key: cord-0719143-nna1icb6
authors: Roussel, M.; Ferrant, J.; Reizine, F.; Le Gallou, S.; Dulong, J.; Carl, S.; Lesouhaitier, M.; Gregoire, M.; Bescher, N.; Verdy, C.; Latour, M.; Bézier, I.; Cornic, M.; Leonard, S.; Feuillard, J.; Tiwari, V.K.; Tadié, J.M.; Cogné, M.; Tarte, K.
title: Mass cytometry and artificial intelligence define CD169 as a specific marker of SARS-CoV2-induced acute respiratory distress syndrome
date: 2020-09-22
journal: bioRxiv
DOI: 10.1101/2020.09.22.307975
sha: 7e63ccafcad00c5ac89dc6d5bdcefea60dc69a0f
doc_id: 719143
cord_uid: nna1icb6

Acute respiratory distress syndrome (ARDS) is the main complication of COVID-19, requiring admission to Intensive Care Unit (ICU). Despite recent immune profiling of COVID-19 patients, to what extent COVID-19-associated ARDS specifically differs from other causes of ARDS remains unknown, To address this question, we built 3 cohorts of patients categorized in COVID-19negARDSpos, COVID-19posARDSpos, and COVID-19posARDSneg, and compared their immune landscape analyzed by high-dimensional mass cytometry on peripheral blood followed by artificial intelligence analysis. A cell signature associating S100A9/calprotectin-producing CD169pos monocytes, plasmablasts, and Th1 cells was specifically found in COVID-19posARDSpos, unlike COVID-19negARDSpos patients. Moreover, this signature was shared by COVID-19posARDSneg patients, suggesting severe COVID-19 patients, whatever they experienced or not ARDS, displayed similar immune dysfunctions. We also showed an increase in CD14posHLA-DRlow and CD14lowCD16pos monocytes correlated to the occurrence of adverse events during ICU stay. Our study demonstrates that COVID-19-associated ARDS display a specific immune profile, and might benefit from personalized therapy in addition to standard ARDS management. One Sentence Summary COVID-19-associated ARDS is biologically distinct from other causes of ARDS.

The SARS-Coronavirus-2 (SARS-CoV-2) virus has currently affected more than 30 million people worldwide, requiring admission to Intensive Care Unit (ICU) for more than 2 million patients (1) .

Whereas most patients exhibit mild-to-moderate symptoms, acute respiratory distress syndrome (ARDS) is the major complication of the coronavirus disease 2019 (COVID-19) (2, 3) , leading to prolonged ICU stays, and high frequency of secondary complications, notably cardiovascular events, thrombosis, pulmonary embolism, and stroke (1, 4) . The immune system plays a dual role in COVID-19, contributing to both virus elimination and ARDS development. Excessive inflammatory response has been proposed as the leading cause of COVID-19-related clinical complications, thus supporting intensive efforts to better understand the specificities and mechanisms of SARS-CoV-2-induced immune dysfunction (5, 6) . Moreover, even if antiviral strategies, such as those provided by remdesivir or convalescent plasma, can lower the viral burden, no antiviral treatment has yet been able to prevent the evolution of some patients towards deregulated inflammation and critical respiratory complications. Recent data however suggest a benefit of corticosteroids in lowering overall mortality in COVID-19 patients with moderate disease, severe disease, and ARDS (7) . However, steroid therapy could be harmful in some specific ARDS etiologies, such as in influenza-associated ARDS (8) . A better understanding of the etiology-specific immune dysfunctions underlying ARDS development and severity is thus a major unmet need to design specific therapeutic strategy.

A number of high-resolution studies have recently concentrated on the determination of circulating markers that can distinguish severe from mild forms of COVID-19, providing a tremendous amount of data describing phenotypic and functional alterations in T cell, B cell, and myeloid cell subsets (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) . In particular, CD14 pos HLA-DR low , CD14 pos CD16 pos , CD14 low CD16 pos , and immature monocytes were demonstrated as increased among peripheral blood mononuclear cells (PBMCs) from critically ill COVID-19 patients (11, 16, 18, (21) (22) (23) . Various alterations of lymphoid cells have also been described, including a T-cell lymphopenia, predictive of patient outcome, a broad T-cell activation including Th1, Th2, and Th17, an alteration of B-cell and T-cell repertoires, and a strong increase of plasmablasts, most prominent in ARDS COVID-19 patients (12, 20, (24) (25) (26) .

