key: cord-0881053-3g6zxnrf
authors: Wilk, Aaron J.; Lee, Madeline J.; Wei, Bei; Parks, Benjamin; Pi, Ruoxi; Martínez-Colón, Giovanny J.; Ranganath, Thanmayi; Zhao, Nancy Q.; Taylor, Shalina; Becker, Winston; Jimenez-Morales, David; Blomkalns, Andra L.; O’Hara, Ruth; Ashley, Euan A.; Nadeau, Kari C.; Yang, Samuel; Holmes, Susan; Rabinovitch, Marlene; Rogers, Angela J.; Greenleaf, William J.; Blish, Catherine A.
title: Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19
date: 2020-12-18
journal: bioRxiv
DOI: 10.1101/2020.12.18.423363
sha: cdea9b89e99f47a436a19d7107a10882f092a821
doc_id: 881053
cord_uid: 3g6zxnrf

Our understanding of protective vs. pathologic immune responses to SARS-CoV-2, the virus that causes Coronavirus disease 2019 (COVID-19), is limited by inadequate profiling of patients at the extremes of the disease severity spectrum. Here, we performed multi-omic single-cell immune profiling of 64 COVID-19 patients across the full range of disease severity, from outpatients with mild disease to fatal cases. Our transcriptomic, epigenomic, and proteomic analyses reveal widespread dysfunction of peripheral innate immunity in severe and fatal COVID-19, with the most profound disturbances including a prominent neutrophil hyperactivation signature and monocytes with anti-inflammatory features. We further demonstrate that emergency myelopoiesis is a prominent feature of fatal COVID-19. Collectively, our results reveal disease severity-associated immune phenotypes in COVID-19 and identify pathogenesis-associated pathways that are potential targets for therapeutic intervention. One Sentence Summary Single-cell profiling demonstrates multifarious dysregulation of innate immune phenotype associated with COVID-19 severity.

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an urgent public health crisis. COVID-19 has a highly variable disease course: approximately 20% of infected individuals require hospitalization, ~5% require critical care, and as many as 40% of cases are asymptomatic (1, 2) . As the immune response to SARS- 5 CoV-2 is a key determinant of COVID-19 severity and outcome, understanding the immunological underpinnings of COVID-19 pathogenesis is critical to predict, prevent, and treat SARS-CoV-2 infection and to prepare for the possibility of future infections caused by emerging betacoronaviruses that may be introduced from existing reservoirs (3) (4) (5) (6) . 10 Severe COVID-19 is associated with a number of distinct immunological signatures. For example, increased serum levels of proinflammatory cytokines such as IL-1β, IL-6, IP-10, and TNFα and the alarmins S100A8 and S100A9 are associated with worse outcomes (7) (8) (9) (10) (11) (12) . Patients with less severe disease tend to have both lower levels of proinflammatory cytokines and higher levels of tissue repair factors (9) . COVID-19 also reconfigures leukocyte phenotype 15 in a severity-specific fashion, with severe COVID-19 associated with lymphocyte exhaustion (13) (14) (15) , neutrophil activation signatures (16) (17) (18) (19) (20) , and hematopoietic alterations (15, 21) . While many of these findings have been established through transcriptomic and proteomic profiling, the gene regulatory changes underlying severe disease manifestations have not been determined. 20 Comparatively less is known about the features of immune responses to SARS-CoV-2 that protect against severe disease, as most cohorts profiled to date have included only hospitalized patients. Neutralizing antibodies and virus-specific T cell responses have been detected in mildly symptomatic patients, providing evidence of a successful adaptive immune response across the disease spectrum (22) (23) (24) (25) (26) (27) . Notably, patients with mild COVID-19 have much lower levels of 25 proinflammatory plasma cytokines, suggesting that the immune response in mild disease can eradicate the virus without triggering the hyperinflammatory state observed in severe cases (9, 11) . Therefore, to define protective versus pathological features of the immune response, we aimed to profile both mild (WHO score 1-3, no oxygen requirement), moderate (WHO score 4-5, noninvasive oxygen requirement), and severe (WHO score 6-8, intubated) cases of COVID-19. 30 To map the immune response at the epigenetic, transcriptional, and proteomic level, we performed single-cell assay for transposase-accessible chromatin (scATAC-seq), single-cell RNA-sequencing (scRNA-seq), and cytometry by time-of-flight (CyTOF) on the peripheral immune cells of a cohort of COVID-19 patients across the entire spectrum of disease severity. 35 We discovered many immunological perturbations associated with disease severity, including robust signatures related to neutrophil activation along with dysfunction of monocytes, type 2 conventional dendritic cells, and natural killer (NK) cells. In addition, we found strong evidence for emergency myelopoiesis in fatal disease. We also identified epigenetic changes correlated to these transcriptional and proteomic changes, demonstrating coordinated changes in regulatory 40 element accessibility and transcription at key pro-inflammatory cytokine-encoding genes in monocytes. Together, this dataset reveals novel mechanistic insights into the pathological and protective mechanisms of the immune response to SARS-CoV-2.

A trimodal single-cell atlas of the peripheral immune response to SARS-CoV-2 To investigate how immune responses vary between different severities of COVID-19, we profiled peripheral blood immune cells from 64 patients with COVID-19 and 12 healthy controls with three high-dimensional single-cell modalities: Seq-Well (28, 29)-based scRNA-seq (33 5 patients, 8 controls; including 7 patients previously profiled by our group (15) ), scATAC-seq (18 patients, 7 controls), and CyTOF (64 patients, 12 controls; Fig. 1A, Fig. S1A-B) . Importantly, we profiled COVID-19 patients across the full range of the disease severity spectrum, including patients with mild disease (WHO score 1-3, see Methods) and hospitalized inpatients with moderate disease (WHO score 4-5) as well as critically ill patients with severe disease (WHO 10 ordinal score [6] [7] [8] . We scored patients by both peak severity (denoted by the colors of cells/patients) and severity at time of sample collection (separated as groups for box plots). The median age of profiled patients was 43.5 years and 51% were female (Fig. 1B, Fig. S1B , Table  S1 ). The majority of patients were sampled during the acute phase of infection; 13 mild and moderately-ill patients were sampled in the convalescent phase (>21 days after first positive 15 nasopharyngeal swab). Demographic information, additional clinical metadata, and the modalities applied to each sample are available in Table S1 .

Peripheral blood mononuclear cells (PBMCs) were sampled by all modalities; additionally, we processed red blood cell-lysed whole blood by scRNA-seq to profile granulocytes like 20 neutrophils (see Fig. 5 ), and we processed isolated NK cells by CyTOF with a panel enabling deep interrogation of NK cell receptor expression (see Fig. 4; Fig. 1A ). In total, we analyzed ~175,000 single transcriptomes, ~50,000 single chromatin accessibility profiles, and >3.2 million single proteomic profiles (Fig. 1A, Tables S2-5 ). After performing modality-specific quality control procedures (see Methods), we created a merged feature matrix of all profiled samples 25 which we subjected to dimensionality reduction, graph-based clustering, and cell type annotation ( Fig. 1C-I, Fig. S1C , see Methods, Table S6 ).

