Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells

Christoph Muus; Malte D Luecken; Gokcen Eraslan; Avinash Waghray; Graham Heimberg; Lisa Sikkema; Yoshihiko Kobayashi; Eeshit Dhaval Vaishnav; Ayshwarya Subramanian; Christopher Smillie; Karthik Jagadeesh; Elizabeth Thu Duong; Evgenij Fiskin; Elena Torlai Triglia; Christophe Becavin; Meshal Ansari; Peiwen Cai; Brian Lin; Justin Buchanan; Sijia Chen; Jian Shu; Adam L Haber; Hattie Chung; Daniel T Montoro; Taylor Adams; Hananeh Aliee; Samuel J Allon; Zaneta Andrusivova; Ilias Angelidis; Orr Ashenberg; Kevin Bassler; Christophe Becavin; Inbal Benhar; Joseph Bergenstrahle; Ludvig Bergenstrahle; Liam Bolt; Emelie Braun; Linh T Bui; Mark Chaffin; Evgeny Chichelnitskiy; Joshua Chiou; Thomas M Conlon; Michael S Cuoco; Marie Deprez; David S Fischer; Astrid Gillich; Joshua Gould; Minzhe Guo; Austin J Gutierrez; Arun C Habermann; Tyler Harvey; Peng He; Xiaomeng Hou; Lijuan Hu; Alok Jaiswal; Peiyong Jiang; Theodoros Kapellos; Christin S Kuo; Ludvig Larsson; Michael A Leney-Greene; Kyungtae Lim; Monika Litvinukova; Ji Lu; Leif S Ludwig; Wendy Luo; Henrike Maatz; Elo Maddissoon; Lira Mamanova; Kasidet Manakongtreecheep; Charles-Hugo Marquette; Ian Mbano; Alexi M McAdams; Ross J Metzger; Ahmad N Nabhan; Sarah K Nyquist; Jose Ordovas-Montanes; Lolita Penland; Olivier B Poirion; Segio Poli; CanCan Qi; Daniel Reichart; Ivan Rosas; Jonas Schupp; Rahul Sinha; Rene V Sit; Kamil Slowikowski; Michal Slyper; Neal Smith; Alex Sountoulidis; Maximilian Strunz; Dawei Sun; Carlos Talavera-Lopez; Peng Tan; Jessica Tantivit; Kyle J Travaglini; Nathan R Tucker; Katherine Vernon; Marc H Wadsworth; Julia Waldman; Xiuting Wang; Wenjun Yan; Ali Onder Yildirim; William Zhao; Carly G K Ziegler; Aviv Regev

To build gene modules for DP cells, we prioritized genes by requiring that they be expressed in at least 10% of DP cells, and to have a model coefficient greater than 0 with an FDR-adjusted pvalue less than 0.05 (for the combined coefficient in the hurdle model). After this filtering step, genes were ranked by their model coefficient (i.e. estimated effect size). The top 12 genes were selected for network visualization within each cell type (Fig. 4c,d, Extended Data Fig. 11a,b) . In The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.19.049254 doi: bioRxiv preprint three cases (gut Cycling TA, TA-2 and BEST4 + cells), RP11-* antisense genes were flagged and excluded from visualizations. To visualize overlap across each network, we indicated whether each gene was among the top 250 genes from each of the other cell types. Putative drug targets were identified by querying the Drugbank database 88 . Gene set enrichment analysis was performed using the R package EnrichR (version 1.0) 181 , selecting the top 25 genes from each cell type for the pan-tissue analysis ("All" category; Fig. 4e ), and the top 50 genes from each cell type for the tissue-specific analyses ("Gut", "Nasal", and "Lung" categories; Fig. 4e ). We note a few caveats/challenges/limitations that may influence our results, including non uniform sampling across donors; variation in cell compositions across regions (e.g., distal lung vs carina), and additional cellular heterogeneity that the current level of broad subset annotation may not have been captured.