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Peer-reviewedCC BY 4.0GigaScience

Multiscale Transcriptomic Network Models of Parkinson’s Disease at the Single Cell Level

  • Linh Chu1,2,3,4
  • Gefei Yu1,2,5
  • Xianxiao Zhou1,2,5
  • Minghui Wang1,2,5
  • Zhenyu Yue3,6,7,8
  • Bin Zhang1,2,4,5,9
9 affiliations
  1. 1Icahn School of Medicine at Mount Sinai Department of Genetics and Genomic Sciences, , 1425 Madison Avenue, NY 10029 ,
  2. 2Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai , 1470 Madison Avenue, NY 10029 ,
  3. 3Icahn School of Medicine at Mount Sinai Department of Neurology, , 1425 Madison Avenue, NY 10029 ,
  4. 4Icahn School of Medicine at Mount Sinai Department of Pharmacological Sciences, , 1425 Madison Avenue, NY 10029 ,
  5. 5Icahn Institute of Genomics, Icahn School of Medicine at Mount Sinai , 1425 Madison Avenue, NY 10029 ,
  6. 6Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai , 1470 Madison Avenue, NY 10029 ,
  7. 7Friedman Brain Institute, Icahn School of Medicine at Mount Sinai , 1470 Madison Avenue, NY 10029 ,
  8. 8Center of Parkinson’s Disease Neurobiology, Icahn School of Medicine at Mount Sinai , 1470 Madison Avenue, New York, NY ,
  9. 9Icahn School of Medicine at Mount Sinai Department of Artificial Intelligence & Human Health, , 1470 Madison Avenue, NY 10029 ,
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Abstract

Multiple single nuclei RNA-sequencing (snRNA-seq) studies of the vulnerable brain regions of Parkinson’s Disease (PD) have revealed alterations in brain cell populations and cell type specific transcriptomes. However, a systematic analysis of cell-type–resolved gene regulatory architecture in PD is lacking. Here, we develop an integrative, meta-cell based multiscale network analysis (MCMNA) of snRNA-seq data from the substantia nigra to systematically uncover molecular mechanisms and identify potential therapeutic targets for PD. MCMNA overcomes the inherent sparsity of single cell data by leveraging metacell-based aggregation, enabling construction of robust gene regulatory networks. Gene co-expression network analysis identifies cell-type specific gene modules associated with PD. Integration of meta-cell based differential gene expression and a Bayesian causal network systematically reveals putative driver genes and their hierarchies. Our multiscale network models provide a framework for prioritizing candidate molecular regulators and pathways for further investigation of disease mechanisms and therapeutic targets.

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Cite

BibTeXRIS
BibTeX
@article{chu2026multiscale,
  title = {{Multiscale Transcriptomic Network Models of Parkinson’s Disease at the Single Cell Level}},
  author = {Linh Chu and Gefei Yu and Xianxiao Zhou and Minghui Wang and Zhenyu Yue and Bin Zhang},
  journal = {GigaScience},
  year = {2026},
  month = oct,
  doi = {10.1093/gigascience/giag096},
  url = {https://academic.oup.com/gigascience/advance-article/doi/10.1093/gigascience/giag096/8868918},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Multiscale Transcriptomic Network Models of Parkinson’s Disease at the Single Cell Level
AU  - Linh Chu
AU  - Gefei Yu
AU  - Xianxiao Zhou
AU  - Minghui Wang
AU  - Zhenyu Yue
AU  - Bin Zhang
PY  - 2026
DA  - 2026/10/06
JO  - GigaScience
DO  - 10.1093/gigascience/giag096
UR  - https://academic.oup.com/gigascience/advance-article/doi/10.1093/gigascience/giag096/8868918
AB  - Multiple single nuclei RNA-sequencing (snRNA-seq) studies of the vulnerable brain regions of Parkinson’s Disease (PD) have revealed alterations in brain cell populations and cell type specific transcriptomes. However, a systematic analysis of cell-type–resolved gene regulatory architecture in PD is lacking. Here, we develop an integrative, meta-cell based multiscale network analysis (MCMNA) of snRNA-seq data from the substantia nigra to systematically uncover molecular mechanisms and identify potential therapeutic targets for PD. MCMNA overcomes the inherent sparsity of single cell data by leveraging metacell-based aggregation, enabling construction of robust gene regulatory networks. Gene co-expression network analysis identifies cell-type specific gene modules associated with PD. Integration of meta-cell based differential gene expression and a Bayesian causal network systematically reveals putative driver genes and their hierarchies. Our multiscale network models provide a framework for prioritizing candidate molecular regulators and pathways for further investigation of disease mechanisms and therapeutic targets.
SN  - 2047-217X
ER  -