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Transcriptomic profiling of Neural Progenitors Cell and Neural Crest Cell Differentiation of Wild-Type and TSC2-Knockout Cultures from the Pluripotent State

GSE137614 Homo sapiens (human) RNA-seq (gene expression) 96 samples Submitted 2024/06/07 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 96 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
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.
Summary (written by the submitters)
TSC2 inactivating mutations elicit mTORC1 hyperactivation and underlie neurological dysfunction and the development of neural and mesenchymal tumors in the monogenic disease tuberous sclerosis complex (TSC). We present a multi-lineage model of TSC2-deficiency employing CRISPR-Cas9 engineering in human pluripotent stem cells (hPSCs) and differentiation into neural and neural crest lineages, cell types predicted to drive TSC manifestations.
Read the full summary ↓
Temporal RNA-sequencing reveals a massive proteostatic stress response underlying early neuroepithelial induction of TSC2-deficient cells, which is resolved upon neural crest cell (NCC) specification but persists in neural precursor cells (NPCs). This culminates in long-term endosomal and metabolic reprogramming as cells age, and sensitivity of TSC2-/- NPCs, but not NCCs, to death via proteasome inhibition independent of mTORC1 activity. Thus, TSC2-deficiency induces lineage-specific stress adaptations which confer differential sensitivity to a commonly targeted pathway. These results exemplify the complexity of elucidating underlying biological mechanisms and therapeutic approaches for multisystem diseases, illustrating the power of utilizing hPSC disease models with tissue-specific relevance.
This dataset
Open GSE137614 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 GSE137614_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 PRJNA566008 and SRA study SRP222104. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
96 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 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.

Samples in this study

+ 56 more — browse all 96 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.