Implicating Gene and Cell Networks Responsible for Differential COVID

1 Implicating Gene and Cell Networks Responsible for

2 Differential COVID-19 Host Responses via an Interactive

3 Single Cell Web Portal 4 5 Kang Jin1,2, Eric E. Bardes1, Alexis Mitelpunkt1,3,4, Jake Y. Wang1, Surbhi Bhatnagar1,5, 6 Soma Sengupta6, Daniel Pomeranz Krummel6, Marc E. Rothenberg7, Bruce J. 7 Aronow1,8,9,* 8 9 1Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 10 Cincinnati, OH, 45229, USA 11 2Department of Biomedical Informatics, University of Cincinnati, Cincinnati, OH, 45229, 12 USA 13 3Pediatric Rehabilitation, Dana-Dwek Children's Hospital, Tel Aviv Medical Center, Tel 14 Aviv, 6423906, Israel 15 4Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel 16 5Department of Electrical Engineering and Computer Science, University of Cincinnati, 17 Cincinnati, OH, 45221, USA 18 6Department of Neurology and Rehabilitation Medicine, University of Cincinnati College 19 of Medicine, Cincinnati, OH, 45267, USA. 20 7Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children's 21 Hospital Medical Center, University of Cincinnati, Cincinnati, OH, 45229, USA 22 8Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, OH, 23 45256, USA 24 9Lead contact 25 *Correspondence: [email protected] (B.A.) 26 27 28 29 30 31

32 Summary

33 Numerous studies have provided single-cell transcriptome profiles of host responses to 34 SARS-CoV-2 infection. Critically lacking however is a datamine that allows users to 35 compare and explore cell profiles to gain insights and develop new hypotheses. To 36 accomplish this, we harmonized datasets from COVID-19 and other control condition 37 blood, bronchoalveolar lavage, and tissue samples, and derived a compendium of gene 38 signature modules per cell type, subtype, clinical condition, and compartment. We 39 demonstrate approaches to probe these via a new interactive web portal 40 (http://toppcell.cchmc.org/ COVID-19). As examples, we develop three hypotheses: (1) 41 a multicellular signaling cascade among alternatively differentiated monocyte-derived 42 macrophages whose tasks include T cell recruitment and activation; (2) novel platelet 43 subtypes with drastically modulated expression of genes responsible for adhesion, 44 coagulation and thrombosis; and (3) a multilineage cell activator network able to drive 45 extrafollicular B maturation via an ensemble of genes strongly associated with risk for 46 developing post-viral autoimmunity.

47 Keywords

48 49 COVID-19, SARS-CoV-2, single-cell RNA-seq, host-pathogen cell atlas, interactive 50 datamining, bronchoalveolar lavage, systems biology, antiviral host defense, platelets, 51 inflammatory thrombosis, autoimmune disorder.

57 Introduction

58 59 COVID-19 clinical outcomes are variable. The poorer outcomes due to this infection are 60 highly associated with immunological and inflammatory responses to SARS-Cov-2 61 infection (Shi et al., 2020; Tay et al., 2020) and many recent single cell expression 62 profiling studies have characterized patterns of immunoinflammatory responses among 63 individuals, mostly during acute infection phases. Different studies have revealed a 64 spectrum of responses that range from lymphopenia (Cao, 2020; Wang et al., 2020), 65 cytokine storms (Mehta et al., 2020; Pedersen and Ho, 2020), differential interferon 66 responses (Blanco-Melo et al., 2020; Hadjadj et al., 2020) and emergency myelopoiesis 67 (Schulte-Schrepping et al., 2020; Silvin et al., 2020). However, a variety of obstacles 68 limit the ability of the research and medical communities to explore and compare these 69 studies to pursue additional questions and gain additional insights that could improve 70 our understanding of cell type specific responses to SARS-Cov-2 infection and their 71 impact on clinical outcome. 72 73 Whereas many studies have focused on the peripheral blood mononuclear cells 74 (PBMC) (Arunachalam et al., 2020; Guo et al., 2020; Lee et al., 2020; Schulte- 75 Schrepping et al., 2020; Wilk et al., 2020a) due to ease of procurement, other studies 76 have profiled airway locations via bronchoalveolar lavage (BAL) (Grant et al., 2020; Liao 77 et al., 2020), nasopharyngeal swabs, and bronchial brushes (Chua et al., 2020). 78 Additional sampling sites that could also be infected or affected have also been 79 approached in autopsy-derived materials from the central nervous system (Heming et 80 al., 2021; Yang et al., 2020), and other sites (Delorey et al., 2021). Moreover, as major 81 COVID-19 consortiums working on the collection and integration of each of their 82 individual studies and interpreting important features of these individual datasets as 83 downloadable datasets or browsable versions, such as single cell portal 84 (https://singlecell.broadinstitute.org/single_cell/covid19) and COVID-19 Cell Atlas 85 (https://www.covid19cellatlas.org/), using these data beyond markers, cell types, and 86 individual signatures is either not possible or not accomplishable across-datasets. Thus, 87 a well-organized and systematic study of immune cells across tissues for in-depth

88 biological explorations is an unmet need for a deeper understanding of the underlying 89 basis of the breadth of COVID-19 host defense and pathobiology. 90 91 Here we harmonized and analyzed eight high quality publicly available single-cell RNA- 92 seq datasets from COVID-19 and immunologically-related studies that in total covered 93 more than 480,000 cells isolated from peripheral blood, bronchial alveolar lavage and 94 lung parenchyma samples, and assembled an integrated COVID-19 atlas 95 (https://toppcell.cchmc.org/). We established a framework for deriving, characterizing, 96 and establishing reference gene expression signatures from these harmonized datasets 97 using modular and hierarchical approaches based on signatures per class, subclass, 98 and signaling/activation and clinical status per each sample group. Leveraging these 99 gene expression signature modules, we demonstrate datamining approaches that allow 100 for the identification of a series of fundamental disease processes: (1) an intercellular 101 monocytic activation cascade capable of mediating the emergence of 102 hyperinflammatory monocyte-derived alveolar macrophages in severe COVID-19 103 patients; (2) the generation of several alternatively differentiated platelet subtypes with 104 dramatically different expression of sets of genes associated with critical platelet tasks 105 capable of altering vascular and tissue responses to infectious agents; and (3) a 106 multilineage and multi cell type cooperative signaling network with the potential to drive 107 extrafollicular B maturation at a lesion site, but do so with high risk for the development 108 of B cell-associated immunity. Additionally, immune hallmarks of COVID-19 patients 109 were compared with other immune-mediated diseases using single-cell data from 110 patients with influenza, sepsis, or multiple sclerosis. Consistent and varied 111 compositional and gene patterns were identified across these implicating striking 112 COVID-19 effects in some individuals.

113 Results

114 115 Creating the First COVID-19 Signature Atlas Using ToppCell Portal 116 To have a comprehensive coverage of cells, we collated single-cell data of COVID-19 117 patients from eight public datasets, which in total contains 231,800 PBMCs, 101,800

118 BAL cells and 146,361 lung parenchyma cells from donors: 43 healthy; 22 mild; 42 119 severe; and 2 convalescent patients (Figure 1A, Table S1). 120 121 In order to assemble an integrated atlas of human cell responses to COVID-19, we 122 sought to harmonize metadata encompassing clinical information, sampling 123 compartments, and cell and gene expression module designations. Doing so provides a 124 rich framework for detecting perturbations of cell repertoire and differentiative state 125 adaptations. We first integrated single cell RNA-seq data in Seurat (Stuart et al., 2019) 126 and annotated cell types using canonical markers (Table S2). Further annotations of B 127 cell and T cell subtypes were completed using the reference-based labeling tool 128 Azimuth (Hao et al., 2020). Sub-clustering was applied for some cell types, such as 129 neutrophils and platelets, to interrogate finer resolutions of disease-specific sub- 130 populations (Figure 1B). Using the ToppCell toolkit (https://toppcell.cchmc.org/), we 131 created over 3,000 hierarchical gene modules of the most significant differentially 132 expressed genes (DEGs) for all cell classes and sub-clusters across compartments and 133 disease severity (Table S1). These modules were then used to infer cell-cell 134 interactions as well as upregulated pathways, which were further combined for 135 functional comparative analysis in a specific cell manner in ToppCluster (Kaimal et al., 136 2010) (Figure 1B), such as sub-clusters of platelets. Integration of ToppCluster output 137 of cells from multiple compartments and disease conditions built pathogenic maps, 138 highlighted by the coagulation map of COVID-19 (Figure S12). In addition, perturbation 139 of cell abundance was evaluated either in one cell population, or in multiple populations 140 across diseases. Taken together, we investigated cell abundance changes, severity- 141 associated signatures, mechanisms of COVID-19 specific symptoms and unique 142 features of COVID-19 as an immune-mediated disease (Figure 1B). 143 144 Dynamic Changes and Balance of COVID-19 Immune Repository in Blood and 145 Lung 146 After the aforementioned cell annotation procedure, we identified 28 and 24 distinct cell 147 types in PBMC and BAL respectively (Figure 2A, 2C; Table S2). Shifts of Uniform 148 Manifold Approximation and Projection (UMAP) of cell type distributions were observed

149 in both compartments of mild and severe patients (Figure 2A, 2C, S1A and 3A). In 150 PBMC, conventional dendritic cells (cDC), plasmacytoid dendritic cells (pDC) and non- 151 classical monocytes displayed a prominent reduction in severe patients (Figure 2B, 152 S1C), consistent with prior reports (He et al., 2020; Laing et al., 2020; Wilk et al., 153 2020a). In contrast, severe patients demonstrated dramatic expansion of neutrophils, 154 especially immature stages (Figure S1C, S2). Integration with evoked pathways in the 155 following analysis implicated that neutrophil expansion was likely the consequence of 156 emergency myelopoiesis (Wilk et al., 2020b). Additionally, a general down-regulation of 157 T cell and NK cell was observed, consistent with lymphopenia reported in clinical 158 practices (Pedersen and Ho, 2020; Terpos et al., 2020) (Figure S1C, S2). However, the 159 trend of T cell subtypes varies across studies and individuals, apart from proliferative T 160 cells which have a dramatic increase in mild and severe patients (Figure S2). Notably, 161 plasmablasts substantially increased in COVID-19 patients, and especially so in severe 162 patients, suggesting upregulated antibody production (De Biasi et al., 2020) (Figure 2B 163 and S1C). Expansion of platelets is another significant change observed in severe 164 patients, possibly leading to immunothrombosis in the lung, which could be closely 165 associated with the severity of the disease (Middleton et al., 2020; Nicolai et al., 2020) 166 (Figure 2B and S1C). 167 168 In samples obtained from patients’ lungs, we observed the depletion of FABP4high 169 tissue-resident alveolar macrophages (TRAM) and dramatic expansion of FCN1high 170 monocyte-derived alveolar macrophages (MoAM) in severe patients (Figure 2C, 2D 171 and S3D). Mild patients exhibited a moderate reduction of tissue-resident 172 macrophages, but no evidence of aggregation of monocyte-derived macrophages 173 (Figure 2C, 2D, S3A and S3D). Dynamic changes of these two subtypes suggest 174 increased tissue chemoattraction (Merad and Martin, 2020) and potential damage of 175 patients' lungs (McGonagle et al., 2020). In addition, neutrophils were only identified in 176 severe patients in the integrated BAL data (Figure 2C and S3A), which might be 177 related with neutrophil extracellular traps (NETs) in the lung (Barnes et al., 2020). 178 However, more samples are required to draw a solid conclusion. We also noted 179 conventional dendritic cells decreased in the severe patients, which is consistent with

180 the trend of the counterpart in PBMC data. Opposite to the change in PBMC, an 181 expansion of plasmacytoid dendritic cells is observed in both mild and severe patients 182 (Figure 2D). Other cell types, including T cell and NK cell in the BAL, also have 183 converse changes of their counterparts in PBMC, which could be attracted by lung 184 macrophages or epithelial cells after infection or damages (Chua et al., 2020) (Figure 185 2D and Figure S3D). These changes were consistently observed in lung parenchyma 186 samples from severe COVID-19 patients (Figure S4). With cells well-annotated in the 187 integrated COVID-19 atlas, we drew a global heatmap for cells in both blood and lung 188 using ToppCell gene modules (top 50 DEG in each module) of all identified cell classes. 189 While there was conservation of gene patterns involved in healthy donors and severe 190 COVID-19 patients, there were substantial differences most notably in myeloid cells 191 (Figure 2E). Such hierarchically ordered ToppCell gene modules were broadly used in 192 visualization, large-scale comparisons and fine-resolution investigations in the following 193 analyses. 194 195 Myeloid Cell Atlas: Functionally Distinct Neutrophils at Different Levels of 196 Maturation and Derailed Macrophages in the Lung 197 Dysregulated myeloid cells have been reported as an important marker of severe 198 COVID-19 patients (Schulte-Schrepping et al., 2020; Silvin et al., 2020). In order to gain 199 a deeper and comprehensive understanding of these cells, we applied the sub- 200 clustering strategy on the integrated data of key cell types, such as neutrophils and 201 macrophages, and then generated gene modules for comparative functional analysis 202 and interactome inference. We successfully identified 5 neutrophil sub-clusters after the 203 integration of PBMC and BAL data, including 3 FCGR3B+ mature sub-clusters and 2 204 FCGR3B- immature sub-clusters (Figure 3A and S5B; Table S3). They’re mainly from 205 severe patients and their gene modules were generated and subjected to comparative 206 functional enrichment using ToppCell and ToppCluster (Figure 3C, 3D and S5A). We 207 identified proliferative neutrophils (referred to as pro-neutrophils and Neu4) and 208 MMP8high precursor immature neutrophils (referred to as pre-neutrophils and Neu2) 209 (Figure 3A and S5B) consistent with prior studies (Schulte-Schrepping et al., 2020). 210 While immune response genes and pathways were barely activated in the immature

211 neutrophils, they displayed upregulation of granule formation pathways and NETosis- 212 associated proteins, including ELANE, DEFA4 and MPO, especially in Neu4 (Schulte- 213 Schrepping et al., 2020; Wilk et al., 2020b) (Figure 3C, S5B and S5C). Upregulated 214 myeloid leukocyte mediated immunity in Neu2 suggests involvement of this cell type in 215 anti-viral function (Figure S5D). Yet, the absence of cytokine and interferon response 216 pathways suggests the lack of mature immune responses (Figure 3D). Notably, 217 compared to mature neutrophils (Neu0 and Neu1) in the blood, the extravasated 218 hyperinflammatory sub-cluster (Neu3) from BAL of severe patients shows 219 extraordinarily high expression of interferon-stimulated genes, as well as prominent 220 upregulation of productions and responses to cytokines and interferons (Figure 3C, 3D, 221 S5B and S5D). 222 223 MoAM and TRAM were two main macrophage types in the BAL (Figure 2C); both are 224 known to have distinct roles in immune responses in the lung (Liao et al., 2020). As 225 described above, five sub-clusters among the expanded COVID-19 patient-specific 226 MoAM (Figure 3B; Table S3) were found, where the loss of HLA class II genes and 227 elevation of interferon-stimulated genes (ISGs) were consistently observed (Figure 3F 228 and S6A). Relative to MoAM3,4, MoAM1,2,5 displayed an upregulation of interferon 229 responses and cytokine production (Figure 3D and S6B; Table S3), indicating their 230 pro-inflammatory characteristics. Notably, MoAM5 shows dramatic upregulation of IL-6 231 secretion and cytokine receptor binding activities (Figure S7A-D). However, cells in this 232 sub-cluster were mainly from one severe patient (Figure S3C). We still need more data 233 to fully understand such dramatic upregulation of IL-6 secretion in some severe 234 patients. Similar to MoAM, we also identified two distinct groups of TRAM in BAL 235 (Figure 3B and S6B), including quiescent TRAM (TRAM1 and TRAM2) and activated 236 TRAM (TRAM3). The quiescent group was mainly from healthy donors with enriched 237 pathways of ATP metabolism (Figure 3D), while the activated group from mild and 238 severe patients displays upregulation of ISGs and cytokine signaling pathways (Figure 239 S6B; Table S3). However, the magnitude of activation and inflammatory responses in 240 TRAM3 is smaller than MoAM1,2,5. Not surprisingly, stronger antigen processing and 241 presentation activities were observed in TRAM3 relative to MoAM1,2,5 (Figure 3D and