Importantly, whereas non-COVID-19 ARDS is associated with a large panel of immune alterations, COVID-19 ARDS immune profiling was performed using healthy donors as a control, thus precluding any conclusions on whether reported immune alterations could be related to COVID- 19 and/or ARDS status. Answering this question has potential to decipher whether ARDS induced by SARS-CoV-2 is mechanistically different from other ARDS etiologies.

To fill this gap, we performed a high-throughput mass cytometry approach on PBMCs obtained from 3 complementary cohorts of 12 COVID-19 neg ARDS pos , 13 COVID-19 pos ARDS pos , and 17 COVID-19 pos ARDS neg patients. We report common myeloid cell alterations in all COVID-19 patients, which are absent from non-COVID-19 ARDS patients. This includes in particular a strong increase of an unusual population of activated monocytes showing upregulated expression of CD169, associated with major COVID-19-specific alterations of T and B-cell compartments.

Analyses were performed on a cohort of 63 cryopreserved PBMC samples isolated from 42 patients included in ICU (n = 36) or infectious standard ward (n = 6). The demographic characteristics of patients included are provided in Table 1 . All patients but one were classified as severe at admission, requiring oxygen at a flow rate higher than 2 liters/min. ARDS was defined in accordance with international guidelines (27) . Patients were classified in 3 groups: COVID-19 neg ARDS pos (n = 12, ARDS stages: 1 mild, 4 moderate, 7 severe), COVID-19 pos ARDS pos (n = 13, ARDS stages: 8 moderate, 5 severe), and COVID-19 pos ARDS neg (n = 17, including 11 from ICU and 6 from infectious standard ward). In the COVID-19 pos ARDS neg , no statistical differences were noticed for immune cell abundance or phenotype between ICU and standard ward patients. Within the COVID-19 neg ARDS pos group, ARDS etiologies were bacterial pneumonia (n = 9), antisynthetase syndrome (n = 1), and unknown (n = 2). No corticoid therapy was started at the time of sampling. For 21 patients, a second blood sample obtained on day 7 after enrollment was studied (n = 7 for COVID-19 neg ARDS pos , n = 8 for COVID-19 pos ARDS pos , and n = 6 for COVID-19 pos ARDS neg ).

To decipher the impact of SARS-CoV2 on circulating immune cells, we characterized PBMCs from COVID-19 pos versus COVID-19 neg patients at admission (D0) using two separate mass cytometry panels exploring myeloid and lymphoid subsets, respectively (Table S1 ). The full pipeline of analysis is depicted in fig. S1 . First, we performed an unbiased discovery approach with CellCnn, a neural network-based artificial intelligence algorithm allowing analysis of single-cell data and detection of cells associated with clinical status (28) (29) (30) . During training, CellCnn learns combinations of weights for each marker in a given panel that best discriminate between groups of patients. These weight combinations, called filters, can be used to highlight the specific profiles of cells associated with patient status. We identified the best-performing CellCnn filters for both the myeloid and the lymphoid panels highlighting a population of cells significantly enriched in COVID-19 pos patients as compared to COVID-19 neg patients (P < 0.0001 for both panels) (Fig. 1A ).

Projecting these cells on tSNE maps generated with either the myeloid or the lymphoid panels revealed that they fell into several distinct areas (Fig. 1B) . The cells selected by the CellCnn filter on the myeloid panel showed high expression for CD169, CD64, S100A9, CD11b, CD33, CD14, and CD36 compared to background, while the cells selected by the CellCnn filter on the lymphoid panel showed high expression for CD38 and CXCR3 ( Fig.1B and Fig. S2 ). This broad and unbiased approach showed that immune markers, in particular related to monocytes, segregated COVID-19 neg and COVID-19 pos patients.

