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Targeted Apoptosis of Senescent Cells Restores Tissue Homeostasis in Response to Chemotoxicity and Aging

GSE94395 Homo sapiens (human) RNA-seq (gene expression) 6 samples Submitted 2017/03/01 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 6 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
6 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)
The accumulation of irreparable cellular damage restricts healthy lifespan after acute stress or natural aging. Senescent cells are thought to impair tissue function and their genetic clearance can successfully delay features of aging. Identifying how senescent cells avoid apoptosis would allow for the prospective design of anti-senescence compounds to address whether homeostasis can be restored.
Read the full summary ↓
Here, we identify FOXO4 as a pivot in the maintenance of senescent cell viability. We designed a FOXO4-based peptide which selectively competes for interaction of FOXO4 with p53. In senescent cells, this results in p53 nuclear exclusion and cell-intrinsic apoptosis. Importantly, under conditions where it was well tolerated, the FOXO4 peptide restored liver function after Doxorubicin-induced chemotoxicity. Moreover, in fast aging XpdTTD/TTD, as well as in naturally aged mice the FOXO4 peptide could counteract the loss of fitness, fur density and renal function. Thus, it is possible to therapeutically target senescent cells and thereby effectively counteract senescence-associated loss of tissue homeostasis.
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 GSE94395_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 PRJNA369561 and SRA study SRP098713. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 6 more — browse all 6 samples with per-sample file links →

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