<p>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’s and Parkinson’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’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.</p>

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Population-scale cross-disorder atlas of the human prefrontal cortex at single-cell resolution

  • John F. Fullard,
  • Prashant NM,
  • Donghoon Lee,
  • Deepika Mathur,
  • Karen Therrien,
  • Aram Hong,
  • Clara Casey,
  • Zhiping Shao,
  • Marcela Alvia,
  • Stathis Argyriou,
  • Tereza Clarence,
  • David Burstein,
  • Sanan Venkatesh,
  • Pavan K. Auluck,
  • Lisa L. Barnes,
  • David A. Bennett,
  • Stefano Marenco,
  • Monika Ahirwar,
  • Sayali A. Alatkar,
  • Marios Anyfantakis,
  • Rachel Bercovitch,
  • Pramod B. Chandrashekar,
  • Jerome Choi,
  • Noah Cohen Kalafut,
  • Pengfei Dong,
  • Logan C. Dumitrescu,
  • Steven Finkbeiner,
  • Chirag Gupta,
  • Kalpana H. Arachchilage,
  • Chenfeng He,
  • Timothy J. Hohman,
  • Xiang Huang,
  • Lars J. Jensen,
  • Ting Jin,
  • Pavel Katsel,
  • Saniya Khullar,
  • Seon Kinrot,
  • Steven P. Kleopoulos,
  • Roman Kosoy,
  • Mikaela Koutrouli,
  • Athan Z. Li,
  • Nicolas Y. Masse,
  • Deepika Mathur,
  • Colleen A. McClung,
  • Jennifer Monteiro Fortes,
  • Milos Pjanic,
  • Christian Porras,
  • Vivek G. Ramaswamy,
  • Genadi Ryan,
  • Madeline R. Scott,
  • Lyra Sheu,
  • Maxim Signaevsky,
  • Collin Spencer,
  • Karen Therrien,
  • Fotios Tsetsos,
  • Sanan Venkatesh,
  • Daifeng Wang,
  • Xinyi Wang,
  • Zhenyi Wu,
  • Hui Yang,
  • Biao Zeng,
  • Kiran Girdhar,
  • Vahram Haroutunian,
  • Gabriel E. Hoffman,
  • Georgios Voloudakis,
  • Jaroslav Bendl,
  • Panos Roussos

摘要

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’s and Parkinson’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’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.