GEO series

Integrative multi-omics analysis reveals conserved cell-projection deficits in human Down syndrome brains

GSE221179 Homo sapiens (human) Non-coding RNA profiling 84 samples Submitted 2024/06/02 Platform GPL24676

At a glance

Non-coding RNA profiling · human · 84 samples

What it is: Non-coding RNA profilingLevels of RNAs that do not code for proteins, such as miRNA or lncRNA.
Organism: Homo sapiens (human)
Samples: 84 samples: 84 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.
Paper: Integrative multi-omic analysis reveals conserved cell-projection deficits in human Down syndrome brainsNeuron · 2024

Summary (written by the submitters)

Down syndrome (DS) is the most common genetic cause of cognitive disability. However, it is largely unclear how triplication of a small gene subset may impinge on diverse aspects of DS brain physiopathology. Here, we took a multi-omic approach and simultaneously analyzed by RNA-seq and proteomics the expression signatures of two diverse regions of human postmortem DS brains.

Read the full summary ↓

We found that the overexpression of triplicated genes triggered global expression dysregulation, differentially affecting transcripts, miRNAs, and proteins involved in both known and novel biological candidate pathways. Among the latter, we observed an alteration in RNA splicing, specifically modulating the expression of genes involved in cytoskeleton and axonal dynamics in DS brains. Accordingly, we found an alteration in axonal polarization in neurons from DS human iPSCs and mice. Thus, our study provides an integrated multilayer expression database capable of identifying new potential targets to aid in designing future clinical interventions for DS.

Published in

Integrative multi-omic analysis reveals conserved cell-projection deficits in human Down syndrome brains

Rastogi M, Bartolucci M, Nanni M et al. · Neuron 2024 · PMID 38810652 · doi:10.1016/j.neuron.2024.05.002

This dataset

Open GSE221179 in finder → Find more human 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 GSE221179_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 ↗

Also filed as BioProject PRJNA913151 and SRA study SRP413410. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38810652) ↗

Study design

84 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 84 sample titles: 84 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

GSM6845756 Control sample 1, hippocampus (small RNA)
GSM6845757 Control sample 2, hippocampus (small RNA)
GSM6845758 Control sample 3, hippocampus (small RNA)
GSM6845759 Control sample 4, hippocampus (small RNA)
GSM6845760 Control sample 5, hippocampus (small RNA)
GSM6845761 Control sample 6, hippocampus (small RNA)
GSM6845762 Control sample 7, hippocampus (small RNA)
GSM6845763 Control sample 8, hippocampus (small RNA)
GSM6845764 Control sample 9, hippocampus (small RNA)
GSM6845765 Control sample 10, hippocampus (small RNA)
GSM6845766 Control sample 11, hippocampus (small RNA)
GSM6845767 Control sample 12, cortex (small RNA)
GSM6845768 Control sample 13, cortex (small RNA)
GSM6845769 Control sample 14, cortex (small RNA)
GSM6845770 Control sample 15, cortex (small RNA)
GSM6845771 Control sample 16, cortex (small RNA)
GSM6845772 Control sample 17, cortex (small RNA)
GSM6845773 Control sample 18, cortex (small RNA)
GSM6845774 Control sample 19, cortex (small RNA)
GSM6845775 Control sample 20, cortex (small RNA)
GSM6845776 Control sample 21, cortex (small RNA)
GSM6845777 Control sample 22, cortex (small RNA)
GSM6845778 Control sample 23, cortex (small RNA)
GSM6845779 DS sample 1, hippocampus (small RNA)
GSM6845780 DS sample 2, hippocampus (small RNA)
GSM6845781 DS sample 3, hippocampus (small RNA)
GSM6845782 DS sample 4, hippocampus (small RNA)
GSM6845783 DS sample 5, hippocampus (small RNA)
GSM6845784 DS sample 6, hippocampus (small RNA)
GSM6845785 DS sample 7, hippocampus (small RNA)
GSM6845786 DS sample 8, hippocampus (small RNA)
GSM6845787 DS sample 9, hippocampus (small RNA)
GSM6845788 DS sample 10, cortex (small RNA)
GSM6845789 DS sample 11, cortex (small RNA)
GSM6845790 DS sample 12, cortex (small RNA)
GSM6845791 DS sample 13, cortex (small RNA)
GSM6845792 DS sample 14, cortex (small RNA)
GSM6845793 DS sample 15, cortex (small RNA)
GSM6845794 DS sample 16, cortex (small RNA)
GSM6845795 DS sample 17, cortex (small RNA)

+ 44 more — browse all 84 samples with per-sample file links →

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