242 S6B; Table S3). Collectively, we concluded that tissue-resident macrophages were 243 greatly depleted in severe patients as the front-line innate immune responders in the 244 lung. Pro-inflammatory monocyte-derived macrophages infiltrate into the lung, leading 245 to the cytokine storm and damage of the lung. Large amounts of infiltration of MoAM 246 were not observed in mild COVID-19 patients, probably due to the controlled infection, 247 which could explain milder lung damages in those patients. 248 249 To develop an understanding of the interaction network in the lung microenvironment of 250 severe COVID-19 patients, we focused on signaling ligands, receptors and pathways 251 using ToppCell and CellChat (Figure 3E, S8A, S8B). Notably, basal cells, MoAMs, 252 neutrophils and T cells all contributed to the cytokine, chemokine and interleukin 253 signaling networks. Strikingly, severe patient specific MoAM2 shows the broadest 254 upregulation of signaling ligands, including CCL2, CCL3, CCL7, CCL8, CXCL9, CXCL9, 255 CXCL10, CXCL11, IL6, IL15 and IL27, suggesting its role as a signaling network hub 256 that is distinct from the other major signaling ligand-expressing cells of BAL such as 257 epithelial and other myeloid cell types such as TRAM3 and proliferating myeloid cells 258 (Figure S8A). Among the MoAM2 top signaling molecules, attractants CXCL8, CXCL9 259 and CXCL10 are known to target CXCR3 on T cells, suggesting their role is to stimulate 260 migration of T cells to the epithelial interface and into BAL fluid (Figure 3E) (Liao et al., 261 2020). In addition, many of MoAM2’s ligands have the potential to cause autocrine 262 signaling activation via IL6-IL6R, IL1RN-ILR2, CCL7-CCR1, CCL2-CCR1 and CCL4- 263 CCR1, indicating its active roles in self-stimulation and development, which further 264 amplify the attraction and migration of T cells and other immune cells. Notably, CCR1 265 was also expressed in activated TRAM3, but with a lower level. Although IL6 expression 266 level is relatively low compared to other ligands in BAL data, substantial expression of 267 IL6R was observed in MoAMs. The CCL and CXCL signaling pathways of neutrophils 268 are less strong than MoAMs (Figure S8B), but they displayed high expression levels of 269 CXCR1 and CXCR2, which binds with a large number of the chemokines from MoAM 270 and epithelial cells (Figure 3E). In addition, neutrophils exhibit an extraordinarily high 271 level of IL1B, which could potentially in turn activate macrophages (Figure S8A and 272 S8B). TRAM3 also displayed a unique pattern of signaling molecules, with a substantial

273 level of CCL23 which could potentially attract MoAM by the interaction with CCR1. 274 Secretion of CXCL3 and CXCL5 in TRAM3 towards CXCR2 could be a potential 275 chemoattraction pathway for neutrophils. In turn, neutrophils could activate TRAM3 by 276 secreting IL1B, which binds with IL1RAP. Additionally, CD4+ T cells could also activate 277 TRAM3 by IL10-IL10RB interaction (Figure 3E, S8A and S8B). 278 279 In addition to neutrophils and macrophages, the upregulation of ISGs was observed in 280 classical monocytes of both mild and severe patients (cMono3, cMono4), while the 281 reduction of the MHC class II cell surface receptor HLA-DR genes was only observed in 282 severe patients (cMono4) (Figure S9). In cDCs, polarization of interleukin secretion was 283 observed in mild-patient and severe-patient specific clusters (Figure S10F). 284 Collectively, dynamic changes of marker genes, transcriptional profiles, signaling 285 molecules and biological activities reveal the heterogeneity of myeloid cell sub-clusters 286 across disease severity (Figure S11C). Pro-inflammatory gene expression was found in 287 all major myeloid cell types, including cMono4, DC1, DC9 in PBMC and Neu3, DC10, 288 MoAM1, MoAM2, MoAM5 in BAL of COVID-19 patients. The reduction of MHC class II 289 (HLA-II) genes is a common feature of classical monocytes and macrophages in 290 COVID-19 patients and implies impaired capacity to activate T cell adaptive immunity. 291 292 COVID-19 Coagulation and Immunothrombosis Map 293 Individuals severely affected during acute phase COVID-19 infection, and in particular 294 those with significantly elevated risk of death, frequently demonstrate striking 295 dysregulation of coagulation and thrombosis characterized by hypercoagulability and 296 microvascular thromboses (endothelial aggregations of platelets and fibrin) and highly 297 elevated D-dimer levels. Yet, COVID-19 does not lead to wide scale consumption of 298 fibrinogen and clotting factors (Iba et al., 2020a; Levi et al., 2020; Middleton et al., 2020; 299 Nicolai et al., 2020; Rapkiewicz et al., 2020). At present, we lack a molecular or cellular 300 explanation of the underlying basis of this pathobiology (Aid et al., 2020; Middleton et 301 al., 2020). To evaluate candidate effectors of this pathobiology, we used a list of genes 302 associated with abnormal thrombosis from mouse and human gene mutation 303 phenotypes and identified parenchymal lung sample endothelial cells and platelets in

304 PBMC as cell types highly enriched with respect to genes responsible for the regulation 305 of hemostasis (Figure S12). Because platelet counts were greatly elevated in severe 306 versus mild individuals, we further examined platelet gene expression signatures and 307 cell type differentiation and identified six distinct platelet sub-clusters shared across all 308 datasets after data integration (Figure 4A and 4B). Severe-patient-specific PLT0 is an 309 interesting sub-cluster with elevated integrin genes, including ITGA2B, ITGB1, ITGB3, 310 ITGB5, as well as thrombosis-related genes, such as SELP, HPSE, ANO6 and PF4V1. 311 Antibodies against the latter are associated with thrombosis including adverse reactions 312 to recent COVID-19 vaccine ChAdOx1 nCoV-19 (Schultz et al., 2021). In addition, 313 upregulated pathways of hemostasis, wound healing and blood coagulation were also 314 observed in PLT0 (Figure S13A; Table S4). Importantly, PLT2 is an inflammatory sub- 315 cluster with an upregulation of ISGs and interferon signaling pathways, while PLT4 is 316 highlighted by upregulated post-transcriptional RNA splicing activities (Figure S13A 317 and S13C). 318 319 Severity-associated gene patterns were also identified by selecting coagulation- 320 associated genes modules (Figure 4C; Table S4), indicating distinct coagulation 321 activities across platelets. Apart from pan-platelet genes, we found dramatic 322 upregulation of genes involved in platelet activation, fibrinogen binding and blood 323 coagulation in platelets of severe COVID-19, including procoagulant heparanase 324 (HPSE) (Osterholm et al., 2013), Anoctamin-6 (ANO6) (Swieringa et al., 2018), and 325 selectin P (SELP) (Sparkenbaugh and Pawlinski, 2013) (Figure 4C and 4D). 326 Heparanase is an endoglycosidase that cleaves heparan sulfate constituents, a major 327 component of anti-coagulation glycocalyx on the surface of vascular endothelium 328 (Edovitsky et al., 2006; Iba et al., 2020b). Upregulated heparanase was related to 329 upregulation of cell-matrix adhesion and coagulation (Figure 4D). Thrombotic vascular 330 damages could be caused by the degradation function of heparinase enriched in 331 platelets of severe patients. Elevation of ANO6 is known to trigger phospholipid 332 scrambling in platelets, resulting in phosphatidylserine exposure which is essential for 333 activation of the clotting system (Heemskerk et al., 2013). In addition, other upregulated 334 genes involved in coagulation-associated activities were also observed, including

335 wound healing, fibrinolysis, platelet aggregation and activation (Figure 4D), which likely 336 collectively contribute to the clotting issue of severe COVID-19 patients. 337 338 Emergence of Developing Plasmablasts and B Cell Association with 339 Autoimmunity 340 Autoimmune disorders in COVID-19 patients such as immune thrombocytopaenic 341 purpura (ITP) is now recognized as a known disease complication (Ehrenfeld et al., 342 2020; Fujii et al., 2020; Gruber et al., 2020; Rodríguez et al., 2020; Zhou et al., 2020). 343 However, little is known about the molecular and cellular mechanism behind it. To 344 examine this further, we integrated B cells and plasmablasts from both PBMC and BAL 345 and conducted systematic analysis (Figure 5A, 5B, S14A and S14B). Several COVID- 346 19 specific sub-clusters were identified in B cells, such as ISGhigh activated B cells 347 (cluster 7) (Figure 5A). Importantly, activated B cells showed dramatic upregulation of 348 interferon signaling pathways and cytokine productions (Figure S15A), indicating its 349 anti-virus characteristics. Notably, plasmablasts were mainly observed in severe 350 COVID-19 patients, where a group of proliferative cells was identified and labeled as 351 developing plasmablasts (Figure 5B). In contrast, non-dividing plasmablasts displayed 352 upregulation of immunoglobulin genes (IGHA1, IGHA2, IGKC), B cell markers (CD79A) 353 (Mason et al., 1995), interleukin receptors (IL2RG) and type II HLA complex (HLA-DOB) 354 (Figure 5C; Table S5). In addition, non-dividing plasmablasts showed unique isotypes 355 of immunoglobulin (Ig) in sub-regions of UMAP, whereas developing plasmablasts 356 displayed obscure Ig types (Figure S14E and S14F). Antibody production activities 357 were upregulated in non-dividing plasmablasts based on gene enrichment analysis 358 (Figure S15A; Table S5). Collectively, we inferred that non-dividing plasmablasts had 359 definite immunoglobulin isotypes and were actively involved in immune responses 360 towards COVID infection, while developing plasmablasts were less mature but highly 361 proliferative to replenish the repertoire of plasma cells. 362 363 Since there are few clues of gene associations of autoimmunity in COVID-19, we 364 brought up a hypothesis-driven, prior knowledge-based approach to discover and 365 prioritize genes for the specific phenotype (Figure 5D). First, gene modules of B cells

366 and other cells in severe patients were collected and subjected to ToppGene for 367 enrichment analysis. Then we queried autoimmunity-associated terms in the enriched 368 output and identified associated genes. After that, we retrieved interaction pairs using 369 ToppCluster and CellChat database (Jin et al., 2020). In the end, we identified genes 370 that are not only involved in autoimmunity, but have a mediator role in the immune 371 signaling network. Using this approach, we observed several candidate pairs of genes, 372 including TNFSF13B-TNSRSF13, IL10-IL10RA, IL21-IL21RA, IL6-IL6R, CXCL13- 373 CXCR5, CXCL12-CXCR4, CCL21-CCR7, CCL19-CCR7 and CCL20-CCR6 in severe 374 patients, which were enriched for autoimmune diseases, such as autoimmune thyroid 375 diseases, lupus nephritis, autoimmune encephalomyelitis (Aust et al., 2004; Hirota et 376 al., 2007; Kuwabara et al., 2009; Lee et al., 2010; Steinmetz et al., 2008). Candidate 377 cytokine and chemokine ligand genes were expressed in various cell types in PBMC 378 and BAL, including IL21 and CXCL13 from exhausted T cells of BAL, CXCL12 from 379 mesenchymal cells, IL6 and CCL21 from endothelial cells, CCL19 from cDC and 380 CCL20, TNFSF13B, and TNFSF13 from lung macrophages (Figure 5E; Table S5). 381 These interaction pairs have been linked with auto-immunity (Klimatcheva et al., 2015; 382 Wong et al., 2010). In addition, we analyzed single-cell studies (Arazi et al., 2019; 383 Zhang et al., 2019) of rheumatoid arthritis and lupus nephritis patients and found that 384 high expression levels of the candidate receptors in B cells and ligands in other cells 385 were also observed, such as CXCL13 in helper T cells and CXCR5 in B cells in both 386 studies (Figure S15C, S15D). However, more evidence is still required to infer the 387 association between these interactions and autoimmunity in COVID-19 patients. 388 Supported by the evidence above, we drew a network for potential mediator interactions 389 of B cells and their associations with autoimmune disorders, where linkages with 390 diseases, such as rheumatoid arthritis, systemic lupus erythematosus, were highlighted, 391 as well as linkages with mouse phenotypes, such as abnormal immune tolerance and 392 increased susceptibility to autoimmune disorder (Figure 5F). As a caveat, although 393 using prior knowledge to prioritize gene and cell-associated functions and interactions 394 may introduce biases, such approaches also have the potential to highlight key 395 similarities and differences between different disease causes and clinical responses and 396 improve our understanding of the molecular and cellular mechanisms at work.

