. Severe COVID-19 patients exhibit an impaired type I interferon (IFN) response with low IFN production and heterogeneous regulation of IFN-stimulated genes (Blanco-Melo et al., 2020; Hadjadj et al., 2020) . Peripheral blood of severe COVID-19 patients exhibits high frequencies of interleukin (IL)-6 secreting CD14 hi CD16 hi monocytes as well as a decrease of non-classical (CD14 lo CD16 hi ) monocytes (Hadjadj et al., 2020; Schulte-Schrepping et al., 2020) . Hyperinflammatory COVID-19 is associated with the appearance of proliferating, type I IFN activated CD14 + HLA lo suppressive monocytes and emergency granulopoiesis with elevated pre-neutrophil counts. T cell lymphopenia and exhaustion are suggested as hallmarks of severe COVID-19 as well (Diao et al., 2020; Giamarellos-Bourboulis et al., 2020; Guan et al., 2020; Huang et al., 2020; Zheng et al., 2020) . SARS-CoV-2 infection can elicit specific T cell and B cell responses (Braun et al., 2020; Grifoni et al., 2020; Long et al., 2020; Ni et al., 2020) . How these changes in immune cell populations and functions are related to disease outcome lasting immunity are areas of active investigation.
A significant disease burden is mounted by thrombotic complications associated with COVID-19. Pulmonary embolism and thrombosis are frequent clinical features of critically ill COVID-19 cases (Deshpande, 2020) , sometimes despite sufficient anticoagulation. Alveolar capillary microthrombi are nine times more prevalent in COVID-19 than in influenza autopsies (Lax et al., 2020) . Patients exhibit elevated D-dimer levels and widespread thrombotic microvascular injury (De Voeght et al., 2020; Rapkiewicz et al., 2020) . Several studies suggest altered platelet immune cell interactions (Leppkes et al., 2020 , Manne et al., 2020 and the presence of megakaryocytes (MKs) in affected lungs (Meyerholz and McCray, 2020) .
Longitudinal analyses on the dynamics of circulating immune cells in COVID-19 so far have mostly investigated one information layer (e.g. FACS) at a time. Here we aimed to provide a comprehensive, longitudinal view of cellular features using an integrated multi-omics approach. We analysed up to 5 peripheral blood samples from hospitalized COVID-19
patients throughout their disease course by single cell RNA sequencing, transcriptome and DNA methylome profiling, multi-colour FACS as well as multiplex cytokine ELISA. In addition to complex changes of immune cells, our study linked circulating MKs and responses of erythroid cells to COVID-19 clinical outcome.
We applied a multi-omics approach using up to 5 longitudinal peripheral blood samples of 13 hospitalized COVID-19 patients and one additional recovery control from two University hospitals in Germany (Cologne and Kiel). We employed parallel single-cell RNA sequencing J o u r n a l P r e -p r o o f (scRNA-seq, 10x Genomics), single-cell BCR profiling, bulk mRNA sequencing (RNA-seq), BCR amplicon sequencing and multicolour flow cytometry. Array-based DNA methylation profiling and multiplex cytokine ELISA analysis was performed in a subset of seven patients ( Figure 1A ). All patients were recruited at admission and samples were taken at days 0, 2, 7, 10, 13 and/or at discharge. Three patients were diagnosed with Acute Respiratory Distress Syndrome (ARDS), two of which had a fatal disease course. Five patients received remdesivir after inclusion into this study. Patient demographics and clinical characteristics are described in Table S1 . 14 age-and gender-matched healthy controls were processed in parallel. To describe the heterogenous disease trajectories over time, we used a modified WHO ordinal scale (WHO, 2020) , which also considers several inflammatory markers (serum CRP, serum IL-6 and ferritin, Table S2 ). The score was used to classify patients along their disease course ( Figures 1B, 1C ).
We first analysed scRNA-seq data from 358,930 cells with 10,900 cells on average per sample ( Figure 2A ). Up to four longitudinal samples were analysed per patient. Uniform
Manifold Approximation and Projection (UMAP) was used to visualize the structure of cellular populations ( Figure 2B -E). Graph-based clustering identified 37 distinct cell clusters in the data set ( Figure S1A ). Cell type classification was performed based on signature genes of each cluster and confirmed using reference transcriptomes (Aran et al., 2019; Wilk et al., 2020; Zheng et al., 2017) (Figures 2B, 2C , S1B, S1C). Cells from individual patients were dispersed evenly in the UMAP representation, with the exception of a higher degree of interindividual heterogeneity in the monocytic subpopulations ( Figure 2D , E, S2).
Enumeration across timepoints revealed changes of several cell types including monocytes, proliferative lymphocytes and NK cells (Figures 2E, 2F and S1D) . We observed an increase of classical CD14 + monocytes especially in convalescence stages, while nonclassical CD16 + monocytes levels were increased in pseudotimes 2 and 3 ( Figure S2 ). Moreover, we confirmed suppression of HLA-DR expression in the critical phase (pseudotime 2) (Schulte- Schrepping et al., 2020) . Lymphopenia of CD4 + cells was present, but less pronounced compared to other data sets. We also noted increased plasmablast (PB) proportions from the incremental phase to early convalescence, while B cells were depleted during incremental and complicated stages (Wilk et al., 2020) . Of note, we recognized the increased presence of several bone marrow-derived precursor cell types in the peripheral blood of COVID-19
patients. Prominently, MK proportions were elevated throughout the course of the disease ( Figure 2F ). To define potential cell populations, which might be directly infected by SARS-CoV2, we specifically interrogated ACE2 mRNA levels in the data set, which were below detection limit (<7 reads) in all cell populations (data not shown).
