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Non-Invasive, Label-free Image Approaches to Predict Multimodal Molecular Markers in Pluripotency Assessment

GSE256303 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/08/08 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 12 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
Noninvasive, label-free image approaches to predict multimodal molecular markers in pluripotency assessmentScientific reports · 2024
Summary (written by the submitters)
We detail an innovative non-invasive technique to predict gene and protein expression in pluripotent stem cells using advanced bright-field microscopy. The method employs machine learning algorithms to classify cells based on brightfield images, avoiding the need for traditional staining and manual annotation. Our approach uses DeepLearning technology to predict the gene expression (qPCR, RNA-seq) and protein expression (immunostaining, Flowcytometry) of cells from brightfield microscopy images.
Read the full summary ↓
It provides a robust tool for non-destructive and continuous monitoring of the pluripotency status of stem cells, which will greatly advance regenerative medicine. It will be an approach that will contribute significantly to the manufacturing process of cellular products, especially where non-destructive and continuous monitoring is required.
Published in
Noninvasive, label-free image approaches to predict multimodal molecular markers in pluripotency assessment
Akiyoshi R, Hase T, Sathiyananthavel M et al. · Scientific reports 2024 · PMID 38977828 · doi:10.1038/s41598-024-66591-z
This dataset
Open GSE256303 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 GSE256303_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 PRJNA1078901 and SRA study SRP490878. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38977828) ↗
Study design
12 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 12 sample titles: 12 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

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.