Identification of replicative aging and inflammatory aging signatures via whole-genome CRISPRi screens
RNA-seq (gene expression) · human · 16 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
- 16 samples: 14 distinct titles, 2 of them repeatedGroups 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
- Identification of replicative aging and inflammatory aging signatures via whole-genome CRISPRi screensGenome biology · 2025
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 GSE300879_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 PRJNA1282582 and SRA study SRP594801. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 16 sample titles: 14 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.
- GSM9070957 MSC-NC-rep1
- GSM9070958 MSC-NC-rep2
- GSM9070959 MSC-si-SAMM50-rep1
- GSM9070960 MSC-si-SAMM50-rep2
- GSM9070961 MSC-NC-rep1_IL6-
- GSM9070962 MSC-NC-rep2_IL6-
- GSM9070963 MSC-si-CMKLR1-rep1_IL6-
- GSM9070964 MSC-si-CMKLR1-rep2_IL6-
- GSM9070965 MSC-si-SERPING-rep1_IL6-
- GSM9070966 MSC-si-SERPING-rep2_IL6-
- GSM9070967 MSC-NC-rep1-R1_IL6+
- GSM9070968 MSC-NC-rep2-R1_IL6+
- GSM9070969 MSC-si-CMKLR1-rep1_IL6+
- GSM9070970 MSC-si-CMKLR1-rep2_IL6+
- GSM9070971 MSC-si-SERPING-rep1_IL6+
- GSM9070972 MSC-si-SERPING-rep2_IL6+
- GSE301164 Comparative analysis of replicative and doxorubicin-induced senescence identifies conserved transcriptomic signatures in aging fibroblasts [RNA-Seq1] 21 samples shares replicative, signatures, aging
- GSE325674 Leveraging experimental evolution to extract predictive collateral drug response signatures in Ewings Sarcoma 73 samples shares signatures, aging
- GSE281048 Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens 4 samples shares signatures, screens
- GSE298243 Single-Cell Profiling of DNA Methylation in Autism Spectrum Disorder Prefrontal Cortex Reveals Distinct Regulatory and Aging Signatures 49 samples shares signatures, aging
- GSE282258 Imaging-based drug screening combined with moleuclar profiling identifies signatures and drivers of therapy resistance in pediatric AML [ATAC-seq] 44 samples shares signatures, aging
- GSE282423 Imaging-based drug screening combined with moleuclar profiling identifies signatures and drivers of therapy resistance in pediatric AML [RNA-seq] 28 samples shares signatures, aging
- GSE291471 Integrative blood profiling uncovers inflammatory network signatures in high-altitude pulmonary edema 83 samples shares signatures
- GSE243625 Inflammatory transcriptomic signatures and cell type compositions in inflamed and non-inflamed colonic mucosa of ulcerative colitis 45 samples shares signatures
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.