To investigate circulating monocyte heterogeneity and define consistent phenotypes, we used the FlowSOM algorithm. This approach led to the identification of 15 monocyte metaclusters from the myeloid panel ( Fig. 2A ). In particular, Mo30, Mo11, and Mo28 metaclusters were defined by higher expression of CD16 and lower expression of CD14, CD36, and CD64, corresponding to a non-classical monocyte phenotype. Mo21 and Mo22 were defined by the high expression of S100A9 and the low expression of CD36. Finally, Mo243 and Mo180 strongly expressed S100A9, CD169, and CD36. To assess the phenotypic changes in monocytes during SARS-CoV2 infection, we determined the frequencies of these metaclusters in each patient at admission and performed hierarchical clustering on these values (Fig. 2B) . The upper branch of the hierarchical clustering included 20 COVID pos (10 ARDS neg and 10 ARDS pos ) and 1 COVID neg ARDS pos patient whereas the lower branch included 10 COVID pos (7 ARDS neg and 3 ARDS pos ) and 11 COVID neg ARDS pos (chi-square = 0.001) (Fig. 2B) . We then analyzed the abundance of individual metaclusters and identified only 4 metaclusters out of 15 as differentially represented between the 3 groups of patients ( Fig. 2C and Fig. S3 ). In particular, within ARDS pos patients, Mo11 and M181 were less abundant in COVID-19 pos patients (P < 0.01 and P < 0.05, respectively), while Mo243 and Mo180 were more abundant (P < 0.05 and P < 0.001) (Fig. 2C ). No differences were detected within COVID-19 pos groups (ARDS pos versus ARDS neg ) (Fig. 2C) . Interestingly, Mo243 and Mo180 were both enriched in cells highly expressing CD169, CD64, CD36, and CD14 ( Fig. 2A and 2D ).

Additionally, Mo22 was present only in some COVID pos patients and also expressed CD169 (Fig.   2B ). Taken as a whole, Mo243, Mo180, and Mo22 clusters were highly enriched in COVID-19 pos patients when compared to COVID-19 neg patients (P < 0.0001), with no difference regarding the ARDS status (Fig. 2E) . Accordingly, CD169 was differentially expressed in COVID-19 pos versus COVID-19 neg patients (P < 0.001) (Fig. 2E) . As a whole, our study including COVID-19 and non-COVID-19 critically ill patients suggest a specificity of CD169 expression in COVID-19 patients, and greatly extend previous scRNAseq data showing an expansion of CD169-expressing monocytes in COVID-19 patients compared to healthy donors ( Fig. 2F ) (20) .

To define a more global immune pattern and the relationship between immune cells in the context of the SARS-CoV2 infection, we sought for correlation between frequencies of clusters of T, NK, B, and plasma cells (n = 136 clusters from the lymphoid panel, fig. S4 ) and the 4 monocyte metaclusters (Mo11, Mo181, Mo243, and Mo180) previously described. This analysis identified 70 clusters with significantly correlated variations (P < 0.05) (Fig. S5 ). To strengthen the relevance of these correlations, we restrained further analysis to the 29 strongest relationships (R > 0.5 or < -0.5 and P < 0.01) between Mo180 or Mo243 (the two metaclusters enriched in COVID-19 patients) and other immune cell subsets ( Fig. 3A and Table S2 ). As expected, Mo180 and Mo243 clusters were correlated (R = 0.93). Moreover, they were positively correlated with 18 clusters of T (n = 6), NK (n = 10), and plasma cells (n = 2), and inversely correlated with 11 clusters of T (n = 9), and NK cells (n = 2) (Fig. 3A) . Among positively correlated clusters, plasmo_183 and plasmo_198 similarly expressed CD38, CD44, and CD27, whereas plasmo_183 was high for Ki-67 and HLA-DR, corresponding to an early plasma cell phenotype (Fig. 3B ). NK cells were all marked by CD7

