Tumor cell-based liquid biopsy using high-throughput microfluidic enrichment of entire leukapheresis product
RNA-seq (gene expression) · human · 110 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
- 110 samples: 108 distinct titles, 1 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
- Tumor cell-based liquid biopsy using high-throughput microfluidic enrichment of entire leukapheresis productNature communications · 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 GSE255889_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 PRJNA1076920 and SRA study SRP489922. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 110 sample titles: 108 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.
- GSM8082251 SH374_TR_GU01CTC
- GSM8082252 SH376_TR_GU01CTC
- GSM8082253 SH378_TR_GU01CTC
- GSM8082254 SH383_TR_GU01CTC
- GSM8082255 SH384_TR_GU01CTC
- GSM8082256 SH385_TR_GU01CTC
- GSM8082257 SH386_TR_GU01CTC
- GSM8082258 SH388_TR_GU01CTC
- GSM8082259 SH389_TR_GU01CTC
- GSM8082260 SH390_TR_GU01CTC
- GSM8082261 SH391_TR_GU01CTC
- GSM8082262 SH393_TR_GU01CTC
- GSM8082263 SH394_TR_GU01CTC
- GSM8082264 SH396_TR_GU01CTC
- GSM8082265 SH436_TR_GU01CTC
- GSM8082266 SH437_TR_GU01CTC
- GSM8082267 SH440_TR_GU01CTC
- GSM8082268 SH443_TR_GU01CTC
- GSM8082269 SH445_TR_GU01CTC
- GSM8082270 SH446_TR_GU01CTC
- GSM8082271 SH453_TR_GU01CTC
- GSM8082272 SH454_TR_GU01CTC
- GSM8082273 SH457_TR_GU01CTC
- GSM8082274 SH461_TR_GU01CTC
- GSM8082275 SH462_TR_GU01CTC
- GSM8082276 SH464_TR_GU01CTC
- GSM8082277 SH466_TR_GU01CTC
- GSM8082278 SH611_TR_GU01DN
- GSM8082279 SH626_TR_GU01DN
- GSM8082280 SH636_TR_GU01DN
- GSM8082281 BRx68_single_cell_1
- GSM8082282 BRx68_single_cell_2
- GSM8082283 BRx68_single_cell_3
- GSM8082284 WBC1_healthy donor1
- GSM8082285 WBC4_healthy donor1
- GSM8082286 WBC6_healthy donor1
- GSM8443775 SH689_TR_GU02CTC
- GSM8443776 SH691_TR_GU02CTC
- GSM8443777 SH699_TR_GU02CTC
- GSM8443778 SH702_TR_GU02CTC
+ 70 more — browse all 110 samples with per-sample file links →
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- GSE252117 High-throughput transcriptomics of water extracts detects reductions in biological activity with water treatment processes 510 samples shares high-throughput
- GSE271332 High-Throughput Transcriptomics Toxicity Assessment of Eleven Data-Poor Bisphenol A Alternatives 286 samples shares high-throughput
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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.