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JAX Study 2- RNA-seq of male KOLF2.2J hiPSC-derived trophoblast cell lines homozygous null for seven different transcription factors

GSE288289 Homo sapiens (human) RNA-seq (gene expression) 82 samples Submitted 2025/02/03 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 82 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
82 samples: 82 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)
This study aims to phenotype iPSC-derived trophoblast lines that contain homozygous null alleles for transcription factors expressed within the extra-embryonic lineage, with a focus on differentiation toward either primitive syncytium or extra-embryonic mesenchymal cells, depending on the TF being analyzed. Null alleles were generated using three distinct genetic engineering approaches: full protein coding region deletion (KO), critical exon deletion (CE), and insertion of a premature termination codon with frameshift (PTC+1).
Read the full summary ↓
RNA-seq data was generated after 6 days of initiating differentiation from iPSCs. The study included the homozygous null alleles for the following transcription factors: MEIS1, MXD1, MEIS2, RUNX1, MEF2C, NCOA3, and BHLHE40. This analysis provided insight into the differences between the various CRISPR-Cas9-based approaches and the impact of loss of function for these transcription factors in trophoblast lineage differentiation.
This dataset
Open GSE288289 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 GSE288289_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 PRJEB82699 and SRA study ERP166375. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
82 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 82 sample titles: 82 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

+ 42 more — browse all 82 samples with per-sample file links →

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.