sign in sign up
<<
Home , CD163, Monocyte, Phagocyte

Major alterations in the mononuclear landscape associated with COVID-19 severity

Egle Kvedaraitea,b,1, Laura Hertwiga, Indranil Sinhab, Andrea Ponzettaa, Ida Hed Myrbergb, Magda Lourdaa,b, Majda Dzidica, Mira Akbera, Jonas Klingströma, Elin Folkessonc, Jagadeeswara Rao Muvvaa, Puran Chena, Sara Gredmark-Russa,c, Susanna Brighentia, Anna Norrby-Teglunda, Lars I. Erikssond,e, Olav Rooyackerse,f, Soo Alemanc,g, Kristoffer Strålinc,g, Hans-Gustaf Ljunggrena, Florent Ginhouxh,i,j, Niklas K. Björkströma, Jan-Inge Henterb,k, Mattias Svenssona, and Karolinska KI/K COVID-19 Study Group2

aCenter for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Karolinska University Hospital, 171 77 Stockholm, Sweden; bChildhood Cancer Research Unit, Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden; cDepartment of Infectious Diseases, Karolinska University Hospital, 171 77 Stockholm, Sweden; dDepartment of Physiology and Pharmacology, Section for Anesthesiology and Intensive Care, Karolinska Institutet, 171 77 Stockholm, Sweden; eFunction Perioperative Medicine and Intensive Care, Karolinska University Hospital, 171 77 Stockholm, Sweden; fDivision of Anesthesiology and Intensive Care, Department of Clinical Science, Intervention, and Technology, Karolinska Institutet, 141 52 Huddinge, Sweden; gDivision of Infectious Diseases, Department of Medicine Huddinge, Karolinska Institutet, 171 77 Stockholm, Sweden; hSingapore Network, Agency for Science, Technology and Research, BIOPOLIS, 138648 Singapore, Singapore; iShanghai Institute of Immunology, Shanghai JiaoTong University School of Medicine, 200240 Shanghai, China; jTranslational Immunology Institute, SingHealth Duke-National University of Singapore Academic Medical Centre, 168753 Singapore, Singapore; and kPediatric Oncology, Theme of Children’s Health, Karolinska University Hospital, 171 77 Stockholm, Sweden

Edited by Philippa Marrack, National Jewish Health, Denver, CO, and approved January 8, 2021 (received for review September 4, 2020) Dendritic cells (DCs) and are crucial mediators of innate capable of recognizing, processing, and presenting to and adaptive immune responses during viral infection, but mis- immune cells and at the same time produce and reg- directed responses by these cells may contribute to immunopa- ulate immune responses (3, 4). They are classified with respect to thology. Here, we performed high-dimensional flow cytometry- their ontogeny and specialized functions. Monocytes are rapidly analysis focusing on mononuclear phagocyte (MNP) lineages in recruited to the site of infection, and can be split into three SARS-CoV-2–infected patients with moderate and severe COVID- major populations: that is, classical, intermediate, and nonclas- 19. We provide a deep and comprehensive map of the MNP land- sical monocytes (5–7). Monocytes provide both proinflammatory IMMUNOLOGY AND scape in COVID-19. A redistribution of subsets toward and resolving functions in conjunction with other immune cells intermediate monocytes and a general decrease in circulating DCs present at the site of infection (8, 9). DCs are also a heteroge- was observed in response to infection. Severe disease coincided neous group of cells divided into conventional DCs (cDCs) and with the appearance of monocytic myeloid-derived suppressor type I - (IFN-) producing plasmacytoid DCs (pDCs) -like cells and a higher frequency of pre-DC2. Furthermore, phe- (10, 11). Among cDCs, cDC1s are specialized in cross-presenting notypic alterations in MNPs, and their late precursors, were cell- antigens to CD8+ T cells while cDC2s initiate T-helper cell re- – lineage specific and associated either with the general response sponses, both of which are essential for successful viral clearance against SARS-CoV-2 or COVID-19 severity. This included an (12). cDC1s constitute a discrete population of cells identified interferon-imprint in DC1s observed in all patients and a decreased based on expression of highly specific surface , such as expression of the coinhibitory molecule CD200R in pre-DCs, DC2s, CLEC9A. cDC2s are more heterogeneous as evident by the − and DC3 subsets of severely sick patients. Finally, unsupervised functionally distinct CD5+ DC2 and CD5 DC3 subsets, among analysis revealed that the MNP profile, alone, pointed to a cluster which inflammatory DC3s positive for the classically monocyte of COVID-19 nonsurvivors. This study provides a reference for the MNP response to SARS-CoV-2 infection and unravels mononuclear phagocyte dysregulations associated with severe COVID-19. Significance

