CT26 multi-tissue RNA-seq under immunotherapy combinations
RNA-seq (gene expression) · mouse · 72 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
- 72 samples: 72 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.
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 GSE342349_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 PRJNA1507402 and SRA study SRP724127. 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 72 sample titles: 72 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.
- GSM9928919 RNASeq_144 01_01_T_d23
- GSM9928920 RNASeq_144 01_04_TDLN_d16
- GSM9928921 RNASeq_144 01_05_TDLN_d16
- GSM9928922 RNASeq_144 01_06_TDLN_d16
- GSM9928923 RNASeq_144 01_08_S_d23
- GSM9928924 RNASeq_144 01_08_TDLN_d23
- GSM9928925 RNASeq_144 01_09_S_d23
- GSM9928926 RNASeq_144 01_09_TDLN_d23
- GSM9928927 RNASeq_144 01_09_T_d23
- GSM9928928 RNASeq_144 01_10_S_d23
- GSM9928929 RNASeq_144 01_10_TDLN_d23
- GSM9928930 RNASeq_144 01_10_T_d23
- GSM9928931 RNASeq_144 02_10_TDLN_d16
- GSM9928932 RNASeq_144 02_11_TDLN_d16
- GSM9928933 RNASeq_144 02_12_TDLN_d16
- GSM9928934 RNASeq_144 02_12_T_d23
- GSM9928935 RNASeq_144 02_18_S_d23
- GSM9928936 RNASeq_144 02_18_TDLN_d23
- GSM9928937 RNASeq_144 02_18_T_d23
- GSM9928938 RNASeq_144 02_19_S_d23
- GSM9928939 RNASeq_144 02_19_TDLN_d23
- GSM9928940 RNASeq_144 02_20_S_d23
- GSM9928941 RNASeq_144 02_20_TDLN_d23
- GSM9928942 RNASeq_144 02_20_T_d23
- GSM9928943 RNASeq_144 03_16_TDLN_d16
- GSM9928944 RNASeq_144 03_17_TDLN_d16
- GSM9928945 RNASeq_144 03_18_TDLN_d16
- GSM9928946 RNASeq_144 03_28_S_d23
- GSM9928947 RNASeq_144 03_28_TDLN_d23
- GSM9928948 RNASeq_144 03_28_T_d23
- GSM9928949 RNASeq_144 03_29_S_d23
- GSM9928950 RNASeq_144 03_29_TDLN_d23
- GSM9928951 RNASeq_144 03_29_T_d23
- GSM9928952 RNASeq_144 03_30_S_d23
- GSM9928953 RNASeq_144 03_30_TDLN_d23
- GSM9928954 RNASeq_144 03_30_T_d23
- GSM9928955 RNASeq_144 04_22_TDLN_d16
- GSM9928956 RNASeq_144 04_23_TDLN_d16
- GSM9928957 RNASeq_144 04_24_TDLN_d16
- GSM9928958 RNASeq_144 04_32_T_d23
+ 32 more — browse all 72 samples with per-sample file links →
- GSE250602 RNA-Seq analysis of SUN1 knockout and control CT26 cells and tumors 24 samples shares ct26, rna-seq
- GSE319170 RNA-seq of CT26 cells and tumors treated with or without Midostaurin 12 samples shares ct26, rna-seq
- GSE215960 Subclonal evolution revealed by single-cell profiles of expressed mutations identifies adaptation mechanisms to immunotherapy in melanoma [scRNA-seq] 192 samples shares immunotherapy, rna-seq
- GSE302715 A tumor-associated macrophage-targeted immunocytokine leveraging T and NK cell synergy for potent cancer immunotherapy [scRNA-seq] 187 samples shares immunotherapy, rna-seq
- GSE265835 The tissue-resident regulatory T cell pool is shaped by transient multi-tissue migration and a conserved residency program. Bulk RNA-Seq profiling of mouse tissue Tregs. 40 samples shares multi-tissue, rna-seq
- GSE212259 Whole-course Taming of Tumor-associated Macrophages from Central to Peripheral using Engineered Outer Membrane Vesicle Nanohybrids for Improved Immunotherapy [part 1: RNA-seq] 36 samples shares immunotherapy, rna-seq
- GSE311488 Chemotherapeutic Induction of Single-Stranded DNA Accumulation Sensitizes Triple-Negative Breast Cancer to Immunotherapy [scRNA-seq] 32 samples shares immunotherapy, rna-seq
- GSE281467 Coactivation of innate immune suppressive cells induces acquired resistance against cancer immunotherapy targeting the innate immune system [TIICs RNA-seq] 30 samples shares immunotherapy, rna-seq
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.