Combined antagonism of Interleukin 6 and Oncostatin M provide both anti-inflammatory and anti-fibrotic benefit in pulmonary fibrosis
RNA-seq (gene expression) · human · 171 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
- Homo sapiens (human)
- Samples
- 171 samples: 171 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 GSE263043_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 PRJNA1095501 and SRA study SRP499073. 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 171 sample titles: 171 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.
- GSM8182211 Bleomycin_D14_Alveolar_0231 [NGS2117]
- GSM8182212 Bleomycin_D14_Alveolar_0232 [NGS2117]
- GSM8182213 Bleomycin_D14_Alveolar_0233 [NGS2117]
- GSM8182214 Bleomycin_D14_CD64_0237 [NGS2117]
- GSM8182215 Bleomycin_D14_CD64_0238 [NGS2117]
- GSM8182216 Bleomycin_D14_CD64_0239 [NGS2117]
- GSM8182217 Bleomycin_D5_Alveolar_0219 [NGS2117]
- GSM8182218 Bleomycin_D5_Alveolar_0220 [NGS2117]
- GSM8182219 Bleomycin_D5_Alveolar_0221 [NGS2117]
- GSM8182220 Bleomycin_D5_CD64_0225 [NGS2117]
- GSM8182221 Bleomycin_D5_CD64_0226 [NGS2117]
- GSM8182222 Bleomycin_D5_CD64_0227 [NGS2117]
- GSM8182223 Saline_D14_Alveolar_0228 [NGS2117]
- GSM8182224 Saline_D14_Alveolar_0229 [NGS2117]
- GSM8182225 Saline_D14_Alveolar_0230 [NGS2117]
- GSM8182226 Saline_D14_CD64_0234 [NGS2117]
- GSM8182227 Saline_D14_CD64_0235 [NGS2117]
- GSM8182228 Saline_D14_CD64_0236 [NGS2117]
- GSM8182229 Saline_D5_Alveolar_0216 [NGS2117]
- GSM8182230 Saline_D5_Alveolar_0217 [NGS2117]
- GSM8182231 Saline_D5_Alveolar_0218 [NGS2117]
- GSM8182232 Saline_D5_CD64_0222 [NGS2117]
- GSM8182233 Saline_D5_CD64_0223 [NGS2117]
- GSM8182234 Saline_D5_CD64_0224 [NGS2117]
- GSM8182235 BLM_7838 [NGS3341]
- GSM8182236 BLM_7839 [NGS3341]
- GSM8182237 BLM_7840 [NGS3341]
- GSM8182238 BLM_7841 [NGS3341]
- GSM8182239 BLM_7842 [NGS3341]
- GSM8182240 BLM_aIL6_7843 [NGS3341]
- GSM8182241 BLM_aIL6_7844 [NGS3341]
- GSM8182242 BLM_aIL6_7845 [NGS3341]
- GSM8182243 BLM_aIL6_7846 [NGS3341]
- GSM8182244 BLM_aIL6_7847 [NGS3341]
- GSM8182245 BLM_aIL6_aOSM_7853 [NGS3341]
- GSM8182246 BLM_aIL6_aOSM_7854 [NGS3341]
- GSM8182247 BLM_aIL6_aOSM_7855 [NGS3341]
- GSM8182248 BLM_aIL6_aOSM_7856 [NGS3341]
- GSM8182249 BLM_aIL6_aOSM_7857 [NGS3341]
- GSM8182250 BLM_aOSM_7848 [NGS3341]
+ 131 more — browse all 171 samples with per-sample file links →
- GSE320585 Interleukin-27 remodels the bone marrow niche to suppress B-cell development and leukaemia progression in mouse models 20 samples shares interleukin
- GSE261794 Interleukin-11 causes alveolar type 2 cell dysfunction and prevents alveolar regeneration 17 samples shares interleukin
- GSE311920 Fibroblasts resist Staphylococcus aureus on the skin surface by responding to interleukin-1 and recruiting neutrophils 16 samples shares interleukin
- GSE262850 Discovery and Anti-inflammatory Activity of a Cyanobacterial Macrolide Glycoside Targeting the Keap1/Nrf2 Pathway 14 samples shares anti-inflammatory
- GSE260742 DOT1L provides transcriptional memory through PRC1.1 antagonism [ChIP-Seq] 158 samples shares antagonism
- GSE277367 The functional antagonism between SIRT2 and MOF regulates cell cycle progression and genome stability [ChIP-seq] 24 samples shares antagonism
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.