We first examined how COVID-19 impacted the composition of peripheral immune cells. We saw similar trends in immune cell composition between the three modalities, including depletion 30 of CD16 monocytes, dendritic cells (DCs), and natural killer (NK) cells, as well as increases in plasmablasts in patients with severe and fatal COVID-19 ( Fig. 1E; Fig. S2-3) . Notably, cell subset proportions that were altered in moderate and severe disease were generally unchanged in mild cases, with the exception of plasmacytoid DCs (pDCs), which were depleted in all severity groups ( Fig. 1E; Fig. S2-3) . Further, a population of developing (or immature) neutrophils first identified in our prior study of seven patients (15) was confirmed in 17 additional patients (Fig.  1G, Fig. S1C ) and is similar to that observed by other groups (21) . Fig. 1 . A trimodal single-cell atlas of the peripheral immune response to COVID-19 across a range of disease severities. A) Pipeline for sample processing and number of patients analyzed summarized by modality and peak disease severity score. For all display figures, scRNA-seq-derived data are boxed in blue, scATAC-seq-derived data are boxed in green, and CyTOF-derived data are boxed in orange. B) Summary of key patient 5 metadata, including age, peak disease severity score, and days after first positive nasopharyngeal (NP) PCR test. The vertical dotted line placed at 21 days post-positive test indicates the threshold after which patient samples are considered convalescent. C-D, F-I) UMAP projections of complete scRNA-seq (C-D), scATAC-seq (F-G), and CyTOF (H-I) datasets colored by peak disease severity score of sample (C,F,H) or cell type 10 (D,G,I). E) Cell type proportions from scRNA-seq data of PBMCs in each sample are colored by peak disease severity score. Platelets are excluded from the proportion calculations, as their presence is related to sample processing. The x axes correspond to the disease severity score for each sample at the time of collection. *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001, n.s., not significant at p = 0.05 by two-sided 15 Wilcoxon rank-sum test with Bonferroni's correction for multiple hypothesis testing.

Multimodal reference mapping enables accurate annotation and analysis of cellular subtypes Accurate identification of fine-grained immune cell subtypes is crucial to understanding how COVID-19 reconfigures the immune system; however, these cell types can be difficult to 20 identify de novo in single-cell RNA-seq data due to data sparsity and lack of information on canonical surface marker expression in each cell. To address this, we mapped our transcriptomic dataset to a large multimodal reference dataset introduced by Seurat v4, which incorporated extensive surface marker information to improve cell type calls ( Fig. 2A-B, Fig. S4A-B ) (30) . 25 Alignment to the Seurat v4 reference dataset revealed gene expression profiles of cell subtypes matching their expected biological signatures ( Fig. S4A-B , Tables S7-8) . For example, multiple T cell subsets, including γδ T cells, mucosal-associated invariant T (MAIT) cells, and regulatory T cells (Tregs) were revealed with Seurat v4 at the expected proportions ( Fig. 2B) and with the expected transcriptomic phenotype (eg. highly-specific expression of TRDC and TRGC1 by γδ T 30 cells, and FOXP3 by Tregs; Fig. S4A ). These annotations closely matched the manuallygenerated labels; importantly, cell types present in our dataset but absent from the reference (ie. neutrophils) were not successfully mapped (Fig. S4C,D) . To orthogonally confirm the accuracy of these annotations, we compared the abundances of MAITs measured by CyTOF to the proportions of MAITs in the transcriptomic dataset predicted by Seurat v4. This analysis 35 revealed high concordance between modalities, supporting the accuracy of these annotations (Fig. S4E) .

This strategy revealed previously unreported changes in peripheral immune cell composition in COVID-19, including the depletion of both CD4 and CD8 T effector memory (TEM) cells and 40 double-negative T cells in severe and fatal COVID-19 (Fig. 2B) . While DCs are known to be depleted in COVID-19, our analysis reveals that this was due to depletion of the cDC2 subset, while cDC1 cells were unchanged (Fig. 2B) . To prioritize downstream analysis of these cell subsets we calculated a perturbation score (30, 31) for each cell type from each COVID-19 sample relative to healthy controls (see Methods). The perturbation score for each cell type is 45 calculated by first identifying genes that display evidence of differential expression between 7 COVID-19 samples and healthy controls, calculating the difference of pseudobulk expression vectors of these genes between COVID-19 samples and healthy controls, and finally projecting the whole transcriptome of each donor onto this vector. This score therefore represents the magnitude of whole transcriptome shifts in gene expression, and reveals disease severityassociated patterns in cell subtype perturbation (Fig. 2C) . This perturbation score captured 5 phenotypic changes in major cell types like monocytes (explored further in Fig. 3 ) as well as more granular subtypes like CD8 TEM cells. We focused our downstream analysis on cell subtypes with COVID-19 severity-associated perturbation with a high number of DEGs relative to other cell subtypes. 10 For example, we identified disease severity-associated phenotypes in cDC2 cells but not other DC subsets (Fig. 2C-E) . In cDC2 cells, FCER1A, known to be involved in inflammatory DC signaling (32), CD83, an activation marker of mature DCs (33), and CTSS, which is involved in antigen presentation (Fig. 2F ) (34) were downregulated with increased disease severity while genes associated with tolerogenic or anti-inflammatory responses, like PKM and CD163, were 15 upregulated (35, 36) . Collectively, these results indicate that dysfunctional and antiinflammatory cDC2 cells may be a feature of severe COVID-19, with important potential implications for Tfh development and mucosal immunity.

CD8 TEM cells also displayed severity-associated transcriptional perturbations (Fig. 2C, Fig.   20 S5D-E). Notably, several markers of CD8 effector capacity, like PRF1, GZMB, and CX3CR1 (37, 38) , were downregulated primarily in patients with mild COVID-19 (Fig. S5F) .

Additionally, in severe and fatal COVID-19 patients, CD8 TEM retained expression of markers of effector capacity without showing features of exhaustion (Fig. S5G) . Together, these data imply that over-exuberant peripheral cytotoxic T cell responses may be associated with severe disease, 25 similar to previous reports (39).

We next examined the impact of disease time point on immune phenotype, given the heterogeneity of sampling times between patients (Fig. 1B) . These analyses are limited by our small sample size of acutely infected mild patients in our transcriptional dataset. Nonetheless, 30 these analyses suggest that disease acuity has more impact on transcriptional perturbation in moderate patients than in mild patients ( Fig. S6) : patients with a peak WHO severity score of 1-3 had similar transcriptional profiles marked by minimal perturbation, regardless of acuity status, while those with a peak score of 4-5 appeared to cluster by acuity. These results imply that mild COVID-19 may be marked by minimal, or rapidly resolved, systemic immune responses, a 35 finding that is orthogonally supported by our CyTOF analyses that include a greater number of subjects. Cell types most perturbed in convalescence included CD8 TEM and CD14 monocytes; in moderate patients, B cells were also perturbed in convalescence, displaying downregulation of Ig genes (Fig. S6) . Additional longitudinal sampling across all severity groups is necessary to clarify these signatures. Table S9 for complete list) by cDC2 cells for each sample. For all box plots: points are colored by the peak disease severity score, shaped according to disease acuity, and grouped by the disease severity score at time of sample collection. *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001, n.s., not significant at p = 0.05 by two-sided 10 Wilcoxon rank-sum test with Bonferroni's correction for multiple hypothesis testing.

