GEO series

17q21.31 locus regulates Parkinson’s disease relevant pathways through KANSL1 activity

GSE283747 Homo sapiens (human) Single-nucleus RNA-seq 40 samples Submitted 2025/03/17 Platform GPL24676

At a glance

Single-nucleus RNA-seq · human · 40 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 40 samples: 40 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.

Summary (written by the submitters)

An inversion polymorphism at the 17q21.31 locus defines the H1 and H2 haplotypes, with the former linked to multiple neurodegenerative disorders, including an increased risk of Parkinson’s disease (PD). Although the high linkage disequilibrium at this locus has made it difficult to decipher which gene(s) drive the PD association, there is increasing evidence to support the role of KANSL1 as a risk gene.

Read the full summary ↓

KANSL1 has been shown to regulate the expression of some PD-associated genes and pathways, likely as part of the histone acetylating non-specific lethal (NSL) complex. Here for the first time, we studied the global effects of 17q21.31 haplotype variation using bulk and single-nuclear RNA-sequencing data from control and PD patient brain. We first analysed differential gene expression across haplotype groups, and then assessed the contribution of KANSL1 by comparing with the results of an siRNA knockdown in neuronal and glial human cell lines. We demonstrated that the PD risk-associated H1 haplotype downregulates autophagy, lysosomal and mitochondrial processes, all of which have already been implicated in PD aetiology. Furthermore, these effects were apparent in both neuronal and glial cell types, and in the case of the latter, appear to be associated with the modulation of innate and adaptive immune responses. Thus, we identify important links between NSL complex activity and PD pathophysiology that can be leveraged for novel therapeutic interventions.

This dataset

Open GSE283747 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 GSE283747_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1195127 and SRA study SRP549862. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

40 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 40 sample titles: 40 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

GSM8671545 siSCR SH-SY5Ys, replicate 1, sequencing batch 1
GSM8671546 siKAT8 SH-SY5Ys, replicate 1, sequencing batch 1
GSM8671547 siSCR SH-SY5Ys, replicate 2, sequencing batch 1
GSM8671548 siKANSL1 SH-SY5Ys, replicate 2, sequencing batch 1
GSM8671549 siKAT8 SH-SY5Ys, replicate 2, sequencing batch 1
GSM8671550 siSCR SH-SY5Ys, replicate 3, sequencing batch 1
GSM8671551 siKAT8 SH-SY5Ys, replicate 3, sequencing batch 1
GSM8671552 siSCR SH-SY5Ys, replicate 4, sequencing batch 1
GSM8671553 siKANSL1 SH-SY5Ys, replicate 4, sequencing batch 1
GSM8671554 siKAT8 SH-SY5Ys, replicate 4, sequencing batch 1
GSM8671555 siSCR SH-SY5Ys, replicate 5, sequencing batch 2
GSM8671556 siKANSL1 SH-SY5Ys, replicate 5, sequencing batch 2
GSM8671557 siKAT8 SH-SY5Ys, replicate 5, sequencing batch 2
GSM8671558 siSCR SH-SY5Ys, replicate 6, sequencing batch 2
GSM8671559 siKANSL1 SH-SY5Ys, replicate 6, sequencing batch 2
GSM8671560 siKAT8 SH-SY5Ys, replicate 6, sequencing batch 2
GSM8671561 siSCR SH-SY5Ys, replicate 7, sequencing batch 2
GSM8671562 siKANSL1 SH-SY5Ys, replicate 7, sequencing batch 2
GSM8671563 siKAT8 SH-SY5Ys, replicate 7, sequencing batch 2
GSM8671564 siSCR SH-SY5Ys, replicate 8, sequencing batch 2
GSM8671565 siKANSL1 SH-SY5Ys, replicate 8, sequencing batch 2
GSM8671566 siKAT8 SH-SY5Ys, replicate 8, sequencing batch 2
GSM8671567 siSCR H4s, replicate 1, sequencing batch 3
GSM8671568 siKANSL1 H4s, replicate 1, sequencing batch 3
GSM8671569 siKAT8 H4s, replicate 1, sequencing batch 3
GSM8671570 siSCR H4s, replicate 2, sequencing batch 3
GSM8671571 siKANSL1 H4s, replicate 2, sequencing batch 3
GSM8671572 siKAT8 H4s, replicate 2, sequencing batch 3
GSM8671573 siSCR H4s, replicate 3, sequencing batch 3
GSM8671574 siKANSL1 H4s, replicate 3, sequencing batch 3
GSM8671575 siKAT8 H4s, replicate 3, sequencing batch 3
GSM8671576 siSCR H4s, replicate 4, sequencing batch 3
GSM8671577 siKANSL1 H4s, replicate 4, sequencing batch 3
GSM8671578 siKAT8 H4s, replicate 4, sequencing batch 3
GSM8671579 siSCR H4s, replicate 5, sequencing batch 3
GSM8671580 siKANSL1 H4s, replicate 5, sequencing batch 3
GSM8671581 siKAT8 H4s, replicate 5, sequencing batch 3
GSM8671582 siSCR H4s, replicate 6, sequencing batch 3
GSM8671583 siKANSL1 H4s, replicate 6, sequencing batch 3
GSM8671584 siKAT8 H4s, replicate 6, sequencing batch 3

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