397 398 Functional Map and Immune Cell Interplay Landscape in COVID-19 399 As above, where highly significant enrichments of unique functions and pathways could 400 be identified in the subtypes of multiple cell classes, such as neutrophils, platelets and B 401 cells, we sought to get a more holistic understanding of COVID-19 specific cell class 402 and subclass-level signatures, including T cell subtypes (Figure S16 and S17), we built 403 an integrative functional map of all cell types in three compartments across multiple 404 disease conditions using a highly integrated gene module set (Figure 6A; Table S6). All 405 enriched functional associations in ToppCluster for gene modules of cell types and sub- 406 clusters were depicted. They were grouped by disease conditions and compartments to 407 show heterogeneity of cellular functions in different circumstances. 408 409 In the heatmap (Figure 6A), most enrichments were consistently observed across cells 410 of healthy donors and COVID-19 patients. However, some unique patterns were also 411 identified. For example, T cells and NK cells in healthy donors show enrichments of 412 mitochondrial transport and ATP metabolic process, while activated T cells in mild 413 patients show upregulation of type I interferon production and cytokine signaling. 414 Enrichments of macrophage differentiation and neutrophil migration regulation were 415 uniquely found in MoAM1 in severe patients (Figure 6A). The function map provides a 416 high-level approach to investigate functional variations of cells across disease 417 conditions and compartments. The predicted interplay of immune cells across multiple 418 compartments and disease conditions is displayed in Figure 6B. Cell proportion 419 changes, sub-cluster specific signatures and cell-cell interaction are also depicted. 420 421 Similarity and Heterogeneity Between COVID-19 and Other Immune-mediated 422 Diseases 423 To further analyze COVID-19 specific immune signatures, we compared immune cells 424 from COVID-19 patients with cells in other immune-mediated diseases, including severe 425 influenza (Lee et al., 2020), sepsis (Reyes et al., 2020) and multiple sclerosis (Schafflick 426 et al., 2020). 404,125 cells were included after the integration of PBMC single-cell 427 datasets (Figure 7A, S18; Table S7). Dynamic changes of cell abundance were

428 compared in diseases versus healthy donors. Similar to COVID-19 patients, severe 429 influenza patients also exhibited the reduction of non-classical monocytes, pDC, cDC 430 and CD4+ TCM, but the effect of the former two types was smaller in magnitude (Figure 431 7B). However, the reduction of non-classical monocytes is more significant in severe 432 COVID-19 patients than severe influenza or mild COVID-19 patients (Figure 7B). 433 Notably, NK cell reduction is associated with COVID-19 severity, whereas T cell 434 depletion is a more dramatic perturbation in severe influenza. Within these 435 comparisons, the expansion of plasmablasts is consistently observed, whereas the 436 accumulation of platelets is unique to SARS-CoV-2 and in particular, to severe COVID- 437 19 clinical status (Figure 7B). 438 439 In addition to dynamic changes of cell ratios, we also investigated the regulation of 440 immune mediator genes across various diseases (Figure 7C; Table S7). IL-6 is an 441 important factor of cytokine storms in COVID-19 (Zhao, 2020). As shown in the 442 heatmap, naive B cells are the main sources of IL-6 in COVID-19 patients while CD14+ 443 monocytes show the highest expression levels in severe influenza patients (Figure 7C). 444 Specific ligands, including CXCL2, CXCL3, CCL20 were upregulated in both severe 445 COVID-19 patients and severe influenza patients. CCR4 and IL2RA is uniquely high in 446 CD4+ T cells of COVID-19 patients. Interestingly, most PBMC myeloid cell types 447 displayed upregulated levels of interferon-stimulated genes in both COVID-19 and 448 influenza, especially in COVID-19, where highest levels of ISGs in CD14+ Monocytes, 449 cDC and pDC were observed. 450

451

452 Discussion

453 In this work, we have constructed an innovative immune signature atlas of the blood 454 and lung of COVID-19 patients using the integrated single cell RNA-sequencing data 455 and Topp-toolkit. By virtue of systemic analysis of large sample size from multiple 456 sampling sites, consistent immunopathology-associated changes of cell abundance and 457 transcriptional profiles were observed in the circulating and lung immune repertoire of 458 COVID-19 patients. The established single cell atlas and the provided public portal 459 (https://toppcell.cchmc.org/) enables the query of candidate molecules and pathways in 460 each of these processes. 461 462 Leveraging this approach, we identified three major candidate mechanisms capable of 463 driving COVID-19 severity: (1) a cascade-like network of proinflammatory autocrine and 464 paracrine ligand receptor interactions among subtypes of differentiating mononuclear, 465 lymphoid, as well as other cell types; (2) the production of emergency platelets whose 466 gene expression signatures implicate significantly elevated potential for adhesion, 467 thrombosis, attenuated fibrinolysis, and potential to enhance the release of heparin- 468 bound cytokines as well as further influence the activation of neutrophils causing further 469 inflammatory cell recruitment and neutrophil netosis; and (3) the extrafollicular activation 470 of naive and immature B cells via a multilineage network that includes monocytic 471 subtypes and exhausted T cells of cytokines and interleukins with the potential to 472 generate local antigen specific response to virus infected targets and collateral 473 autoimmunity. More details will be discussed below. 474

475 We identified dramatically expanded macrophages which were marked by the loss of 476 HLA class II genes and upregulation of interferon-stimulated genes. It implicates a key 477 role for these activated macrophages involved in signaling network and less so in 478 activation of adaptive T cell immunity. Among them, MoAM2 displayed 479 hyperinflammatory responses and extraordinary high levels of signaling molecules, 480 which are involved in both autocrine (e.g. IL-6, CCL2, CCL4 and CCL8) and paracrine 481 (e.g. CXCL2, CXCL9, CXCL10 and CXCL11) signaling pathways. The former pathway

482 contributed to the self-stimulation and development, which amplified the paracrine 483 pathway for T cell and neutrophil chemoattraction. The latter two cell types in turn 484 activated MoAMs with cytokines genes (CCL5, IL10 of T cells and IL1B of neutrophils, 485 respectively). Based on the intercellular and multifactor complexity of the signaling 486 cascade we have outlined, to effectively control a malignant inflammatory cascade, it 487 may be essential to consider simultaneously targeting multiple nodes of this network of 488 cytokines and interleukins. In addition, HLA-DRlow monocytes, likely reflecting 489 dysfunctional cells, were observed in severe infection. This, along with evidence of 490 emergency myelopoiesis with immature circulating neutrophils into the circulation was 491 detected in severe COVID-19. These neutrophils had transcriptional programs 492 suggestive of dysfunction and immunosuppression not seen in patients with mild 493 COVID-19. As such, we have presented evidence for the contribution of defective 494 monocyte activation and dysregulated myelopoiesis to severe COVID.

495

496 Platelet expansion is uniquely observed in COVID-19 versus other immune-mediated 497 diseases. 498 Strikingly, these activated platelets were highlighted with abnormal thrombosis and 499 upregulated heparanase, a procoagulant endoglycosidase that cleaves anti-coagulation 500 heparan sulfate constituents on endothelial cells and potentially causes thrombotic 501 vascular damages. Additionally, heparanase-cleaved heparan sulphate (HS) fragments 502 were capable of stimulating the release of pro-inflammatory cytokines, such as IL1B, 503 IL6, IL8, IL10 and TNF through the TLR-4 pathway in PBMC (Goodall et al., 2014), 504 further contributing to the hyperinflammatory environment in COVID-19 patients. Since 505 heparanase is recognized as a hallmark in tumor progression and metastasis 506 (Jayatilleke and Hulett, 2020), we hypothesize COVID-19 infection could be associated 507 with higher occurrence of lung tumor metastasis. However, more data is required to 508 support it. Pro-neutrophil secreted proteins (e.g. ELANE, DEF4) of neutrophil 509 extracellular trap (NET), which have been reported to be associated with higher risk of 510 morbid thrombotic events (Zuo et al., 2021). Approaches to combatting NETs could a 511 potential anticoagulation treatment (Thålin et al., 2019).

512 513 We propose a signaling network which potentially shapes the differentiation of B cells 514 towards the formation of autoantibodies. Proliferation and activation of inflammatory 515 myeloid cells and the formation of exhausted CD4+ T helper around an area of direct or 516 indirect viral tissue injury leads to the production of a set of interleukins and cytokines 517 known to have both direct cell activating and maturing effects on naïve and immature B 518 cells. Previous report had revealed the exaggerated extrafollicular B cell response, 519 which is part of a mechanism that stimulates somatic mutation and maturation of B cells 520 to produce plasma cells with specificity for antigens present in the vicinity of tissue 521 damage sites (Farris and Guthridge, 2020). In the absence of macrophages or dendritic 522 cells to restrict self vs non-self, the presence of IL-10, IL-21, CXCL13 CXC10, IL-6 and 523 others acting on receptors present in naïve and immature B cells leads to the selection 524 and maturation of self-reactive maturation of B cells clones with formation of 525 autoantibodies. Many of these COVID-19-activated genes (e.g. CXCL13, CCL19, 526 CCL20, TNFRSF13) are known to be genetically associated with rheumatoid arthritis, 527 lupus, and risk of developing autoimmune disease in humans and mouse models. The 528 development of different patterns of autoimmunity may be the main hallmark of “Long 529 Haul” Covid disease and could explain why some individuals develop different 530 autoantibodies and suffer different forms of clinical consequences depending on which 531 antigens drive the B-cell maturation. Thus, an additional prediction that could be made 532 based on these findings and our network model is that among individuals treated with 533 corticosteroids at the time these auto-immunogenic processes are activated, there 534 should be a protective effect and lower likelihood of developing post acute sequela of 535 Covid. 536 537 Consistent and varied compositional changes and gene patterns of immune cells were 538 identified in COVID-19, influenza and sepsis. Expansion of plasmablasts, as well as the 539 reduction of non-classical monocytes, are more significant changes in severe COVID-19 540 patients, while the depletion of T cells is more dramatic in severe influenza patients. The 541 accumulation is a unique immune hallmark of COVID-19 within the selected diseases, 542 which contributes to the coagulation abnormalities and thrombosis, a key cause of

543 fatality in COVID-19 patients. Different signaling gene patterns were identified across 544 immune-mediated diseases, with CCR4 only highly expressed in CD4+ T cells of 545 COVID-19 patients, which might be related with extravasation of these cells (Spoerl et 546 al., 2021). Upregulated interferon-stimulated genes of myeloid cells in PBMC revealed 547 the inflammatory environment of COVID-19. 548 549 Collectively, using the COVID-19 single cell atlas data exploration environment, we 550 have illustrated is that researchers are now enabled to systematically explore, learn, 551 and formulate new hypotheses within and between compartments, cell types, and 552 biological processes, and provided access to these reprocessed datasets through a 553 suite of explorative and evaluative tools. Moreover, we have shown different hypotheses 554 can be developed and explored using the approaches that we have outlined and the 555 database that we have provided. Certainly additional critical information will also be 556 obtained using approaches that include in situ spatial, temporal data as well as those of 557 viral products and viral and inflammatory-process affected complexes. Next steps for 558 improving its ability to be mined more deeply will be based on additional statistical 559 methods that extend the current ToppCell / ToppGene Suite based on fuzzy measure 560 similarity, Page-Rank, and cell-cell signaling approaches. 561 562 There are several limitations in our study. Different studies used various standards of 563 COVID-19 severity definition. To generalize conclusions, we simplified disease 564 conditions into several universal groups. Prospectively, a standardized definition of 565 disease stages will assist to the accuracy of future studies. Additionally, the timing of 566 sample collection was not considered as a variable in this study, rather disease stages 567 were used to consolidate data across samples. W lack follow-up data of patients with 568 sequela, which will be helpful for understanding the long-haul effects of the disease. 569 570 571 572 573

574 Acknowledgements

575 We thank Pablo Garcia-Nieto, Ambrose Carr and Jonah Cool and the Chan Zuckerberg 576 Initiative for hosting the data on cellxgene. We acknowledge suggestions and help from 577 Greta Beekhuis. Some figures were created using https://biorender.com. Funding for 578 this study was provided by LungMap (U24 and HL148865), Digestive Health Center 579 (P30, DK078392) and Harold C. Schott Foundation funding of the Harold C. Schott 580 Endowed Chair, UC College of Medicine. We thank the support from Pediatric Cell Atlas 581 and high performance computational cluster of CCHMC. 582 583

584 Author Contributions

585 586 Conceptualization, K.J. and B.A.; Methodology, K.J., B.A., E.B., A.M. and J.Y.W; 587 Investigation, K.J. and B.A.; Writing – Original Draft, K.J.; Writing – Review & Editing, 588 K.J., B.A., M.E.R., A.M., D.A.P.K, S.S.G. and S.B.; Funding Acquisition, B.A.; 589 Resources, K.J.; Data Curation, K.J. and E.B.; Visualization, E.B.; Supervision, B.A.. 590

591 Declaration of Interests

592 The authors declare no competing interests. 593

594 Resource availability

595 Lead contact 596 Further information and requests for resources should be directed to and will be fulfilled 597 by the Lead Contact, Bruce Aronow ([email protected]). 598 599 Materials Availability 600 This study did not generate new unique reagents. 601 602 Data and Code Availability

603 Public single-cell RNA-seq datasets of PBMC in COVID-19 patients are available on 604 NCBI Gene Expression Omnibus and European Genome-phenome Archive, including 605 GSE150728, GSE155673, GSE150861, GSE149689 and EGAS00001004571 (or 606 FastGenomics). BAL single-cell RNA-seq datasets of COVID-19 patients are available 607 on GSE145926 and GSE155249. Lung Parenchyma single-cell RNA-seq data are 608 available on GSE158127. 609 Single-cell RNA-seq data of sepsis patients are available on the Single Cell Portal 610 SCP548 and SCP550. Data of multiple sclerosis patients are available on GSE128266. 611 Data of severe influenza patients are available on GSE149689. 612 Gene modules of all datasets analyzed using ToppCell web portal are available on 613 COVID-19 Atlas in ToppCell, including gene modules from either a single dataset or an 614 integrated dataset. Gene modules from the integration of specific cell types, such as B 615 cells and neutrophils are also listed in ToppCell. More details are listed in Figure1A and 616 Table S1. An interactive interface of integrated PBMC data and subclusters of immune 617 cells will be public on cellxgene. 618 Codes of preprocessing, normalization, clustering and plotting of single-cell datasets will 619 be available on github. 620

621 Methods 622 623 Single-cell RNA-seq data source 624 To have a comprehensive understanding of immune cells in different repertoires, we 625 collected 8 public COVID-19 single-cell RNA-seq datasets of multiple compartments, 626 including peripheral blood mononuclear cells, bronchoalveolar lavage and lung biopsy, 627 which in total covered over 43 healthy donors, 22 mild/moderate, 42 severe and 2 628 convalescent COVID-19 patients. More details can be found in Figure 1A and Table S1. 629 Lung biopsy samples were taken from the explanted lung or post-mortem lungs of 630 COVID-19 patients(Bharat et al., 2020). Various criteria were used in these publications 631 to describe COVID-19 severity. For example, we found asymptomatic, mild, moderate 632 and floor COVID-19 patients under the definition of non-severe COVID-19 patients in 633 our data sources. A recent paper used the WHO score of COVID-19 severity to

634 categorize disease conditions of patients(Wilk et al., 2020b), which is a more 635 standardized and robust approach for the description of disease stages. However, in 636 order to address the issue of missing information for disease stratification and to 637 simplify the comparison, we grouped disease conditions into three groups, including 638 healthy donors, mild COVID-19 patients and severe COVID-19 patients. Convalescent 639 patients were excluded in some of our analysis for simplification. Sequencing data of 640 healthy donors in Guo et al. was excluded since it was not from the same institute(Guo 641 et al., 2020). 642 We also collected PBMC single-cell RNA-seq data from 29 sepsis patients(Reyes et al., 643 2020) and 4 multiple sclerosis(Schafflick et al., 2020) patients for comparative analysis 644 of immune-mediated diseases (Figure 1A; Table S1). Data sources can be found in 645 Data Availability. 646 647 Data preprocessing and normalization 648 For datasets with raw UMI counts, we first removed cells with less than 300 detected 649 genes or less than 600 UMI counts. Then cells with more than 15% counts of 650 mitochondrial genes were filtered out. Genes expressed in less than 5 cells were 651 removed. After quality control, we finally harvested 483,765 high-quality cells from 8 652 studies (Table S1). We normalized the total UMI counts per gene to 1 million (CPM)

653 and applied log2(CPM+1) transformation for heatmap visualization and downstream 654 differential gene expression analysis. Steps above were done in Scanpy(Wolf et al., 655 2018). 656 For some datasets that only provide processed and normalized h5ad or rds files, we 657 checked their preprocessing procedures in the original publications and confirmed that 658 stringent quality control procedures were used. Most of them used the default 659 normalization approach in the Seurat or Scanpy pipeline. We transferred them to

660 log2(CPM+1) to make data consistently normalized. We also prepared corresponding 661 raw count files for data integration. 662 663 Integration of PBMC datasets and BAL datasets using Reciprocal PCA in Seurat

664 We input raw count files of 5 preprocessed PBMC datasets into Seurat and created a 665 list of Seurat objects. Reciprocal PCA procedure 666 (https://satijalab.org/seurat/v3.2/integration.html#reciprocal-pca) was used for data 667 integration. First, normalization and variable feature detection were applied for each 668 dataset in the list. Then we used SelectIntegrationFeatures to select features for 669 downstream integration. Next, we scaled data and ran the principal component analysis 670 with selected features using ScaleData and RunPCA. Then we found integration 671 anchors and integrated data using FindIntegrationAchnors and IntegrateData. RPCA 672 was used as the reduction method. After integration, we scaled data and ran PCA on 673 integrated expression values. UMAP was generated using the top 30 reduced 674 dimensions with RunUMAP. The same approach was also used in BAL data integration 675 and multi-disease integration. We also used it for the integration of specific cell types 676 across multiple datasets, for example, the integration of neutrophils from PBMC and 677 BAL datasets. Compared with standard workflow and SCTransform 678 (https://satijalab.org/seurat/v3.2/integration.html) in Seurat, we found Reciprocal PCA is 679 much less computation-intensive and time-consuming, making the integration of 680 multiple large single-cell datasets feasible. 681 682 Cell Annotations using canonical markers after unsupervised clustering 683 Cell annotations were assigned in each dataset and then mapped to the integrated 684 data. For some datasets without available cell annotations, we first used unsupervised 685 clustering in Scanpy. Detailed steps include (1) detecting top 3,000 highly variable 686 genes using pp.highly_variable_genes; (2) scaling each gene to unit variance on highly 687 variable genes using pp.scale; (3) running PCA using arpack approach in tl.pca; (4) 688 finding neighbors using pp.neighbors; (5) running leiden clustering with resolution of 1 689 using tl.leiden (resolutions were determined swiftly based on the size and complexity of 690 data). More details can be found in the code (point to it). For datasets with available 691 annotations, we checked their validity and corrected wrong annotations. For example, 692 hematopoietic stem and progenitor cells (HSPC) were mistakenly annotated as 693 “SC&Eosinophil” in the original paper(Wilk et al., 2020a) and were corrected in our 694 annotation.