A correlation analysis of relative cell proportions with clinical activity parameters and multiplex serum ELISA revealed that PB proportions were correlated with serum levels of e.g. TNF, IL-10 and IL-21, a factor critically involved in B cell differentiation to PBs via STAT3 and BLIMP-1 (PRDM1) (Ozaki et al., 2004) . Increased bone marrow precursor cells were associated with increased levels of IL-33, a Th2 cytokine involved in regulating haematopoietic stem cell regeneration (Kim et al., 2014) and elevated MKs were linked to heightened inflammatory parameters, e.g. serum CRP, IL-6 and IFN-α ( Figure 2G ).
To delineate the transcriptional response to SARS-CoV-2 infection at higher temporal resolution and to instruct further cell type selection from the single-cell data set, we next analysed whole-blood transcriptomic data of the 13 hospitalized patients from up to five timepoints along with the recovery control and compared the signatures against 14 healthy controls ( Figure 3A ). Principal component analysis showed a separation between healthy controls and COVID-19 patients on the first principal component (PC1) ( Figure 3B ). In total, 5,915 genes were differentially expressed between healthy controls and COVID-19 patients in a pairwise comparison between controls and each of the disease trajectory pseudotimes ( Figure 3C , S3A). Most of the differentially expressed genes (DEGs) were expressed at higher levels in COVID-19 patients ( Figures 3C, S3A) . Notably, early changes comprised increase of immunoglobulin transcripts (IGHA1, IGHG1) and lactoferrin (LTF), while RORC mRNA levels, encoding for a Th17-specific transcription factor (TF) and class II HLA transcripts were decreased throughout the disease course.
Next, we employed ImpulseDE2 to construct a model of intra-individual variation of DEGs over time (Sander et al., 2017) . Since two of three patients reaching pseudotime 2 were deceased soon after reaching this timepoint and did not experience a convalescent disease course, this category was excluded from this analysis and was only used to contrast identified transcripts post hoc. We identified 935 DEGs following impulse-like progression patterns across the disease trajectory ( Figure 3D , S3A). Genes involved in IL-1β and vasodilatory signalling were highly expressed during the incremental trajectory (pseudotime 1). A broad decrease in transcripts encoding ribosomal structural proteins was present from the incremental to the late convalescent state (pseudotimes 1,3 and 4), which might reflect a general suppression of the protein synthetic apparatus by type I IFN (Jiang et al., 1997) .
Opposingly, there was a strong increase in IFN-related transcripts during the peak of disease activity, which was, however, suppressed in the critical disease category ( Figure 3D , S3B), confirming earlier reports (Blanco-Melo et al., 2020; Hadjadj et al., 2020) . Transcripts involved in myeloid cell-mediated immunity and neutrophil degranulation were modestly increased during pseudotime 1, 3, and 4, but were strongly increased in the critical J o u r n a l P r e -p r o o f pseudotime 2. Notably, during late convalescence (pseudotimes 5 and 6) a strong signal of erythroid cell differentiation (e.g., HBD, OPTN, FIS1) was detectable indicating a response to hypoxia (Ashrafi and Schwarz, 2013) .
We used the gene set resource DoRothEA to infer TF activity by enriched regulon analysis.
TFs in active disease were related to inflammation and IFN signalling (e.g. IRF1, STAT3) as well as hypoxic signalling (HIF1A) ( Figure S3C ). Supporting the IL-21-PB axis identified in the first part of the single-cell analysis, PRDM1 (BLIMP-1) was predicted as significantly active both at the critical as well as at convalescent timepoints. A hypergeometric test of all active TFs against the REACTOME database showed an enrichment of "cell differentiation pathway" (q-value = 0.03) and "MK development and platelet production" (q-value = 0.05).
Using upstream network topology of gene expression changes using signed and directed protein-protein interactions , we could show that MAPK3 (ERK1) and MAPK1 (ERK2) had the highest centralities across all pseudotimes ( Figure S3D ). We further constructed metabolic models of each pseudotime using the respective DEGs (Gebauer et al., 2016) and found that the inflammatory disease states were associated with higher numbers of predicted metabolically active pathways when compared to convalescence and healthy individuals ( Figure S3E ). We also investigated the bulk RNA data set for presence of viral reads and did not detect relevant amounts (>10) of viral reads in any sample. This finding was also confirmed by negative routine real time-PCR (E gene and S gene amplicon, data not shown).
We next used all DEGs identified from the combination of pairwise and longitudinal analysis (6,317 genes) for weighted gene co-expression network analysis (Langfelder and Horvath, 2008; Lee et al., 2004) . The analysis identified a total of 10 modules, which we refer to as M1-M10, of co-expressed genes following distinct expression patterns throughout the COVID-19 disease phases (Figures 4A, S3F, Table S3 ). We calculated the eigengene values, which represent a single expression profile for all genes within a module, to assess the individual correlation between the modules and scRNA-seq-derived cellular composition ( Figure 4B ) and clinical parameters ( Figure 4C ). Projecting the expression of the hub genes of each module on the scRNA-seq data revealed cell type and pseudotime-specific expression patterns ( Figure S4 ). Gene set enrichment analysis revealed biological processes and pathways enriched in each of the modules. Transcription factor binding site (TFBS)
inference (Breuer et al., 2013) was used to depict putatively involved TFs. Module M2
represented a signature of a type I IFN response and proliferative activity (G2M transition) ( Figure 4D ) and was enriched in binding sites for IRF1, STAT1, and STAT2 ( Figure S3G ).
