Comparative immune profiling of acute respiratory distress syndrome patients with or without SARS-CoV-2 infection

Mikael Roussel, Juliette Ferrant, Florian Reizine, Simon Le Gallou, Joelle Dulong, Sarah Carl, Matheiu Lesouhaitier, Murielle Gregoire, Nadège Bescher, Clotilde Verdy, Maelle Latour, Isabelle Bézier, Marie Cornic, Angélique Vinit, Céline Monvoisin, Birgit Sawitzki, Simon Leonard, Stéphane Paul, Jean Feuillard, Robin Jeannet, Thomas Daix, Vijay K Tiwari, Jean Marc Tadié, Michel Cogné, Karin Tarte, Mikael Roussel, Juliette Ferrant, Florian Reizine, Simon Le Gallou, Joelle Dulong, Sarah Carl, Matheiu Lesouhaitier, Murielle Gregoire, Nadège Bescher, Clotilde Verdy, Maelle Latour, Isabelle Bézier, Marie Cornic, Angélique Vinit, Céline Monvoisin, Birgit Sawitzki, Simon Leonard, Stéphane Paul, Jean Feuillard, Robin Jeannet, Thomas Daix, Vijay K Tiwari, Jean Marc Tadié, Michel Cogné, Karin Tarte

Abstract

Acute respiratory distress syndrome (ARDS) is the main complication of coronavirus disease 2019 (COVID-19), requiring admission to the intensive care unit (ICU). Despite extensive immune profiling of COVID-19 patients, to what extent COVID-19-associated ARDS differs from other causes of ARDS remains unknown. To address this question, here, we build 3 cohorts of patients categorized in COVID-19-ARDS+, COVID-19+ARDS+, and COVID-19+ARDS-, and compare, by high-dimensional mass cytometry, their immune landscape. A cell signature associating S100A9/calprotectin-producing CD169+ monocytes, plasmablasts, and Th1 cells is found in COVID-19+ARDS+, unlike COVID-19-ARDS+ patients. Moreover, this signature is essentially shared with COVID-19+ARDS- patients, suggesting that severe COVID-19 patients, whether or not they experience ARDS, display similar immune profiles. We show an increase in CD14+HLA-DRlow and CD14lowCD16+ monocytes correlating to the occurrence of adverse events during the ICU stay. We demonstrate that COVID-19-associated ARDS displays a specific immune profile and may benefit from personalized therapy in addition to standard ARDS management.

Conflict of interest statement

J. Ferrant, F.R., S.L.G., J.D., M. Lesouhaitier, M.G., N.B., C.V., M. Latour, I.B., M. Cornic, A.V., C.M., B.S., S.L., S.P., J. Feuillard, R.J., T.D., and M. Cogné declare no competing interests. M.R., S.C., V.K.T., J.M.T., and K.T. are the inventors of a patent, EP 20305642.9, “A method for early detection of propensity to severe clinical manifestations methods” submitted June 11, 2020 under University Hospital of Rennes and Scailyte AG names.

© 2021 The Authors.

