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Transcriptomic Landscape of Human iPSCs-derived Osteogenic Differentiation Identifies a Key Regulator, KLF16

GSE200492 Homo sapiens (human) RNA-seq (gene expression) 60 samples Submitted 2024/03/06 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 60 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
60 samples: 20 × iPSC-derived MSC, sample vs 20 × iPSC-derived preosteoblast, sample vs 20 × iPSC-derived osteoblast, sampleGroups 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
Integrated transcriptomic analysis of human induced pluripotent stem cell-derived osteogenic differentiation reveals a regulatory role of KLF16bioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
The osteogenic differentiation process is essential for bone development and metabolism, but the underlying gene regulatory networks have not been well investigated. In this study, we performed RNA-seq analysis of mesenchymal stem cell differentiation to preosteoblasts and osteoblasts, derived from normal human induced pluripotent stem cell (iPSC) lines.
Read the full summary ↓
Systematic analyses revealed potential transcription factor (TF) - TF regulatory networks, regulatory roles of long noncoding RNAs (lncRNAs), and differential splicing of extracellular matrix, TF, lncRNA, and splicing factor genes. TF-TF regulatory and gene co-expression network analyses predicted a key role for KLF16 in osteogenesis. In vitro and in vivo biological functional studies further established the inhibitory role of Klf16 in osteogenesis. Our model system and transcriptomic approach identify novel osteogenic genes and highlight the regulatory complexity of osteogenesis.
Published in
Integrated transcriptomic analysis of human induced pluripotent stem cell-derived osteogenic differentiation reveals a regulatory role of KLF16
Ru Y, Ma M, Zhou X et al. · bioRxiv : the preprint server for biology 2025 · PMID 38405902 · doi:10.1101/2024.02.11.579844
This dataset
Open GSE200492 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 GSE200492_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 PRJNA824819 and SRA study SRP368287. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38405902) ↗
Study design
20 × iPSC-derived MSC, sample vs 20 × iPSC-derived preosteoblast, sample vs 20 × iPSC-derived osteoblast, sample

Supports a between-group comparison across 60 samples.

How we read this: 3 replicated groups read from 60 sample titles; they account for 60 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

+ 20 more — browse all 60 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.