695 696 After unsupervised clustering, well recognized immune cell markers were used to 697 annotate clusters, including CD4+ T cell markers such as TRAC, CD3D, CD3E, CD3G, 698 CD4; CD8+ T cell markers such as CD8A, CD8B, NKG7; NK cell markers such as 699 NKG7, GNLY, KLRD1; B cell markers such as CD19, MS4A1, CD79A; plasmablast 700 markers such as MZB1, XBP1; monocyte markers such as S100A8, S100A9, CST3, 701 CD14; conventional dendritic cell markers such as XCR1, plasmacytoid dendritic cell 702 markers such as TCF4; megakaryocyte/platelet marker PPBP; red blood cell markers 703 HBA1, HBA2; HSPC marker CD34. Exhaustion-associated markers, including PDCD1, 704 HAVCR2, CTLA4 and LAG3 were used to identify exhausted T cells. 705 706 Additionally, other markers were used for annotations of lung-specific cells, including 707 AGER, MSLN for AT1 cells; SFTPC, SFTPB for AT2 cells; SCGB3A2, SCGB1A1 for 708 Club cells; TPPP3, FOXJ1 for Ciliated cells; KRT5 for Basal cells; CFTR for Ionocytes; 709 FABP4, CD68 for tissue-resident macrophages; FCN1 for monocyte-derived 710 macrophages, TPSB2 for Mast cells. More details can be found in Table S2. 711 712 Cell Annotations using Azimuth 713 To better annotate T cells in our study, we applied Azimuth 714 (https://satijalab.org/azimuth/), a tool for reference-based single-cell analysis developed 715 in Seurat version 4.0(Hao et al., 2020). High-quality PBMC single-cell data in Azimuth 716 was used as the reference for label projection. After removing annotations with low 717 prediction scores or low mapping scores, we got a collection of well-annotated T cell 718 subtypes, including CD4+ Cytotoxic T cell, CD4+ Naive T cell, CD4+ Central Memory T 719 cell, CD8+ Naive T cell, CD8+ Effector Memory cell, gamma-delta T cell, double- 720 negative T cell. CD4+ Effector Memory T cell and CD8+ Central Memory T cell were 721 found by Azimuth but removed later because of low scores. Apart from annotations of T 722 cell subtypes, we also found CD56-bright NK cell, intermediate B cell and Memory B cell 723 using Azimuth. 724 725 Sub-clustering for specific cell types

726 Sub-clustering was used for the discovery of subtypes or distinct stages of a specific 727 cell type. In our work, we applied sub-cluster for various immune cell types, including 728 classical monocytes, neutrophils, conventional dendritic, B cells and platelets. First, all 729 cells in the specific cell type were integrated using the same procedure as PBMC data 730 integration. Then Louvain clustering (resolution = 0.5, except for sub-clustering of 731 classical monocytes where resolution = 0.3) was applied to detect sub-clusters of those 732 cells. Importantly, neutrophils, cDCs and B cells were retrieved from both PBMC and 733 BAL, whereas classical monocytes and platelets were only retrieved from PBMC. 734 735 Generation of ToppCell gene modules 736 ToppCell (https://toppcell.cchmc.org/) was designed to parallelly analyze transcriptional 737 profiles of single-cell datasets by organizing differential expressed gene modules in a 738 customized hierarchical order. In our study, we hierarchically annotated cells with 739 multiple layers, including compartments, disease conditions, lineages, cell classes and 740 sub-clusters. All the cells were grouped into specific hierarchical categories. For 741 example, “PBMC_severe COVID-19_myeloid cells_classical-monocytes_cMono1” 742 represents cells belonging to cMono1 (a sub-cluster of classical monocytes) in PBMC of 743 severe COVID-19 patients. With hierarchically ordered cell annotations, we calculated 744 their DEGs in a hierarchical way as well. We defined customized ranges for 745 comparisons and applied t-test based on normalized expression values. More details 746 can be seen on ToppCell website. Usually, the top 200 most differentially genes in each 747 comparison were picked up as the gene modules for the selected cell group, which are 748 the starting point of downstream analysis, including gene enrichment in ToppGene and 749 interaction inference in ToppCluster. All gene modules in our study were curated in 750 COVID-19 Atlas (https://toppcell.cchmc.org/biosystems/go/index3/COVID-19 Atlas) and 751 ImmuneMap (https://toppcell.cchmc.org/biosystems/go/index3/ImmuneMap) on the 752 ToppCell website. 753 754 Gene Enrichment Analysis using ToppGene 755 Abundant gene modules were generated with ToppCell. After that, we used ToppGene 756 (https://toppgene.cchmc.org/) for gene enrichment analysis. Genes in each gene

757 module were sent to ToppGene platform as input for enrichment in different domains. 758 GO-Molecular Function, GO-Biological Process and GO-Cellular Component and 759 Mouse Phenotype were usually used for enrichment. P values of enrichment results 760 were adjusted using the Benjamini-Hochberg procedure. 761 762 Generation of Functional Association Heatmap using ToppCluster 763 Genes in gene modules of selected cell types or sub-clusters were sent to ToppCluster 764 (https://toppcluster.cchmc.org/). Then multi-group functional enrichment was drawn for

765 input gene modules and -log10(adjusted p-value) was used as the gene enrichment 766 score to represent the strength of association between gene modules and pathways. 767 Scores greater than 10 were trimmed to 10. Pathways from Gene Ontologies, including 768 Molecular Functions, Biological Process and Cellular Component in the option list were 769 used for the enrichment of gene modules in myeloid cells, B cells and platelets. In order 770 to gain a broader knowledge of immunothrombosis-related pathways, “Pathway” and 771 “Mouse Phenotype'' in the option list were also selected for enrichment. Morpheus was 772 used for visualization of the heatmap (https://software.broadinstitute.org/morpheus/). 773 774 Cell Interaction Inference in immunothrombosis activities and cytokine signaling 775 pathways 776 CellChat was used to infer the signaling network in the BAL of severe patients (Figure 777 S8B). All 3 categories of interactions were used in the database CellChatDB.human. 778 Over-expressed ligands or receptors in each cell type were first identified for further 779 identification of over-expressed interaction pairs. Then cytokine, chemokine and IL 780 signaling probability between multiple cell types was inferred using 781 computeCommunProb and computeCommunProbPathway. 782 783 ToppCell was used to infer interactions in immunothrombosis. We first selected genes 784 related to coagulation or immunothrombosis pathways from subtypes of endothelial 785 cells, platelets, neutrophils, classical monocytes and monocyte-derived macrophages 786 by filtering the output of ToppCluster (Figure S12A). Then we used CellChatDB as the 787 knowledge base to find the subset of genes participating in cell-cell interaction, including

788 genes involved in signaling via secretion, cell-cell contact and extracellular matrix 789 interaction. These genes in each cluster were sent to ToppCluster to infer the 790 interaction network using protein-protein interactions (PPI) between those genes. 791 792 Statistics of Cell Proportion Changes in Different Disease Stages 793 Cell proportion differences between disease groups for specific types and subtypes 794 (Figure 2, Figure S2-S4) shown on box plots were measured by Mann-Whitney test 795 (Wilcoxon, paired=False). Significance between two disease conditions were shown on 796 the top. 797 To investigate the dynamic changes of cell proportions across various immune- 798 mediated diseases, we followed the approach in recent literature (Lee et al., 2020) 799 (Figure 7B). For each disease condition, we computed the relative ratio of each cell

800 type in individual disease samples divided by individual healthy samples. Log2 801 transformed values were shown in the box plot. Then we calculated relative ratios of 802 each cell type between all sample pairs of healthy donors as a control. To compute the 803 significance, we used a two-sided Kolmogorov-Smirnov (KS) test using relative ratios in 804 diseases and those values in healthy donors. 805 806 Generation of Volcano Plots 807 We first calculated differential expressed genes using tl.rank_genes_groups in Scanpy. 808 Adjusted p values and log fold changes in the output were used as the input of volcano 809 plots. R package EnhancedVolcano (Blighe et al., 2018) was used to draw figures. 810 811 Construction of COVID-19 Functional Enrichment Map 812 In order to characterize functional properties of cell types and subtypes observed in 813 BAL, PBMC, and lung parenchymal samples from control, mild, and severe COVID-19 814 patient samples, we used the library of gene expression signatures (“Gene Module 815 Report” from ToppCell) as an input to the ToppCluster enrichment analyzer web server 816 (Kaimal et al 2010). Using categories of Gene Ontology, Human Phenotype, Mouse 817 Phenotype, Pathway and Protein Interaction, a matrix was constructed using minus log 818 P enrichment values for each celltype gene list and then all cells and enriched features

819 could be clustered and ordered based on their shared or distinct properties that could 820 then be associated with lineage, cell subclass, tissue compartment, and disease state. 821

822 Supplementary Information

823 Table S1. Metadata of data sources and generated resources in this study, relative to 824 Figure 1. 825 826 Table S2. Severity-associated cell distribution in multiple compartments of COVID-19 827 patients, relative to Figure 2. 828 829 Table S3. Gene modules and DEGs of neutrophil and macrophage sub-clusters, relative 830 to Figure 3. 831 832 Table S4. Gene modules and pro-thrombosis genes in platelets of COVID-19 patients, 833 relative to Figure 4. 834 835 Table S5. Developing plasmablast signatures and autoimmunity-associated signatures 836 of COVID-19 B cells, relative to Figure 5. 837 838 Table S6. Enrichment scores of COVID-19 Functional Map, relative to Figure 6. 839 840 Table S7. Dynamic changes of abundances and signatures across immune-mediated 841 diseases, relative to Figure 7. 842 843

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Figures

Fig. 1. Creating a COVID-19 Signature Atlas. (A) Representative aggregation of multiple single-cell RNA-sequencing datasets from COVID-19 and related studies. The present study is derived from a total of 231,800 peripheral blood mononuclear cells (PBMCs), 101,800 bronchoalveolar lavage (BAL) cells and 146,361 lung parenchyma cells from 43 healthy; 22 mild, 42 severe, and 2 convalescent patients. Data was collated from eight public datasets (right). (B) Data analysis pipeline of the study using Topp-toolkit. It includes three phases: (1) clustering and annotation; (2) downstream analysis using Topp-toolkit; (3) biological exploration. Output includes the evaluation of abundance of cell populations, cell type (cluster) specific gene modules, functional associations of disease-associated cell classes and clusters, inference of cell-cell interactions, as well as comparative analysis across diseases, including influenza, sepsis and multiple sclerosis. Additional newer datasets not included in this manuscript are present and will continue to be added to ToppCell (http://toppcell.cchmc.org).

Fig. 2. Modularized representation of cell type specific gene signatures and dynamic changes of cell abundance. (A) Uniform Manifold Approximation and Projection (UMAP) of 28 distinct cell types identified in the integrated peripheral blood mononuclear cell (PBMC) data. (B) Comparative analysis of cell abundance effects of COVID-19. Reproducible multi-study data present high impact effects on 5 cell types in PBMC. Percentages of selected cell types in each sample are shown (where Vent: Ventilated patients; Non Vent: Non-ventilated patients). Significance between two conditions was measured by the Mann-Whitney rank sum test (Wilcoxon, paired=False), which was also used in following significance tests of cell abundance changes in this study. *: p <= 0.05; **: p <= 0.01; ***: p <= 0.001; ****: p <= 0.0001. (C) UMAP of 24 distinct cell types identified in the integrated BAL data. (D) Dynamic changes of cell abundances for cell types in two bronchoalveolar lavage (BAL) single-cell datasets. (E) ToppCell allows for gene signatures to be hierarchically organized by lineage, cell type, subtype, and disease condition. The global heatmap shows gene modules with top 50 upregulated genes (student t test) for each cell type in a specific disease condition and compartment. Gene modules from control donors and severe COVID-19 patients were included in the figure.

Fig. 3. Functional analysis of compartment-specific immature and subtype- differentiated neutrophils and monocytic macrophages in COVID-19 patients. (A) Five sub-clusters and three cell groups were identified after the integration of neutrophils in peripheral blood mononuclear cells (PBMC) and bronchoalveolar lavage (BAL) (Left). The distribution of compartments is shown on the right. (B) Sub-clusters (Left) and COVID-19 conditions (Right) of monocyte-derived macrophages and tissue- resident macrophages were identified after integration of BAL datasets. (C) Heatmap of gene modules from ToppCell with top 200 upregulated genes for each neutrophil sub- cluster. Important neutrophil-associated genes and inferred roles of sub-clusters were shown on two sides. (D) Heatmap of associations between subclusters of neutrophils and macrophages and myeloid-cell-associated pathways (Gene Ontology). Gene modules with 200 upregulated genes for sub-clusters were used for enrichment in ToppCluster. Additionally, enrichment of top 200 differentially expressed genes (DEGs) for comparisons in fig. S5D and fig. S6B were appended on the right. Gene enrichment

scores, defined as -log10(adjusted p-value), were calculated as the strength of associations. Pie charts showed the proportions of COVID-19 conditions in each cluster. (E) Gene interaction network in the BAL of severe patients. Highly expressed ligands and receptors of each cell type were drawn based on fig. S8. Interaction was inferred using both CellChat database and embedded cell interaction database in ToppCell.

