tumor-promoting neutrophils 9 samples shares neutrophils
- GSE301965 Type I IFN impedes early bacterial control in TB-resistant and -susceptible mice and activates neutrophils to restrict lung macrophage-T cell interactions 72 samples shares neutrophils
- GSE298786 Type I IFN impedes early bacterial control in TB-resistant and -susceptible mice and activates neutrophils to restrict lung macrophage-T cell interactions [RNA-seq] 70 samples shares neutrophils
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.