and T-bet expression, NK_209 being CD8 high , and NK_241 and NK_197 displaying a Ki-67 high proliferating phenotype. The related T8_147 and T8_161 clusters exhibited a CD45RA high CD45RO low CCD7 low CD27 low Tbet high CD38 high effector phenotype. Few T4 clusters were positively correlated with Mo180 and Mo243, among them T4_106 displayed an effector memory proliferating phenotype (Ki-67 high CD45RA low CCR7 low CD45RO high CD27 high and CTLA4 high PD1 high ). T4_25 was also marked by an effector memory phenotype (CD45RA low CCR7 low CD45RO pos ) and displayed a CD27 low CD127 pos CCR6 pos CxCR3 neg CD161 pos Th17 profile (Fig. 3B ). Conversely, some T4 clusters were inversely correlated with Mo_180 and Mo_243, in particular clusters T4_6, T4_20, and T4_34, all three corresponding to naïve cells (CD45RA high CD45RO low CCR7 high ), and T4_59 expressing a Th2 phenotype (CCR4 high ). We then As previously discussed, T4_6, T4_20, and T4_34 corresponded to naïve cells, whereas within the effector memory cells, T4_7 and T4_45 were CD127 low , T4_24, T8_99, and T8_113 were CD127 high , and T4_59 was CCR4 high . Conversely, 13 clusters were enriched in COVID-19 pos ARDS pos compared to COVID-19 neg ARDS pos including: i) CTLA4 high PD1 high effector memory activated CD4 Tcells (T4_106); ii) Tbet high Th1-like CD8 effector phenotype (T8_146, T8_147, and T8_161); iii) cytotoxic mature CD16 pos CD56 low CD7 pos Tbet pos CD127 neg NK cells (NK_209, NK_241, NK_242, and NK_244) with in particular proliferating Ki-67 high NK cells (NK_241); and iv) proliferating plasmablasts (plasmo_183) and mature plasma cells (plasmo_198) (Fig. 3B and   Fig. 3C ). Of note, no cluster was differentially expressed between COVID-19 pos ARDS pos and COVID-19 pos ARDS neg groups ( Fig. 3C and Fig. S6) . Then, to explore the whole immune profile and define relationship with groups of patients, we performed correspondence analysis (CA) using, as a variable, the abundance of the myeloid (n = 4) and the lymphoid (n = 22) clusters differentially expressed between groups of patients (Fig. 3D) . CA was developed to analyze frequency tables and visualize similarities between patients and co-occurrence of cell subsets (31) . The first and second dimension of the correspondence analysis explained 80.5 % and 13.5 % of the difference, respectively (Fig. 3D) . The top-ten cell populations explaining the difference between COVID pos and COVID neg patients were Mo243, Mo180, T8_146, NK_244, and T8_161 being increased and Mo181, T4_6, Mo11, T8_99, and T4_45 being decreased in COVID pos . Altogether, these subsets corresponded to an increase in inflammatory monocytes (CD169 high CD64 high ), Tbet high Th1-like CD8 T cells, and mature NK cells and a decrease in naïve T4 cells and effector memory T4 and T8 cells. Interestingly, only the first dimension of the correspondence analysis segregated COVID-19 pos ARDS pos from COVID-19 neg ARDS pos (P < 0.001) and no statistical differences were found between COVID-19 pos ARDS pos and COVID-19 pos ARDS neg (Fig. 3D) .

We next performed mass cytometry analysis for 21 patients at day 7 of hospitalization, including 7 COVID-19 neg ARDS pos , 8 COVID-19 pos ARDS pos , and 6 COVID pos ARDS neg patients, in order to follow up the kinetic of PBMC phenotypic alterations. The 42 samples (21 at day 0 and 21 at day 7) were parsed by correspondence analysis using, as a variable, the abundance of myeloid and lymphoid clusters (Fig. 4A ). The first and second dimensions of the correspondence analysis explained 85.1 % and 9 % of the differences acquired between D0 and D7. The first dimension captured the difference between D0 and D7 only for COVID-19 pos ARDS pos (P < 0.01) (Fig. 4A) .