A C Compartment Pattern2 Compartment PBMC COVID_Mild + Severe Disease Condition COVID-19_Severe plt_5 plt_1 plt_0 plt_3 plt_3 plt_3 plt_2 plt_1 plt_1 plt_4 plt_4 Pan-platelet Platelet cluster Control Mild Disease Condition Severe 317 257 390 771 216 606 705 567 361 n_cells 1191 1775 Gene Gene Name Pattern Pattern2 abnormalPlateletIncreased platelet antibodyabnormal aggregationFormation platelet positiveplatelet plateletnegative aggregation of activation ResponseFibrinactivation focalregulation Clotcell adhesion to(Clotting junctionabnormalelevated of woundinflammatory Cascade)cytoskeletal platelet vascular healingpositive cytosolic vesicleresponse development anchor regulation budding Ca2+ activity of from coagulation membrane CD151 CD151 molecule (Raph blood group) Mild-Severe-down A MYL9 myosin light chain 9 Mild-Severe-down A ITGA2B integrin subunit alpha 2b Mild-Severe-down A F13A1 coagulation factor XIII A chain Mild-Severe-down B GP9 glycoprotein IX platelet Mild-Severe-down B TGFB1 transforming growth factor beta 1 Mild-Severe-down B FERMT3 fermitin family member 3 Mild-Severe-down B TLN1 talin 1 Mild-Severe-down B PF4 platelet factor 4 Mild-Severe-down B CD9 CD9 molecule Mild-Severe-down C FHL1 four and a half LIM domains 1 Mild-Severe-down C CXCL5 C-X-C motif chemokine ligand 5 Mild-Severe-down D LIMS1 LIM zinc finger domain containing 1 Mild-Severe-down E Sub-cluster CLDN5 claudin 5 Mild-Severe-down E NCK2 NCK adaptor protein 2 Mild-Severe-down E LMNA lamin A/C Mild-Severe-down E DMTN dematin actin binding protein Mild-Severe-down E B PDGFA platelet derived growth factor subunit A Mild-Severe-down F MYLK myosin light chain kinase Mild-Severe-down F TBXA2R thromboxane A2 receptor Mild-Severe-down F ALOX12 arachidonate 12-lipoxygenase, 12S type Mild-Severe-down G F2RL3 F2R like thrombin or trypsin receptor 3 Mild-Severe-down G RGS18 regulator of G protein signaling 18 Mild-Severe-down H PDLIM1 PDZ and LIM domain 1 Mild-Severe-down I RAB4A RAB4A RAS oncogene Mild-Severe-up A TFPI tissue factor pathway inhibitor Mild-Severe-up A GP1BA glycoprotein Ib platelet subunit alpha Mild-Severe-up B PDCD10 programmed cell death 10 Mild-Severe-up B SERPINE1 serpin family E member 1 Mild-Severe-up B PDGFB platelet derived growth factor subunit B Severe-up A SERPINE2 serpin family E member 2 Severe-up A ANK1 ankyrin 1 Severe-up B PTGS1 prostaglandin-endoperoxide synthase 1 Severe-up B ITGB3 integrin subunit beta 3 Severe-up B THBS1 thrombospondin 1 Severe-up B PROS1 protein S Severe-up B ANO6 anoctamin 6 Severe-up C JAM3 junctional adhesion molecule 3 Severe-up C SELP selectin P Severe-up C CD68 CD68 molecule Severe-up D SNCA synuclein alpha Severe-up D FCER1G Fc fragment of IgE receptor Ig Severe-up D PF4V1 platelet factor 4 variant 1 Severe-up D Disease Condition HPSE heparanase Severe-up D FCGR2A Fc fragment of IgG receptor IIa Severe-up D row min row max

D Pan-platelet COVID_mild+severe COVID_severe-high

integrin binding cell-cell adhesion

Focal adhesion SYK interactions hemorrhage LIMS1 cell junction abnormal response to infection PDLIM1 NRGN TUBB1 GNG11 Thrombocytopenia RGS18 PF4 LMNA Purpura PPBP CD151NCK2 Autoimmune thrombocytopenia CD9 PDCD10 FERMT3 CLDN5 F13A1 abnormal thrombosis Allergic Reaction FHL1 FCGR2A FCER1G ITGA2B CD68 F2RL3 negative regulation of platelet activation F2 interactions TLN1 GP9 MYL9 ITGB3 ANK1 fibrinogen binding TGFB1 negative regulation of wound healing PROS1 SNCA actin cytoskeleton MYLK PDGFA PDGFB negative regulation of blood coagulation

V$CEBPA_01 CXCL5 GP1BA protease binding ALOX12 Rap1 signaling pathway fibrinolysis SERPINE2 Signaling by GPCR PTGS1 JAM3 increased inflammatory response THBS1 DMTN PF4V1 Platelet activation, signaling and aggregation heparin binding SELP ANO6 positive regulation of wound healing Abnormal blood coagulation TBXA2R HPSE

Thromboembolism cell-matrix adhesion

leukotriene B4

positive regulation of blood coagulation

Fig. 4. COVID-19 driven reprogramming of platelets leads to drastically altered expression of genes associated with platelet adhesion, activation, coagulation and thrombosis. (A-B) Uniform Manifold Approximation and Projections (UMAPs) show distributions of sub-clusters (A) and COVID-19 conditions (B) of platelets after the integration of PBMC datasets. (C) Severity-associated coagulation genes were selected and shown on the heatmap, with disease and sub-cluster specific gene patterns identified and labeled. Their functional associations with coagulation pathways were retrieved from ToppGene and shown on the right. (D) Functional and phenotypical associations of coagulation-association genes in each gene pattern from (B). Associations were retrieved from ToppGene enrichment. Fibrinolysis is highlighted.

Fig. 5. Implicating a multi-lineage cell network capable of driving extrafollicular B cell maturation and the emergence of humoral autoimmunity in COVID-19 patients. (A) Uniform Manifold Approximation and Projections (UMAPs) of sub-clusters (Left) and COVID-19 conditions (Right) of B cells after integration of peripheral blood mononuclear cells (PBMC) and bronchoalveolar lavage (BAL) datasets. (B) UMAPs of subtypes (Left) and COVID-19 conditions (Right) of plasmablasts after integration of PBMC and BAL datasets. (C) Volcano plot depicts differentially expressed genes between plasmablasts and developing plasmablasts. Student t-tests were applied and p values were adjusted by the Benjamini-Hochberg procedure. (D) Workflow of discovering and prioritizing candidate genes related to a disease-specific phenotype with limited understanding. (E) The heatmap shows the normalized expression levels of candidate ligands and receptors for COVID-19 autoimmunity in multiple compartments in healthy donors and COVID-19 patients. Binding ligands of receptor genes were shown in parentheses on the right. Hot spots of expression are highlighted. (F) Network analysis of autoimmunity-associated gene expression by COVID-19 cell types. Prior knowledge associated gene associations include GWAS, OMIM, mouse knockout phenotype, and additional recent manuscripts were selected from ToppGene enrichment results of differentially expressed ligands and receptors and shown on the network. Orange arrows present the interaction directions from ligands (green) to receptors (pink) on B cells. Annotations for these genes, including single-cell co- expression (blue), mouse phenotype (light blue), transcription factor binding site (purple) and signaling pathways (green) are shown.

Fig. 6. Comparative analysis of cell type specific gene signatures associated with lineage, class, subclass, compartment, and disease state in the COVID-19 atlas. (A) Enrichment scores of gene modules for all cell types across different compartments and COVID-19 conditions were generated by ToppCluster and shown on the heatmap. ToppCluster enriched functions from Gene Ontology, Human Phenotype, Mouse Phenotype, Pathway and Interaction databases were used to generate a feature matrix (cell types by features) and hierarchically clustered. Hot spots of the disease-specific enrichments were highlighted and details were shown on the left. More details can be found in Methods. (B) Summarizing predicted functions and interplay of immune cells in COVID-19 blood and lung. Aforementioned key observations in this study were shown in peripheral blood mononuclear cells (PBMC) and bronchoalveolar lavage (BAL) in healthy donors, mild and severe COVID-19 patients, including changes of cell abundance, specific marker genes, upregulated secretion, cell development and cell- cell interactions.

A B

Control IIH Disease Log (proportion of cell type (disease / control )) MS 2 Cell Class Sepsis Influenza Severe 6 4 20 2 4 6 4 20 2 4 6 4 20 2 4 Control Mild 6 4 20 2 4 6 4 20 2 4 6 4 20 2 4 Control Severe *** *** CD14+ Monocyte *** *** * * *** CD16+ Monocyte ** *** *** *** cDC *** *** * * *** pDC *** *** *** CD4+ T naive CD4+ T ** *** *** *** CM ** *** CD8+ T naive B naive dn T CD8+ T B memory gd T *** EM Other T Other

B cell B intermediate T/NK proliferative Plasmablast NK *** * * NK NK CD56bright CD4+ CTL ILC NK activated B naive CD4+ T naive ILC *** ** CD4+ T CM CD14+ Monocyte B memory

CD4+ T CD4+ CD4+ TEM CD16+ Monocyte CD4+ T activated Neutrophil Plasmablast Treg immature Neu * *** *** * cDC1 CD8+ T naive Myeloid cDC2 * *** Platelet CD8+ TCM tDC pDC CD8+ TEM CD8+ T CD8+ Multiple Sclerosis MAIT HSPC Leuk-UTI* Sepsis-ICU Severe influenza COVID-19 COVID-19 CD8+ T activated RBC Mild Severe Others Platelet

Disease Influenza Severe Lineage COVID-19 Mild MS C COVID-19 Severe SEP-ICU Lineage Lymphocyte-B Lymphocyte-T/NK Myeloid Others CTL SEP-Mild Category B Cell Signatures Lineage Cell Cycle CD14+ Monocyte immature Neutrophil Chemokines (Receptors) B memory B naive Plasma cell CD4+ T naive CD4+ Tcm CD8+ T naive CD8+ Tem NK CD16+ Monocyte cDC pDC Neutrophil Platelet Cytokines (Receptors) Dendritic Cell Signatures Cell class HLA-II interferon (receptors) interleukines (receptors) ISG Mono/Macro Signatures

row min row max CTL SEP-ICU SEP-Mild SEP-Mild COVID-19 Severe COVID-19 Mild MS SEP-ICU COVID-19 Mild COVID-19 Severe CTL MS Influenza Severe Influenza Severe COVID-19 Mild COVID-19 Severe CTL SEP-ICU MS Influenza Severe COVID-19 Mild COVID-19 Severe COVID-19 Mild COVID-19 Severe CTL CTL MS MS CTL SEP-ICU COVID-19 Mild COVID-19 Severe MS Influenza Severe SEP-ICU SEP-Mild SEP-Mild SEP-Mild SEP-ICU COVID-19 Mild COVID-19 Severe CTL MS Influenza Severe Influenza Severe SEP-ICU SEP-Mild SEP-ICU SEP-Mild CTL COVID-19 Mild COVID-19 Severe MS Disease COVID-19 Mild COVID-19 Severe Influenza Severe CTL MS SEP-ICU SEP-Mild COVID-19 Severe COVID-19 Mild Influenza Severe CTL MS SEP-ICU SEP-Mild COVID-19 Mild COVID-19 Severe CTL MS COVID-19 Mild COVID-19 Severe SEP-ICU CTL SEP-Mild MS COVID-19 Mild COVID-19 Severe CTL COVID-19 Mild COVID-19 Severe Influenza Severe CTL COVID-19 Mild Influenza Severe COVID-19 Severe MS n_cells Gene Category IL20RB IL20RB IL15RA IL15RA CCR10 CCR10 CCL25 CCL25 IL10RB IL10RB IL17RE IL17RE OAS1 OAS1 IFNAR2 IFNAR2 IFNAR1 IFNAR1 ISG15 ISG15 IFI44 IFI44 STAT1 STAT1 IFI16 IFI16 CCR2 CCR2 IL17RC IL17RC CD4 CD4 IL6R IL6R IL13RA1 IL13RA1 IL18 IL18 IL1RL2 IL1RL2 IL1A IL1A IL1B IL1B IL10 IL10 CCL2 CCL2 IL31RA IL31RA CCL20 CCL20 CXCL3 CXCL3 CXCL2 CXCL2 IFIT3 IFIT3 IFIT1 IFIT1 CXCL9 CXCL9 IL17RA IL17RA CCR1 CCR1 IFNGR1 IFNGR1 IFITM3 IFITM3 IL15 IL15 CSF1R CSF1R CXCL16 CXCL16 IFNGR2 IFNGR2 CXCL10 CXCL10 ACKR3 ACKR3 IL1R2 IL1R2 CSF2RB CSF2RB IL1RAP IL1RAP IL1R1 IL1R1 CXCL1 CXCL1 CCR3 CCR3 CXCR2 CXCR2 CXCR1 CXCR1 IL18R1 IL18R1 IL3RA IL3RA CXCR3 CXCR3 IL6 IL6 IL4R IL4R IL16 IL16 HLA-DQA2 HLA-DQA2 HLA-DRB5 HLA-DRB5 HLA-DQA1 HLA-DQA1 HLA-DQB1 HLA-DQB1 HLA-DMB HLA-DMB HLA-DMA HLA-DMA HLA-DRA HLA-DRA HLA-DRB1 HLA-DRB1 HLA-DPA1 HLA-DPA1 HLA-DPB1 HLA-DPB1 HLA-DOA HLA-DOA IL7 IL7 HLA-DOB HLA-DOB CXCR5 CXCR5 CCR6 CCR6 IL5RA IL5RA IL23R IL23R IL6ST IL6ST IL11RA IL11RA IL23A IL23A IL7R IL7R CCR7 CCR7 IL2RA IL2RA CCR4 CCR4 IL24 IL24 CCL28 CCL28 CXCR4 CXCR4 IL2RG IL2RG CCR5 CCR5 CCL23 CCL23 IL21R IL21R IL27RA IL27RA IL12RB1 IL12RB1 IL10RA IL10RA CXCR6 CXCR6 IL18RAP IL18RAP IL17C IL17C IL12RB2 IL12RB2 IL2RB IL2RB CCL18 CCL18 IFNG IFNG CCL4 CCL4 CCL5 CCL5 CCL3 CCL3 PF4V1 PF4V1 PF4 PF4 PPBP PPBP CXCL5 CXCL5

Fig. 7. Comparative analysis of differentially-expressed immunoregulatory genes between COVID-19 and other immune-mediated diseases. (A) Uniform Manifold Approximation and Projection (UMAP) shows the distributions of cell types (Left) and diseases (Top right) after the integration of datasets in multiple studies. MS: multiple sclerosis; IIH: idiopathic intracranial hypertension. IIH patients were recruited as controls in the multiple sclerosis study. (B) Dynamic changes of immune cell types in different immune-mediated diseases compared to healthy controls. Log2(ratio) was calculated to show the levels of changes. *, p<0.05, **, p<0.01, ***, p<0.001. Statistical models can be found in the Methods. Leuk-UTI: sepsis patients that enrolled into UTI with leukocytosis (blood WBC ≥ 12,000 per mm3) but no organ dysfunction. (C) Normalized expression values of key genes involved in immune signaling and responses are shown for cell types across multiple diseases. Lowly expressed genes (maximal average expression level across all cell types in the heatmap is less than 0.5

after Log2CPM normalization) were removed.

A Disease Group Dataset B Disease group Healthy Mild/Convalescent Severe

Arunachalam et al.

Healthy Guo et al. COVID-19 Mild Lee et al. COVID-19 Remission Schulte-Schrepping et al. COVID-19 Severe Wilk et al.

pDC cDC HSPC Platelet RBC CD16+ CD4+ T cell B cell NK cell CD8+ T cell Other T Plasmablast Monocyte CD14+ Neutrophil Monocyte

Figure S1. Cell distribution and abundance in the integrated COVID-19 PBMC data, relative to Figure 2. (A) Distributions of COVID-19 conditions (Left) and data sources (Right) for the integrated PBMC data are shown on the same UMAP of Figure 2A. (B) Bar plot depicts distributions of disease conditions in 5 individual PBMC single- cell datasets. Percentages of 3 disease conditions in each dataset is shown on y axis. (C) The integrated bar plot shows percentages of 3 disease conditions in each cell type per dataset. Dataset abbreviations and cell types were concatenated to show disease distributions of specific cell types in the selected datasets. These labels are colored by their cell type designations and ordered by the ascending percentages of COVID-19 conditions.