Figures

Graphical abstract
Graphical abstract
Figure 1
Figure 1
SARS-CoV-2 induces specific phenotype of circulating immune cells CellCnn analysis performed on single cells from myeloid (top) and lymphoid (bottom) panels on 39 samples at admission (day 0) (COVID-19− [n = 9] and COVID-19+ [n = 30]) (A) Frequencies of cells discovered by the best-performing CellCnn filter in COVID-19− (blue) and COVID-19+ (orange) patients for each panel. Mann-Whitney tests, ∗∗∗∗p < 0.0001. (B) Cells defined by the best-performing CellCnn filters enrichment shown on tSNE and representative markers for each panel (CD14 and CD38 [see additional markers in Figure S2]).
Figure 2
Figure 2
CD169 monocytes are enriched in SARS-CoV-2-infected patients (A) Heatmap of the 15 monocyte metaclusters defined after FlowSOM analysis. (B) Relative abundance of metaclusters among monocytes for each patient and hierarchical clustering of COVID-19−ARDS+ (n = 12, green), COVID-19+ARDS+ (n = 13, blue), and COVID-19+ARDS− (n = 17, red). (C) Abundance of metaclusters differentially expressed between groups, among singlet cells analyzed. (D) Expression of the corresponding markers (mean metal intensity) for background (gray), Mo11 and Mo181 (orange), and Mo243 and Mo180 (blue) metaclusters. (E) Abundance of Mo22, Mo180, and Mo243 and expression of CD169 (box and whiskers with 10th and 90th percentiles). (F) Uniform manifold approximation and projection (UMAP) from scRNA-seq of COVID-19 patients (COVID-19) and healthy donors (healthy) highlighting CD14 and CD169 expression (data adapted from Wilk et al.25). Kruskal-Wallis test with Dunn’s multiple comparison correction, ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.
Figure 3
Figure 3
Monocyte metaclusters enriched in COVID-19 are correlated with effector memory T cells and plasma cells (A) Correlation between Mo180 and Mo243 and lymphoid clusters (see heatmap for all lymphoid clusters and markers in Figure S2) from all patients at D0 (COVID-19−ARDS+ [n = 12], COVID-19+ARDS+ [n = 13], and COVID-19+ARDS− [n = 17]). Only strong correlations (Spearman R > 0.5 or R < −0.5 and p < 0.01) are shown (see all significant correlations [p < 0.05] in Figure S2 and Table S4). (B) Heatmap showing marker expression for the lymphoid clusters (Spearman R > 0.5 or R 

Figure 4

Evolution of immune cell subsets…

Figure 4

Evolution of immune cell subsets between D0 and D7, defines high-risk clinical grade…

Figure 4
Evolution of immune cell subsets between D0 and D7, defines high-risk clinical grade COVID-19 patients (A) Two first dimensions of correspondence analysis accounting for 94.1% of the association between immune clusters differentially expressed between groups (n = 4 monocyte and n = 22 lymphoid clusters) and patients for which a follow-up of 7 days was available (COVID-19−ARDS+ [n = 7], COVID-19+ARDS+ [n = 8], and COVID-19+ARDS− [n = 6]). For clarity, patients and immune cells are shown on 2 different plots. Dimensions 1 and 2 coordinates were compared between D0 and D7 for each group of patients. Wilcoxon matched-pairs signed rank tests, ∗∗p < 0.01. (B) Spearman correlation between immune and clinical score for COVID-19+ patients (ARDS+ [n = 8] and ARDS– [n = 6]).
Figure 4
Figure 4
Evolution of immune cell subsets between D0 and D7, defines high-risk clinical grade COVID-19 patients (A) Two first dimensions of correspondence analysis accounting for 94.1% of the association between immune clusters differentially expressed between groups (n = 4 monocyte and n = 22 lymphoid clusters) and patients for which a follow-up of 7 days was available (COVID-19−ARDS+ [n = 7], COVID-19+ARDS+ [n = 8], and COVID-19+ARDS− [n = 6]). For clarity, patients and immune cells are shown on 2 different plots. Dimensions 1 and 2 coordinates were compared between D0 and D7 for each group of patients. Wilcoxon matched-pairs signed rank tests, ∗∗p < 0.01. (B) Spearman correlation between immune and clinical score for COVID-19+ patients (ARDS+ [n = 8] and ARDS– [n = 6]).

