Psychotropic-drug and neurotransmitter perturbation RNA-seq of human iPSC-derived cardiac and retinal organoids (ToxiPred)
Single-cell RNA-seq · human · 96 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Homo sapiens (human)
- Samples
- 96 samples: 96 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.
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE334500_RAW.tar (it can run to tens of gigabytes).
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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1475359 and SRA study SRP707143. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 96 sample titles: 96 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.
- GSM9789701 cardiac organoid; No_drug; well A1
- GSM9789702 cardiac organoid; aripiprazole; well B1
- GSM9789703 cardiac organoid; loxapine; well C1
- GSM9789704 cardiac organoid; sertraline; well D1
- GSM9789705 cardiac organoid; quetiapine; well E1
- GSM9789706 cardiac organoid; xanomeline; well F1
- GSM9789707 cardiac organoid; control No drug; well G1
- GSM9789708 cardiac organoid; control No drug; well H1
- GSM9789709 cardiac organoid; No_drug + adrenaline; well A2
- GSM9789710 cardiac organoid; aripiprazole + adrenaline; well B2
- GSM9789711 cardiac organoid; loxapine + adrenaline; well C2
- GSM9789712 cardiac organoid; sertraline + adrenaline; well D2
- GSM9789713 cardiac organoid; quetiapine + adrenaline; well E2
- GSM9789714 cardiac organoid; xanomeline + adrenaline; well F2
- GSM9789715 cardiac organoid; control 10 uM NaCl; well G2
- GSM9789716 cardiac organoid; control 2 uM NaCl; well H2
- GSM9789717 cardiac organoid; No_drug + dopamine; well A3
- GSM9789718 cardiac organoid; aripiprazole + dopamine; well B3
- GSM9789719 cardiac organoid; loxapine + dopamine; well C3
- GSM9789720 cardiac organoid; sertraline + dopamine; well D3
- GSM9789721 cardiac organoid; quetiapine + dopamine; well E3
- GSM9789722 cardiac organoid; xanomeline + dopamine; well F3
- GSM9789723 cardiac organoid; control 10 uM NaNO2; well G3
- GSM9789724 cardiac organoid; control 2 uM NaNO2; well H3
- GSM9789725 cardiac organoid; No_drug + histamine; well A4
- GSM9789726 cardiac organoid; aripiprazole + histamine; well B4
- GSM9789727 cardiac organoid; loxapine + histamine; well C4
- GSM9789728 cardiac organoid; sertraline + histamine; well D4
- GSM9789729 cardiac organoid; quetiapine + histamine; well E4
- GSM9789730 cardiac organoid; xanomeline + histamine; well F4
- GSM9789731 cardiac organoid; control 2 uM C8H16NaO8; well G4
- GSM9789732 cardiac organoid; control 2 uM C8H16NaO8; well H4
- GSM9789733 cardiac organoid; No_drug + retinoic acid; well A5
- GSM9789734 cardiac organoid; aripiprazole + retinoic acid; well B5
- GSM9789735 cardiac organoid; loxapine + retinoic acid; well C5
- GSM9789736 cardiac organoid; sertraline + retinoic acid; well D5
- GSM9789737 cardiac organoid; quetiapine + retinoic acid; well E5
- GSM9789738 cardiac organoid; xanomeline + retinoic acid; well F5
- GSM9789739 cardiac organoid; control 2 uM Na4P2O7; well G5
- GSM9789740 cardiac organoid; control 2 uM Na4P2O7; well H5
+ 56 more — browse all 96 samples with per-sample file links →
- GSE287843 JAX Study 4- RNA-seq of male KOLF2.2J hiPSC-derived cortical brain organoids and extra-embryonic lineages homozygous null for six different transcription factors 28 samples shares ipsc-derived, organoids, rna-seq
- GSE303979 JAX Study 11- RNA-seq of male KOLF2.2J hiPSC-derived cortical brain organoids during different time points. 18 samples shares ipsc-derived, organoids, rna-seq
- GSE326554 RNA-seq of wild-type human iPSC-derived retinal organoids at different developmental stages 18 samples shares ipsc-derived, organoids, rna-seq
- GSE341748 3D Human iPSC-Derived Cardiac Organoids for Antifibrotic Drug Screening 12 samples shares ipsc-derived, organoids, cardiac
- GSE263372 Efficient and reproducible generation of human iPSC-derived cardiomyocytes and cardiac organoids in stirred suspension systems 5 samples shares ipsc-derived, organoids, cardiac
- GSE279590 BMP, MEK and WNT modulation with NGN2 expression for rapid generation of hiPSC-derived neurons amenable to regional patterning [bulk RNA-seq] 61 samples shares ipsc-derived, rna-seq
- GSE289264 Integral approach to organelle profiling in human iPSC-derived cardiomyocytes enhances in-vitro cardiac-safety classification 54 samples shares ipsc-derived, cardiac
- GSE302997 JAX Study 8- RNA-seq of male KOLF2.2J hiPSC-derived trophoblast cell lines homozygous null for nine different transcription factors. 53 samples shares ipsc-derived, rna-seq
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.