Module M4 was associated with the presence of MKs in the peripheral blood as well as D-J o u r n a l P r e -p r o o f dimers and had two expression peaks, in the critical state (pseudotime 2) and in the early convalescent state (pseudotime 4), respectively. Module M7 contained transcripts related to haemoglobin biosynthesis and coincided with TFBS motifs for GATA-1 and GATA-2, which are TFs related to erythroid cell differentiation ( Figure S3G ). This biphasic pattern is likely related to hypoxia during highly acute inflammation (first peak) and weaning off supplemental oxygen during convalescence (second peak). Taken together, analysis of temporal gene expression patterns in whole blood of patients clearly depicted pathophysiological consequences of a SARS-CoV-2 virus infection along an idealized disease trajectory.
Epigenetic changes have been shown to contribute to the pathophysiology of systemic inflammatory states (Lorente-Sorolla et al., 2019) . We thus investigated DNA methylation (DNAm) patterns along the COVID-19 disease course following the workflow depicted in Figure 4E in a subset of the same patients (n = 6) ( (Müller et al., 2019) identified that major parts of the COVID-19 associated DNAm signatures originated from granulocytes, B cells, NK cells and monocytes ( Figure S5A ). Using Locus
Overlap Analysis for inferring differentially methylated TF binding sites (Sheffield and Bock, 2016) , we observed a significant overrepresentation of binding sites of the CCAAT Enhancer
Binding Protein Beta (CEBPB) in hypomethylated regions ( Figure 4H ), which has a critical role for emergency granulopoiesis (Hirai et al., 2006) and B lymphocyte-to-granulocyte transdifferentiation, a process, which has been proposed in severe COVID-19 (Wilk et al., 2020) We employed a hierarchical testing approach (Pan et al., 2018) to identify interactions between transcriptome and DNAm signatures in cis ( Figure 4E ). To that end, we screened all DEGs (combined set) for DMPs within a 5kb window up-and downstream of their respective transcription start site. Of all 3,280 DMP-DEG pairs, 68.3% showed increased expression with reduced methylation or decreased expression with increased methylation, which is in line with previous studies (Häsler et al., 2012) . We next investigated the representation of DMP-DEG pairs in the co-expression modules M1 to M10. Rank-based correlation analysis identified DMP-DEG pairs in all modules, with a significant overrepresentation in M3 and M8
( Figures S5C, D) . GO term enrichment analysis of DMP-DEG pairs showed an enrichment of innate-immunity related terms in the DEGs with increased expression (e.g. TNF and IL-6 signalling, TLR pathway), and also identified gene sets related to platelet function and metabolic processes (ATP metabolism and autophagy) ( Figure 4I ). DMP-DEG pair genes, J o u r n a l P r e -p r o o f which were either increased in the inflammatory phase (pseudotime 1-4) or decreased in late convalescence (pseudotime 5 and 6) were mapped to scRNA-seq data, identifying larger cell type specific clusters of DMP-DEG pairs for PBs, monocytes, MKs ( Figures S5E, F) .
As our findings pointed to changes of the B cell compartment along the disease trajectory of COVID-19, we next investigated the B cell lineage in greater detail ( Figure 5A ). We first extracted 22,190 cells identified as part of the B cell lineage from scRNA-seq data in cohort Figure 5B ). PBs were largely expanded during COVID-19 with highest levels in the hyperinflammatory phase ( Figure 5C ). Multicolour FACS confirmed high levels of circulating CD27 hi CD20cells in the fraction of CD19 + cells, which dropped along the disease convalescence. Likewise, the early relative decrease of naïve (CD20 + CD27 -) and memory B cell (CD20 + CD27 + ) levels normalized at later timepoints ( Figure 5D ). We also identified a large fraction of HLA-DR + CD138 + double positive PBs in the inflammatory phase (pseudotimes 1 to 4) . Whereas CD138 + cells were only abundant in the active disease and not in the convalescence phase, 82-98% of all CD27 hi CD20cells remained HLA-DR positive ( Figure S6F ). Of note, as PBs are notoriously sensitive to manipulation, we recognized that the handling procedures for scRNA-seq, also for cohort 2, diminished the levels of intact PBs compared to FACS data, while the longitudinal dynamics of increased PB numbers was retained between the two methods.
We could distinguish a smaller cluster of PBs, which expressed genes associated with neutrophils (e.g., ELANE, MPO and CAMP) ( Figure 5E ). Unlike in the previous study (Wilk et al., 2020) , we also found such cells in healthy subjects, albeit at lower frequency ( Figure 5C ).
Using monocle3 (Qiu et al., 2017) , we highlighted the cellular trajectory of transitional B cells into naïve and memory B cells, with a separate memory B cell cluster being most likely CD45RBcells (Glass et al., 2020 ) ( Figure 5F ). We could retrace the progression of PBs, which did not cluster based on immunoglobulin classes but rather based on disease state with cells from hyperinflammatory phases being more distant from the root of the trajectory ( Figures S6A, B) . Neutrophil-like PB cells were not continuously linked to the PB trajectory ( Figure 5F ).
We next analysed the BCR repertoire in the different B cell compartments using heavy-chain We next analysed the longitudinal gene expression patterns of the 6,295 PBs ( Figure 5G ).
The incremental inflammatory phase of COVID-19 patients was characterized by ER stress/protein folding (XBP1) and cell proliferation (e.g. PIM2, S100A4). Type I IFN response genes (IFI27, IFI6, IFITM1) were present in the PBs until late in the disease (up to pseudotime 5), yet such genes were absent from PBs in critically ill patients (pseudotime 2).
Throughout disease we also identified an increase in SLC1A4 expression, a potential upstream regulator of metabolic changes ( Figure 5I ). IL-16 secretion, which supports migration of CD4 + T cells and circulating blood DCs into lymphoid organs during the initiation of a humoral immune response (Kaser et al., 2000) , was a feature of late PBs ( Figure 5G ).
