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Resilience to bioprinting-induced cellular stress by heat shock preconditioning

GSE337799 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2026/07/13 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 24 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
24 samples: 24 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.
Summary (written by the submitters)
Bioprinting holds the promise to revolutionize therapeutic development and healthcare; however, we still lack an in-depth understanding of how cells respond to stress experienced during the bioprinting process. Here, we assess at the molecular and cellular level the effects of bioprinting-induced stress on cell morphology, metabolism, and behavior using human umbilical vein endothelial cells.
Read the full summary ↓
We demonstrate that mild preconditioning of the stress response in cells using heat shock before bioprinting helps cells to rapidly respond to bioprinting-associated stress, mitigating negative effects. We hypothesize this is in part mediated by the small heat shock protein 27 (Hsp27) which is known to play a role in stabilizing actin in the cytoskeleton. This results in restoration of cellular viability, morphology, metabolism, and function, including vascularization. Our study outlines an easy-to-implement strategy to improve cell viability following bioprinting, facilitating the real-life application of bioprinting.
This dataset
Open GSE337799 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 GSE337799_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 PRJNA1491101 and SRA study SRP715887. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 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 24 sample titles: 24 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
Datasets on a similar topic

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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.