Because of the limited number of samples, only a trend was observed for COVID pos ARDS neg (P = 0.062). The top-five enriched populations explaining the differences between D0 and D7 for COVID-19 pos ARDS pos patients were Mo11, Mo181, T8_113, T4_34, and NK_197, corresponding to an enrichment in non-classical monocytes (CD14 low CD16 high CD64 low CD36 low S100A9 high ), in M-MDSC-like (HLA-DR low S100A9 high ), in effector memory CD127 high T8 cells, in T4 naïve cells, and in Ki-67 high proliferating NK cells. These 5 cell subsets were integrated in an immune score combining their fold change between D0 and D7. To define the relevance of this immune score in discriminating COVID-19 patients with unfavorable prognosis, we built a clinical score as the sum of events occurring during ICU stay (thromboembolic, ICU-acquired infection, septic shock, renal failure, and deaths) ( Table 1) . Interestingly, both the clinical and the immune scores were found correlated in severe COVID-19 patients, irrespectively of their ARDS status (Spearman R = 0.58; P < 0.05) (Fig. 4B ).

Immune response to COVID-19 infection has been recently intensively studied at transcriptomic and proteomic levels. However, most studies focused on either the lymphoid (15, 17, 19) or the myeloid compartments (9, 16, 18) , and only few performed a wide analysis of the circulating immune landscape (10, 12, 13, 20, 32) , thus precluding the definition of complex patterns of immune parameter alterations associated with COVID-19 severity or physiopathology. Moreover, these works were designed to identify differences in immune cell subset frequencies between COVID-19 patients and healthy donors, and eventually correlated with the severity of the disease, but did not include severe non-COVID-19 patients as controls, although critically ill patients were largely demonstrated previously to display immune reprogramming (33) . ARDS is a major adverse event occurring during ICU stay, leading to an overall mortality rate of 40 % to 60 %. Whether COVID-19 associated ARDS is clinically and biologically similar to other causes of ARDS remains controversial (34, 35) . To address this point, we characterized for the first time, by mass cytometry, the immune landscape in COVID-19-associated ARDS compared to other causes of ARDS. We demonstrated that an increase of CD169 pos monocytes, correlated with specific changes of T, plasma, and NK cell subsets, defines COVID-19-associated ARDS and is not found for bacteriaassociated ARDS, suggesting a COVID-19 specific immune reprogramming.

The amplification of CD169 pos circulating monocytes has already been highlighted in the context of COVID-19 (11, 18, 36, 37) , and is reminiscent of other inflammatory conditions found in viral infections, such as with Human Immunodeficiency Virus or Epstein-Barr Virus, in which the CD169 sialoadhesin is induced in an IFN-dependent manner on the surface of circulating monocytes (38, 39) . Consistent with the inflammatory response, we showed that the accumulation of CD169 pos monocytes in COVID-19 pos patients is positively correlated with an increase of plasmablasts and mature plasma cells, Th1-like CD8 effector T cells, cytotoxic mature NK cells, and activated CD4 effector memory T cells displaying a CTLA4 high PD1 high phenotype. CD169 pos activated monocytes were detected in mild disease (18) , and were proposed to rise rapidly and transiently in patients with COVID-19, in association with a high expression of IFNγ and CCL8 (11) . This could be due to the transient nature of this monocytic population, either losing CD169, being short-lived, or being recruited into tissues as CD169 pos macrophages, as suggested by the high expression of CCR2 on Mo243 and Mo180, the two monocyte subsets identified here in COVID-19 patients, and the local inflammation and lung tissue destruction mediated by monocytederived macrophages in severe cases of SARS-CoV2 infections (40, 41) . Interestingly, we also found an upregulation of cytoplasmic S100A9 in monocyte subsets specifically amplified in COVID-19 patients irrespectively of their ARDS status. These data suggest that, in the early stage of the disease, monocytes could contribute to the burst of circulating calprotectin (S100A8/S100A9), recently proposed to contribute to the secondary cytokine release syndrome described in severe COVID-19 and attributed to neutrophils (18) .