GO enrichment analysis identified increased unfolded protein response and mitochondrial ATP synthesis during active disease ( Figure 5H ). These findings were also corroborated in an independent cohort (cohort 2) of mild and severe COVID-19 patients using another PBs can modulate immune responses by serving as a nutrient sink (Vijay et al., 2020) . Thus, a constraint-based model was used to reconstruct the metabolic state of individual cells from scRNA-seq data (Joshi et al., 2020) . PBs from inflammatory states displayed a higher metabolic activity, which was reduced only upon recovery, while memory and naïve B cells displayed no significant differences in overall metabolic activity between disease states ( Figure S7L ). Increased metabolic processes in PBs were oxidative phosphorylation, glyoxylate and dicarboxylate metabolism, NAD synthesis and an increase of various amino acid metabolic pathways (including glycine, serine, alanine, threonine, valine, leucine and isoleucine). Glycolysis was predicted to have a low activity state in inflammatory disease phases, whereas it was highly active at clinical recovery (pseudotime 6) ( Figure 5J ). Taken together, the analysis identified the broad activation of PBs and suggested a strong J o u r n a l P r e -p r o o f immunometabolic shift of the cells towards amino acid metabolism, which may contribute to the immunopathology seen in severe COVID-19.
Systemic inflammatory responses are known to consume platelets, which exert broad immune and inflammatory functions in addition to their well-established haemostatic role (Semple et al., 2011; Yeaman, 2014) . Pulmonary and cerebral embolism is an important contributor to morbidity and mortality in COVID-19 . However, we could discern a significant increase of HSCs and MEPs at the convalescence state in comparison with healthy controls ( Figure S7J ).
By focusing exclusively on MKs there was a clear separation between samples belonging to healthy controls and patients with active COVID-19 ( Figure 6C ), particularly cells from complicated disease phases form distinct subclusters from cells of healthy and long-term recovery disease phases ( Figure 6D , E). Strong increase in PKM transcript levels (PKM2), encoding a pyruvate kinase that is involved in ATP formation, interacts with HIF1A and promotes its activity, was observed in critical patients (pseudotime 2). In this group, high expression of FCER1G, encoding the common FcR γ-chain adaptor responsible for integrin (ITGA2/GP6)-mediated platelet adhesion was also present ( Figure 6E ). GO enrichment analysis identified broad terms related to immune responses, type I IFN response and platelet aggregation to be altered along the disease trajectory ( Figure 6G ).
Transiently decreased transcripts comprised of ODC1 (ornithine decarboxylase), the ratelimiting enzyme for polyamine synthesis (Kanerva et al., 2008) , and TGFB1 ( Figure 6F ).
IFITM3, IFI27, and IFITM2 had increased expression in the inflammatory pseudotimes 1, 2, 3, and 4, indicating a lasting IFN response throughout the disease trajectory in MKs.
Furthermore, we validated our findings in an independent cohort of mild and severe COVID-19 (Schulte-Schrepping et al., 2020), which confirmed not only higher numbers of MKs in severe patients ( Figure 6H ), but also confirmed increased expression of IFITM3, PKM, J o u r n a l P r e -p r o o f ITGA2B (CD41), and IFITM2, and decreased expression of ODC1 and TGFB1 in the severely ill patients ( Figure 6F , lower panels).
Metabolic modelling identified an increased metabolic activity of MKs along the disease trajectory compared to healthy controls, albeit at a lower level compared to PBs ( Figure 6I ).
Notable predicted processes were related to energy metabolism (pyruvate metabolism, glycolysis, and ROS detoxification) ( Figure 6J , I). The metabolic model inferred a pronounced increase of glycolytic flux towards lactate, an induction of the methylglyoxal pathway (Kalapos, 2008 ) and a decrease of spermidine/polyamine products known to inhibit platelet aggregation (de la Peña et al., 2000) .
Lastly, we focused on the potential importance of the obtained signatures in a clinical context in a longitudinal cohort of 40 mechanically ventilated, critically ill COVID-19 patients from Radboud University Medical Center (UMC) in Nijmegen (cohort 3) (Table S4) .
Bulk RNA sequencing data in this cohort were obtained at two timepoints early upon admission to the ICU. We used matched sample pairs with similar increasing inflammatory activity changes from survivors (n = 33) and non-survivors (n = 7) and interrogated the change of expression levels of module genes (M1 to M10, as defined in the German longitudinal cohort (cohort 1) initially shown in Figure 4A ) between the two timepoints ( Figure   7A ). The first sample was obtained in median 3 days after ICU admission, the median period In a reverse approach, we asked which transcripts displayed longitudinally different expression patterns between the two timepoints in survivors vs. non-survivors. We found that while in survivors only 3 transcripts were regulated, 182 transcripts were significantly different in non-survivors ( Figure 7C ). Of these transcripts, 130 were contained in the previously defined temporal co-expression modules from cohort 1 with a significant enrichment of transcripts related to M2, M4, and M7. Cell-type specific expression patterns of these fatality-associated transcripts (average expression value per cell type, derived from J o u r n a l P r e -p r o o f scRNAseq cohort 1) showed that the signature for M2 genes marked a broad array of cell types, such as monocytes, granulocytes, NK cells, proliferative lymphocytes and CD8 + T cells, M3 and M4 genes were mostly specific for monocytes, with few highly expressed transcripts attributed to MKs. A large proportion of M7 genes painted the erythroid lineage, a separate cluster was expressed specifically in MKs, e.g., PBX1, TRIM58, and PDZK1IP1
( Figure 7D ).
