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Human iPSC-based Modeling of Late-Onset Disease using Progerin-induced Aging

GSE52431 Homo sapiens (human) RNA-seq (gene expression) 8 samples Submitted 2014/01/23 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 8 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
8 samples
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)
Reprogramming somatic cells to induced pluripotent stem cells (iPSCs) sets their identity back to an embryonic age. This presents a fundamental hurdle for modeling late-onset disorders using iPSC-derived cells. We therefore developed a strategy to induce age-like features in multiple iPSC-derived lineages and tested its impact on modeling Parkinson’s disease (PD).
Read the full summary ↓
We first describe markers that predict fibroblast donor age and observed the loss of these age-related markers following iPSC induction and re-differentiation into fibroblasts. Remarkably, age-related markers were readily induced in iPSC-derived fibroblasts or neurons following exposure to progerin including dopamine neuron-specific phenotypes such as neuromelanin accumulation. Induced aging in PD-iPSC-derived dopamine neurons revealed disease phenotypes requiring both aging and genetic susceptibility such as frank dendrite degeneration, progressive loss of tyrosine-hydroxylase expression and enlarged mitochondria or Lewy body-precursor inclusions. Our study presents a strategy for inducing age-related cellular properties and enables the modeling of late-onset disease features.
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 GSE52431_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 PRJNA228920 and SRA study SRP033078. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 8 more — browse all 8 samples with per-sample file links →

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