Within severe COVID-19 patients, we detected no significant differences between ARDS pos and ARDS neg immune profiles, indicating a specificity of the phenotype induced by SARS-CoV2 infection, irrespectively of the respiratory complications. While most published studies showed differences between mild and severe COVID-19 diseases, some of their conclusions might be obscured by the fact that ARDS by itself, mechanical ventilation, and/or nonspecific treatments might impact immune parameters (42) . A strength of our study comparing two groups of severe COVID-19 patients with or without ARDS is to highlight features directly related to the viral infection rather than to its respiratory complications or their treatment. Importantly, our cohort was homogeneous regarding treatment with in particular no immunosuppressive therapy at the time of sampling.

The small size of our cohort did not allow us to pinpoint a mortality prognostic factor based on our phenotypic data. However, we identified a specific immune pattern associated with the occurrence of the major adverse clinical events (thrombosis, nosocomial infection, septic shock, acute renal failure, and death) described in COVID-19 and combined as a clinical score. In particular, an increase of non-classical CD14 low CD16 pos monocytes (Mo11), and CD14 pos HLA-DR low M-MDSClike (Mo181), both not expressing CD169, are markers of adverse events. This suggests that besides the early increase of CD169 pos monocytes in all COVID-19 patients associated with T-cell dysfunctions, the immunological response to SARS-CoV2 infection features multiple alterations of monocytic subsets reflecting the severity of the disease. Consistent with these data, it was shown that CD14 pos HLA-DR low cells were increased in critical COVID-19 patients (16, 21) , while CD14 low CD16 pos monocytes were correlated with the length of stay in ICU (11, 18) . To our knowledge, our study is the first to correlate the accumulation of non-classical monocytes and M-MDSCs occurring during the first days of ICU to adverse events.

Besides the low number of included patients, our study has some limitations. By focusing on severe patients with and without ARDS, we cannot make conclusions about phenotypic changes in mild and moderate diseases. Moreover, since the mass cytometry was conducted on PBMCs, we lack information on the neutrophil lineage, which appears affected in the COVID-19 (18) . It would also be interesting to link these data with in situ data from lung tissue samples and bronchoalveolar lavages. However, our detailed analysis of circulating immune cells shows that immune monitoring of severe COVID-19 patients brings interesting prognostic biomarkers independently of their clinical classification in ARDS pos versus ARDS neg . Moreover, we demonstrated that at the biological level, COVID-19 associated ARDS is different from other causes of ARDS, and might benefit from personalized therapy in addition to standard ARDS management (18, 43) .

This study was performed in the infectious diseases department and intensive care unit (ICU) at Rennes University Hospital. The study design was approved by our ethic committee (CHU Rennes, n°35RC20_9795_HARMONICOV, ClinicalTrials.gov Identifier: NCT04373200) and informed consent was obtained from patients in accordance with the Declaration of Helsinki. Peripheral blood was collected in tubes containing lithium heparin from COVID-19 neg ARDS pos , COVID-19 pos ARDS pos , and COVID-19 pos ARDS neg patients. Peripheral blood samples were drawn at D0 and D7. PBMC were isolated from whole blood using ficoll before cryopreservation. All patients provided written informed consent. The following data were recorded: gender, age, preexisting chronic kidney disease and acute kidney failure during the ICU stay (44) , preexisting chronic heart failure (45), Body Mass Index (BMI), SAPS II at admission (46) , duration of mechanical ventilation, length of hospital stay, and outcome (alive or dead) on day 7, day 30 and day 90. The occurrence of nosocomial infection, defined following CDC criteria as previously described (47) , was also recorded during hospital stay. For each patient, a clinical score was built to summarize the occurrence of adverse clinical events frequently encountered during hospitalization (47, 48) .

Each of the following events: thromboembolic events, nosocomial infection, septic shock, acute renal failure, and death counting as one point, the score varies from 0 (no adverse events) to 5.

Patients characteristics are reported in table 1.

PBMC from patients were thawed. Briefly, cells were stained 5 minutes in RPMI supplemented 

After acquisition, intrafile signal drift was normalized and .fcs files were obtained using CyTOF software. To diminish batch effects, all files were normalized on EQ Beads (Fluidigm Sciences) using the premessa R package (https://github.com/ParkerICI/premessa). Files were then uploaded to the Cytobank cloud-based platform (Cytobank, Inc.). Data were first arcsinh-transformed using a cofactor of 5. For all files, live single cells were selected by applying a gate on DNA1 vs. DNA2 followed by a gate on DNA1 vs. Cisplatin, then beads were removed by applying a gate on the beads channel (Ce140Di) vs. DNA.1 Normalized, transformed and gated values were exported as FCS files.