Lastly, we quantified the differential regulation of TFs over time in survivors vs. non-survivors via a moderated t-test using limma (Ritchie et al., 2015) . The analysis identified 16 TFs that were differentially regulated in non-survivors only and 7 TFs in survivors ( Figure 7E ).
Pathway analysis (REACTOME) revealed significant enrichment of the terms "MK development and platelet production" (p-value=0.0001) and "TRAF6 mediated induction of proinflammatory cytokines" (p-value=0.001) in the non-survivor TFs.
The clinically heterogeneous disease presentation renders individual molecular dynamics of the haematopoietic and immune cell compartments system in response to COVID-19 an important topic to understand the pivot points of the disease. Our longitudinal analysis provided a chronological rank order to changes observed in prior cross-sectional studies. We disease support the theory that these cells are activated after being non-specifically mobilized from the bone marrow or other tissues. Of note, we found that the COVID-19 PBs were predicted to be highly metabolically active in a systems biology modelling approach (Joshi et al., 2020) . The observed changes suggest a role of PBs as a nutrient sink, which was already observed in extrafollicular PBs as a hallmark of a systemic inflammatory response in severe malaria (Vijay et al., 2020) . The predicted lower energy availability at the J o u r n a l P r e -p r o o f peak of COVID-19 may indicate excessive shuttling of glucose into antibody glycosylation, which may contribute to metabolic exhaustion of the cells (Corcoran and Nutt, 2016; Lam et al., 2016) and/or altered glycosylation patterns of antibodies, which were linked to severe COVID-19 (Hoepel et al., 2020; Larsen et al., 2020) .
Our longitudinal approach identified two other cellular features induced by COVID-19, which were unrecognized in previous studies. First, we found a significant increase of MKs, which carried a strong type I IFN signature. The change was associated with two longitudinal coexpression modules from the bulk RNA sequencing data (M3 and M4). Both modules were positively correlated with serum levels of D-dimers, suggesting a link to the inflammationinduced pro-coagulative state as a potentially fatal complication in COVID-19 patients. One of the top transcripts with increased expression in MKs was IFITM3, which confers antiviral activity in MKs and platelets (Campbell et al., 2019) . Metabolic modelling suggested that in the hyperinflammatory states, a shift towards higher rates of pyruvate metabolism and glycolysis occurs, which sensitizes platelets towards activation and aggregation (Nayak et al., 2019) .
Although thrombopenia has been observed as a clinical correlate of critical COVID-19
(metanalysis in (Lippi et al., 2020) ), it is unlikely that direct infection of MKs as seen in Dengue fever is responsible for this alteration. We found no evidence of either ACE2 expression in MKs or any virus-associated reads in the cell population (data not shown). The observed increase of circulating precursors including MK-erythroid progenitors (MEPs)
argues for an influence of COVID-19, which could not only represent a bystander response to increased platelet consumption but may rather reflect inflammation-induced emergency megakaryopoiesis (Haas et al., 2015) . We did not observe a direct correlation of MK levels with neither platelet counts nor D-Dimers, which might be limited by the small number of scRNAseq data sets. A continuous presence of type I IFN signals, as seen in our data set, may additionally increase of the aggregation potential of platelets (Leppkes et al., 2020; Middleton et al., 2020) . In line with this, the MK-associated module M4 is strongly correlated with D-Dimer levels in the larger bulk data set from cohort 1.
The second cellular feature was primarily observed in the bulk RNA-seq data and was related to the co-expression module M7. We saw a biphasic up-regulation of a transcript group that comprises canonical components of erythropoiesis, which are most likely related to the presence of reticulocytes. GATA-1 TF binding motifs linked to hypoxia-induced stress erythropoiesis (Zhang et al., 2012) were significantly enriched in the module. We thus reasoned that this feature reflects a canonical response to hypoxia as it is present in critically ill patients and at a later stage when patients are weaned of supplemental oxygen. Posthypoxia polycythaemia and the presence of different erythroid progenitors in the circulation has been studied as a response mechanism of the bone marrow to acute hypoxic insults and J o u r n a l P r e -p r o o f critical illness since decades (Loeffler et al., 1984; Peschle et al., 1977) . Mobilization of erythroid progenitor cells and their presence in the circulation has been linked to augmented immune responses (Serebrovskaya et al., 2011) . While we did not directly profile erythrocytes in the scRNA-seq data due to red blood cell lysis and size/feature filtering steps in the data processing, we found evidence for the increased presence of committed 
Limitations of our study are given by the relatively low sample size of the initial two-centre cohort (cohort 1), which we aimed to compensate by two independent validation cohorts. The 
The authors declare no conflicting interests. Table S2 .
C, Overview of the cohort 1. All patients are temporally aligned to the day of initial admission.
Squares mark the day of COVID-19-related symptom onset. Frames mark days of in-patient care, while the colour is representing the disease pseudotime. Sampling days were marked with a white circle. Intubations and extubations are depicted with triangle symbols, if applicable. See also Tables S1 and S2. See also Figure S1 . See also Figure S2 . See also Figure S6L . See also Figure S6L . See also 
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Philip Rosenstiel, p.rosenstiel@mucosa.de
This study did not generate unique reagents.
The bulk RNA-seq and BCR-seq data generated during this study is available in GEO database (GSE161777).
The Illumina EPIC Array data generated during this study is available in GEO database (GSE161678).
The raw scRNA-seq data and scBCR-seq data generated during this study is available in EGA (Accession number to be confirmed).
The processed scRNA-seq and scBCR-seq data generated during this study is available in FastGenomics (https://www.fastgenomics.org).