pre-DCs | DCs | monocytes | COVID-19 While broad efforts toward getting an overview of immune cell and soluble factor alterations in COVID-19 are under way, a deep and comprehensive understanding of the mononuclear he current COVID-19 pandemic has claimed more than one phagocyte system, including circulating progenitors, is still million lives worldwide during the first 10 mo of 2020 (https:// T largely lacking. This study provides a reference for the mono- www.who.int/emergencies/diseases/novel-coronavirus-2019). The nuclear phagocyte response to SARS-CoV-2 infection and un- clinical presentation of COVID-19 can vary from asymptomatic ravels mononuclear phagocyte dysregulations associated with to life-threatening acute respiratory distress syndrome and severe COVID-19. multiple failure. Additional potentially lethal complica- tions include profound abnormalities associated with Author contributions: E.K., H.-G.L., N.K.B., M.S., and K.K.C.S.G. designed research; E.K., systemic thrombogenicity combined with a hyperinflammatory L.H., A.P., I.H.M., M.L., M.D., M.A., J.K., E.F., J.R.M., P.C., S.G.-R., L.I.E., O.R., S.A., and K.S. state (1, 2). Despite a steadily growing body of information re- performed research; E.K., S.B., A.N.-T., H.-G.L., F.G., N.K.B., J.-I.H., and M.S. contributed new reagents/analytic tools; E.K., L.H., I.S., and I.H.M. analyzed data; and E.K., H.-G.L., garding the host to SARS-CoV-2 infection and N.K.B., J.-I.H., and M.S. wrote the paper. the pathophysiology behind COVID-19, it is still unclear why The authors declare no competing interest. certain patients enter the detrimental courses of the disease This article is a PNAS Direct Submission. while others merely present with mild or no symptoms (2). Thus, This open access article is distributed under Creative Commons Attribution License 4.0 there is an urgent need to characterize, in-depth, the immuno- (CC BY). logical and inflammatory aspects of SARS-CoV-2 infection and 1To whom correspondence may be addressed. Email: [email protected]. ensuing COVID-19. 2A complete list of the Karolinska KI/K COVID-19 Study Group can be found in Mononuclear (MNPs) in peripheral com- SI Appendix. prise dendritic cells (DCs) and monocytes, both with central This article contains supporting information online at https://www.pnas.org/lookup/suppl/ roles in orchestrating the induction of innate and adaptive im- doi:10.1073/pnas.2018587118/-/DCSupplemental. mune responses. MNPs are at the front line during an infection, Published January 21, 2021.