References

    1. Williamson E.J., Walker A.J., Bhaskaran K., Bacon S., Bates C., Morton C.E., Curtis H.J., Mehrkar A., Evans D., Inglesby P. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584:430–436.
    1. Guan W.-J., Ni Z.-Y., Hu Y., Liang W.-H., Ou C.-Q., He J.-X., Liu L., Shan H., Lei C.L., Hui D.S.C., China Medical Treatment Expert Group for Covid-19 Clinical Characteristics of Coronavirus Disease 2019 in China. N. Engl. J. Med. 2020;382:1708–1720.
    1. Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., Zhang L., Fan G., Xu J., Gu X. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497–506.
    1. Helms J., Tacquard C., Severac F., Leonard-Lorant I., Ohana M., Delabranche X., Merdji H., Clere-Jehl R., Schenck M., Fagot Gandet F. High risk of thrombosis in patients with severe SARS-CoV-2 infection: a multicenter prospective cohort study. Intens. Care Med. 2020;46:1089–1098.
    1. Schultze J.L., Aschenbrenner A.C. COVID-19 and the human innate immune system. Cell. 2021;184:1671–1692.
    1. Chen G., Wu D., Guo W., Cao Y., Huang D., Wang H., Wang T., Zhang X., Chen H., Yu H. Clinical and immunological features of severe and moderate coronavirus disease 2019. J. Clin. Invest. 2020;130:2620–2629.
    1. Jeannet R., Daix T., Formento R., Feuillard J., François B. Severe COVID-19 is associated with deep and sustained multifaceted cellular immunosuppression. Intensive Care Med. 2020;46:1769–1771.
    1. Libster R., Pérez Marc G., Wappner D., Coviello S., Bianchi A., Braem V., Esteban I., Caballero M.T., Wood C., Berrueta M., Fundación INFANT–COVID-19 Group Early High-Titer Plasma Therapy to Prevent Severe Covid-19 in Older Adults. N. Engl. J. Med. 2021;384:610–618.
    1. Arabi Y.M., Murthy S., Webb S. COVID-19: a novel coronavirus and a novel challenge for critical care. Intensive Care Med. 2020;46:833–836.
    1. van Paassen J., Vos J.S., Hoekstra E.M., Neumann K.M.I., Boot P.C., Arbous S.M. Corticosteroid use in COVID-19 patients: a systematic review and meta-analysis on clinical outcomes. Crit. Care. 2020;24:696.
    1. Ni Y.N., Chen G., Sun J., Liang B.M., Liang Z.A. The effect of corticosteroids on mortality of patients with influenza pneumonia: a systematic review and meta-analysis. Crit. Care. 2019;23:99.
    1. Agrati C., Sacchi A., Bordoni V., Cimini E., Notari S., Grassi G., Casetti R., Tartaglia E., Lalle E., D’Abramo A. Expansion of myeloid-derived suppressor cells in patients with severe coronavirus disease (COVID-19) Cell Death Differ. 2020;27:3196–3207.
    1. Arunachalam P.S., Wimmers F., Mok C.K.P., Perera R.A.P.M., Scott M., Hagan T., Sigal N., Feng Y., Bristow L., Tak-Yin Tsang O. Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans. Science. 2020;369:1210–1220.
    1. Chen Z., John Wherry E. T cell responses in patients with COVID-19. Nat. Rev. Immunol. 2020;20:529–536.
    1. Chevrier S., Zurbuchen Y., Cervia C., Adamo S., Raeber M.E., de Souza N., Sivapatham S., Jacobs A., Bachli E., Rudiger A. A distinct innate immune signature marks progression from mild to severe COVID-19. Cell Rep. Med. 2020;2:100166.
    1. Hadjadj J., Yatim N., Barnabei L., Corneau A., Boussier J., Smith N., Péré H., Charbit B., Bondet V., Chenevier-Gobeaux C. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science. 2020;369:718–724.
