Characteristics of Age-Associated Changes in the Whole Transcriptome of Human Peripheral Blood Leukocytes and Establishment of an Aging Clock
RNA-seq (gene expression) · human · 35 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
- 35 samples: 6 conditions, mostly with about 5 replicates eachGroups 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
- Human Peripheral Blood Leukocyte Transcriptome-Based Aging Clock Reveals Acceleration of Aging by Bacterial or Viral InfectionsThe journals of gerontology. Series A, Biological sciences and medical sciences · 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 GSE262619_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 PRJNA1092652 and SRA study SRP498208. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 35 samples.
How we read this: 6 replicated groups read from 35 sample titles; they account for 35 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.
- GSM8172471 30y_F1 30y_F
- GSM8172472 30y_F2 30y_F
- GSM8172473 30y_F3 30y_F
- GSM8172474 30y_F4 30y_F
- GSM8172475 30y_F5 30y_F
- GSM8172476 30y_M1 30y_M
- GSM8172477 30y_M2 30y_M
- GSM8172478 30y_M3 30y_M
- GSM8172479 30y_M4 30y_M
- GSM8172480 30y_M5 30y_M
- GSM8172481 30y_M6 30y_M
- GSM8172482 60y_F1 60y_F
- GSM8172483 60y_F2 60y_F
- GSM8172484 60y_F3 60y_F
- GSM8172485 60y_F4 60y_F
- GSM8172486 60y_F5 60y_F
- GSM8172487 60y_M1 60y_M
- GSM8172488 60y_M2 60y_M
- GSM8172489 60y_M3 60y_M
- GSM8172490 60y_M4 60y_M
- GSM8172491 60y_M5 60y_M
- GSM8172492 75y_F1 75y_F
- GSM8172493 75y_F2 75y_F
- GSM8172494 75y_F3 75y_F
- GSM8172495 75y_F4 75y_F
- GSM8172496 75y_F5 75y_F
- GSM8172497 75y_F6 75y_F
- GSM8172498 75y_F7 75y_F
- GSM8172499 75y_M1 75y_M
- GSM8172500 75y_M2 75y_M
- GSM8172501 75y_M3 75y_M
- GSM8172502 75y_M4 75y_M
- GSM8172503 75y_M5 75y_M
- GSM8172504 75y_M6 75y_M
- GSM8172505 75y_M7 75y_M
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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.