Healthy Donors Mild COVID-19 Patients dn T Convalescent HSPC COVID-19 Patients Severe COVID-19 Progenitor Patients

CD4+ T Naive gd T Other T cell

T/NK

CD4+ TCM proliferative

CD4+ T activated NK CD4+ T Naive NK cell CD4+ CTL NK_CD56 bright

Treg Classical Monocyte

CD8+ T activated Myeloid Neutrophil

MAIT immature Neutrophil CD8+ T Naive CD8+ T naive B naive B cell

CD8+ T EM B memory

Mild Vent Mild Vent Healthy Severe Severe Healthy Severe Healthy Severe Severe Healthy Severe Moderate Healthy Mild late Healthy Moderate Healthy Mild late Healthy Mild early Non Vent Remission Mild early Non Vent Remission SevereSevere early late SevereSevere early late Asymptomatic Asymptomatic Arunachalam Lee et al. Schulte- Wilk et al. Guo et al. Arunachalam Lee et al. Schulte- Wilk et al. Guo et al. et al. Schrepping et al. Schrepping et al. et al.

Figure S2. Dynamic changes of cell type abundances in five COVID-19 PBMC datasets, relative to Figure 2. Relative abundances and differences of major cell types in each single cell dataset are shown and compared to controls per each disease condition, per each single-cell dataset. Box plots of all cell types in PBMC are shown except for the 5 highlighted cell types shown in Figure 2B. Statistical methods are the same with Figure 2B.

A D Disease Condition TRAM1 Neutrophil TRAM AT1 / AT2 / AT1 TRAM2 Endothelial TRAM3 Basal / Club

B Ciliated MoAM1 Dataset Grant et al. Liao et al. CD4+ T_1 CD4+ MoAM2 MoAM3 MoAM CD4+ T_2 CD4+ T / NK Cell T CD8+ T CD8+ MoAM4

C Sample MoAM5 Prolif T/NK Prolif

Mild Mild Severe Severe Healthy Healthy HealthySevere Healthy Severe Grant et al. Liao et al. Grant et al. Liao et al.

Figure S3. Cell distributions and dynamic changes in the integrated COVID-19 BAL data, relative to Figure 2. (A-C) Distributions of disease conditions (A), data sources (B) and samples (C) are shown on the same UMAP of Figure 2C. (D) Box plots depict dynamic changes of cell types across COVID-19 conditions in BAL that are not covered in Figure 2D. Statistical methods are the same with Figure 2B.

bioRxiv preprint doi: https://doi.org/10.1101/2021.06.07.447287; this version posted June 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. AT 2-2 AT Basal 1 Smooth DCs Muscle-1 TRAM (A) Fibroblast-1 Stromal Cells Epithelial Cell Basal 2 Smooth AT2 Muscle-2 TRAM (B) Fibroblast-2 Transitional Endothelial-1

HealthySevere Basal 3 T cells T TRAM (C) Fibroblast-3 Endothelial-2 Stromal Cells Club Epithelial Cell B cells Hematopoietic Cells Artery MoAM (A) Fibroblast-4 Endothelial Cells Ciliated Plasma cells MoAM (B) Capillary Mesothelial-1 Hematopoietic Cells AT 1 AT Vessel Erythrocytes MoAM (C) Bronchial Mesothelial-2

HealthySevere AT 2-1 AT Mast cells MoAM (D) Lymphatic

HealthySevere HealthySevere HealthySevere Severe Healthy Figure S4. Cell type abundance changes in COVID-19 lung parenchyma dataset, relative to Figure 2. Box plots depict percentages of cell types in control samples and severe COVID-19 samples. We used cell type clusters identified in the original publication but modified cell naming of macrophage subtypes to distinguish monocyte derived macrophage subtypes present in BAL fluid samples. Statistical methods are the same with Figure 2B.

Figure S5. Sub-cluster-specific genes of neutrophils of COVID-19 patients, relative to Figure 3. (A) Distribution of disease conditions (Left) and data sources

(Right) for the integrated neutrophil data on the same UMAP of Figure 3A. (B) UMAPs of neutrophil sub-cluster-associated genes from Figure 3C. Normalized expression values for each gene were used. (C) Normalized expression values of neutrophil- associated genes and other important immune signatures are shown for 5 neutrophil sub-clusters. Lowly expressed genes (genes with maximal average expression level

across all neutrophil sub-clusters less than 0.5 after Log2CPM normalization) were removed from the gene pool of cytokines, chemokines, ISGs, interleukins, interferons, corresponding receptors and MHC-II. (D) The volcano plot depicts differentially expressed genes between circulating mature neutrophils (Neu0,1) and extravasated neutrophils (Neu3) (Left); as well as DEGs between pro-neutrophils (Neu4) and pre- neutrophils (Neu2) (Right). Statistical methods are the same with Figure 5C. Representative enriched biological processes (Gene Ontology) are shown in the bottom.

Normalized Expression (log2(CPM + 1))

0.00 6.00 14.00 Cell class Cluter

MoAM1 MoAM2 MoAM5 MoAM3 MoAM4 TRAM2 TRAM1 Trans TRAM3 Gene ISG15 ISG15 OAS1 IFIT1 IFI44 IFITM3 IL6R IL4R IL10RA IL27RA IL17RA IL15 IL12RB1 IL7R CSF1R IL6 IL1B IL1R2 IL1R1 IL10 IL2RG IL18 IFNGR1 IFNAR1 IFNGR2 HLA-DQA1 HLA-DQB1 HLA-DRA HLA-DRB1 HLA-DPB1 HLA-DPA1 HLA-DMB HLA-DMA CCL8 CCL4 CCL3L1 CCL3 CCL7 CCL2 CCL13 CXCL8 CXCL2 CXCL1 CXCL3 CXCL11 CXCL10 SPP1 S100A9 S100A8 FCN1 CD68 CD14 MARCO FABP4 C1QA Cell class MoAM TRAM Category

ISG interleukines (receptors) interferon HLA-II Cytokines Chemokines Myeloid receptors Signatures

B Padj Padj 10 10 Padj 10 -Log -Log -Log

Figure S6. Macrophage-related signatures in the integrated BAL data, relative to Figure 3. (A) Normalized expression values of myeloid-cell-associated genes and other important immune signatures are shown for 9 macrophage sub-clusters. Lowly expressed genes (genes with maximal average expression level across all macrophage

sub-clusters less than 0.5 after Log2CPM normalization) were removed from the gene pool of MHC-II, cytokines, chemokines, ISGs, interleukins, interferons and their

receptors. (B) Volcano plots were drawn for DEGs of MoAM3,4 versus MoAM1,2,5 (Left) and TRAM 1,2 versus TRAM3 (Middle) and TRAM3 versus MoAM1,2,5 (Right). Statistical methods are the same with Figure 5C. (C) Normalized expression values were shown on the same UMAP of Figure 3B for important genes including macrophage signatures, ISGs, interferons, receptors and MHC-II.

Figure S7. A uniquely-activated monocyte-derived cell type (MoAM5) exhibits a broad signature of cytokines, chemokines, and interleukins including IL6, relative to Figure 3. (A) Normalized expression values of IL6 on the same reference UMAP of integrated BAL data as Figure 3B. (B) Scale expression levels of IL6 for each macrophage sub-cluster on the violin plot. (C) Heatmap of expression levels of pan- MoAM signatures and MoAM5-specifc signatures in all myeloid cells in both PBMC and BAL. (D) Network of functional and phenotypic associated pan-MoAM signatures and

MoAM5-specific signatures from (C). Associations were retrieved from ToppGene enrichment results. IL6 is highlighted in the network. As a caveat, the MoAM5 subtype represented a small fraction among the BAL MoAM subtypes and the majority of these cells were observed in a single severely-affected individual.

Figure S8. Cell type and cell subtype-specific divisions of cytokine, chemokine, and interleukin signaling pathways in BAL of severe COVID-19 patients, relative to Figure 3. (A) Heatmap of expression patterns of ligands and receptors in cytokine, chemokine, interleukin, CSF and TNFSF signaling pathways across cell types of BAL in severe patients. Average normalized expression values were shown and lowly expressed ligands or receptors (maximal normalized expression value for a row in the heatmap < 0.5) were removed. To reduce bias, MoAM5 was removed because cells in the cluster were mainly from one patient. Cell types that have less than 5% cells from severe patients were removed, including TRAM1 and TRAM2. Neutrophils are

highlighted in the heatmap. (B) Interaction network of BAL cells in severe patients using CellChat. CCL, CXCL and IL1 signaling pathways were shown. The width of edges represents the strength of interactions and the size of nodes represents the abundance of cell types.

A B Cluster

Normalized Expression (log2(CPM+1))

C row min row max 13.32 13.33 2.16 1.38 2.04 0.95 1.31 0.81 2.44 0.73 0.82 2.69 1.95 0.73 0.99 2.97 2.83 2.19 3.11 6.44 4.45 0.74 1.41 5.12 8.25 1.01 0.54 0.04 1.25 0.30 0.73 1.15 0.05 0.06 0.21 0.65 0.03 0.72 1.14 0.20 0.39 0.03 0.73 5.79 9.56 8.10 3.42 0.17 4.11 0.40 0.70 1.92 1.92 0.90 2.06 2.13 0.20 0.26 0.80 1.53 1.19 0.49 0.45 0.09 0.59 0.20 0.17 0.77 cMono1 14.97 14.96 10.68 11.34 2.74 5.02 0.03 2.11 1.73 0.62 0.90 0.57 3.94 2.34 2.14 2.23 3.32 2.27 5.96 2.33 2.49 5.75 4.78 1.87 3.46 2.88 3.07 2.33 3.39 6.62 3.70 0.35 0.75 3.16 6.97 2.23 0.81 0.48 4.78 0.78 1.79 0.63 0.94 1.53 2.40 9.35 5.57 1.35 7.15 1.87 2.86 4.08 4.08 2.89 4.39 4.85 1.41 1.56 1.40 1.60 2.18 1.27 0.79 0.21 0.95 0.55 0.46 2.80 cMono3 13.51 13.02 10.78 10.32 1.09 4.10 0.13 2.32 3.48 1.63 2.16 0.06 5.36 3.60 4.58 2.48 3.96 2.28 5.76 1.92 2.32 5.49 4.91 1.99 3.01 4.25 5.12 5.21 5.25 8.78 7.38 1.75 2.79 6.99 9.98 2.28 1.06 0.10 3.59 1.41 3.04 1.29 0.23 0.34 2.01 8.81 6.97 0.52 5.03 1.13 2.03 3.23 3.23 2.93 4.42 4.99 0.71 0.88 1.02 1.93 3.51 1.72 1.33 0.32 1.28 0.66 0.54 4.41 cMono2 12.92 12.52 10.62 10.23 10.02 1.15 3.45 0.88 2.72 2.94 1.44 1.43 0.07 2.24 8.85 9.78 7.32 2.62 3.60 5.71 7.80 7.80 4.07 6.39 5.73 0.18 1.29 1.02 1.62 2.55 1.22 1.67 0.51 1.82 1.10 0.68 4.73 5.79 3.25 4.71 2.37 3.89 2.09 5.20 2.12 2.72 6.55 5.57 2.68 3.10 5.86 4.60 4.93 5.35 9.14 7.61 1.50 2.37 6.82 1.32 0.71 1.17 5.81 1.40 2.49 0.97 0.23 0.55 cMono4 Gene S100A9 S100A8 MARCO CD14 FCN1 VCAN CD68 IFIT1 IFITM3 IFIT3 IFI44 ISG15 ISG15 OAS1 STAT1 IFI16 IL1R2 IL1RAP IL10RB IL16 IL13RA1 IL18 IL15 IL3RA IL2RG IL15RA IL12RB1 IL1B IL10RA CSF1R CD4 CSF2RB IL6R IL27RA IL17RA IL4R IL6ST IFNGR2 IFNGR1 IFNAR2 IFNAR1 HLA-DRB5 HLA-DQB1 HLA-DMB HLA-DMA HLA-DRB1 HLA-DPA1 HLA-DQA2 HLA-DQA1 HLA-DPB1 HLA-DRA CCR2 CCL4 CCL2 CCR1 CCL3L1 CCL3 CCL5 CXCL1 CXCL3 CXCL2 CXCL8 CXCL10 CXCL16 CXCR4 PF4 PPBP MKI67 Category Cell Cycle Chemokines (Receptors)

Category Cytokines (Receptors) HLA-II interferon (receptors) interleukines (receptors) ISG D E Mono/Macro Signatures Neu immature Neu ncMono cMono1 cMono2 cMono3 cMono4 cDC pDC

Cell Class Gene Cluster FCGR3B CXCR2 MME CXCR1 cMono1 MMP9 Enriched Term FDR Hits of genes (B&H) MPO 15 / 23 ELANE mitochondrial ATP synthesis coupled 1.460E-26 proton transport DEFA1 GO:0042776 DEFA4 nucleotide metabolic process 3.534E-11 23 / 694 GO:0009117 CDKN1C cation transport 1.575E-6 25 / 1423 FCGR31 GO:0006812 C1QA CSF1R cMono2 Enriched Term FDR Hits of genes ATP5E (B&H) F ATP5L myeloid leukocyte activation 4.052E-16 37 / 683 ATP5D GO:0002274 cMono1 inflammatory response 2.572E-12 34 / 805 GO:0006954 CD68 cytokine-mediated signaling 1.003E-10 32 / 814 HLA-DMB pathway JUN GO:0019221 cMono2

S100A8 cMono3 S100A9 CXCL2 Enriched Term FDR Hits of genes CXCL3 (B&H)

CCL7 cMono3 myeloid leukocyte activation 1.619E-33 56 / 683 GO:0002274 IFI44L inflammatory response 1.083E-17 42 / 805 IFI6 GO:0006954 ISG15 cell migration 2.141E-11 51 / 1751 CCL2 GO:0016477 OAS2 cMono4 FCER1A cMono4 CD1C CD1E Enriched Term FDR Hits of genes HLA-DOA (B&H) HLA-DQA response to cytokine 6.557E-22 59 / 1287 GO:0034097 CLEC4C type I interferon signaling pathway 7.338E-22 22 / 95 LILRA4 GO:0060337 MZB1 immune effector process 4.833E-19 56 / 1332 TPM2 GO:0002252 row min row max

Figure S9. Characteristics of sub-clusters of classical monocytes in the integrated COVID-19 PBMC data, relative to Figure 3. (A) UMAPs of 4 sub-clusters (Left) and COVID-19 conditions (Right) of classical monocytes are shown. Grey dots are other myeloid cells in the UMAP of integrated PBMC myeloid data. (B) UMAPs of normalized expression values of specific signatures for classical monocyte sub-clusters.

(C) Normalized expression values of monocyte-associated genes and other important immune signatures are shown for 4 classical monocyte sub-clusters. (D) Gene modules of classical monocyte sub-clusters, as well as other myeloid cell types in the integrated PBMC myeloid data. Representative genes in each module are shown on the left. ToppGene enrichment results for classical monocyte sub-clusters are shown on the right. Columns are clustered using hierarchical clustering. (E) Similarity matrix of myeloid cell types using genes in (D). Pearson correlation was used to evaluate similarity. (F) Dot plot of MHC-II, ISGs, interleukin genes and cell cycle genes for each myeloid cell type. Scale values were used.

