Back

Dissecting the molecular landscape of Parkinson's disease and Parkinson's disease dementia using highly efficient snRNA-seq (HIF-snRNA-seq)

KANG, S.-U.; Park, J.; Ha, S.; Kim, D.; Pletnikova, O.; Redding, J.; Troncoso, J.; Peng, Q.; Emburgh, B.; Trivedi, J.; Brahmachari, S.; Nezami, B.; Dawson, V.; Dawson, T.

2025-03-07 genomics
10.1101/2025.03.01.640894 bioRxiv
Show abstract

This study presents a transcriptomic analysis of the cingulate cortex (CING) in Parkinsons disease (PD) and Parkinsons disease dementia (PDD) using a High-efficiency single-nucleus RNA sequencing (HiF-snRNA-seq) protocol optimized for post-mortem brain samples. RNA quality prediction, poly-A tailing, and dCas9-targeted depletion enabled analysis of 77 high-quality samples from 240 cases, yielding over 2 million nuclei classified into seven major cell types. Disease conditions revealed altered astrocyte and microglia proportions, implicating their roles in neuroinflammation. Differential expression analysis identified unique and shared genes across PD and PDD, linked to synaptic remodeling, stress responses, and inflammation. Stage-specific analysis uncovered tau-dependent early-stage genes and inflammation-associated late-stage genes. This study highlights the CINGs central role in PD and PDD pathophysiology, offering insights into disease mechanisms and identifying candidate genes and pathways for therapeutic and biomarker development.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
npj Parkinson's Disease
○ 105 papers in training set
Top 0.1%
22.4%
Similar papers in this journal
2
Molecular Neurodegeneration
○ 55 papers in training set
Top 0.2%
7.4%
Similar papers in this journal
3
Neurobiology of Disease
○ 148 papers in training set
Top 0.7%
4.9%
Similar papers in this journal
4
Movement Disorders
○ 71 papers in training set
Top 0.3%
4.4%
Similar papers in this journal
5
Acta Neuropathologica
○ 58 papers in training set
Top 0.3%
4.4%
Similar papers in this journal
6
Acta Neuropathologica Communications
○ 89 papers in training set
Top 0.5%
4.1%
7
Molecular Omics
◐ 23 papers in training set
Top 0.1%
4.1%
50% of probability mass above
8
Brain
◐ 168 papers in training set
Top 0.9%
4.1%
9
Scientific Reports
○ 3612 papers in training set
Top 30%
3.3%
10
Nature Communications
○ 5641 papers in training set
Top 39%
2.5%
11
Brain Communications
◐ 166 papers in training set
Top 2%
2.4%
12
Neurology Genetics
○ 15 papers in training set
Top 0.1%
2.2%
13
Annals of Neurology
○ 64 papers in training set
Top 0.9%
1.8%
14
Alzheimer's & Dementia
177 papers in training set
Top 2%
1.7%
15
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 4%
1.5%
16
eBioMedicine
○ 183 papers in training set
Top 3%
1.5%
17
International Journal of Molecular Sciences
○ 494 papers in training set
Top 10%
1.4%
18
Cells
○ 249 papers in training set
Top 4%
1.1%
19
Molecular Neurobiology
○ 53 papers in training set
Top 1%
1.0%
20
Genome Medicine
○ 183 papers in training set
Top 4%
1.0%
21
PLOS ONE
● 5266 papers in training set
Top 58%
0.9%
22
Human Genetics and Genomics Advances
○ 84 papers in training set
Top 2%
0.9%
23
iScience
○ 1154 papers in training set
Top 34%
0.9%
24
Communications Biology
○ 993 papers in training set
Top 33%
0.6%
25
NAR Molecular Medicine
◐ 22 papers in training set
Top 0.4%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.