Emergence of monocytes with dysfunctional features in severe COVID-19 We next analyzed the phenotypes of peripheral monocytes in COVID-19, as these cells appeared to be strongly reconfigured in nonlinear dimensionality reduction projections for all three 15 modalities ( Fig. 1C -D, F-I). Embedding of CD14 monocytes alone from the transcriptomic dataset recapitulated this phenotypic shift (Fig. 3A) . Similar to previous reports (11, 15, 21, 40, 41) , multiple ISGs ( Fig (Fig. 3D) . Additionally, we noted a severity-associated loss of 25 CD4 expression ( Fig. 3B-C) , which is involved in monocyte-to-macrophage differentiation and pro-inflammatory cytokine induction in CD14 monocytes (44, 45) . These results collectively suggest that suppressive and dysfunctional monocytes are a feature of severe and fatal COVID-19, in agreement with previous reports (11, 15, 21) . Importantly, mild COVID-19 generally did not lead to this shift towards suppressive and dysfunctional monocytes. 30 We also noted that C19orf59, which encodes MCEMP1, a key biomarker for stroke risk and outcome (46, 47) , was upregulated in the monocytes of severe and fatal COVID-19 patients (Fig.  S8) . Given the accumulating data that COVID-19 can drive thrombotic complications including ischemic stroke, we also examined expression of other transcripts reported to predict stroke risk 35 by Raman, et al. (47). We found that each of the five most predictive transcripts for stroke risk and prognosis reported by Raman, et al. (C19orf59, IRAK3, ANXA3, RBM47, and TLR5) were abundantly expressed in monocytes and neutrophils, and that each of these transcripts was significantly upregulated in severe COVID-19 in either monocytes or neutrophils (Fig. S8 ). 40 We next examined the expression of pro-inflammatory cytokine-encoding genes by peripheral monocytes. Similar to previous work (11, 15) , we found minimal expression of key monocytederived pro-inflammatory cytokine-encoding genes in severe and fatal COVID-19 patients; in fact, IL1B and TNF were significantly downregulated in the monocytes of severe and fatal COVID-19 patients ( Fig. 3E-F) . Similar phenotypic changes were also observed in CD16 45 monocytes in the scRNA-seq dataset (Fig. S7) . Further, monocytes from mild COVID-19

patients also did not upregulate many pro-inflammatory cytokine-encoding genes ( Fig. 3E-F) , in contrast to mild cases of similar viral infections like influenza (48) (49) (50) .

To investigate potential gene regulatory mechanisms that could explain the downregulation of IL1B in the CD14 monocytes of severe and fatal COVID-19 patients, we examined changes in 5 chromatin accessibility around the IL1B locus. We identified a putative enhancer downstream of IL1B, which shows linkage to the IL1B promoter via single cell co-accessibility analysis and chromosome conformation capture Hi-C data from the THP-1 monocytic cell line (Fig. 3G , see Methods) (51) . This putative enhancer showed significantly decreased accessibility in severe COVID-19 patients (p=0.0081, Wilcoxon test, Fig. 3H ). Furthermore, this element contains an 10 NF-κB binding motif, suggesting it may be regulated by NF-κB family transcription factors. We identify similar changes in peaks containing NF-κB motifs at loci for other monocyte-produced inflammatory cytokines including CCL2 and CCL3 (Fig. S9) .

Consistent with this hypothesis, we further observed COVID-19-associated changes in genome- 15 wide NF-κB family transcription factor activity. Using ChromVAR analysis to quantify transcription factor activity from the chromatin accessibility of each cell, we found increased NF-κB activity in mild cases and significantly decreased activity in severe cases, consistent with the changes we observed at the IL1B locus (p=0.0047, Wilcoxon test, Fig. 3I ) (52). Our scRNAseq data did not show significant transcriptional changes for most NF-κB-family TFs, although 20 REL and RELB are significantly downregulated in severe COVID-19 (Fig. S9 ). This could either reflect technical limitations of measuring lowly-expressed transcription factor transcripts, or it could indicate that our observed NF-κB activity changes are caused by post-translational modifications (53) . The NF-κB pathway is crucial for the inflammatory responses to virus infections in innate immune cells (53) (54) (55) , and its activation relies on various proinflammatory 25 cytokines including IL-1β and TNFα (56) . Activated NF-κB can further induce TNF and IL1B expression (53, 57) , leading to a positive feedback loop. Our results suggest that aberrant decreases in NF-κB activity in severe COVID-19 may result in loss of accessibility at putative enhancers of key cytokine genes, providing a potential mechanism for the previously reported impairment in cytokine production by peripheral monocytes in COVID-19 (11). 30 We also examined the epigenetic regulation at the CD4 locus, as this gene was significantly downregulated with increasing disease severity. Although there was no change in chromatin accessibility of the CD4 gene promoter between severity groups, we found that the accessibility of monocyte-specific CD4 gene putative regulatory regions was significantly reduced in severe 35 samples (Fig. S9) . This monocyte-specific loss of CD4 expression may provide an additional mechanism explaining the previously reported impairment of cytokine production by monocytes in COVID-19 (11, 21) , as the interaction between IL-16 and monocytic CD4 induces the expression of proinflammatory cytokines, including IL-1β (44). A) UMAP projection of CD14 monocytes from scRNA-seq dataset, colored by peak disease severity score. B,E) Feature plots depicting expression of selected DEGs and proinflammatory cytokine-encoding genes by CD14 monocytes (see Table S10 for complete DEG list). C,F) Box plots depicting average expression of features plotted in (B,E) in 5 each sample. D) Box plot showing average module score for gene signatures of monocytes in bacterial sepsis (43) and MDSCs (42) (see Methods). G) Genome track showing 100 kb around the IL1B gene locus. Top panel shows the coverage at different peak regions for different severity groups; the box below shows peaks called from all CD14 monocytes in that region (dark blue) and peaks containing putative strong NF-κB2 10 binding sites (red); the "CoAccessibility" box shows the accessibility correlated peak pairs across all CD14 monocytes; the "Genes" box shows location of IL1B gene, blue color means the gene is located on the minus strand; the bottom "Virtual 4C" track shows KR-normalized contact frequencies to the IL1B promoter in THP1 monocytic cells. A locus with significantly altered accessibility is marked by a black arrow. H) Box plot 15 depicting the accessibility of the peak marked by a black arrow in (G) in CD14 monocytes from each sample, measured by number of Tn5 insertions per million. I) Box plot depicting the average chromVAR z-scores of NF-κB2 binding motifs in CD14 monocytes from different samples. J) UMAP projection of all monocytes from CyTOF dataset, colored by peak disease severity score. K-L) Feature plots (K) and box plots (L) 20 depicting arcsinh-transformed expression of selected protein markers by monocytes in CyTOF dataset. For all box plots: points are colored by the peak disease severity score, shaped according to disease acuity, and grouped by the disease severity score at time of sample collection. *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001, n.s., not significant at p = 0.05 by two-sided Wilcoxon rank-sum test with Bonferroni's correction 25 for multiple hypothesis testing.