A B

Healthy_BAL Healthy_PBMC COVID-19_BAL C COVID-19 Mild_PBMC COVID-19 Severe_PBMC E Normalized Expression (log2(CPM + 1))

0.00 2.00 5.00 10.00 15.00 Cluster

DC_10 DC_11 DC_2 DC_8 DC_12 DC_0 DC_5 DC_3 DC_4 DC_6 DC_7 DC_1 row minDC_9 row max id IFIT1 IFIT3 IFI44 STAT1 ISG15 ISG15 OAS1 IFITM3 IFI16 IL10 IL10RB IL21R IL2RA IL7R IL15 CSF2RB IL15RA IL3RA IL4R IL6ST IL1R1 IL18R1 IL1RAP IL17RA CSF1R IL17RC IL19 IL1B IL2RG IL12RB1 IL27RA IL10RA IL6R IL1R2 IL18 CD4 IL16 IL13RA1 IFNGR2 IFNAR2 IFNAR1 IFNGR1 HLA-DOB HLA-DQA2 HLA-DRB5 HLA-DOA HLA-DMB HLA-DMA HLA-DPB1 HLA-DQB1 HLA-DPA1 HLA-DRA HLA-DQA1 HLA-DRB1 CCL18 CCL22 CCL19 CCR7 CCL17 CCR6 CCL7 CCL8 CCL2 CCR5 CCR1 CCR2 CCL5 CCL4 CCL3L1 CCL3 CXCL13 CXCL9 CXCL11 CXCL10 CXCR3 CXCL16 PF4 PPBP CXCL3 CXCL2 CXCL8 CXCR4 CCNE2 TYMS CLSPN MKI67 CDKN3 CD1E ENHO CD1D CLEC10A FCER1A integrated_snn_res.0.5Category type Human Symbol B Cell Signatures 0 Cell Cycle ligands 1 Chemokines (Receptors)receptors 10 Category Cytokines (Receptors) 11 Dendritic Cell Signatures HLA-II 12 interferon (receptors) 2 interleukines (receptors) 3 ISG 4 Mono/Macro Signatures 5 6 D F 7 8 9 Cluster row min row max Gene Counts Modules Pro-inflammatory integrated_snn_res.0.5Protective type Human Symbol CCL19 CCR7 0 ligands CCL17 CSF2RA Severe Module I 10 1 9 3 2 0 12 1 4 5 6 7 8 11 receptors CCL22 CSF2RB (lung) integrated_snn_res.0.5 10 type Human Symbol CXCL10 IL2RA Enriched Term FDR Hits of genes CCL4L2 IL32 IL7R (B&H) 11 CCR1 IL12B IL23R cytokine-mediated signaling pathway 2.331E-9 33 / 814 12 GO:0019221 2 CCR5 regulation of interleukin-12 production 7.188E-9 11 / 62 IL1RN GO:0032655 3 CCL5 cell adhesion 1.473E-7 41 / 1509 4 GO:0007155 CCL3L1 5 CCL3 6 IL10RB-DT CCL2 IFITM3 7 CXCL8 CCL7 IFI44 Severe Module II CCL8 IFI16 8 IL1B (shared) CXCL11 STAT1 9 CXCR4 CCR5 OAS1 IL18 IL27 ISG15 Enriched Term FDR Hits of genes IL13RA1 (B&H) CCR2 response to virus 4.187E-45 60 / 350 IL18R1 GO:0009615 IL1R1 response to type I interferon 7.351E-36 34 / 99 GO:0034340 CCL7

response to interferon-gamma 9.700E-28 37 / 208 10 1 CCL89 3 2 0 12 4 5 6 7 8 11 GO:0034341 CXCL10 integrated_snn_res.0.5 type Human Symbol CXCL11 CCL2 CCL4L2 CDKN3 IL15RA CCR1 CDCA5 Proliferation IL27 CCNE2 Module CCL17 CCR5 CCR6 IL1RN CCL22 CXCL9 CCL5 CCR7 CCL3L1 Severe Module III AREG CCL19 CCL3 VEGFA (blood) IL12B CREM IL23R IL10RB-DT Enriched Term FDR Hits of genes (B&H) IL32 CXCL8 CXCL13 regulation of immune system process 2.675E-18 64 / 1775 IL1B GO:0002682 IL2RA negative regulation of cell 1.535E-14 55 / 1632 IL4I1 CXCR4 communication GO:0010648 CCL18 IL18 cytokine production 2.758E-14 40 / 875 CXCR6 GO:0001816 CXCL12 IL13RA1 CCL3 IL2RB CCR2 CCL3L1 CXCL14 IL1B Mild Module IL18R1 CXCL8 IL12RB2 OAS1 Enriched Term FDR Hits of genes CXCL16 IL1R1 (B&H) ISG15 CXCR3 CCL7 immune effector process 7.911E-9 38 / 1332 GO:0002252 CCL8 row min row max cytokine-mediated signaling pathway 8.067E-7 26 / 814 GO:0019221 CXCL10 type I interferon signaling pathway 9.020E-8 9 / 95 row min row max integrated_snn_res.0.5 GO:0060337 CXCL11 CCL2 0 integrated_snn_res.0.5 IL15RA 1 0 IL27 10 1 11 CCL17 10 Figure S10.12 Features of conventional dendritic cell sub-clusters and polarized CCR6 2 11 CCL22 3 12 CXCL9 signaling genes4 , relative to Figure 3. (A) UMAPs of 13 sub-2clusters (Left) and CCR7 5 3 CCL19 6 4 IL12B sources (Right)7 of conventional dendritic cells after data integration.5 (B) Normalized IL23R 8 6 IL32 9 7 CXCL13 8 IL2RA expression values of sub-cluster-specific genes on the UMAP.9 (C) Normalized IL4I1 CCL18 CXCR6

expression values of10 cDC11 2 -associated3 1 9 0 12 genes4 5 and6 7 other8 important immune signatures are CXCL12 integrated_snn_res.0.5 Human Symbol IL2RB CXCL14 0.43 0.54 0.47 0.41 1.24 0.33 0.77 0.43 0.03 1.48 0.08 0.17 0.53 CCL4L2 10 11 2 3 1 9 0 12 4 5 6 7 8 IL12RB2 integrated_snn_res.0.5 HumanCXCL16 Symbol 0.60 0.04 0.63 1.26 3.75 4.76 2.72 1.99 1.45 1.24 0.73 0.62 2.37 CCR1 CXCR3 0.43 0.54 0.47 0.41 1.24 0.33 0.77 0.43 0.03 1.48 0.08 0.17 0.53 CCL4L2 0.20 0.09 0.38 1.32 2.19 1.37 1.13 0.92 0.65 1.04 630.64 0.35 0.76 CCR5 0.60 0.04 0.63 1.26 3.75 4.76 2.72 1.99 1.45 1.24 0.73 0.62 2.37 CCR1 0.72 0.46 0.68 1.07 3.85 3.39 2.34 3.10 1.46 3.12 2.03 1.91 3.69 IL1RN 0.20 0.09 0.38 1.32 2.19 1.37 1.13 0.92 0.65 1.04 0.64 0.35 0.76 CCR5 0.62 0.82 1.62 0.57 1.11 0.61 1.05 1.18 0.78 0.99 1.62 0.95 1.48 CCL5 0.72 0.46 0.68 1.07 3.85 3.39 2.34 3.10 1.46 3.12 2.03 1.91 3.69 0.19 0.20 0.45 0.51 1.53 0.56 1.30 0.56 0.58 1.85 0.14 0.75 4.18 CCL3L1 IL1RN 0.43 0.50 0.51 1.08 1.76 0.95 2.42 1.37 1.50 1.95 2.19 1.83 5.64 CCL3 0.62 0.82 1.62 0.57 1.11 0.61 1.05 1.18 0.78 0.99 1.62 0.95 1.48 CCL5 0.24 0.31 0.14 0.66 0.82 0.82 0.97 1.21 1.24 1.06 1.88 1.01 3.43 IL10RB-DT 0.19 0.20 0.45 0.51 1.53 0.56 1.30 0.56 0.58 1.85 0.14 0.75 4.18 CCL3L1 0.59 0.58 0.35 0.65 1.23 0.80 2.50 1.51 2.06 2.60 4.35 4.25 6.74 CXCL8 0.43 0.50 0.51 1.08 1.76 0.95 2.42 1.37 1.50 1.95 2.19 1.83 5.64 CCL3 0.17 0.41 0.76 2.85 2.74 1.46 3.99 2.45 3.82 5.14 6.45 6.40 7.96 IL1B 0.24 0.31 0.14 0.66 0.82 0.82 0.97 1.21 1.24 1.06 1.88 1.01 3.43 IL10RB-DT 3.14 2.42 1.78 4.41 4.31 5.45 4.41 5.39 6.24 7.89 8.15 8.96 8.24 CXCR4 0.59 0.58 0.35 0.65 1.23 0.80 2.50 1.51 2.06 2.60 4.35 4.25 6.74 CXCL8 1.14 2.22 1.96 5.94 4.15 4.71 3.62 4.11 5.67 5.45 5.13 5.31 5.30 IL18 0.17 0.41 0.76 2.85 2.74 1.46 3.99 2.45 3.82 5.14 6.45 6.40 7.96 IL1B 4.95 0.90 2.02 6.16 4.91 5.71 5.37 4.52 6.29 5.85 5.59 5.00 5.76 IL13RA1 3.14 2.42 1.78 4.41 4.31 5.45 4.41 5.39 6.24 7.89 8.15 8.96 8.24 CXCR4 0.08 0.15 0.61 2.35 1.20 1.34 2.16 2.38 3.11 0.67 1.71 0.75 1.46 CCR2 1.14 2.22 1.96 5.94 4.15 4.71 3.62 4.11 5.67 5.45 5.13 5.31 5.30 IL18 0.29 1.21 0.26 1.58 0.88 0.66 0.30 1.42 1.61 0.96 1.52 0.94 1.18 IL18R1 4.95 0.90 2.02 6.16 4.91 5.71 5.37 4.52 6.29 5.85 5.59 5.00 5.76 IL13RA1 0.49 0.11 0.29 1.62 0.22 0.16 0.50 0.40 0.70 0.90 0.61 0.59 0.23 IL1R1 0.08 0.15 0.61 2.35 1.20 1.34 2.16 2.38 3.11 0.67 1.71 0.75 1.46 CCR2 0.23 0.10 0.08 0.01 0.23 0.73 0.16 0.27 0.00 0.04 0.00 0.00 0.00 CCL7 0.29 1.21 0.26 1.58 0.88 0.66 0.30 1.42 1.61 0.96 1.52 0.94 1.18 IL18R1 0.63 0.23 0.23 0.01 1.17 1.38 0.32 0.41 0.00 0.21 0.01 0.00 0.00 CCL8 0.49 0.11 0.29 1.62 0.22 0.16 0.50 0.40 0.70 0.90 0.61 0.59 0.23 IL1R1 5.07 1.52 0.79 0.14 3.58 2.38 0.56 0.74 0.04 0.61 0.08 0.02 0.14 CXCL10 0.23 0.10 0.08 0.01 0.23 0.73 0.16 0.27 0.00 0.04 0.00 0.00 0.00 2.69 0.24 0.25 0.04 2.34 1.40 0.33 0.45 0.01 0.30 0.03 0.00 0.02 CXCL11 CCL7 2.16 0.67 0.63 0.28 2.35 1.89 1.11 0.78 0.01 0.52 0.09 0.00 0.02 CCL2 0.63 0.23 0.23 0.01 1.17 1.38 0.32 0.41 0.00 0.21 0.01 0.00 0.00 CCL8 1.78 0.83 0.17 0.19 1.50 1.79 0.63 0.59 0.21 0.49 0.09 0.17 0.47 IL15RA 5.07 1.52 0.79 0.14 3.58 2.38 0.56 0.74 0.04 0.61 0.08 0.02 0.14 CXCL10 1.06 0.63 0.13 0.03 1.12 0.87 0.22 0.52 0.04 0.08 0.01 0.00 0.09 IL27 2.69 0.24 0.25 0.04 2.34 1.40 0.33 0.45 0.01 0.30 0.03 0.00 0.02 CXCL11 3.95 0.57 1.16 3.82 0.44 0.03 0.61 0.44 0.05 0.19 0.05 0.00 0.00 CCL17 2.16 0.67 0.63 0.28 2.35 1.89 1.11 0.78 0.01 0.52 0.09 0.00 0.02 CCL2 2.96 0.80 0.67 3.02 1.04 1.04 0.69 1.08 0.89 1.00 0.45 0.30 0.52 CCR6 1.78 0.83 0.17 0.19 1.50 1.79 0.63 0.59 0.21 0.49 0.09 0.17 0.47 IL15RA 5.03 0.04 0.61 1.33 0.21 0.03 0.30 0.18 0.02 1.08 0.10 0.04 0.00 CCL22 1.06 0.63 0.13 0.03 1.12 0.87 0.22 0.52 0.04 0.08 0.01 0.00 0.09 IL27 4.68 1.75 0.45 0.18 1.59 1.25 0.36 0.35 0.01 0.43 0.04 0.04 0.08 CXCL9 3.95 0.57 1.16 3.82 0.44 0.03 0.61 0.44 0.05 0.19 0.05 0.00 0.00 CCL17 9.31 0.31 0.53 0.23 1.14 1.15 0.49 0.34 0.02 1.99 0.35 0.22 0.31 CCR7 2.96 0.80 0.67 3.02 1.04 1.04 0.69 1.08 0.89 1.00 0.45 0.30 0.52 CCR6 6.20 0.06 0.20 0.03 0.18 1.02 0.21 0.11 0.00 0.04 0.01 0.00 0.00 CCL19 5.03 0.04 0.61 1.33 0.21 0.03 0.30 0.18 0.02 1.08 0.10 0.04 0.00 CCL22 0.57 0.09 0.00 0.00 0.01 0.07 0.01 0.00 0.01 0.00 0.00 0.00 0.00 IL12B 4.68 1.75 0.45 0.18 1.59 1.25 0.36 0.35 0.01 0.43 0.04 0.04 0.08 CXCL9 0.36 0.06 0.02 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 IL23R 9.31 0.31 0.53 0.23 1.14 1.15 0.49 0.34 0.02 1.99 0.35 0.22 0.31 CCR7 5.78 0.66 1.49 0.41 0.43 0.83 0.61 0.60 0.12 0.45 0.14 0.25 0.43 IL32 6.20 0.06 0.20 0.03 0.18 1.02 0.21 0.11 0.00 0.04 0.01 0.00 0.00 CCL19 0.93 0.00 0.06 0.00 0.27 0.25 0.02 0.00 0.00 0.00 0.00 0.00 0.00 CXCL13 0.57 0.09 0.00 0.00 0.01 0.07 0.01 0.00 0.01 0.00 0.00 0.00 0.00 IL12B 0.90 0.03 0.11 0.03 0.11 0.35 0.09 0.00 0.00 0.14 0.00 0.00 0.02 IL2RA 0.36 0.06 0.02 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.56 0.90 0.80 1.15 2.93 1.28 1.09 1.02 0.28 2.29 0.40 0.42 0.88 IL4I1 IL23R 0.36 0.25 0.44 0.09 0.18 0.11 0.66 0.20 0.03 0.05 0.00 0.00 0.00 CCL18 5.78 0.66 1.49 0.41 0.43 0.83 0.61 0.60 0.12 0.45 0.14 0.25 0.43 IL32 0.07 0.00 0.13 0.00 0.02 0.08 0.04 0.00 0.00 0.00 0.01 0.00 0.00 CXCR6 0.93 0.00 0.06 0.00 0.27 0.25 0.02 0.00 0.00 0.00 0.00 0.00 0.00 CXCL13 0.00 0.00 0.05 0.01 0.01 0.00 0.01 0.00 0.00 0.02 0.00 0.00 0.00 CXCL12 0.90 0.03 0.11 0.03 0.11 0.35 0.09 0.00 0.00 0.14 0.00 0.00 0.02 IL2RA 0.00 0.06 0.26 0.01 0.03 0.03 0.08 0.17 0.06 0.06 0.03 0.04 0.02 IL2RB 5.56 0.90 0.80 1.15 2.93 1.28 1.09 1.02 0.28 2.29 0.40 0.42 0.88 IL4I1 0.04 0.10 0.04 0.06 0.08 0.37 0.07 0.74 0.01 0.07 0.02 0.00 0.09 CXCL14 0.36 0.25 0.44 0.09 0.18 0.11 0.66 0.20 0.03 0.05 0.00 0.00 0.00 CCL18 0.03 0.03 0.04 0.00 0.01 0.00 0.00 0.10 0.01 0.00 0.00 0.01 0.00 IL12RB2 0.07 0.00 0.13 0.00 0.02 0.08 0.04 0.00 0.00 0.00 0.01 0.00 0.00 CXCR6 4.46 5.57 2.07 5.46 4.00 2.12 4.14 2.00 2.43 5.20 2.95 3.83 3.18 CXCL16 0.00 0.00 0.05 0.01 0.01 0.00 0.01 0.00 0.00 0.02 0.00 0.00 0.00 CXCL12 0.19 2.11 0.27 0.40 0.18 0.17 0.11 0.21 0.15 0.25 0.19 0.59 0.09 CXCR3 0.00 0.06 0.26 0.01 0.03 0.03 0.08 0.17 0.06 0.06 0.03 0.04 0.02 IL2RB 0.04 0.10 0.04 0.06 0.08 0.37 0.07 0.74 0.01 0.07 0.02 0.00 0.09 CXCL14 0.03 0.03 0.04 0.00 0.01 0.00 0.00 0.10 0.01 0.00 0.00 0.01 0.00 IL12RB2 4.46 5.57 2.07 5.46 4.00 2.12 4.14 2.00 2.43 5.20 2.95 3.83 3.18 CXCL16 0.19 2.11 0.27 0.40 0.18 0.17 0.11 0.21 0.15 0.25 0.19 0.59 0.09 CXCR3 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.07.447287; this version posted June 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