Additional processed data is available at GitHub (https://github.com/Systems-Immunology- 
14 patients from two independent University hospitals (Cologne, Kiel) were recruited for the longitudinal multi-Omics study. Eligibility criteria included age ≥18 years and admission to the respective hospitals (either normal ward or ICU) with a positive SARS-CoV-2 nasopharyngeal swab by RT-PCR. Sex, age and additional demographic information, as well as patient comorbidities can be found in Table S1 . This study did not particularly focused on impact of sex on patient outcome due to the limit number of individuals; larger cohorts have been used to study the influence of sex upon SARS-CoV-2 infection. Five patients were co- To describe the heterogenous disease trajectories over time, a modified WHO ordinal scale (WHO, 2020) , which also considers the behaviour of several inflammatory markers (serum CRP, serum IL-6 and ferritin) was used (Table S2) minor signs of inflammation), recovery/pre-discharge (pseudotime 6, no supplemental oxygen, absent inflammation markers) and long-term follow-up (pseudotime 7, at least two weeks after hospital discharge) ( Table S2 ). Note that pseudotimes 2 and 3 do not directly reflect the chronological order, but can also represent peak levels, i.e. a given patient might have gone from 1 to 3 without intubation/mechanical ventilation.
For validation of cellular findings from our prospective cohorts, data from two independent cohorts were analysed:
The scRNA-seq data from 18 patients admitted to the University Hospital Bonn 
Anti-SARS-CoV-2-specific IgA and IgG was quantified using commercial ELISA kits (EUROIMMUN, Lübeck, Germany) .
Blood ( 
Bulk BCR libraries were prepared starting from 100 ng of total RNA isolated from PAXGene tubes. Library construction protocol was performed as previously described (Bashford-Rogers et al., 2019) . In brief, primers for the constant (C) regions of the BCR were used during cDNA synthesis. Product was then amplified via PCR using a multiplex primer set for the variable (V) regions using the Real-Time PCR library amplification kit from KAPA Biosystems. Libraries were sequenced on Illumina MiSeq machine 2×300 bp.
SARS-CoV-2-specific viral RNA from the RNA extracts from the PAXGene tubes was quantified by RT-PCR using the RealStar® SARS-CoV-2 RT-PCR Kit RUO (altona diagnostics, Hamburg, Germany). The two amplicons were in the E gene and in the S glycoprotein in Spike protein 2 gene.
Blood ( Plasmablasts and plasma cells: CD19 + CD20 -CD27 hi ).
Tissue material was obtained in the autopsy procedure of the deceased patient 002 and from an age-and gender-matched patient (control), who had suffered from pulmonary adenocarcinoma and died from bronchopneumonia. SARS-CoV-2-testing was negative repeatedly in the control patient. Immunostaining was done on formalin-fixed and paraffin- 
Single-cell libraries were generated using the Chromium Next GEM Single 
An in-house RNA-seq pipeline was used to map and align the sequenced data (https://github.com/nf-core/rnaseq). Adapters and low-quality bases from the RNA-seq reads were removed using Trim Galore (version 0.4.4), which is a wrapper tool for Cutadapt and FastQC. Reads that were shorter than 35 bp after trimming were discarded. The filtered reads were mapped to the human genome (GRCh38, gencode version 25) using STAR aligner (version 2.5.2b) (Dobin et al., 2013) . featureCounts (version 1.5.2) was used to estimate the expression counts of the genes. The expression counts were normalized across samples using the DESeq normalization method.
Differentially expressed genes between healthy controls and each of the COVID pseudotime samples were identified using the Bioconductor package DESeq2 (version 1.20.0). Genes with FDR adjusted p-value of less than 0.05, log fold change greater than 0.5 or less than -0.5 and average expression counts of more than 100 were regarded as differentially expressed (DEGs). Longitudinal differential expression analysis of the COVID samples was performed by applying the case-only analysis from the Bioconductor package ImpulseDE2
(version 1.4.0). Pseudotimes 1, 3, 4, 5, and 6 were used as single timepoints of a timecourse experiment and the patient IDs were regarded as batch effects in order to perform a paired analysis. To identify the transcripts regulated longitudinally in survivors and nonsurvivors from the Nijmegen cohort, differentially expressed genes between in the two selected time points were identified using DESeq2 for survivors and non-survivors separately. To perform a paired analysis, patient ID was used as batch effect.
Modules of co-expressed genes were identified using the WGCNA package for R (version 1.69). All differentially expressed genes identified from the pairwise and longitudinal analysis 
Gene set enrichment analysis (GSEA) was conducted for the co-expression modules using GSEA desktop application (version 4.0.3). Pre-ranked analyses against Hallmark, KEGG and GO (Biological Processes) genes sets were conducted for each of the modules by ranking all genes by the module membership score. FDR of 0.05 was used as the significance threshold.
All gene ontology enrichment analyses were conducted using the Bioconductor package topGO (version 2.32.0), with all expressed genes as the universe set. In the topGO analysis, the Fisher.elim p-value, calculated using the weight algorithm, of 0.05 was used as the significance threshold.
Transcription factor binding sites (TFBS) enriched in the promoter regions of the coexpression module genes were identified by conducting enrichment analysis using the Bioconductor package LOLA (version 1.14.0). Promoter regions were defined as the region between 1,500 bp upstream to 500 bp downstream of the transcription start site.
Predicted transcription factor binding sites (TFBS) enriched in DMPs were identified by conducting enrichment analysis using the Bioconductor package LOLA (version 1.14.0).
For the integrated analysis of gene expression with DNA methylation, we first identified DMPs located 5,000 bp upstream and downstream of the transcription start sites of DEGs.