    1. Lucas C., Wong P., Klein J., Castro T.B.R., Silva J., Sundaram M., Ellingson M.K., Mao T., Oh J.E., Israelow B., Yale IMPACT Team Longitudinal analyses reveal immunological misfiring in severe COVID-19. Nature. 2020;584:463–469.
    1. Mann E.R., Menon M., Knight S.B., Konkel J.E., Jagger C., Shaw T.N., Krishnan S., Rattray M., Ustianowski A., Bakerly N.D., NIHR Respiratory TRC. CIRCO Longitudinal immune profiling reveals key myeloid signatures associated with COVID-19. Sci. Immunol. 2020;5:eabd6197.
    1. Mathew D., Giles J.R., Baxter A.E., Oldridge D.A., Greenplate A.R., Wu J.E., Alanio C., Kuri-Cervantes L., Pampena M.B., D’Andrea K., UPenn COVID Processing Unit Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science. 2020;369:eabc8511.
    1. Ren X., Wen W., Fan X., Hou W., Su B., Cai P., Li J., Liu Y., Tang F., Zhang F. Large-scale single-cell analysis reveals critical immune characteristics of COVID-19 patients. bioRxiv. 2020 doi: 10.1101/2020.10.29.360479.
    1. Schulte-Schrepping J., Reusch N., Paclik D., Baßler K., Schlickeiser S., Zhang B., Krämer B., Krammer T., Brumhard S., Bonaguro L., Deutsche COVID-19 OMICS Initiative (DeCOI) Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment. Cell. 2020;182:1419–1440.e23.
    1. Sekine T., Perez-Potti A., Rivera-Ballesteros O., Strålin K., Gorin J.-B., Olsson A., Llewellyn-Lacey S., Kamal H., Bogdanovic G., Muschiol S., Karolinska COVID-19 Study Group Robust T Cell Immunity in Convalescent Individuals with Asymptomatic or Mild COVID-19. Cell. 2020;183:158–168.e14.
    1. Silvin A., Chapuis N., Dunsmore G., Goubet A.-G., Dubuisson A., Derosa L., Almire C., Hénon C., Kosmider O., Droin N. Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19. Cell. 2020;182:1401–1418.e18.
    1. Song J.-W., Zhang C., Fan X., Meng F.-P., Xu Z., Xia P., Cao W.J., Yang T., Dai X.P., Wang S.Y. Immunological and inflammatory profiles in mild and severe cases of COVID-19. Nat. Commun. 2020;11:3410.
    1. Wilk A.J., Rustagi A., Zhao N.Q., Roque J., Martínez-Colón G.J., McKechnie J.L., Ivison G.T., Ranganath T., Vergara R., Hollis T. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat. Med. 2020;26:1070–1076.
    1. Giamarellos-Bourboulis E.J., Netea M.G., Rovina N., Akinosoglou K., Antoniadou A., Antonakos N., Damoraki G., Gkavogianni T., Adami M.E., Katsaounou P. Complex Immune Dysregulation in COVID-19 Patients with Severe Respiratory Failure. Cell Host Microbe. 2020;27:992–1000.e3.
    1. Merad M., Martin J.C. Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages. Nat. Rev. Immunol. 2020;20:355–362.
    1. Ong E.Z., Chan Y.F.Z., Leong W.Y., Lee N.M.Y., Kalimuddin S., Haja Mohideen S.M., Chan K.S., Tan A.T., Bertoletti A., Ooi E.E., Low J.G.H. A Dynamic Immune Response Shapes COVID-19 Progression. Cell Host Microbe. 2020;27:879–882.e2.
    1. Reizine F., Lesouhaitier M., Gregoire M., Pinceaux K., Gacouin A., Maamar A., Painvin B., Camus C., Le Tulzo Y., Tattevin P. SARS-CoV-2-Induced ARDS Associates with MDSC Expansion, Lymphocyte Dysfunction, and Arginine Shortage. J. Clin. Immunol. 2021;41:515–525.
