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

We next sought to understand how the expression of each of these three key genes --ACE2, TMPRSS2, and CTSL --in specific cell subsets may relate to three key covariates that have been associated with disease severity: age (older individuals are more severely affected), sex (males are more severely affected), and smoking (smokers are more severely affected) 62 . We integrated samples across many studies, as no single dataset generated to date is sufficiently large to address this question. We assembled 22 datasets (Supplementary Table 2 , Supplementary Data File D3), comprised of 1,176,683 cells from 164 individuals, spanning 282 healthy nasal, lung, and airway samples profiled by scRNA-seq or snRNA-seq from either biopsies, resections, entire lungs that could not be used for transplant, or post mortem examinations, allowing us to study a diversity of respiratory regions and cell types (Fig. 3a) . These included 6 published datasets 63-68 and 16 datasets that are not yet published [69] [70] [71] [72] [73] . In the case of unpublished data, we only obtained singlecell expression counts for the three genes, as well as the total UMI counts per cell, cell identity annotations, and the relevant anonymous clinical variables (age and sex, as well as smoking status when ascertained). Cell identity annotations were manually harmonized using an ontology with three levels of annotation specificity (Fig. 3b, Supplementary Table 2 ); focusing on levels 2 and 3 allowed us to include a large number of datasets, while retaining relatively high cell subset specificity (Fig. 3a,b) . To facilitate rapid data sharing, we analyzed data pre-processed by each data-generating team at the level of gene counts, using total counts as a size factor. We used Poisson regression (diffxpy package; Methods) to model the association between the expression counts of the three genes and age, sex, and smoking status, and their possible pair-wise interactions (Fig. 3c) , using total counts as an offset, and dataset as a technical covariate to capture sampling and processing differences. It should be noted that modeling interaction terms was crucial as their omission resulted in reversed effects for age and sex for particular cell types (Discussion). This model was fitted to non-fetal lung data (761,693 cells, 165 samples, 77 donors, 10 datasets) within each cell type to assess cell-type specific association of these covariates with the three genes. To further validate sex and age associations, we fit a simplified version of the model without smoking status covariates to the full non-fetal lung data (986,342 cells, 225 samples, 125 donors, 16 datasets).