Profiling of COVID-19 monocytes by CyTOF recapitulated many of these findings (Fig. 3J,K) . This included a loss of CD16 + monocytes as well as a distinct shift in the phenotype of CD14 + monocytes (Fig. 3J,K; Fig. S10 ). As observed in our transcriptional data, expression of HLA- 30 DR and CD4 was lost in the CD14 + monocytes of severe COVID-19 samples. Importantly, this proteomic reconfiguration was not observed in patients with mild COVID-19, evident in nonlinear dimensionality reduction (Fig. 3J) . Mild patients experienced no significant increase in expression of the stress marker CD112, nor did they upregulate CCR2, which is involved in monocyte recruitment to the airways in the setting of severe COVID-19, although both of these 35 markers were upregulated in severe patients (Fig. 3J-L) (58, 59) . They also displayed a less dramatic loss of HLA-DR and CD4 expression compared to monocytes in severe cases (Fig. 3J-L) . These results indicate that while monocytes are dramatically remodeled in severe COVID-19, mild COVID-19 has minimal, or rapidly resolved, impact on the monocyte proteome. 40 Peripheral NK cells are depleted in severe COVID-19 and have a highly activated phenotype We next interrogated changes in the NK cells of COVID-19 samples. As demonstrated previously (15, 60) , peripheral NK cells were substantially depleted across all three modalities and transcriptionally reconfigured in severe COVID-19 samples (Fig. 4A-B, Fig. S11) , which may reflect their trafficking to the site of infection. Transcriptional changes appeared to be 45 driven by upregulation of several canonical NK cell activation genes, including higher expression of cytotoxic effector molecule-encoding genes GZMB and PRF1, as well as proliferation marker MKI67 and ISGs like XAF1 (Fig. 4B-C) . NK cells from moderate and severe, but not mild, COVID-19 cases, also displayed transcriptional evidence of exhaustion (Fig. 4C) . 5 We next examined this NK cell activation signature at the protein level. We corroborate previously known changes in NK cell biology, including increased expression of the activation markers CD38 and CD69 (60) , and also demonstrate that surface expression of the death receptor ligand Fas-L is increased in moderate and severe COVID-19 patients ( Fig. 4D; Fig. S11 ). While perforin was also upregulated in moderately and severely ill patients, surprisingly, NK cells from 10 fatal COVID-19 patients failed to upregulate both this cytotoxic effector and the proliferation marker Ki-67 (Fig. 4D-E) . These data, coupled with transcriptomic evidence of NK cell exhaustion in severe and fatal COVID-19 (Fig. 4C) , suggest that defects in NK cell cytotoxicity may be associated with adverse outcomes. 15 Dynamic changes in NK cell receptors and ligands may underlie COVID-19 severity-associated NK activation To assess mechanisms of NK cell activation, we interrogated changes in the NK cell repertoire of surface-expressed activating and inhibitory receptors as well as their cognate ligands. Notably, surface expression of the activating receptors DNAM-1 (CD226) and NKG2D was significantly 20 downregulated in the activated (CD69 + CD38 + ) (Fig. 4F) and total (Fig. S11F) NK cells of severe COVID-19 samples compared to healthy controls, despite no change in the expression of the genes encoding these proteins in the total NK cells within our scRNA-seq data (Fig. 4G) . As expression of both DNAM-1 and NKG2D can be downmodulated following ligation (61, 62) , we investigated the abundance of the two ligands for DNAM-1, poliovirus receptor (CD155) and 25 Nectin-2 (CD112), and of the ULBP proteins, which are recognized by NKG2D. Both Nectin-2 and the ULBPs were significantly upregulated on the peripheral monocytes of hospitalized COVID-19 patients compared to healthy controls (Fig. 4H) , which supports the hypothesis that SARS-CoV-2 may decrease surface expression of DNAM-1 and NKG2D through internalization following ligation of overexpressed Nectin-2 and ULBP proteins due to stress. We found no 30 change in the expression of the other ligand for DNAM-1, CD155, nor did we observe any changes in the protein-or RNA-level expression of either TIGIT, an inhibitory receptor that competes with DNAM-1 for binding of Nectin-2, or TACTILE (CD96), which recognizes CD155 (Fig. S11E) . Alternatively, activated NK cells expressing DNAM-1 or NKG2D may migrate to the tissue in the setting of severe disease, depleting these markers from the circulating 35 population.

We also investigated the surface expression levels of other ligands for NK cell receptors and observed a loss of LLT-1 expression on CD14 + monocytes that appears to correlate with disease severity, with a near-total loss in fatal samples (Fig. 4I) ; however, we found no change in the 40 expression of the inhibitory receptor that recognizes LLT-1, CD161, on NK cells (Fig. S11G) . The overall profile of activating and inhibitory receptors and ligands expressed in severe COVID-19 is summarized in Fig. 4J and suggests that the activated phenotype observed in these samples may be driven by activating signals received through DNAM-1 and NKG2D as well as a lack of inhibitory signaling through CD161. 20 depending on the context (yellow). Arrows and dashes indicate whether abundance of a protein is increased, decreased, or unchanged in severe COVID-19 compared to healthy controls. Dashed lines indicate interactions between receptors and ligands. For all box plots except (E): points are colored by peak disease severity score, shaped according to disease acuity, and grouped by the severity score at sample collection. *, p < 0.05; **, p < 25 0.01; ***, p < 0.001; ****, p < 0.0001, n.s., not significant at p = 0.05 by two-sided Wilcoxon rank-sum test with Bonferroni's correction for multiple hypothesis testing.

A hyperactivated neutrophil signature marks severe and fatal COVID-19 Despite evidence that neutrophils are major players in the dysregulated immune response 30 defining severe and fatal COVID-19 (16, (18) (19) (20) (63) (64) (65) (66) , there is a relative dearth of deep profiling of neutrophils from COVID-19 patients given their sensitivity to both cryopreservation and mechanical stimulation (67, 68) . To address this, we demonstrated that Seq-Well generated high-quality scRNA-seq data of primary human neutrophils from a healthy blood donor (Fig.  S12) . While fewer genes were detected in sequenced neutrophils, the number of unique 35 molecular identifiers (UMIs) sequenced in neutrophils was comparable to the expected recovery based on known RNA content (69, 70) . We also found that neutrophils from ACK-lysed whole blood were phenotypically similar to neutrophils isolated by magnetic-activated cell sorting (MACS; Fig. S12D-E) ; the former strategy was also able to uncover other granulocytic cell types like eosinophils (Fig. S12C) . GBP5  GBP1  GBP2  RNF213  NEAT1  HBB  MX1  ISG15  IFIT1  XAF1  RSAD2  IFIT2  IFIT3  IFITM3 S100A8 S100A9 

projections of complete scRNA-seq dataset colored by expression of canonical neutrophil markers (top) and of canonical neutrophils alone colored by peak disease severity score (bottom). B) Heatmap of DEGs between neutrophils of each COVID-19 sample 5 compared to neutrophils of all healthy controls, colored by average log(fold-change). All displayed DEGs are statistically significant at the p < 0.05 confidence level by Seurat's implementation of the Wilcoxon rank-sum test (two-sided, adjusted for multiple comparisons using Bonferroni's correction). C) Box plots depicting average expression of selected neutrophil DEGs by severity group (see Table S13 for complete DEG list). D) 10 Plots depicting median ISG signature score of neutrophils in each sample grouped by disease severity score at time of sample collection (left) and by days after first positive NP swab (right). All points are colored by peak disease severity score. For scatter plot at right, Pearson's r, exact two-sided p values and the 95% confidence interval are shown for each peak disease severity score grouping. E) Box plots depicting average module 15 scores for genes sets of neutrophil phagocytosis and neutrophil degranulation (see Methods, Table S12 ). F) Dot plots depicting average and percent expression of proinflammatory cytokine encoding genes (left) and epigenetic regulators (right) by canonical neutrophils. The y axis corresponds to the peak disease severity score. G) Results of transcription factor activity prediction analysis performed by iRegulon (76) . 20 DEGs between neutrophils from severely ill (peak severity 6-8) and neutrophils from healthy controls were used as input (see Methods, Table S14 ). H) Box plots of average module scores for PD-L1 + neutrophils in an in vitro model of endotoxemia (74) and granulocytes in the setting of sepsis and ARDS (75) (see Methods, Table S12 ). For all box plots: points are colored by the peak disease severity score, shaped according to 25 disease acuity, and grouped by the disease severity score at time of sample collection. *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001, n.s., not significant at p = 0.05 by two-sided Wilcoxon rank-sum test with Bonferroni's correction for multiple hypothesis testing. 30 Seq-Well processing of red blood cell-lysed whole blood yielded 33,276 high-quality single transcriptomes of primary neutrophils (Fig. 5A) . These cells uniformly and specifically expressed neutrophil lineage marker-encoding genes, including CSF3R and CXCR2, indicating their identity as canonical neutrophils (Fig. 5A, Table S6 ). Non-linear dimensionality reduction revealed that neutrophil transcriptional phenotype was strongly remodeled in COVID-19 ( Fig.   35 5A), driven in part by upregulation of PADI4, which is required for NETosis, the IL-8 receptor CXCR1, and multiple alarmins, including S100A8 and S100A9 (Fig. 5B-C) , which together induce neutrophil chemotaxis and adhesion (71) . We also noted disease severity-specific induction of interferon-stimulated genes (ISGs) in moderate and severe, but not in mild, COVID-19 patients (Fig. 5D) . While this ISG signature was detected across most cell types in moderately 40 and severely ill patients, neutrophils upregulated the broadest number of ISGs (Fig. S13) . Importantly, the differential expression of ISGs by neutrophils between COVID-19 severity groups was not due to a difference in infection time point between patients: neutrophils from patients with mild COVID-19 did not upregulate ISGs at any point during infection (Fig. 5D) .