    1. Mudd P.A., Crawford J.C., Turner J.S., Souquette A., Reynolds D., Bender D., Bosanquet J.P., Anand N.J., Striker D.A., Martin R.S. Distinct inflammatory profiles distinguish COVID-19 from influenza with limited contributions from cytokine storm. Sci. Adv. 2020;6:eabe3024.
    1. De Biasi S., Meschiari M., Gibellini L., Bellinazzi C., Borella R., Fidanza L., Gozzi L., Iannone A., Lo Tartaro D., Mattioli M. Marked T cell activation, senescence, exhaustion and skewing towards TH17 in patients with COVID-19 pneumonia. Nat. Commun. 2020;11:3434.
    1. De Biasi S., Lo Tartaro D., Meschiari M., Gibellini L., Bellinazzi C., Borella R., Fidanza L., Mattioli M., Paolini A., Gozzi L. Expansion of plasmablasts and loss of memory B cells in peripheral blood from COVID-19 patients with pneumonia. Eur. J. Immunol. 2020;50:1283–1294.
    1. Vabret N., Britton G.J., Gruber C., Hegde S., Kim J., Kuksin M., Levantovsky R., Malle L., Moreira A., Park M.D., Sinai Immunology Review Project Immunology of COVID-19: Current State of the Science. Immunity. 2020;52:910–941.
    1. Ranieri V.M., Rubenfeld G.D., Thompson B.T., Ferguson N.D., Caldwell E., Fan E., Camporota L., Slutsky A.S., ARDS Definition Task Force Acute respiratory distress syndrome: the Berlin Definition. JAMA. 2012;307:2526–2533.
    1. Arvaniti E., Claassen M. Sensitive detection of rare disease-associated cell subsets via representation learning. Nat. Commun. 2017;8:14825.
    1. Galli E., Hartmann F.J., Schreiner B., Ingelfinger F., Arvaniti E., Diebold M., Mrdjen D., van der Meer F., Krieg C., Nimer F.A. GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis. Nat. Med. 2019;25:1290–1300.
    1. Krieg C., Nowicka M., Guglietta S., Schindler S., Hartmann F.J., Weber L.M., Dummer R., Robinson M.D., Levesque M.P., Becher B. High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat. Med. 2018;24:144–153.
    1. Bedin A.-S., Makinson A., Picot M.-C., Mennechet F., Malergue F., Pisoni A., Nyiramigisha E., Montagnier L., Bollore K., Debiesse S. Monocyte CD169 Expression as a Biomarker in the Early Diagnosis of Coronavirus Disease 2019. J. Infect. Dis. 2021;223:562–567.
    1. Bourgoin P., Soliveres T., Barbaresi A., Loundou A., Belkacem I.A., Arnoux I., Bernot D., Loosveld M., Morange P.-E., Michelet P. CD169 and CD64 could help differentiate bacterial from CoVID-19 or other viral infections in the Emergency Department. Cytometry A. 2021 doi: 10.1002/cyto.a.24314.
    1. Ortillon M., Coudereau R., Cour M., Rimmelé T., Godignon M., Gossez M., Yonis H., Argaud L., Lukaszewicz A.-C., Venet F. Monocyte CD169 expression in COVID-19 patients upon intensive care unit admission. Cytometry A. 2021 doi: 10.1002/cyto.a.24315.
    1. Chevrier S., Levine J.H., Zanotelli V.R.T., Silina K., Schulz D., Bacac M., Ries C.H., Ailles L., Jewett M.A.S., Moch H. An Immune Atlas of Clear Cell Renal Cell Carcinoma. Cell. 2017;169:736–749.e18.
    1. Kuri-Cervantes L., Pampena M.B., Meng W., Rosenfeld A.M., Ittner C.A.G., Weisman A.R., Agyekum R.S., Mathew D., Baxter A.E., Vella L.A. Comprehensive mapping of immune perturbations associated with severe COVID-19. Sci. Immunol. 2020;5:eabd7114.
    1. Laing A.G., Lorenc A., Del Molino Del Barrio I., Das A., Fish M., Monin L., Muñoz-Ruiz M., McKenzie D.R., Hayday T.S., Francos-Quijorna I. A dynamic COVID-19 immune signature includes associations with poor prognosis. Nat. Med. 2020;26:1623–1635.
