Cell-Type-Resolved Transcriptional Remodelling in Parkinson's Disease Substantia Nigra: An Integrated Framework Implicates NPAS3 and BNC2 Regulatory Subnetworks in Dopaminergic Neurons and Glial Subpopulations
Noor, S.; Zahoor, F.
Show abstract
Background: Parkinson's disease (PD) is the second most common progressive neurological disorder that is pathologically characterized by the loss of dopaminergic neurons within the substantia nigra (SN). However, disease progression probably involves coordinated changes across both neuronal and glial cell populations. Although single-nucleus RNA-seq resolved cell-type-specific transcriptional profiling, differential expression and regulatory interpretation are commonly reported separately; however, they may limit the mechanistic prioritization to uncover novel therapeutic targets. Methods: Here, we performed sample-aware pseudobulk framework analysis on single-nucleus transcriptomes obtained SN of PD and control donors. Cell-type-specific differential expression for PD vs. control was identified using edgeR quasi-likelihood modeling (FDR < 0.05; |log2FC| > 0.5). Further, to quantify disease-specific remodelling, we computed one-vs-rest cell-type specificity scores in each condition and defined delta-specificity as the PD-control shift. We further prioritized the gene-set for dopaminergic neurons and microglia based on edge R significance and delta-specificity shifts, followed by upstream regulatory assessment using transcription factor enrichment and subnetwork visualization using ChEA-KG. Moreover, we used Cellchat to identify altered cell-cell communication networks to infer differences between both conditions. Results: Dopaminergic neurons demonstrated upregulation of neuronal-state remodeling transcriptional programs related gene sets in PD group, including receptor signaling and contact/guidance pathways (e.g., CHRM3, ROBO1, PLXNA4, UNC5D, EFNA5), neuronal excitability homeostatsis, RNA components, cellular traffickings and proteostasis, suggesting coordinated remodeling in surviving neuronal population. Microglia exhibited a compact PD-associated signature enriched for regulatory and activation state-related genes. TF networks analysis revealed distinct regulatory subnetwork in each population,including BNC2-centered network in microglia and an NPAS3-centered network in dopaminergic neurons with embedded ZNF804A and chromatin-associated components. Conclusions: In summary, integrating pseudobulk, delta-specificity scoring and TF-network enrichment analysis provides coherent dopaminergic and microglial programs in PD substantia nigra. This framework prioritizes cell-type-specific potential candidate mechanisms for downstream validation. The inferred regulatory networks and interactions are hypothesis generating and need orthogonal validation, such as spatial or proteomics approaches and independent cohorts.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Transcriptional analysis of peripheral memory T cells reveals Parkinson's disease-specific gene signatures 95%
- Genome-wide determinants of mortality and motor progression in Parkinson's disease 95%
- Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts. 95%
Similar papers in this journal
- Multi-omics dissection of Parkinson's patient subgroups associated with motor and cognitive severity 94%
- Brain-first forms of Parkinson’s Disease are over-represented in patients with non-responsive resting tremor 94%
- Bioenergetic and Protein Processing Imbalances Synergize in iPSC-Dopamine neurons from Individuals with Idiopathic Parkinsons Disease 94%
Similar papers in this journal
- Genetic modifiers of risk and age at onset in GBA associated Parkinson disease and Lewy body dementia 96%
- The chromatin modulating NSL complex regulates genes and pathways genetically linked to Parkinsons disease 95%
- Genetic Analysis and Natural History of Parkinson’s Disease Due to the LRRK2 G2019S Variant 95%
Similar papers in this journal
Similar papers in this journal
"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.