GEO series

A flexible enrichment strategy for the isolation and single-cell characterization of disease-specific circulating tumor cells

GSE291830 Homo sapiens (human) Single-cell RNA-seq 34 samples Submitted 2026/03/16 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 34 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 34 samples: 23 × 22Rv1_CTC vs 11 × A375_CTCGroups 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.

Summary (written by the submitters)

Background: Circulating tumor cells (CTCs) are cells that have broken off from a primary solid tumor, entered the blood stream, and have the potential to metastasize to nearby tissues. Thus, CTCs represent an important biomarker to monitor cancer progression and patient outcome. The isolation, identification and characterization of CTCs are critical for this goal.

Read the full summary ↓

We previously developed a method for the detection and isolation of melanoma CTCs from peripheral blood through negative and positive antibody enrichment. Methods: Here we show that this protocol is easily applied to other cancers; in this case we expand and validate this method to include prostate cancer. Results: We show that this combined negative and positive enrichment protocol successfully isolates CTCs for characterization using single cell RNA sequencing and have updated the protocol to include a newer version of single cell library preparation, Smart-seq3. In our analysis, cells prepared for sequencing with Smart-seq3, which uses unique molecular identifiers (UMIs), showed no considerable difference compared to those prepared using the previous iteration of the protocol, Smart-seq2. Conclusion: This study demonstrates a robust and flexible method for the isolation and characterization of CTCs from various cancers for potential use as biomarkers for disease management and for characterization with single cell RNA-seq.

This dataset

Open GSE291830 in finder → Find more human RNA-seq datasets →

Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE291830_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

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.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1235312 and SRA study SRP569984. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

23 × 22Rv1_CTC vs 11 × A375_CTC

Supports a between-group comparison across 34 samples.

How we read this: 2 replicated groups read from 34 sample titles; they account for 34 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.

Samples in this study

GSM8842996 22Rv1_CTC_1 22Rv1_CTC
GSM8842997 22Rv1_CTC_2 22Rv1_CTC
GSM8842998 22Rv1_CTC_3 22Rv1_CTC
GSM8842999 22Rv1_CTC_4 22Rv1_CTC
GSM8843000 22Rv1_CTC_5 22Rv1_CTC
GSM8843001 22Rv1_CTC_6 22Rv1_CTC
GSM8843002 22Rv1_CTC_7 22Rv1_CTC
GSM8843003 22Rv1_CTC_8 22Rv1_CTC
GSM8843004 22Rv1_CTC_9 22Rv1_CTC
GSM8843005 22Rv1_CTC_10 22Rv1_CTC
GSM8843006 22Rv1_CTC_11 22Rv1_CTC
GSM8843007 22Rv1_CTC_12 22Rv1_CTC
GSM8843008 22Rv1_CTC_13 22Rv1_CTC
GSM8843009 22Rv1_CTC_14 22Rv1_CTC
GSM8843010 22Rv1_CTC_15 22Rv1_CTC
GSM8843011 22Rv1_CTC_16 22Rv1_CTC
GSM8843012 22Rv1_CTC_17 22Rv1_CTC
GSM8843013 22Rv1_CTC_18 22Rv1_CTC
GSM8843014 22Rv1_CTC_19 22Rv1_CTC
GSM8843015 22Rv1_CTC_20 22Rv1_CTC
GSM8843016 22Rv1_CTC_21 22Rv1_CTC
GSM8843017 22Rv1_CTC_22 22Rv1_CTC
GSM8843018 22Rv1_CTC_23 22Rv1_CTC
GSM8843019 A375_CTC_1 A375_CTC
GSM8843020 A375_CTC_2 A375_CTC
GSM8843021 A375_CTC_3 A375_CTC
GSM8843022 A375_CTC_4 A375_CTC
GSM8843023 A375_CTC_5 A375_CTC
GSM8843024 A375_CTC_6 A375_CTC
GSM8843025 A375_CTC_7 A375_CTC
GSM8843026 A375_CTC_8 A375_CTC
GSM8843027 A375_CTC_9 A375_CTC
GSM8843028 A375_CTC_10 A375_CTC
GSM8843029 A375_CTC_11 A375_CTC

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