    1. Delano M.J., Ward P.A. The immune system’s role in sepsis progression, resolution, and long-term outcome. Immunol. Rev. 2016;274:330–353.
    1. Ferrando C., Suarez-Sipmann F., Mellado-Artigas R., Hernández M., Gea A., Arruti E., Aldecoa C., Martínez-Pallí G., Martínez-González M.A., Slutsky A.S., Villar J., COVID-19 Spanish ICU Network Clinical features, ventilatory management, and outcome of ARDS caused by COVID-19 are similar to other causes of ARDS. Intensive Care Med. 2020;46:2200–2211.
    1. Gattinoni L., Coppola S., Cressoni M., Busana M., Rossi S., Chiumello D. COVID-19 Does Not Lead to a “Typical” Acute Respiratory Distress Syndrome. Am. J. Respir. Crit. Care Med. 2020;201:1299–1300.
    1. Carissimo G., Xu W., Kwok I., Abdad M.Y., Chan Y.-H., Fong S.-W., Puan K.J., Lee C.Y., Yeo N.K., Amrun S.N. Whole blood immunophenotyping uncovers immature neutrophil-to-VD2 T-cell ratio as an early marker for severe COVID-19. Nat. Commun. 2020;11:5243.
    1. Farina A., Peruzzi G., Lacconi V., Lenna S., Quarta S., Rosato E., Vestri A.R., York M., Dreyfus D.H., Faggioni A. Epstein-Barr virus lytic infection promotes activation of Toll-like receptor 8 innate immune response in systemic sclerosis monocytes. Arthritis Res. Ther. 2017;19:39.
    1. Rempel H., Calosing C., Sun B., Pulliam L. Sialoadhesin expressed on IFN-induced monocytes binds HIV-1 and enhances infectivity. PLoS ONE. 2008;3:e1967.
    1. Carvelli J., Demaria O., Vély F., Batista L., Chouaki Benmansour N., Fares J., Carpentier S., Thibult M.L., Morel A., Remark R., Explore COVID-19 IPH group. Explore COVID-19 Marseille Immunopole group Association of COVID-19 inflammation with activation of the C5a-C5aR1 axis. Nature. 2020;588:146–150.
    1. Liao M., Liu Y., Yuan J., Wen Y., Xu G., Zhao J., Cheng L., Li J., Wang X., Wang F. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat. Med. 2020;26:842–844.
    1. Bastard P., Rosen L.B., Zhang Q., Michailidis E., Hoffmann H.-H., Zhang Y., Dorgham K., Philippot Q., Rosain J., Béziat V., HGID Lab. NIAID-USUHS Immune Response to COVID Group. COVID Clinicians. COVID-STORM Clinicians. Imagine COVID Group. French COVID Cohort Study Group. Milieu Intérieur Consortium. CoV-Contact Cohort. Amsterdam UMC Covid-19 Biobank. COVID Human Genetic Effort Autoantibodies against type I IFNs in patients with life-threatening COVID-19. Science. 2020;370:eabd4585.
    1. Zhang Q., Bastard P., Liu Z., Le Pen J., Moncada-Velez M., Chen J., Ogishi M., Sabli I.K.D., Hodeib S., Korol C., COVID-STORM Clinicians. COVID Clinicians. Imagine COVID Group. French COVID Cohort Study Group. CoV-Contact Cohort. Amsterdam UMC Covid-19 Biobank. COVID Human Genetic Effort. NIAID-USUHS/TAGC COVID Immunity Group Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science. 2020;370:eabd4570.
    1. Galbraith M.D., Kinning K.T., Sullivan K.D., Baxter R., Araya P., Jordan K.R., Russell S., Smith K.P., Granrath R.E., Shaw J.R. Seroconversion stages COVID19 into distinct pathophysiological states. eLife. 2021;10:e65508.
    1. Stolk R.F., van der Pasch E., Naumann F., Schouwstra J., Bressers S., van Herwaarden A.E., Gerretsen J., Schambergen R., Ruth M.M., van der Hoeven J.G. Norepinephrine Dysregulates the Immune Response and Compromises Host Defense during Sepsis. Am. J. Respir. Crit. Care Med. 2020;202:830–842.
