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Live-cell transcriptomics with engineered virus-like particles

GSE304897 Homo sapiens (human) RNA-seq (gene expression) 509 samples Submitted 2026/09/02 Platform GPL21697Platform GPL22790Platform GPL34295
At a glance

RNA-seq (gene expression) · human · 509 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
509 samples
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)
Transcriptomic profiling is widely applied to characterize cellular gene expression, yet existing approaches lyse cells and preclude direct analysis of transcriptional dynamics in the same sample over time. We addressed this limitation by engineering mammalian cells to “self-report” their transcriptional states via mRNA export in virus-like particles (VLPs).
Read the full summary ↓
Repeated sampling of culture media from VLP-producing cell populations faithfully captured evolving transcriptional states in complex biological settings, including acute inflammatory stimulation of primary cell spheroids and multi-day differentiation of pluripotent stem cells. We engineered VLP components for multiplexed readouts from distinct cell-types in co-culture and to tune self-reported RNA profiles. Finally, we demonstrated the unique utility of self-reporting for selective longitudinal tracking of endothelial cell dynamics within the enclosed architecture of a microphysiological co-culture system to identify perivascular stroma-dependent temporal gene programs underlying vasculogenesis. Altogether, this work establishes cellular self-reporting as a broadly enabling technology for live-cell transcriptome-wide gene expression profiling.
This dataset
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.

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 GSE304897_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1303199 and SRA study SRP607085. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 509 more — browse all 509 samples with per-sample file links →

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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.