shown for 13 cDC sub-clusters. (D) Gene modules of cDC sub-clusters with 200 most significantly upregulated genes in each module. Representative genes are shown on the left. Gene enrichment results of some modules from ToppGene are shown on the right. (E) Similarity matrix of sub-clusters using genes in (D). Pearson correlation was used for similarity scores and hierarchical clustering was applied for rows and columns. (F) The heatmap shows the clustering of signaling genes, including cytokines, chemokines, interleukins and their receptors. Red boxes highlight severe patients associated sub-clusters and their upregulated genes. Green boxes highlight mild patients-associated sub-clusters and their upregulated genes.

A B

Neu Non Myeloid Neutrophil MoAM1 MoAM2 MoAM3 MoAM4 MoAM5 Macro Transitional Macro TRAM1 TRAM2 cDC TRAM3 cDC pDC Proliferative Myeloid UMAP2 UMAP2 UMAP1 UMAP1

C Source BAL PBMC PBMC+BAL

Source Cell type MoAM TRAM Monocyte cDC Neutrophil Cell type Disease Condition Source + Disease Condition PBMC_Severe PBMC_Mild PBMC_Control BAL_Severe BAL_Mild BAL_Control

cluster MoAM1 MoAM2 MoAM5 MoAM4 MoAM3 TRAM2 TRAM1 transitional TRAM3 cMono3 cMono2 cMono1 cMono4 cDC_0 cDC_7 cDC_6 cDC_4 cDC_2 cDC_3 cDC_11 cDC_10 cDC_5 cDC_1 cDC_9 cDC_8 cDC_12 Neu_0 Neu_1 Neu_3 Neu_2 Neu_4 Gene Enrichment Term Category antigen processing and presentation of endogenous peptide antigen antigen processing and presentation Antigen Presenting peptide antigen assembly with MHC protein complex programmed cell death regulation of programmed cell death regulation of neutrophil degranulation Apoptosis positive regulation of neutrophil degranulation neutrophil degranulation ATP biosynthetic process ATP metabolic process ATP Biosynthesis cell cycle DNA replication Cell Cycle cell cycle process secretory granule lumen secretory granule Granule Formation tertiary granule azurophil granule membrane lipid metabolic process cellular lipid metabolic process neutral lipid metabolic process Lipid Metabolism positive regulation of lipid metabolic process leukocyte migration cell migration regulation of cell migration chemotaxis Migration myeloid leukocyte migration leukocyte chemotaxis cell chemotaxis regulation of phagocytosis phagocytosis phagocytosis, engulfment Phagocytosis phagocytosis, recognition cytokine production cytokine secretion Production of Cytokines cytokine biosynthetic process interferon-gamma production regulation of interferon-gamma production type I interferon production Production of interferon type I interferon secretion response to reactive oxygen species respiratory burst involved in inflammatory response respiratory burst involved in defense response Respiratory Burst respiratory burst cellular response to cytokine stimulus cytokine-mediated signaling pathway Response to Cytokines response to cytokine response to interferon-alpha type I interferon signaling pathway cellular response to type I interferon Response to interferon response to interferon-gamma interferon-gamma-mediated signaling pathway secretion exocytosis Secretion regulated exocytosis T cell activation involved in immune response regulation of alpha-beta T cell activation T cell activation T Cell Activation regulation of T cell activation angiogenesis blood vessel development response to wounding Wound Healing & Angiogenesis wound healing vascular wound healing

Gene Enrichment Score (-log10P)

row min row max

Figure S11. Landscape of myeloid cells in the integrated PBMC and BAL data, relative to Figure 3. (A-B) UMAPs of myeloid cells in integrated PBMC (A) and BAL (B)

data. Cell types which were further clustered are highlighted in different colors. (C) The heatmap shows associations between subclusters of myeloid cells and myeloid-cell- associated pathways, such as antigen presenting, T cell activation, phagocytosis etc.

Gene enrichment scores, defined as -log10(adjusted p value), were calculated as the strength of associations. Pie charts showed the proportions of COVID-19 conditions in each sub-cluster.

Figure S12. Gene expression signatures of cell types and subtypes activated by COVID-19 are extensively associated with coagulation, hemostasis, and thrombosis-associated pathways, functions, and knockout phenotypes, relative to Figure 4. (A) Functional association heatmap of gene signatures from COVID-19 cell types demonstrates differential enrichment for pathways associated with coagulation, vascular permeability, complement, extravasation, platelet activation and aggregation, response to wounding, as shown. Gene modules of cell types and sub-clusters that participate in these pathways were used to calculate enrichment scores. (B) Network of upregulated genes in coagulation/thrombosis-associated pathways (A) shows the

potential gene-gene interactions in immunothrombosis of COVID-19 patients. CellChat and ToppCell/ToppGene protein-protein ligand receptor and cell adhesion interaction databases were used to find interaction pairs among upregulated genes. (C) A new network derived from (B) shows integrin-associated interactions between platelets and other cells.

A C Category Apoptosis Metabolism

PLT_1 PLT_2 PLT_0 PLT_3 PLT_5 PLT_4 ATP synthesis Neutrophil activation Cluster cell adhesion neutrophil migration Count Gene Module Cell Migration response to cytokines BMP6 coagulation response to interferon KIFC3 ESAM Cytokine Production RNA Splicing

LIMS1 PLT_1 PLT_0 PLT_3 PLT_2 PLT_5 PLT_4 CLDN5 secretion PARD3 Cluster Gene Enrichment Term Category DMTN platelet degranulation IFI27 regulation of cell death IFI6 regulation of apoptotic process STAT1 ISG20 regulation of programmed cell death IFIT1 apoptotic process XAF1 cell death IRF7 ATP metabolic process SELP ATP biosynthetic process ITGB3 mitochondrial ATP synthesis coupled electron transport PF4V1 ATP synthesis coupled electron transport ITGA2B HPSE cell-cell adhesion THBS1 cell adhesion ANO6 cell-matrix adhesion cell adhesion mediated by integrin PF4 ITGA2B cell migration F13A1 cell motility PPBP localization of cell FCER1G FLNA wound healing TMSB4X platelet aggregation hemostasis RPL21 blood coagulation RPL22 coagulation RPL28 blood coagulation, fibrin clot formation regulation of wound healing regulation of hemostasis regulation of coagulation SRSF3 positive regulation of hemostasis SRSF7 angiogenesis involved in wound healing CKJ4 SRM1 regulation of response to wounding RBM17 negative regulation of hemostasis positive regulation of wound healing cytokine secretion regulation of cytokine secretion row min row max positive regulation of cytokine secretion translational initiation mRNA catabolic process RNA catabolic process translational elongation translation neutrophil activation B granulocyte activation neutrophil degranulation neutrophil activation involved in immune response neutrophil mediated immunity neutrophil chemotaxis granulocyte migration neutrophil migration granulocyte chemotaxis neutrophil aggregation cellular response to cytokine stimulus response to cytokine cytokine-mediated signaling pathway response to interferon-gamma cellular response to interferon-gamma interferon-gamma-mediated signaling pathway cellular response to type I interferon type I interferon signaling pathway response to type I interferon response to interferon-beta response to interferon-alpha RNA splicing mRNA splicing, via spliceosome RNA splicing, via transesterification reactions regulation of RNA splicing regulation of mRNA splicing, via spliceosome export from cell secretion by cell regulated exocytosis exocytosis exocytic process secretion peptide secretion amide transport peptide transport protein transport 0.00 5.00 10.00

Figure S13. Emergence of platelet subtypes implicating functionally significant alternative roles in hemostasis, coagulation, wound response, and neutrophil recruitment and activation, relative to Figure 4. (A) The heatmap shows ToppCell gene modules of 6 platelet sub-clusters in COVID-19 PBMC. Each gene module contains 200 most significant genes for each sub-cluster and important genes are shown on the left. Gene enrichment analysis was conducted using ToppGene and top enrichment results from biological processes (Gene Ontology) are shown on the right. (B) Dot plot of integrin and other platelet-associated genes. Scale values are shown on the figure. (C) Heatmap of associations between subclusters of platelets and platelet- associated pathways (Gene Ontology). Gene enrichment scores, defined as -

log10(adjusted p value), were calculated and shown.

A B G B cells Plasma cells Cell subtype B activate B intermediate B memory B naive Dev Plasmablast Plasmablast Cell type B cell Cluster B intermediate-1 B naive-1 B intermediate-10 B intermediate-0 B memory-0 B memory-3 B intermediate-14 B intermediate-6 B memory-6 B naive-12 B activate-7 B naive-4 B naive-2 B naive-9 B naive-8 B naive-13 B naive-11 B naive-5 Dev Plasmablast Plasmablast Plasmablast

n cells Cell subtype Cell type Gene Cluster Source Source

PBMC BAL PBMC BAL C E

D F row min row max

H PB Dev Plasmablast response to oxygen- containing compound cell cycle

B cell activation ribosome biogenesis

positive regulation of apoptotic process nuclear transport

12345 4 3 2 01 20 40 60 80 100 Gene Enrichment Score (-log Padj) 10

Figure S14. Consistent emergence of a series of early and maturing B cells and plasmablasts in BAL fluid and PBMC across multiple datasets, relative to Figure 5. (A-B) UMAPs of B cells (A) and plasmablasts (B) from multiple datasets. (C-D) UMAP of normalized expression values of immunoglobulin genes (C) and ISGs (D) for B cells. (E-F) UMAP of normalized expression values of immunoglobulin genes (E) and sub-cluster associated genes, such as cell cycle genes and B cell markers (F) for plasmablasts. (G) Gene modules of B cell sub-clusters and plasmablast subtypes with 200 most significant genes in each module. Hierarchical clustering was applied for columns. (H) Three representative enriched biological processes (Gene Ontology) are shown for these two subtypes using DEGs of plasmablasts in Figure 5C.

Figure S15. Gene Enrichment analysis of B cell subtypes and autoimmune- associated signatures, relative to Figure 5. (A) Heatmap shows gene enrichment scores of B-cell-associated pathways for each B cell sub-cluster and plasmablast subtype. (B) Pathway and function association network of upregulated genes in B cells of BAL in mild COVID-19 patients. (C-D) Heatmaps show normalized expression levels of autoimmune-associated ligands and receptors (Figure 5E) in lupus nephritis (C) and rheumatoid arthritis (D).

A B C T cell subtypes Dataset CD4+ T Disease (BAL) CD4+ T activated CD4+ T exhausted CD8+ T CD8+ T activated MAIT NK T/NK proliferative Treg

D E

Figure S16. Distinct subtypes of T cells and NK cells in COVID-19 BAL data. (A-C) UMAPs of subtypes (A), COVID-19 conditions (B) and data sources (C) of T cells and NK cells in the integrated BAL data. (D-E) UMAPs of normalized expression values of exhausted T cell markers (D) and ISGs (E).

A B Healthy Disease Conditions T cell subtypes COVID-19 Mild (PBMC) CD4+ T CD8+ T / NK COVID-19 Remission COVID-19 Severe CD4+ CTL CD8+ T activated CD4+ T activated CD8+ T naive MAIT CD4+ TCM CD4+ T naive NK cell CD4+ T activated NK activated

Treg NK CD56bright Others dn T gd T T/NK proliferative

Figure S17. Various T cell and NK cell subtypes in the integrated PBMC data. (A- B) UMAPs of T cell and NK cell subtypes (A) and COVID-19 conditions (B) after integration of T cells in 5 PBMC single-cell datasets. (C) Dot plot shows T cell and NK cell subtype associated genes for each subtype per disease condition. Labels of cell types of healthy donors, mild patients and severe patients are colored by blue, yellow and red. Scaled expression values are shown using a color scheme.

A B Influenza

B cell

pDC B Naive T/NK Cell gamma-delta T B Cell B Memory NK

Monocyte B Intermediate NK CD56bright RBC Plasmablast T/NK proliferative Platelet

CD4+ CTL Myeloid Cell Classical Monocyte T cell PB CD4+ TCM Non-classical Monocyte T/NK Cell CD4+ T naive Neutrophil CD8+ T naive cDC

CD8+ TEM pDC Others MAIT Platelet Treg RBC NK cell

C Sepsis D RBC

pDC NK cell cDC B Naive NK B Cell B Memory NK CD56bright

B Intermediate Other T dn T B cell Plasmablast gamma-delta T CD4+ CTL T/NK proliferative CD4+ T CD4+ T naive Mono Classical Monocyte NK cell CD4+ TCM Non-classical Monocyte CD4+ T Monocyte EM Dendritic cDC1 Treg cDC2 CD8+ T naive CD8+ T tDC

CD8+ TCM pDC

CD8+ TEM RBC

T cell MAIT

E Multiple Sclerosis F

RBC PB

pDC B cell B Naive Other T dn T B Cell B Memory gamma-delta T B Intermediate T/NK proliferative

Plasmablast NK

Platelet ILC CD4+ CTL NK CD56bright CD4+ T CD4+ T naive cDC ILC

CD4+ TCM Monocyte Myeloid Classical Monocyte CD4+ T EM Non-classical Monocyte Treg T cell cDC CD8+ T naive CD8+ T pDC

CD8+ TCM Others HSPC Platelet CD8+ TEM MAIT RBC NK cell

Figure S18. Various cell types in immune-mediated diseases, relative to Figure 7. (A, C, E) Distributions of cell types identified in influenza (A), sepsis (C) and multiple sclerosis (E) patients were shown on UMAPs. (B, D, F) Distributions of disease conditions in influenza (B), sepsis (D) and multiple sclerosis (F) patients were shown on UMAPs.