Additionally, gene set enrichment analysis demonstrated upregulation of genes associated with neutrophil phagocytosis and degranulation in a disease severity-associated fashion (Fig. 5E , see Table S12 ).

We also identified two distinct neutrophil immunophenotypes of fatal COVID-19. Neutrophils from 4/6 fatal COVID-19 cases had robust ISG induction and expressed CD274 (encoding PD-5 L1; Fig. 5B-C) , in line with previous work (21) . However, we also identified 2 fatal COVID-19 cases with less pronounced ISG induction but with upregulation of additional neutrophil activation markers not observed in other samples, including CXCR4, CLEC12A, EGR1, and the decoy IL1β receptor IL1R2 (Fig. 5B-C) . Additional severity-associated changes in neutrophil phenotype included the upregulation of pro-inflammatory cytokine-encoding genes, including 10 CXCL16 and TNFSF10 (encoding TRAIL), as well as upregulation of several epigenetic regulators involved in neutrophil activation, like PADI4, which is required for formation of neutrophil extracellular traps (NETs; Fig. 5F ) (19, 72, 73) .

Transcription factor activity analysis implicated STAT1, STAT2, STAT3, and IRF1 as key 15 upstream regulators of the observed transcriptional reconfiguration, further suggesting that COVID-19 neutrophils are strongly activated by type I IFN in a disease severity-specific fashion (Fig. 5G) . To better contextualize these findings, we scored the neutrophils in our dataset against gene modules upregulated in a model of endotoxemia (74) and in ARDS-complicated sepsis (75) . This analysis revealed that both of these signatures are upregulated with increasing 20 COVID-19 severity (Fig. 5H) , implying similarities in neutrophil phenotype between these clinical conditions. Collectively, profiling fresh whole blood rather than isolated PBMCs reveals a prominent neutrophil hyperactivation signature in severe and fatal COVID-19.

Developing neutrophils are a feature of fatal COVID-19 25 We next analyzed a population of developing neutrophils from the transcriptomic dataset that was enriched in patients with severe and fatal COVID-19 (Fig. 1E, Fig. 6A ). This population has been identified in other COVID-19 datasets but is not yet well-characterized (21, 63) . These cells specifically and highly expressed genes encoding markers expressed at distinct stages in neutrophil development, including DEFA1B, LCN2, and MMP8 (Fig. S1C, Table S6 ), indicating 30 that they likely represent immature neutrophils derived from emergency granulopoiesis. As we hypothesized that these cells were not a static population but rather were dynamically differentiating, we embedded them in two and three dimensions using PHATE, a dimensionality reduction method developed to visualize phenotypic continua, branches, and continual progressions ( Fig. 6B-C, Fig. S14) (77) . Clustering of these cells revealed 5 clusters that 35 corresponded to sequential stages in neutrophil development, beginning with cluster 0 (proneutrophils) expressing primary neutrophil granule protein-encoding genes, followed by clusters 1-3 (pre-neutrophils) consecutively expressing secondary and tertiary neutrophil granule proteinencoding genes, and finally cluster 4 (mature neutrophils) which expresses markers of canonical neutrophils (Fig. 6C-D) (78) . Importantly, ELANE (which encodes neutrophil elastase), was 40 specifically expressed by pro-neutrophils, implying that these cells may be capable of NETosis (79, 80 ). An orthogonal approach ordering each cell in latent time modeled by splicing kinetics of RNA velocity (81, 82) (Fig. 6E) also revealed a similar developmental trajectory with respect to both granule protein-encoding genes (Fig. 6F) and transcription factors known to be involved in neutrophil development, like the C/EBP family (Fig. 6G, Fig. S14 ). In our earlier work, we 45 hypothesized that developing neutrophils may arise via transdifferentiation from plasmablasts based on their phenotypic similarity in nonlinear dimensionality reduction manifold space and subsequent analysis of cellular trajectory by RNA velocity. In this larger dataset, a phenotypic relationship between developing neutrophils and plasmablasts was still evident (Fig. 1C-D) , but RNA velocity analysis of plasmablasts, developing neutrophils, and mature neutrophils did not reveal a clear transdifferentiation bridge (Fig. S14) . Orthogonal experiments are necessary to 5 conclusively determine the development origins of these cells.

As developing neutrophils were present uniformly, often at high frequencies, in patients with fatal COVID-19, and these cells specifically expressed many genes not found in other peripheral blood cell types, we hypothesized that the developing neutrophil gene signature might be an 10 accurate predictor of mortality in COVID-19. We therefore identified the five most positively differentially-expressed genes between developing neutrophils (6,569 cells across 21 patients) and all other cells in our dataset: DEFA1B, DEFA3, LTF, DEFA1, and S100A8. We next obtained a publicly-available whole blood bulk transcriptomic dataset of 103 COVID-19 patients as a validation cohort and scored each sample in this dataset by the aggregated expression of 15 these five genes (see Methods). After scoring each sample, we used the associated patient metadata to determine the 28-day mortality of each scored sample. We then constructed a receiver operating characteristic (ROC) plot using the gene score as predictor and the 28-day mortality as the response variable. We found that the developing neutrophil gene score accurately predicted 28-day mortality of the 17 patients who succumbed to infection (AUC = 20 0.81; Fig. 6H, Fig. S14) . Importantly, the sequential organ failure assessment (SOFA) score at the time of sample collection did not strongly predict 28-day mortality (AUC = 0.67), indicating that the presence of developing neutrophils is a better prognostic indicator than current clinical status measured by clinically used severity scales (Fig. S14) . Thus, developing neutrophils are likely enriched in the blood of fatal COVID-19 cases in other cohorts, and gene signatures from 25 these cells have promise as a potential prognostic indicator.

