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Depletion-assisted multiplexing cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicle

GSE216561 Homo sapiens (human) RNA-seq (gene expression) 113 samples Submitted 2024/03/27 Platform GPL20795
At a glance

RNA-seq (gene expression) · human · 113 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)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
113 samples: 113 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.
Paper
Depletion-assisted multiplexed cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesiclesClinical and translational medicine · 2024
Summary (written by the submitters)
Cell-free RNAs in biofluids provide opportunities to monitor cancer in a non-invasive manner. Although extracellular microRNAs are extensively characterized, fragmented cell-free long RNAs are not well investigated. Here, we developed Detector-seq (depletion-assisted multiplexing cell-free total RNA sequencing) to enable the deciphering of the cell-free transcriptome.
Read the full summary ↓
After demonstrating the superior performance of detecting fragmented cell-free long RNAs, we applied Detector-seq to compare cell-free RNAs in human plasma and its extracellular vesicle (EV). Distinct human and microbial RNA signatures were revealed. Structured circular RNA, tRNA, and Y RNA were enriched in plasma, while mRNA and srpRNA were enriched in EV. Meanwhile, cell-free RNAs derived from the virus were more enriched in plasma than in EV. We identified RNAs that showed a selective distribution between plasma and EV and uncovered their distinct functional pathways, that is RNA splicing, antimicrobial humoral response enriched in plasma and transcriptional activity, cell migration, and antigen receptor-mediated immune signals enriched in EV. Although distinctive cancer-relevant RNA signals were identified in plasma and EV, a comparable performance of distinguishing cancer patients from normal individuals could be achieved. Compared to human RNAs, microbe-derived RNA features enabled better classification between colorectal and lung cancer. And for these microbial RNAs, plasma RNAs outperformed EV RNAs for the discrimination of cancer types. Overall, our work provides insights into the unexplored difference of cell-free RNA signals between plasma and EV, thus offering practical guidance for proper selection (with/without EV enrichment) when launching an RNA-based liquid biopsy study. Furthermore, with the ability to capture understudied cell-free long RNA fragments, Detector-seq offers new possibilities for transcriptome-wide characterization of cell-free RNAs to facilitate the understanding of extracellular RNA biology and clinical advances of liquid biopsy.
Published in
Depletion-assisted multiplexed cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicles
Wang H, Zhan Q, Ning M et al. · Clinical and translational medicine 2024 · PMID 39031987 · doi:10.1002/ctm2.1760
This dataset
Open GSE216561 in finder → Find more human RNA-seq datasets → Lung cancer landscape →
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 GSE216561_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: 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 PRJNA894211 and SRA study SRP404515. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39031987) ↗
Study design
113 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 113 sample titles: 113 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.

Samples in this study

+ 73 more — browse all 113 samples with per-sample file links →

More Lung cancer datasets

Search all human RNA-seq datasets in GEO →

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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.