Single-Cell Co-expression Analysis Reveals Distinct Functional Modules, Co-regulation Mechanisms and Clinical Outcomes.
Co-expression analysis has been employed to predict gene function, identify functional modules, and determine tumor subtypes. Previous co-expression analysis was mainly conducted at bulk tissue level. It is unclear whether co-expression analysis at the single-cell level will provide novel insights into transcriptional regulation. Here we developed a computational approach to compare glioblastoma expression profiles at the single-cell level with those obtained from bulk tumors. We found that the co-expressed genes observed in single cells and bulk tumors have little overlap and show distinct characteristics. The co-expressed genes identified in bulk tumors tend to have similar biological functions, and are enriched for intrachromosomal interactions with synchronized promoter activity. In contrast, single-cell co-expressed genes are enriched for known protein-protein interactions, and are regulated through interchromosomal interactions. Moreover, gene members of some protein complexes are co-expressed only at the bulk level, while those of other complexes are co-expressed at both single-cell and bulk levels. Finally, we identified a set of co-expressed genes that can predict the survival of glioblastoma patients. Our study highlights that comparative analyses of single-cell and bulk gene expression profiles enable us to identify functional modules that are regulated at different levels and hold great translational potential.
The Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Jie
J
Wang
Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.|Hugo W Moser Research Institute at Kennedy Krieger, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Shuli
S
Xia
Department of Computer Science and Engineering, the Ohio State University, Columbus, Ohio, United States of America.
Brian
B
Arand
The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.|Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.|Center for High-Throughput Biology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Heng
H
Zhu
Department of Computer Science and Engineering, the Ohio State University, Columbus, Ohio, United States of America.|Department of Biomedical Informatics, the Ohio State University, Columbus, Ohio, United States of America.
Raghu
R
Machiraju
Department of Computer Science and Engineering, the Ohio State University, Columbus, Ohio, United States of America.|Department of Biomedical Informatics, the Ohio State University, Columbus, Ohio, United States of America.
Kun
K
Huang
Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Hongkai
H
Ji
The Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.|The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Jiang
J
Qian
D001932:Brain Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D019295:Computational Biology; D003198:Computer Simulation; D005909:Glioblastoma / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D006801:Humans; D008297:Male; D008957:Models, Genetic; D005810:Multigene Family; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D060066:Protein Interaction Maps / Q000235:genetics; D059010:Single-Cell Analysis / Q000706:statistics & numerical data*; D059467:Transcriptome