Single-Cell Co-expression Analysis Reveals Distinct Functional Modules, Co-regulation Mechanisms and Clinical Outcomes

• Published in: PLoS computational biology 2016-04, Vol.12 (4), p.e1004892-e1004892
• Main Authors: Wang, Jie, Xia, Shuli, Arand, Brian, Zhu, Heng, Machiraju, Raghu, Huang, Kun, Ji, Hongkai, Qian, Jiang
• Format: Article
• Language: English
• Subjects: Biology and Life Sciences; Brain Neoplasms - genetics; Brain Neoplasms - metabolism; Brain Neoplasms - pathology; Cancer treatment; Care and treatment; Clinical outcomes; Comparative analysis; Computational Biology; Computer Simulation; Gene expression; Genetic aspects; Glioblastoma - genetics; Glioblastoma - metabolism; Glioblastoma - pathology; Gliomas; Health aspects; Humans; Male; Medicine and Health Sciences; Models, Genetic; Multigene Family; Patient outcomes; Prognosis; Prostatic Neoplasms - genetics; Prostatic Neoplasms - metabolism; Prostatic Neoplasms - pathology; Protein Interaction Maps - genetics; Proteins; Single-Cell Analysis - statistics & numerical data; Transcriptome; Tumors
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1004892

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.