    1. Reyes, M., Filbin, M.R., Bhattacharyya, R.P., Sonny, A., Mehta, A., Billman, K., Kays, K.R., Pinilla-Vera, M., Benson, M.E., MGH COVID-19 Collection & Processing Team, et al. (2020). Induction of a regulatory myeloid program in bacterial sepsis and severe COVID-19. bioRxiv 10.1101/2020.09.02.280180.
    1. Thompson E.A., Cascino K., Ordonez A.A., Zhou W., Vaghasia A., Hamacher-Brady A., Brady N.R., Sun I.-H., Wang R., Rosenberg A.Z. Mitochondrial induced T cell apoptosis and aberrant myeloid metabolic programs define distinct immune cell subsets during acute and recovered SARS-CoV-2 infection. MedRxiv. 2020 doi: 10.1101/2020.09.10.20186064.
    1. Xu G., Qi F., Li H., Yang Q., Wang H., Wang X., Liu X., Zhao J., Liao X., Liu Y. The differential immune responses to COVID-19 in peripheral and lung revealed by single-cell RNA sequencing. Cell Discov. 2020;6:73.
    1. Sánchez-Cerrillo I., Landete P., Aldave B., Sánchez-Alonso S., Sánchez-Azofra A., Marcos-Jiménez A., Ávalos E., Alcaraz-Serna A., de Los Santos I., Mateu-Albero T., REINMUN-COVID and EDEPIMIC groups COVID-19 severity associates with pulmonary redistribution of CD1c+ DCs and inflammatory transitional and nonclassical monocytes. J. Clin. Invest. 2020;130:6290–6300.
    1. Fan E., Beitler J.R., Brochard L., Calfee C.S., Ferguson N.D., Slutsky A.S., Brodie D. COVID-19-associated acute respiratory distress syndrome: is a different approach to management warranted? Lancet Respir. Med. 2020;8:816–821.
    1. Amir E.D., Davis K.L., Tadmor M.D., Simonds E.F., Levine J.H., Bendall S.C. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol. 2013;31:545–552. doi: 10.1038/nbt.2594.
    1. Van Gassen S., Callebaut B., Van Helden M.J., Lambrecht B.N., Demeester P., Dhaene T. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A. 2015;87:636–645. doi: 10.1002/cyto.a.22625.
    1. Kotecha N., Krutzik P.O., Irish J.M. Web-based analysis and publication of flow cytometry experiments. Current Protoc Cytom. 2010;53:10–17. doi: 10.1002/0471142956.cy1017s53.
    1. Levey A.S., Eckardt K.-U., Tsukamoto Y., Levin A., Coresh J., Rossert J., De Zeeuw D., Hostetter T.H., Lameire N., Eknoyan G. Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO) Kidney Int. 2005;67:2089–2100.
    1. Ponikowski P., Voors A.A., Anker S.D., Bueno H., Cleland J.G.F., Coats A.J.S., Falk V., González-Juanatey J.R., Harjola V.P., Jankowska E.A., ESC Scientific Document Group 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur. Heart J. 2016;37:2129–2200.
    1. Le Gall J.R., Lemeshow S., Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA. 1993;270:2957–2963.
    1. Gaudriot B., Uhel F., Gregoire M., Gacouin A., Biedermann S., Roisne A., Flecher E., Le Tulzo Y., Tarte K., Tadié J.M. Immune Dysfunction After Cardiac Surgery with Cardiopulmonary Bypass: Beneficial Effects of Maintaining Mechanical Ventilation. Shock. 2015;44:228–233.
    1. Le Balc’h P., Pinceaux K., Pronier C., Seguin P., Tadié J.-M., Reizine F. Herpes simplex virus and cytomegalovirus reactivations among severe COVID-19 patients. Crit. Care. 2020;24:530.

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