Myeloid skewing of hematopoietic progenitors in severe and fatal COVID-19 Considering that severe and fatal COVID-19 patients displayed evidence of emergency myelopoiesis in the periphery, we hypothesized that there also may be severity-associated 30 aberrations in a small population of hematopoietic stem and progenitor cells (HSPCs) we identified in our transcriptomic dataset ( Fig. 1C-D, Fig. 2C ). Although the frequency of these cells did not change in COVID-19 (Fig. 6I) , we found that several genes involved in HSPC maintenance and self-renewal, including CDK6, SOX4, and CHD4 (83) (84) (85) (86) (87) (88) (89) , were generally upregulated in COVID-19 patients relative to healthy controls (Fig. 6J) . These DEGs suggest 35 that the hematopoietic progenitor compartment in COVID-19 patients has been transcriptionally reshaped to accommodate the stress of emergency hematopoiesis. To better understand the identities of these cells, we leveraged a publicly available single-cell transcriptomic dataset of hematopoiesis in healthy human blood and bone marrow (Fig. 6K) (90) , into which we projected HSPCs from our COVID-19 dataset (Fig. 6L) . We found a trend towards myeloid skewing in 40 COVID-19 circulating HSPCs with increasing disease severity, with lower frequencies of common lymphoid progenitors in severe and fatal patients, as well as granulocyte/macrophage progenitor (GMP)/neutrophil-like cells appearing in severe and fatal cases (Fig. 6M) . Together, these results indicate severity-associated changes in hematopoiesis in COVID-19 with greater myeloid skewing evident in circulating HSPCs. Severity score at draw (Granja, et al. 2019) Projection of COVID-19 dataset HSPCs

Cluster Percent Expressed Table S15 ). E) Twodimensional PHATE projection of developing neutrophils colored by latent time calculated by scVelo (82). F-G) Scaled expression of selected neutrophil granuleencoding genes (F) and C/EBP transcription factor family-encoding genes (G) by developing neutrophils across inferred latent time. H) Receiver operating characteristic 10 (ROC) curve depicting sensitivity and specificity of 28-day mortality prediction of a fivegene signature of developing neutrophils (DEFA1B, DEFA3, LTF, DEFA1, and S100A8) in an independent validation cohort of 103 samples where 17 cases are fatal (91) . I) Box plot depicting proportions of Seurat v4-annotated HSPCs in scRNA-seq dataset. J) Box plots of average expression of selected HSPC DEGs (see Table S16 ). K-L) UMAP 15 projection of Seurat v4-annotated HSPCs from scRNA-seq dataset into a publiclyavailable blood and bone marrow hematopoiesis dataset (90) colored by published cell type annotations (K) and with projected HSPCs colored in red (L). M) Bar plot depicting proportions of cell type identities transferred after projection into the publicly-available hematopoiesis dataset for each peak disease severity score bin. For all box plots: points 20 are colored by the peak disease severity score, shaped according to disease acuity, and grouped by the disease severity score at time of sample collection. *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001, n.s., not significant at p = 0.05 by two-sided Wilcoxon rank-sum test with Bonferroni's correction for multiple hypothesis testing.

In this work, we have compiled a trimodal single-cell atlas of immune cells from COVID-19 patients with a wide range of disease severities through scRNA-seq, scATAC-seq, and CyTOF. By virtue of our whole blood analyses and multi-modal approach, our analysis reveals novel mechanisms of immune activation and dysregulation in the setting of severe COVID-19, in 5 addition to providing critical validation of results from other studies. We found that neutrophils and NK cells appeared strongly activated with increasing disease severity, with heightened ISG induction and increased expression of cytotoxic machinery. Conversely, monocytes and dendritic cells displayed dysregulated and tolerogenic features in severe and fatal COVID-19. Finally, we found dramatic changes in hematopoietic development in severe COVID-19, with the appearance 10 of a population of developing neutrophils in the periphery and skewing of circulating hematopoietic precursors towards the myeloid lineage. Importantly, our profiling of patients with mild COVID-19 allows us to demonstrate that many of these changes occur largely in severe and fatal COVID-19 and not in milder forms of the 15 disease. For example, the kinetics and role of local and systemic IFN signaling in ameloriating or exacerbating SARS-CoV-2 remain controversial (41, (92) (93) (94) (95) . Here, we noted minimal induction of ISGs in mild COVID-19 cases, regardless of the time point of infection. This suggests that robust IFN responses detectable in the periphery may not be required for disease resolution. Additionally, we observed surprisingly little perturbation of monocytes and NK cells from mild 20 COVID-19 patients, whereas mild cases of influenza are known to induce systemic activation of these cells (96, 97) . Direct comparative analyses and larger sample sizes will be necessary to identify conserved or differential features between mild COVID-19 and other mild respiratory virus infections. 25 Our analysis demonstrated a strong severity-associated hyperactivation phenotype in peripheral neutrophils marked by broad ISG induction, pro-inflammatory cytokine-encoding gene production, enrichment of phagocytosis and degranulation gene sets, and upregulation of epigenetic regulators associated with inflammatory neutrophils, such as PADI4 which is required for NETosis. Neutrophils in severe COVID-19 also strongly upregulated S100A8 and S100A9, 30 which dimerize to form the inflammatory molecule calprotectin, which is involved in neutrophil activation and chemotaxis (71) . Additionally, we found features associated with neutrophil exhaustion, like upregulation of CD274, similar to other studies (21) . While this finding has led some groups to conclude that neutrophils become "suppressive" in severe COVID-19 (21), we believe our findings, combined with accumulating evidence that NETosis contributes to tissue 35 injury and thrombotic complications in severe COVID-19 (16, 18, (64) (65) (66) , suggest a predominantly pathogenic role for circulating neutrophils in severe COVID-19. Importantly, these data provide insight into the mechanistic pathways that drive neutrophil activation. Targeting such pathways may provide new therapeutic opportunities. For example, an agonist of a neutrophil-expressed inhibitory receptor Siglec-10 (SACCOVID) has shown promising results 40 in suppressing hyperinflammation in severe COVID-19 and is in a phase III clinical trial (98- 100) . Agonists against other neutrophil-expressed inhibitory receptors, like Siglec-9, may also represent novel therapeutic candidates (101) .

In addition to identifying features of severe COVID-19, we were also able to identify key 45 differences between the immune responses of patients with severe COVID-19 who went on to survive the disease versus those who did not. A striking difference between fatal and non-fatal cases was the emergence of a population of developing neutrophils that was first described by our group (15) . In this earlier work we identified these cells in 4/4 patients with acute respiratory distress syndrome (ARDS) requiring mechanical ventilation, including in one patient who succumbed to infection. We now show the presence of these cells in 17 additional patients, 5 including 5/7 patients with severe and 6/6 patients with fatal COVID-19. Our trajectory analyses demonstrate that these cells follow the stages of canonical neutrophil development, beginning with defensin-rich promyelocytes and differentiating through metamyelocytes and bands to form mature neutrophils. This process may be driven by elevated levels of circulating proinflammatory cytokines, like IL-17, that may induce formation of neutrophils (102). Although 10 any functional or pathological role for these cells in COVID-19 pathogenesis remains unclear, their abundance in the periphery of patients with fatal COVID-19 enabled us to demonstrate that their most defining transcripts could be used to predict 28-day mortality in an independent bulk transcriptomic dataset. While emergency myelopoiesis is known to be a feature of bacterial sepsis (103, 104) , it is likely that emergency myelopoiesis is also an underappreciated feature of 15 severe viral infection. For example, in an integrated multicohort analysis of viral disease severity, Zheng, et al. report a gene module that includes several markers of immature neutrophils (eg. CEACAM8, DEFA4, LCN2) that is enriched in other viral infections including influenza and RSV (105) . Additionally, a six-mRNA classifier of viral disease severity, which includes DEFA4, trained on non-COVID-19 viral infections was found to also predict COVIDmultiple severe viral infections and may be used to predict adverse outcomes.