Identification of a Covid-19-specific cell-identity signature was carried out using the CellCnn algorithm (28) , implemented in Pytorch in the ScaiVision platform (version 0.3.6, © Scailyte AG).

Briefly, this is a supervised machine learning algorithm that trains a convolutional neural network with a single layer to predict sample-level labels using single-cell data as inputs. We first analyzed the myeloid panel. Out of 30 metaclusters defined by the FlowSOM approach, we identified 13 metaclusters with monocyte markers, other metaclusters contained other cell types, low count of cells or remaining doublets or dead cells. We visually identified 2 (Mo18 and Mo26) out or the 13 metaclusters that were heterogeneous. These 2 metaclusters were manually splited into 2 new metaclusters (identified respectively as Mo180, Mo181 and Mo214, Mo243) (Fig. S1B) .

Thus, altogether we analyzed 15 metaclusters of myeloid cells. Regarding the lymphoid compartment, we noticed that FlowSOM defined metaclusters at the lineage level, thus we retain all the 136 clusters included in 10 metaclusters of interest (i.e. containing lymphoid lineage markers) (Fig. S1C) . All metaclusters and clusters phenotypes including their abundances and mean marker intensity were then exported from Cytobank for further analyses. Cytometry data was explored with Kaluza Analysis Software (Beckman Coulter). Hierarchical clustering and heatmaps were generated with R v3.6.3, using Rstudio v1.2.5033 and the pheatmap package.

Statistical analyses were performed with Graphpad Prism 8.4.3. P values were defined by a Kruskal-Wallis test followed by a Dunn's post-test for multiple group comparisons or by Wilcoxon matched-pairs signed rank tests as appropriate. Correlations were calculated using Spearman test.

* P < 0.05, ** P < 0.01, *** < 0.001, and **** P < 0.0001. Hierarchical clustering of the patients was performed using euclidean distance and complete clustering. Correspondence analysis was performed using the package factoshiny using as variable the abundance in cell subsets for each patient.

Figure S1: CyTOF experimental design and data analysis pipeline (20) . Kruskal-Wallis test with Dunn's multiple comparison correction, *P < 0.05, **P < 0.01, ***P < 0.001. 

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CFTR² (COVID-19 Fast Track Recherche Rennes) grant (to F.R.) and by the Fondation pour la Recherche Médicale (FRM) and the Agence Nationale de la Recherche (ANR), Flash Covid-19 joint grant (HARMONICOV to M.Cog.)

Author contributions: Conceptualization

A method for early detection of propensity to severe clinical manifestations

Methods" submitted June 11 th 2020 under University hospital of Rennes and Scailyte AG names

and Data and materials availability: All data is available in the main text or the supplementary materials

Correlation between Mo180 and Mo243 and lymphoid clusters (see heatmap for all lymphoid clusters and markers in Fig. S4) from all patients at D0 (COVID-19 neg ARDS pos [n=12], COVID-19 pos ARDS pos [n=13], and COVID-19 pos ARDS neg

001) strongly correlated with Mo180 and Mo243 (see heatmap for all clusters and markers in Fig. S4). (C) Abundance of lymphoid clusters differentially expressed between groups, among singlet cells analyzed. Kruskal-Wallis test with Dunn's multiple comparison correction, *P < 0.05, **P < 0.01, ***P < 0.001 [see all clusters in Fig. S6]). (D) Two first dimensions of correspondence analysis accounting for 84 % of the association between immune clusters differentially expressed between groups (n= 4 monocyte-and n=22 lymphoid-clusters), and patients. For clarity

Age, median (IQR)

We thank all donors, families, and surrogates, as well as the medical personnel in charge of patient care. We thank Catherine Blanc and Aurelien Corneau, from the CyPS core facility at Sorbonne University, Paris for access to the Helios mass cytometer. Funding: This work