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Modeling late-onset Alzheimer's disease neuropathology via direct neuronal reprogramming

GSE252932 Homo sapiens (human) RNA-seq (gene expression) 48 samples Submitted 2024/08/05 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 48 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
48 samples: 18 conditions, mostly in triplicateGroups 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.
Paper
Modeling late-onset Alzheimer's disease neuropathology via direct neuronal reprogrammingScience (New York, N.Y.) · 2024
Summary (written by the submitters)
Late-onset Alzheimer’s disease (LOAD) is the most common form of AD. However, modeling sporadic LOAD, without clear genetic predispositions, to capture hallmark neuronal pathologies such as extracellular amyloid-β (Aβ) plaque deposition, intracellular tau tangles, and neuronal loss, remains an unmet need.
Read the full summary ↓
Here, we demonstrate that neurons generated by microRNA-based direct reprogramming of fibroblasts from patients affected by autosomal dominant AD (ADAD) and LOAD in a three-dimensional (3D) environment, effectively recapitulate key neuropathological features of AD without additional cellular or genetic insults. These LOAD neurons exhibit Aβ-dependent neurodegeneration, as treatment with β- or γ-secretase inhibitors before (but not subsequent to) Aβ deposit formation mitigated neuronal death. Moreover, inhibiting age-associated retrotransposable elements (RTEs) in LOAD neurons reduced both Ab deposition and neurodegeneration. Our study underscores the efficacy of modeling late-onset neuropathology of LOAD through high-efficiency microRNA-based neuronal reprogramming.
Published in
Modeling late-onset Alzheimer's disease neuropathology via direct neuronal reprogramming
Sun Z, Kwon JS, Ren Y et al. · Science (New York, N.Y.) 2024 · PMID 39088624 · doi:10.1126/science.adl2992
This dataset
Open GSE252932 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 GSE252932_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 PRJNA1063270 and SRA study SRP482928. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39088624) ↗
Study design
18 conditions, mostly in triplicate
LOAD89 ×3 HC89 ×3 LOAD72 ×3 LOAD78 ×3 HC78 ×3 LOAD87 ×3 LOAD70 ×3 HC68 ×3 +10 more

Supports a between-group comparison across 48 samples.

How we read this: 18 replicated groups read from 48 sample titles; they account for 48 of them. 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

+ 8 more — browse all 48 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.