Analyzing network diversity of cell-cell interactions in COVID-19 using single-cell transcriptomics

Xinyi Wang; Axel A Almet; Qing Nie

doi:10.3389/fgene.2022.948508

Analyzing network diversity of cell-cell interactions in COVID-19 using single-cell transcriptomics

Front Genet. 2022 Aug 29:13:948508. doi: 10.3389/fgene.2022.948508. eCollection 2022.

Authors

Xinyi Wang¹, Axel A Almet^{1

2}, Qing Nie^{1

2

3}

Affiliations

¹ Department of Mathematics, University of California, Irvine, Irvine, CA, United States.
² The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, United States.
³ Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, United States.

Abstract

Cell-cell interactions (CCI) play significant roles in manipulating biological functions of cells. Analyzing the differences in CCI between healthy and diseased conditions of a biological system yields greater insight than analyzing either conditions alone. There has been a recent and rapid growth of methods to infer CCI from single-cell RNA-sequencing (scRNA-seq), revealing complex CCI networks at a previously inaccessible scale. However, the majority of current CCI analyses from scRNA-seq data focus on direct comparisons between individual CCI networks of individual samples from patients, rather than "group-level" comparisons between sample groups of patients comprising different conditions. To illustrate new biological features among different disease statuses, we investigated the diversity of key network features on groups of CCI networks, as defined by different disease statuses. We considered three levels of network features: node level, as defined by cell type; node-to-node level; and network level. By applying these analysis to a large-scale single-cell RNA-sequencing dataset of coronavirus disease 2019 (COVID-19), we observe biologically meaningful patterns aligned with the progression and subsequent convalescence of COVID-19.

Keywords: COVID-19; cell–cell interactions; diversity; network analysis; single-cell.