Spearman's rank correlation coefficient between the normalized expression count of each DEG and the methylation intensity (β-values) of its corresponding DMPs were calculated. To test the statistical significance of the correlations, we calculated the false discovery rate (FDR) using a permutation approach.
Sequencing reads were aligned to BCR gene reference and clonotypes were identified and grouped using the software MiXCR (Bolotin et al., 2015) . Relative proportions of IGH classes were calculated. Alpha diversity measures were calculated using the R packages vegan and tcR (versions 1.5-6 and 2.3.2).
To quantify the amount of virus present in the blood of COVID-19 patients, reads from whole blood RNA-seq data were first aligned to the human reference genome (GRCh38) using STAR with default parameters. The reads that did not map to the human genome were then aligned to the SARS-CoV-2 reference genome (NC_045512.2) using STAR with slightly relaxed parameters (--outFilterScoreMinOverLread 0.2 --outFilterMatchNminOverLread 0.2 --outFilterMatchNmin 0 --outFilterMismatchNmax 4). The reads that aligned to the SARS-CoV-J o u r n a l P r e -p r o o f 2 genome with at least 40 consecutive matches were then aligned locally to the human genome using the Smith-Waterman algorithm (Smith and Waterman, 1981) in order to filter any reads of human origin. The reads that aligned locally to the human genome or were composed largely of homopolymers were filtered and the remaining reads were considered as viral reads.
Standard curves and cytokine concentrations were calculated using linear regression in Microsoft Excel GraphPad Prism (Graphpad Software Inc, San Diego, US).
Analyses were performed using FlowJo v10 (FlowJo LLC, Beckton Dickinson, Ashland, Oregon, US) and Graphpad Prism 8 (GraphPad Software, San Diego, California USA).
The sequences were processed using cell ranger v3.1.0 (10× Genomics). Each sample was mapped to GRCh38 Homo sapiens reference genome, in order to produce their respective count matrices. Raw feature-barcode matrixes were filtered using Seurat package (version 3.1.5) in R environment (Butler et al., 2018; Stuart et al., 2019) ; low quality cells that were potentially disrupted or doublets cells were removed from the analysis using number of features (number of reads mapping to gene between [200;5000]) or percentage of mitochondria (lower than 25%) ( Figure S1E ). We used a broad threshold in order to encompass cells from a variety of cell-types.
Each filtered sample matrix was then merged into a single object containing 358,930 cells with overall reads mapping to 22,519 human genes. The merge object was normalized and scaled using LogNormalized() and ScaleData() functions respectively. Principal component analysis was performed utilizing the top 2,000 variable genes. We identified the clusters using the standard k-nearest neighbour method based on 80 dimensions with a 0.2 resolution. In total 37 clusters were displayed as a Uniform Manifold Approximation and Projection (UMAP).
Cluster cell types were identified by their corresponding gene signatures using the Wilcoxon rank sum, with a cut-off based on genes expressed in more than 25% of the cluster cells and exhibiting a 0.25-fold difference between clusters. Clusters of interest were identified based on marker genes ( Figure S1C ); In general, monocytes (CD14, ITGAM, S100A8 and Figure 2F ). We found most cell types to be impacted by disease trajectory, with only proliferative lymphocytes and cell precursors not having a significant difference between pseudotimes. Furthermore, we correlated cell type proportion with the clinical parameters available for each sample using spearman correlation ( Figure 2G ).
The clusters identified as cell types of interest -B cell compartment, megakaryocytes and cell precursors were pulled from the merged object. Each cell type of interest was reclustered and analysed separately. B cell compartment and cell precursors were re-clustered using 80 PCs, while megakaryocytes and their respective precursors (HSCs and MEPs)
were re-cluster using 60 PCs. B cell compartment clusters were assigned based on expression of marker genes, with memory B cell expressing CD73/NT5E, naïve B cells expressing IGHD and CD185/CXCR5, transitional B cells expressing CD9, plasmablasts expressing CD27 and CD38) and neutrophil-like cell expressing ELANE, MPO and CAMP (Glass et al., 2020) . We presented the B cell compartment as a cell trajectory analysis using monocle3 (Qiu et al., 2017) . Trajectories were calculated and the cells displayed based on monocle3 pseudotime approach rooted on the previously identified transitional B cells.
Plasmablasts (with neutrophil-like cells included), megakaryocytes and cell precursors signature genes for the individual disease groups were selected based on genes expressed in more than 25% and 0.25-fold difference between pseudotimes. Differentially expressed genes between healthy controls and each of the COVID pseudotimes was identified using MAST (Finak et al., 2015) and GO enrichment analysis was performed with TopGO package for R (version 2.38.1) (Alexa et al., 2006) and GO terms of interest selected based on fisher classic test statistic (p-value < 0.05).
To validate our findings, we performed a parallel analysis from an independent cohort (cohort 2) of mild and severe COVID-19 patients using another scRNA-seq technology (Rhapsody™ BD) (Schulte-Schrepping et al., 2020) . Similarly, we identified plasmablasts and J o u r n a l P r e -p r o o f megakaryocytes, identified differences in cell proportions and tested if genes of interest were expressed differently based on their disease classification (control, mild COVID-19 or severe COVID-19 patients).
BCR sequences were processed using cellranger v3.0.1 vdj function. Relative proportions of IGH classes were calculated. Alpha diversity measures were calculated using the R packages vegan and tcR (versions 1.5-6 and 2.3.2). scBCR-seq information was merged with B cell compartment containing both B cells and plasmablasts using barcode information.
BCR information was merged with scRNA-seq expression, thus, we were able to discern IG class information per cell type and displayed it in the form of a UMAP or monocle3 cell trajectory.