In addition to canonical and immature neutrophils, the phenotype of NK cells in fatal COVID-19 cases was also distinct from that of severe non-fatal cases. While circulating NK cells are known 25 to become activated in severe COVID-19 (60), our work is the first to show that patients with fatal COVID-19 fail to proliferate and upregulate the cytotoxic effector perforin. The absence of this activation could indicate a defect in the functional responses of NK cells in fatal COVID-19, although this finding requires direct validation. We also identify NK cell ligand-receptor axes that may contribute to their activation in severe COVID-19; for example, our data suggest that 30 DNAM-1-mediated recognition of Nectin-2 could drive NK cell activation in COVID-19, as could NKG2D-mediated activation. These data are consistent with other viral infections in which activation of NK cells through DNAM-1 or NKG2D are important (107, 108) , and highlight pathways that should be investigated through in vivo model systems for their role in disease outcome. 35 While neutrophils and NK cells are activated in COVID-19, monocytes and dendritic cells appear to take on an impaired inflammatory phenotype. Perturbations in the cDC2 dendritic cells of patients with severe COVID-19 could inhibit priming of Tfh cells and development of mucosal immunity (109) . Both cDC2s and monocytes of critically ill COVID-19 patients 40 appeared to downregulate or failed to upregulate genes involved in activation and inflammation; for example, cDC2s lost expression of FCER1A and CD83, while CD14 + monocytes notably did not upregulate any genes encoding proinflammatory cytokines such as IL-6 and CCL3, and genes encoding IL-1β and TNF were significantly downregulated in these cells compared to healthy controls. This lack of proinflammatory cytokine expression is of note as other viral 45 infections such as influenza drive an increase in the production of these molecules by peripheral monocytes (97). We identified the inactivation of NF-κB family TFs as a possible epigenetic mechanism for this silencing of proinflammatory cytokine production in monocytes, as we observed a striking loss of accessibility at an NF-κB binding site within the IL1B locus. This observed inactivation is unusual, given that these TFs typically play an important role in antiviral immune responses, including through regulating the production of cytokines (110) . Indeed, NF-5 κB family TFs have been implicated in driving the proinflammatory cytokine production in other cell types during SARS-CoV-2 infection (111) . It is also possible that the absence of NF-κB activity and accessibility at cytokine-encoding genes reflects the immaturity of monocytes in severe COVID-19. 10 There are several limitations to this work that should be noted. Though large for a multi-modal dataset, our sample size is still limited. Moreover, we were unable to profile each patient by all three single-cell modalities, preventing us from performing cross-modality validation on a perpatient basis. Additionally, our CyTOF panels only allowed us to examine a limited number of cell types, preventing us from performing orthogonal validation of some transcriptional or 15 epigenetic findings in proteomic space. This work profiled exclusively circulating immune cells; while understanding the peripheral immune system is critical to understanding aberrant and protective immune responses to SARS-CoV-2 infection, it does not capture the immune response at the site of infection. Finally, further functional experimentation is necessary to validate or refute many of the hypotheses presented here. 20 Collectively, our work represents the first trimodal epigenomic, proteomic, and transcriptomic cell atlas of peripheral immune responses to COVID-19 across a broad spectrum of disease severity. By identifying novel immune features associated with COVID-19 mortality, as well as the immune status of patients with mild disease, our work enhances our understanding of 25 pathological vs. protective immune responses and highlights several opportunities for therapeutic development. Competing interests C.A.B. is a member of the Scientific Advisory Board of Catamaran Bio. W.J.G. is a consultant for 10x Genomics and Guardant Health, Co-founder of Protillion Biosciences, and is named on patents describing ATAC-seq. 5 Data and materials availability Processed matrices (scRNA-seq and scATAC-seq) or fcs files (mass cytometry) with deidentified metadata supporting this publication are available at ImmPort (https://www.immport.org) under study accession SDY1708. Data from scRNA-seq and 10 scATAC-seq will be deposited to GEO. All original code used for analysis and visualization will be available from a Github repository.

Figures S1-S15

Tables S1-S19

Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo

Prevalence and genetic diversity of coronaviruses in bats from China

Origin and evolution of pathogenic coronaviruses

Extensive diversity of coronaviruses in bats from China

Bats and Coronaviruses

Cytokine profile in plasma of severe COVID-19 does not differ from 35 ARDS and sepsis

Longitudinal analyses reveal immunological misfiring in severe COVID-19

HLH Across 5 Speciality Collaboration, UK, COVID-19: consider cytokine storm syndromes and immunosuppression

Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans

A dynamic COVID-19 immune signature includes associations with poor prognosis

Patients With Coronavirus Disease 2019 (COVID-19)

Elevated exhaustion levels and reduced functional diversity of T cells in peripheral blood may predict severe progression in COVID-19 patientsCell

A single-cell atlas of the peripheral immune response in patients with severe COVID-19

SARS-CoV-2-triggered neutrophil extracellular traps mediate COVID-19 27

Excessive Neutrophils and Neutrophil Extracellular Traps in COVID-19

Neutrophil Extracellular Traps (NETs) Contribute to Immunothrombosis in COVID-19 Acute Respiratory Distress Syndrome

Giamarellos-Bourboulis, T. Ulas, Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patientsmedRxiv

A neutrophil activation signature predicts critical illness and mortality in COVID-19, medRxiv

Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment

Functional SARS-CoV-2-specific immune memory persists after mild COVID-19

Defining the features and duration of antibody responses to SARS-CoV-2 infection associated with disease severity and outcomeScience

Human B Cell Clonal Expansion and Convergent Antibody Responses to SARS-CoV-2

Antigen-Specific Adaptive Immunity to SARS-CoV-2 in Acute COVID-19 and Associations with Age and Disease Severity

Cross-reactive memory T cells and herd immunity to SARS-CoV-2

Functional SARS-CoV-2-Specific Immune Memory Persists after Mild COVID-19

Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput

Second-Strand Synthesis-Based Massively Parallel scRNA-Seq Reveals Cellular States and Molecular Features of Human Inflammatory Skin Pathologies

Integrated analysis of multimodal single-cell dataCold Spring Harbor Laboratory

Characterizing the molecular regulation of inhibitory immune checkpoints with multi-modal single-cell screensCold Spring Harbor Laboratory

The role of FcεRI expressed in dendritic cells and monocytes

CD83: Activation Marker for Antigen Presenting Cells and Its Therapeutic Potential

Increased cathepsin S in Prdm1-/-dendritic cells alters the TFH cell repertoire and 10 contributes to lupus

Pyruvate Kinase M2 Is Required for the Expression of the Immune Checkpoint PD-L1 in Immune Cells and Tumors

Coexpression of CD163 and CD141 identifies human circulating IL-10-producing dendritic cells (DC-10)

CX3CR1 identifies PD-1 therapy-responsive CD8+ T cells that withstand chemotherapy during cancer 20 chemoimmunotherapy

The Chemokine Receptor CX3CR1 Defines Three Antigen-Experienced CD8 T Cell Subsets with Distinct Roles in Immune Surveillance and Homeostasis

Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications

Shapes COVID-19 Progression

Complex Immune Dysregulation in COVID-19 Patients with Severe Respiratory Failure

Defining the emergence of myeloid-derived suppressor cells in breast cancer using single-cell transcriptomics

An immune-cell signature of bacterial sepsis

Interleukin-16 stimulates the expression and production of pro-inflammatory cytokines by human monocytes

CD4 ligation on human blood monocytes triggers macrophage differentiation and enhances HIV infection

MCEMP1 -a new prognostic and diagnostic biomarker for stroke?

