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 assess the association of age, sex, and smoking status with the expression of ACE2, TMPRSS2, and CTSL, we aggregated 22 scRNA-seq datasets of healthy human nasal and lung cells, as well as fetal samples. Aggregation of these datasets was enabled by harmonizing the cell type labels of . CC-BY-ND 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the .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 individual datasets within Scanpy 165 (version 1.4.5.1). We harmonized annotations together with data contributors using a preliminary ontology generated on the basis of 5 published datasets 63-66,174 with 3 levels of annotations (level 1 -lowest resolution; Supplementary Table 2) . We further harmonized metadata by collapsing the smoking covariate into "has smoked" and "has never smoked" and by taking mean ages where only age ranges were given. This endeavor produced a dataset of 1,176,683 cells in 282 samples from 164 donors (Supplementary Data D3) . We divided the data into fetal (136, To get an overview of sample diversity, we clustered the samples using the proportion of cells in level 2 cell types as features. Clustering was performed using louvain clustering (resolution 0.3; louvain package version 0.6.1) on a knn-graph (k=15) computed on Euclidean distances over the top 5 principal components of the cell type proportion data within Scanpy. This produced five clusters. Sample cluster labels were assigned based on metadata for anatomical location that was obtained from the published datasets and via input from the data generators.