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Development-inspired approach for the derivation of functional and transplantable salivary gland organoids from human pluripotent stem cells [RNA-Seq]

GSE342790 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2026/08/07 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 8 × BXS0114 - Day vs 8 × WA09 - DayGroups 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)
Salivary gland (SG) hypofunction significantly affects the life quality of patients with hyposalivation. Current treatment options for these conditions are limited and often result in undesirable side effects. To address this issue, we developed a novel and developmentally inspired strategy to derive salivary gland epithelial progenitor (SGEP) cells from human pluripotent stem cells (PSC).
Read the full summary ↓
Then we employed SGEP cells derived from human iPSC with a SOX10::GFP reporter to engineer three-dimensional lumenized SG organoids, with well-defined lumens and secretory function, reminiscent of human salivary glands. Upon transplantation into the submandibular gland of athymic nude mice, mCherry+ salivary gland organoids (SGO) integrated into the gland and differentiated into salivary gland phenotypes, including acinar, ductal and myoepithelial structures. Our findings suggest that SG organoids may hold promise for studying SG development, disease progression and drug screening, as well as to develop cell therapies for salivary gland regeneration.
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 GSE342790_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 PRJNA1508898 and SRA study SRP724918. Searching any of these in the dataset finder brings you back here.

Study design
8 × BXS0114 - Day vs 8 × WA09 - Day

Supports a between-group comparison across 16 samples.

How we read this: 2 replicated groups read from 16 sample titles; they account for 16 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
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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.