Blood metabolites originating from the Human Metabolome Database (HMDB) (Wishart et al., 2018) ; specifically, an advanced search of blood metabolites from healthy adults was initially conducted in March 2019. In the same time period, a list of all human metabolites (Brunk et al., 2018) was downloaded from Virtual Metabolic Human (VMH) database (Noronha et al., 2019) . The latter contains the HMDB indices of the compounds, which enabled the merging of the two databases into one (Spring 2020). Compounds were removed from the database (e.g., drug-related ones or without FooDB IDs (https://foodb.ca/)) and the metabolites nomenclature was altered to be compatible with an adapted version of the model Recon 2.2 (Swainston et al., 2016) . All values were transformed to mM to correspond with mean values of the HMDB database (cut-off of values > 10 -6 ). Water was set to 55,000 mM, and pH was set to 7.4. All calculations were conducted with the R packages sybil (version 2.1.5) (Gelius-Dietrich et al., 2013) and sybilSBML (version 3.0.5) (Gelius-Dietrich et al., 2013) along with their dependencies.
For the reconstruction of metabolic models from bulk sequencing data we used a previously described two-step approach that first discretizes gene expression based on differential gene expression analysis and subsequently reconstructs metabolic models based on gene expression states (Gebauer et al., 2016; Yang et al., 2019) . Differentially expressed genes between healthy controls and each of the COVID pseudotimes from previous differential expression analysis were used separately to build generalized linear models and tested with disease state as an independent variable. Wherever possible, i.e. when models reached full rank, we included the donor identifier to control for paired samples with similar genetic background in the data. The significance cut-off used for optimizing independent filtering was adjusted to α = 0.05 before differentially expressed genes were extracted from the tests. (Swainston et al., 2016) constrained with the serum metabolic environment. In order to test method-inherent uncertainties in the reconstructed context-specific metabolic network, the model reconstruction procedure was repeated fifty times while leaving out gene activity data for 5%
of the genes each time.
Reconstructions of cell-specific metabolic models were created by integrating single-cell transcriptomics with a human genome-scale metabolic network (Swainston et al., 2016) conditioned with the serum metabolic environment. We employed a two-step approach, in which StanDep (Joshi et al., 2020) first identifies a core reaction list across cell types.
Second, the FASTCORE algorithm (Pacheco and Sauter, 2018) 
Putative transcription factor activity from RNA-seq data was assessed per pseudotime against healthy controls using the human gene set resource DoRothEA v1, which provides a Table S1 . Detailed clinical characteristics of the patients included in cohort1 (related to Figure 1 ) Table S3 . List of genes assigned to each co-expression module (related to Figure 4) CD4 + T cells GM2A   LILRA6  BST1  TLR5  HEXB  NAIP  ITGA7  IL10   MARCO  CXCR2  C1R  C5AR1  TNFSF14  ECE1  MYD88  AKIRIN2  TLR2  TIMP2  IFNGR2  RARA  SPI1  PRKCD  SERPINA1  FPR1  NLRP12   GDF15  OPRL1   IRAK2   CCR1  ITFG1   AOC1   HPN   LILRB4  GPR17  AGTRAP  IL1RN  INSL3  ADM  IL1B  NTSR1  ICAM1 DDX60  TRIM22  GBP1  OAS2  STAT1  GCH1  APOL6  EPSTI1  OAS3  IFI44L  RSAD2  MX1  OAS1  FCGR1A  IFI27  ZBP1  TIFA  SAMD9L  CD274  IFIH1  PRKDC  FLT3  PANX2  PIK3AP1  TSC22D3  ZNF608  IL1B  DOCK4  TPST1  PER1  IRS2  LACTB  RCN3  TSPO  ADAMTS2  DAAM2  SAP30  CR1  YWHAH  DBN1  FURIN  CAMP  MMP8  ABCA13  PTX3  BPI  PRTN3  TCN1  OLR1  LCN2  ERG  LTF  NBN  PDE4B  TNFAIP6  FHDC1  DAPP1  GFI1  GSPT1  RGCC  TSPAN5  BPGM  FBXO7  GLRX5  SPTB  MARCH8  SLC14A1  RUNDC3A  CA1  KLC3  SELENBP1  AHSP  SLC4A1  EPB42  MYL4  SLC25A39  FECH  RAP1GAP  TNS1  HEMGN  NFIX  KLF1  FAM46C  KANK2  IGF2BP2  YBX3  RPIA  ASCC2  SLC2A1  RBM38  TRAK2  UBXN6  SIAH2  DNAJB2  TRIM58  PDZK1IP1  TMCC2  ANK1  BCL2L1  DMTN  MKRN1  FKBP8  ARHGEF12  GMPR  PBX1  RANBP10  SRRD  NEDD4L  ALDH5A1  SHARPIN  RNF187  CNPPD1  TEX2  AZU1  ELANE  MYL9  AP001189.4  SPARC  DEFA3  GAS2L1  PTGDR2  LTB  PARP14  IFIT1  IFIT3  SPN  ZBTB16  DDX58  XAF1  RTP4  ISG15  ETV7  LRRC8A  IFITM1  GBP5  RNF167  CIZ1  HERC5  CNOT3  AP2A1  IFIT2  IFI44  ZFHX3  TNFSF13B  ADGRE1  DOC2B  DPYSL2  IFI6  INHBA  S100A10  PLEKHG1  TCF3  PTMS  RP11 20D14.6  TGM2  LSM6  RAD23A  EPB41  TMEM63B  NPRL3  CMPK2  FTH1  SH3BGRL3  RP11 J o u r n a l P r e -p r o o f