key: cord-0755942-eubmiblw authors: Nassir, Nasna; Tambi, Richa; Bankapur, Asma; Al Heialy, Saba; Karuvantevida, Noushad; Khansaheb, Hamdah Hassan; Zehra, Binte; Begum, Ghausia; Hameid, Reem Abdel; Ahmed, Awab; Deesi, Zulfa; Alkhajeh, Abdulmajeed; Uddin, K M Furkan; Akter, Hosneara; Safizadeh Shabestari, Seyed Ali; Almidani, Omar; Islam, Amirul; Gaudet, Mellissa; Kandasamy, Richard Kumaran; Loney, Tom; Tayoun, Ahmad Abou; Nowotny, Norbert; Woodbury-Smith, Marc; Rahman, Proton; Kuebler, Wolfgang M.; Al Mashshadani, Mahmood; Casanova, Jean-Laurent; Berdiev, Bakhrom K.; Alsheikh-Ali, Alawi; Uddin, Mohammed title: Single Cell Transcriptome Identifies FCGR3B Upregulated Subtype of Alveolar Macrophages in Patients with Critical COVID-19 date: 2021-08-25 journal: iScience DOI: 10.1016/j.isci.2021.103030 sha: 01221d2d7b325c40ea12910664becbb3978200f6 doc_id: 755942 cord_uid: eubmiblw Understanding host cell heterogeneity is critical for unravelling disease mechanism. Utilizing large scale single-cell transcriptomics, we analysed multiple tissue specimens from patients with life-threatening COVID-19 pneumonia, compared with healthy controls. We identified a subtype of monocyte-derived alveolar macrophages (MoAM) where genes associated with severe COVID-19 comorbidities are significantly upregulated in broncho-alveolar lavage fluid (BALF) of critical cases. FCGR3B consistently demarcated MoAM subset in different samples from severe COVID-19 cohorts and in CCL3L1-upregulated cells from nasopharyngeal swabs. In silico findings were validated by upregulation of FCGR3B in nasopharyngeal swabs of severe ICU COVID-19 cases, particularly in older patients and those with comorbidities. Additional lines of evidence from transcriptomic data and in vivo of severe COVID-19 cases suggest that FCGR3B may identify a specific subtype of MoAM in patients with severe COVID-19 that may present a novel biomarker for screening and prognosis as well as a potential therapeutic target. Major international initiatives are underway to unravel the pathogenesis of severe COVID- 83 19. Despite these efforts, our knowledge of COVID-19 pathophysiology is very limited and 84 has been compounded by the complex interplay with a range of comorbid conditions. 85 Recently, rare germline mutations impairing TLR3 and IRF7 dependent type I interferon 86 immunity were found to be causal for a minor subset of critical COVID-19 patients(Bastard To assign the corresponding cell type identity to each cluster, we utilized an in-house 137 database (Table S1 ) of specific markers of lung tissue cell types that was created using a (Table S2 , Figure S4 ). Comorbid gene set upregulation in macrophage subtype Cluster 11. We have used genes 148 that are associated with severe COVID-19 comorbid conditions to identify specific activated 149 cell types that are highly regulated in severe COVID-19 phenotypes. The list included 150 encoding cytokines and cytokine receptors, or associated with rare infectious diseases, rare 151 syndromes, chronic obstructive pulmonary disease, cardiovascular disease, hypertension, 152 obesity, and diabetes (Table S3 ). The enrichment analysis of cluster genes with comorbid 153 gene lists revealed that eight of the nine auxiliary COVID-19 comorbid condition genes were 154 exclusively upregulated in cluster 11 in severe COVID samples ( Figure 2B , Figure S5 ). Severe cluster 11 was marked as monocyte-derived alveolar macrophages (MoAM), as 156 indicated by the presence of CCl3L1 (Figure 2A ). This particular MoAM subtype was not 157 found in any of the moderate or control clusters. (Table S4 ). These analyses revealed that the identified MoAM cell 163 subtype is involved in host immune response signalling networks related to TNFα (p < 2.79 x 164 10 -25 ), cytokine and interferon gamma responses (p < 1.80 x 10 -22 ), the response to type1 165 interferon and biotic stimulus (p < 2.75 x 10 -17 ), and innate immune and inflammatory 166 responses (p < 1.09 x 10 -16 ), which were visualized using Cytoscape ( Figure 2C ). Figure S13 , Table S5 ). Further, among COVID-19 219 patients, the frequency of cases with fold change of FCGR3B greater than 1.5 was 220 significantly higher among patients older than 60 years compared to younger patients (20-40 221 years) ( Figure 4D , p = 0.04). Similar analysis was performed for FFAR2 gene ( Figure S14 ), 222 which did not however show any significant association with COVID-19 severity. MoAM (cluster 11) in severe COVID-19 cases ( Figure S7 and Table S10, S11 & S12). We have adopted an in-silico analysis comprising of all genes known to be associated with 280 severe COVID-19 comorbid conditions to identify this disease associated cell types. In the data sets analysed in our present study, FCGR3B showed severe COVID19 306 specific expression that was restricted to a subtype of monocyte-derived macrophages which 307 also showed upregulation of CCL3L1, a known inflammatory marker for MoAMs (Table 308 S10, S11 & S12). This restrictive expression of FCGR3B was not observed in any of the Limitations of the study 359 The small sample size of this cohort is a limitation (31 severe Covid19 patients and 11 360 healthy controls). Although we did qPCR validation for FCGR3B using nasopharyngeal 361 swabs (due to availability), the ideal validation sample would be flow cytometry sorted 362 macrophages from BALF region. Any additional information required to reanalyse the data reported in this paper is available 455 through personnel contact with the corresponding author upon request. Table S5 . The filtered data were normalized (log(expression/total*10000)) using the 'LogNormalize' 516 function, the data were scaled, principal components were calculated and using the top PCs 517 showing maximum variance, clusters were computed using 'FindClusters' which were 518 visualized using UMAP. Different single cell transcriptome data were analysed separately. Hence, each data do not impact the other and downstream analyses has negligible batch effect 520 after the standard corrections. Eventually, the differentially expressed genes (DEG) were 521 identified for each computed cluster using three statistical tests: Wilcoxon-ranked sum test, t-522 test, t-test overestimated variance (Table S9) (Table S1 ). The cell type specific genes were mapped onto the clusters. Box plots were used 533 to analyse their expression and the cell type identity was assigned based on the highest 534 normalized median expression value for each cluster (Table S2) . (Table S3) The node colour (red-white gradient) and size (low to high) was set based on increasing p-560 value and odd's ratio, respectively. Hilden, Germany) and the control (qPCR negative) swabs (n=11) were used in the study. Table S7 shows forward and reverse primers used. The reaction was as Table S2 . Cell identity of severe, moderate and control clusters. Related to Figure 2 . The Lung Macrophage in 678 SARS-CoV-2 Infection: A Friend or a Foe? 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