Data-driven analysis of COVID-19 reveals specific severity patterns distinct from the temporal immune response

Lim, Jackwee; Puan, Kia Joo; Wang, Liang Wei; Weng Teng, Karen Wei; Loh, Chiew Yee; Tan, Kim Peng; Carissimo, Guillaume; Chan, Yi-Hao; Poh, Chek Meng; Lee, Cheryl Yi-Pin; Fong, Siew-Wai; Yeo, Nicholas Kim-Wah; Chee, Rhonda Sin-Ling; Amrun, Siti Naqiah; Chang, Zi Wei; Tay, Matthew Zirui; Torres-Ruesta, Anthony; Fernandez, Norman Leo; How, Wilson; Andiappan, Anand K.; Lee, Wendy; Duan, Kaibo; Tan, Seow-Yen; Yan, Gabriel; Kalimuddin, Shirin; Lye, David Chien; Leo, Yee-Sin; Ong, Sean W. X.; Young, Barnaby E.; Renia, Laurent; Ng, Lisa F.P.; Lee, Bernett; Rötzschke, Olaf

Key immune signatures of SARS-CoV-2 infection may associate with either adverse immune reactions (severity) or simply an ongoing anti-viral response (temporality); how immune signatures contribute to severe manifestations and/or temporal progression of disease and whether longer disease duration correlates with severity remain unknown. Patient blood was comprehensively immunophenotyped via mass cytometry and multiplex cytokine arrays, leading to the identification of 327 basic subsets that were further stratified into more than 5000 immunotypes and correlated with 28 plasma cytokines. Low-density neutrophil abundance was closely correlated with hepatocyte growth factor levels, which in turn correlated with disease severity. Deep analysis also revealed additional players, namely conventional type 2 dendritic cells, natural killer T cells, plasmablasts and CD16+ monocytes, that can influence COVID-19 severity independent of temporal progression. Herein, we provide interactive network analysis and data visualization tools to facilitate data mining and hypothesis generation for elucidating COVID-19 pathogenesis.