Peripheral Blood MCEMP1 Gene Expression as a Biomarker for Stroke Prognosis

The Role of Innate Leukocytes during Influenza 25 Virus Infection

Smed-Sörensen, Respiratory Mononuclear Phagocytes in Human Influenza A Virus Infection: Their Role in Immune Protection and As Targets of the Virus

Influenza Virus A 30 Infection of Human Monocyte and Macrophage Subpopulations Reveals Increased Susceptibility Associated with Cell Differentiation

Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development

chromVAR: inferring transcriptionfactor-associated accessibility from single-cell epigenomic data

NF-κB signaling in inflammation

NF-kappaB in the immune response of Drosophila

Transcriptional control of the inflammatory response

The nuclear factor NF-kappaB pathway in inflammation

Characterization of a functional NF-kappa B site in the human 10 interleukin 1 beta promoter: evidence for a positive autoregulatory loop

Genetic mechanisms of critical illness in Covid-19

Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages

Karolinska COVID-19 Study Group, Natural killer cell immunotypes related to COVID-19 disease severity

Primary human tumor cells expressing CD155 impair tumor targeting by down-regulating DNAM-1 on NK cells

Regulation of NKG2D-Dependent NK Cell Functions: The Yin and the Yang of Receptor Endocytosis

Deciphering the state of immune silence in fatal COVID-19 patients

Targeting potential drivers of COVID-19: Neutrophil extracellular traps

Neutrophil extracellular traps in 10 COVID-19

Neutrophil extracellular traps infiltrate the lung airway, interstitial, and vascular compartments in severe COVID-19Journal of

Mechanotransduction in neutrophil activation and deactivation

Mechanical deformation of neutrophils into narrow channels induces pseudopod projection and changes in biomechanical properties

Single-cell transcriptome profiling reveals neutrophil heterogeneity and orchestrated maturation during homeostasis and bacterial infectionbioRxiv

RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types

Proinflammatory activities of S100: proteins S100A8, S100A9, and S100A8/A9 induce neutrophil chemotaxis and adhesion

PAD4 is essential for antibacterial innate immunity mediated by neutrophil extracellular traps

PAD4-mediated neutrophil 35 extracellular trap formation is not required for immunity against influenza infection

IFN-γ-Stimulated Neutrophils Suppress Lymphocyte Proliferation through Expression of PD-L1

Phosphoinositide 3-Kinase Inhibition

iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections

Visualizing structure and transitions in high-dimensional biological data

Developmental Analysis of Bone Marrow Neutrophils Reveals Populations Specialized in Expansion, Trafficking, and Effector Functions

Neutrophil activation and NETosis are the major drivers of thrombosis in heparin-induced thrombocytopenia

Neutrophil elastase-deficient mice form neutrophil extracellular traps in an experimental model 25 of deep vein thrombosis

RNA velocity of single cells

Generalizing RNA velocity to transient cell states through dynamical modeling

CDK6 levels regulate quiescence 35 exit in human hematopoietic stem cells

YBX1 expression and function in early hematopoiesis and leukemic cells

Nuclear hnRNPA2B1 initiates and amplifies the innate immune response to DNA viruses

Molecular profiling reveals similarities and differences between primitive subsets of hematopoietic cells generated in vitro from human embryonic stem cells and in vivo 5 during embryogenesis

The role of the chromatin remodeler Mi-2beta in hematopoietic stem cell self-renewal and multilineage differentiation

Mi-2/NuRD chromatin remodeling complexes regulate B and Tlymphocyte development and function

The role of SRY-related HMG box transcription factor 4 (SOX4) in tumorigenesis and metastasis: friend or foe?

Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia

Large-Scale Multi-omic Analysis of COVID-19 Severity

Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19

Type III interferons disrupt the lung epithelial barrier upon viral recognition

Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients

The type I interferon response in COVID-19: implications for treatment

Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory Viruses

Zheng, CD24 and Fc fusion protein protects SIVmac239-infected Chinese rhesus macaque 10 against progression to AIDS

CD24Fc protects against viral pneumonia in simian immunodeficiency virus-infected Chinese rhesus monkeys

CD24 and Siglec-10 selectively repress tissue 15 damage-induced immune responses

Synthetic Siglec-9 agonists inhibit neutrophil activation associated with COVID-19chemRxiv

IL-17 have a role in COVID-19 infection?

Dysregulated myelopoiesis and hematopoietic function following acute physiologic insult

Cutting edge: bacterial infection induces hematopoietic stem and progenitor cell expansion in the absence of TLR signaling

Multi-cohort analysis of host immune response identifies 30 conserved protective and detrimental modules associated with severity irrespective of virus

A 6-mRNA host response whole-blood classifier trained using patients with non-COVID-19 viral infections accurately predicts severity of COVID-19

Activating Receptor and Its Ligands: How Do Viruses Affect the NK Cell-Mediated Immune Surveillance during the Various Phases of Infection?

The Role of Dendritic Cells During Infections Caused by Highly Prevalent Viruses

The intricate interplay between RNA viruses and 15 NF-κB

COVID-19: A New Virus, but a Familiar Receptor and Cytokine Release SyndromeImmunity

Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin PathologybioRxiv

Integrated single-cell analysis of multicellular immune dynamics during hyperacute HIV-1 infection

STAR: ultrafast universal RNA-seq aligner

dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments

Comprehensive Integration of Single-Cell Data

Integrating single-cell transcriptomic data across different conditions, technologies, and species

A Single-Cell Transcriptional Atlas of the Developing Murine Cerebellum

Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data

Lymphocyte innateness defined by transcriptional states reflects a balance between 10 proliferation and effector functions

Single-cell RNA-seq reveals new types of human blood dendritic cells, 15 monocytes, and progenitors

Multiplexed droplet single-cell RNA-sequencing using natural genetic variation

Fcgamma receptors as regulators of immune responses

Fcgamma receptors: old friends and new family members

Cell-type specific gene expression 25 profiles of leukocytes in human peripheral blood

Profiling of the Human Natural Killer Cell Receptor-Ligand Repertoire

Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm

Normalization of mass cytometry data with bead standards

CytoNorm: A Normalization Algorithm for Cytometry Data

PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells

ArchR: An integrative and scalable software package for single-cell chromatin accessibility analysisCold Spring Harbor Laboratory

Multimodal single-cell chromatin analysis with SignacCold Spring Harbor Laboratory

Fast, sensitive and accurate integration of single-cell data with Harmony

Juicer Provides a One-Click System for Analyzing Loop-Resolution Hi-C Experiments

Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade

We are grateful to all participants in this study. We thank Sopheak Sim and the Stanford Functional Genomics Facility for the use of the Fragment Analyzer, as well as Angela Detweiler and Michelle Tan at the Chan Zuckerberg Biohub for assistance with sequencing. We thank Anne-Maud Ferreira for insightful conversation on mass cytometry analysis. We thank Yuhan 5 Hao and Rahul Satija for helpful discussions on the calculation and interpretation of the perturbation score. We thank Martin Prlic for assistance with mass cytometry gating schemes. We thank Nima Aghaeepour for his assistance in implementing the CytoNorm package.