@article{bibcite_9196,
  keywords = {Gene expression, Neurodevelopmental disorders, Neurological disorders},
  author = {John F. Fullard and Prashant Nm and Donghoon Lee and Deepika Mathur and Karen Therrien and Aram Hong and Clara Casey and Zhiping Shao and Marcela Alvia and Stathis Argyriou and Tereza Clarence and David Burstein and Sanan Venkatesh and Pavan K. Auluck and Lisa L. Barnes and David A. Bennett and Stefano Marenco and Kiran Girdhar and Vahram Haroutunian and Gabriel E. Hoffman and Georgios Voloudakis and Jaroslav Bendl and Panos Roussos},
  title = {Population-scale cross-disorder atlas of the human prefrontal cortex at single-cell resolution},
  abstract = {Neurodegenerative diseases and serious mental illnesses often exhibit overlapping characteristics, highlighting the potential for shared underlying mechanisms. To facilitate a deeper understanding of these diseases and pave the way for more effective treatments, we have generated a population-scale multi-omics dataset consisting of genotype and single-nucleus transcriptome data from the prefrontal cortex of frozen human brain specimens. Encompassing over 6.3 million nuclei from 1,494 donors, our dataset represents a diverse range of neurodegenerative and serious mental illnesses, including Alzheimer{\textquoteright}s and Parkinson{\textquoteright}s diseases, schizophrenia, bipolar disorder and diffuse Lewy body dementia, as well as neurotypical controls. Our dataset offers a unique opportunity to study disease interactions, as 21\% of donors had comorbid diagnoses of two or more major brain disorders. Additionally, it includes detailed phenotypic information on neuropsychiatric symptoms, such as apathy and weight loss, which commonly accompany Alzheimer{\textquoteright}s disease and related dementias. We have performed stringent preprocessing and quality controls, ensuring the reliability and usability of the data. As a commitment to fostering collaborative research, we provide this valuable resource as an online repository, enabling widespread analyses across the scientific community.},
  year = {2025},
  journal = {Scientific Data},
  volume = {12},
  pages = {954},
  month = {2025-06-06},
  issn = {2052-4463},
  url = {https://www.nature.com/articles/s41597-025-04687-5},
  doi = {10.1038/s41597-025-04687-5},
  language = {en},
}