Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data-Reference-Cited by-同舟云学术

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

Published:2024-05-24 Issue:21 Volume:10 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Dai Rujia¹^ORCID,Chu Tianyao²^ORCID,Zhang Ming²,Wang Xuan²^ORCID,Jourdon Alexandre³^ORCID,Wu Feinan³^ORCID,Mariani Jessica³^ORCID,Vaccarino Flora M.³⁴^ORCID,Lee Donghoon⁵^ORCID,Fullard John F.⁵^ORCID,Hoffman Gabriel E.⁵^ORCID,Roussos Panos⁵^ORCID,Wang Yue⁶,Wang Xusheng⁷^ORCID,Pinto Dalila⁸^ORCID,Wang Sidney H.⁹,Zhang Chunling¹⁰^ORCID, ,Chen Chao²^ORCID,Liu Chunyu¹²¹⁰^ORCID

Affiliation:

1. Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA.

2. MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China.

3. Child Study Center, Yale University, New Haven, CT, USA.

4. Department of Neuroscience, Yale University, New Haven, CT, USA.

5. Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

6. Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.

7. Department of Biology, University of North Dakota, Grand Forks, ND, USA.

8. Departments of Psychiatry and Genetics and Genomic Sciences, Mindich Child Health and Development Institute, and Icahn Genomics Institute for Data Science and Genomic Technology, Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

9. Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA.

10. Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA.

Abstract

Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer’s disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.

Publisher

American Association for the Advancement of Science (AAAS)

Link

https://www.science.org/doi/pdf/10.1126/sciadv.adh2588

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