GEO series

A pattern to rule them all: tracing a human brain map of shared molecular aspects across aging, Alzheimer’s disease and dementia with Lewy bodies

GSE280268 Homo sapiens (human) RNA-seq (gene expression) 90 samples Submitted 2024/10/24 Platform GPL21697

At a glance

RNA-seq (gene expression) · human · 90 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: 90 samples: 25 conditions, mostly in duplicateGroups 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: A Map of Transcriptomic Signatures of Different Brain Areas in Alzheimer's DiseaseInternational journal of molecular sciences · 2024

Summary (written by the submitters)

Aging is often characterized by a progressive loss of cognitive abilities due to the onset of different forms of dementia. Among these, Alzheimer’s Disease (AD) and dementia with Lewy bodies (LBD) are the most widespread ones. However, little is known about the common aspects between these conditions and how these aspects can be linked to aging itself.

Read the full summary ↓

With this work, we aim to identify a molecular fil rouge that could link aging and different dementias in order to possibly highlight strategic targets for novel therapeutical approaches. A whole transcriptome analysis and a Gene Set Enrichment Analysis (GSEA) have been carried out on brain samples from hippocampus (HI), temporal and parietal cortex (TC, PC), cingulate cortex (CG), and substantia nigra (SN) from the Abbiategrasso Brain Bank (ABB) of subjects with a neuropathological diagnosis of AD and LBD (6 per pathology) and 3 age-matched controls with no dementia (CTRLs). Clinical and neuropsychological profile were obtained through serial evaluations, made at the baseline and in the following years. According to the ABB protocol, the neuropathological diagnosis was based on a complete histological characterization, including all the main proteinopathies and vascular scoring. Transcriptomic results showed some major differences in the number of differentially expressed genes (DEGs) between the two pathologies and in particular that AD brains gene expression is more affected compared to LBD’s one (DEGsAD=3156; DEGsLBD=278). Furthermore, the GSEA showed that in AD it is possible to cluster the analysed regions by biological processes (HI and TC: DEGs primarily related to synaptic transmission; PC, CG and SN: DEGs primarily related to proper protein folding and inflammation) while in LBD there is only a strong and peculiar involvement of SN (DEGs primarily related to proper protein folding and inflammation) and PC (DEGs primarily related to myelination and glial system activation). AD and LBD are different forms of dementia characterized by a definite time and severity-related pattern. Therefore, it is possible to draw a molecular map of pathology spreading in the brain that could unveil unexplored roads and possible shortcuts leading to a better understanding of AD and LBD pathogenesis. This could help to discover novel biological targets in order to develop effective and well-timed therapeutical approaches.

Published in

A Map of Transcriptomic Signatures of Different Brain Areas in Alzheimer's Disease

Ferrari RR, Fantini V, Garofalo M et al. · International journal of molecular sciences 2024 · PMID 39456899 · doi:10.3390/ijms252011117

This dataset

Open GSE280268 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 GSE280268_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 PRJNA1177488 and SRA study SRP540706. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39456899) ↗

Study design

25 conditions, mostly in duplicate

BRAIN,AD,HI, ×6 BRAIN,AD,TC, ×6 BRAIN,AD,PC, ×6 BRAIN,AD,CG, ×6 BRAIN,AD,SN, ×6 BRAIN,LBD,HI, ×6 BRAIN,LBD,TC, ×6 BRAIN,LBD,PC, ×6 +17 more

Supports a between-group comparison across 90 samples.

How we read this: 25 replicated groups read from 90 sample titles; they account for 90 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

GSM8593623 BRAIN,AD,HI,1 BRAIN,AD,HI,
GSM8593624 BRAIN,AD,TC,1 BRAIN,AD,TC,
GSM8593625 BRAIN,AD,PC,1 BRAIN,AD,PC,
GSM8593626 BRAIN,AD,CG,1 BRAIN,AD,CG,
GSM8593627 BRAIN,AD,SN,1 BRAIN,AD,SN,
GSM8593628 BRAIN,AD,HI,2 BRAIN,AD,HI,
GSM8593629 BRAIN,AD,TC,2 BRAIN,AD,TC,
GSM8593630 BRAIN,AD,PC,2 BRAIN,AD,PC,
GSM8593631 BRAIN,AD,CG,2 BRAIN,AD,CG,
GSM8593632 BRAIN,AD,SN,2 BRAIN,AD,SN,
GSM8593633 BRAIN,AD,HI,3 BRAIN,AD,HI,
GSM8593634 BRAIN,AD,TC,3 BRAIN,AD,TC,
GSM8593635 BRAIN,AD,PC,3 BRAIN,AD,PC,
GSM8593636 BRAIN,AD,CG,3 BRAIN,AD,CG,
GSM8593637 BRAIN,AD,SN,3 BRAIN,AD,SN,
GSM8593638 BRAIN,AD,HI,4 BRAIN,AD,HI,
GSM8593639 BRAIN,AD,TC,4 BRAIN,AD,TC,
GSM8593640 BRAIN,AD,PC,4 BRAIN,AD,PC,
GSM8593641 BRAIN,AD,CG,4 BRAIN,AD,CG,
GSM8593642 BRAIN,AD,SN,4 BRAIN,AD,SN,
GSM8593643 BRAIN,AD,HI,5 BRAIN,AD,HI,
GSM8593644 BRAIN,AD,TC,5 BRAIN,AD,TC,
GSM8593645 BRAIN,AD,PC,5 BRAIN,AD,PC,
GSM8593646 BRAIN,AD,CG,5 BRAIN,AD,CG,
GSM8593647 BRAIN,AD,SN,5 BRAIN,AD,SN,
GSM8593648 BRAIN,AD,HI,6 BRAIN,AD,HI,
GSM8593649 BRAIN,AD,TC,6 BRAIN,AD,TC,
GSM8593650 BRAIN,AD,PC,6 BRAIN,AD,PC,
GSM8593651 BRAIN,AD,CG,6 BRAIN,AD,CG,
GSM8593652 BRAIN,AD,SN,6 BRAIN,AD,SN,
GSM8593653 BRAIN,LBD,HI,1 BRAIN,LBD,HI,
GSM8593654 BRAIN,LBD,TC,1 BRAIN,LBD,TC,
GSM8593655 BRAIN,LBD,PC,1 BRAIN,LBD,PC,
GSM8593656 BRAIN,LBD,CG,1 BRAIN,LBD,CG,
GSM8593657 BRAIN,LBD,SN,1 BRAIN,LBD,SN,
GSM8593658 BRAIN,LBD,HI,2 BRAIN,LBD,HI,
GSM8593659 BRAIN,LBD,TC,2 BRAIN,LBD,TC,
GSM8593660 BRAIN,LBD,PC,2 BRAIN,LBD,PC,
GSM8593661 BRAIN,LBD,CG,2 BRAIN,LBD,CG,
GSM8593662 BRAIN,LBD,SN,2 BRAIN,LBD,SN,

+ 50 more — browse all 90 samples with per-sample file links →

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