PNAS 2021 Vol. 118 No. 6 e2018587118 https://doi.org/10.1073/pnas.2018587118 | 1of12 Downloaded by guest on September 25, 2021 restricted marker CD14 are found (13–16). In addition, circu- clusters corresponded to all major DC subsets (Fig. 3A). When lating DC precursors, described as pre-DC (17) or AS DC investigating the of DC1, a minor expansion of (AXL+SIGLEC6+ DCs) (18), have recently been discovered. AXL+DC1, possibly related to type-I IFN signaling, was a gen- Although MNPs play a central role in the defense against in- eral feature in COVID-19 along with decreased expression of the fections, misdirected MNP responses may also contribute to differentiation marker c-KIT (Fig. 3 B–D). Of note, IFN- immunopathology. Indeed, the importance of monocytes, signaling was also the most strongly activated pathway in DC1s monocyte-derived cells, and DCs in COVID-19 pathogenesis is at the site of infection as assessed in bronchioalveolar lavage emerging (19–27). Yet, a detailed understanding of develop- (BAL) samples (see Fig. 3E for reanalysis of public sincle-cell mental and phenotypic alterations, especially with respect to DC RNA sequencing [scRNA-seq] data of BAL immune cells of lineages and their circulating precursors in COVID-19, is lack- COVID-19 patients and controls, and SI Appendix, Fig. S1). ing. Moreover, clues on how the altered MNP compartment in Circulating pDCs were observed with decreased levels of COVID-19 is linked to host responses against the , disease CD45RA (Fig. 3 F and G), while pDCs in BAL were enriched severity, clinical complications, and ultimate disease outcome are for and signaling pathways and further still limited. Here, we performed a deep profiling of the pe- presented with reduced expression of LAMP5, known to be ripheral blood MNP landscape using 25-color flow cytometry, down-regulated in response to TLR9 stimulation and type I IFN integrated with publicly available single-cell transcriptional data signaling (28), in severe COVID-19 patients (Fig. 3I). Finally, from tissue, as well as soluble inflammatory serum factors in higher levels of KLF6, a transcription factor induced by TGF-β in SARS-CoV-2–infected patients with moderate and severe DCs (29), was increased in pDCs in BAL from severe COVID-19 COVID-19. Together, these results provide a comprehensive patients, while the MHC class II molecule HLA-DQA2 displayed resource of the MNP landscape in response to SARS-CoV-2 lower transcript levels (Fig. 3I), in line with lower levels infection and ensuing COVID-19. of HLA-DR detected on circulating pDCs. Focusing on cDC2s, an increase in frequencies of CD5+ DC2s Results was detected in moderate COVID-19 (Fig. 3 J and K), while a − − SARS-CoV-2 Infection Causes Declining Numbers of Circulating cDCs, reduction in frequencies of CD163 CD14 DC3s was noted in Their Progenitors, and pDCs. To study the immune profile of MNPs severe COVID-19 with otherwise stable frequencies of the DC3 in SARS-CoV-2 infection, hospitalized patients with ongoing subsets (Fig. 3L). Analysis of the pre-DC subsets (Fig. 3M) moderate or severe COVID-19 were recruited based on inclu- showed a higher percentage of pre-DC2 among total pre-DCs sion and exclusion criteria early on in the course of their disease was seen in severe COVID-19 (Fig. 3N). In pre-DCs, an up- (Fig. 1A and SI Appendix, Material and Methods and Tables S1 regulation of the stem cell marker c-KIT was detected in se- and S2). There was no difference in time from onset of symptoms vere COVID-19 (Fig. 3O), and AXL was decreased in response until hospital admission, nor until study sampling, between the to infection. The levels of AXL correlated positively with serum two severity groups (SI Appendix, Table S2). An integrative levels of FMS-like tyrosine kinase 3 ligand (FLT3L), a factor analysis approach was taken where the detailed phenotype of central for DC development (Fig. 3M). Finally, levels of the distinct MNP lineages was analyzed in relation to the clinical maturation markers HLA-DR and CD86, the ecto-enzyme disease status, combining supervised and unsupervised analysis CD38, the inhibitory CD200R, the GM-CSF receptor strategies, with the aim to comprehensively chart the response of (CD116), the (IL)-6R (CD126), circulating MNPs in response to SARS-CoV-2 infection and (CD141), and a possible SARS-CoV-2 spike protein receptor severity of COVID-19 (Fig. 1B). As immune homeostasis is CD147 were assessed across the DC subsets (Fig. 3Q). Higher significantly disrupted in COVID-19, canonical lineage markers, levels of CD38 were detected specifically in moderately sick such as HLA-DR, normally used to identify discrete subsets of COVID-19 patients, while the maturation markers HLA-DR MNPs, are altered in expression (19). As a point of departure, we and CD86 were decreased over all DC subsets (with an exception designed an MNP-focused 25-color flow cytometry panel for DC1s) in severe disease (Fig. 3Q). In severe disease, all (Fig. 1C). After exclusion of (CD15+), NK cells subsets of the cDC2 linage presented with lower levels of (CD7+), ILCs (CD7+), B cells (CD19+), T cells (CD3+), cir- CD200R, while DC1s specifically down-regulated the IL-6R culating early progenitors (CD34+), (FCER1A+HLA- (Fig. 3Q). Taken together, these analyses suggest lineage-specific − DR ), and plasma cells (CD38+CD45RA+CD19low), the total changes and altered developmental phenotype in DC subsets MNPs were identified among the cells defined as CD88+ and/or from COVID-19 patients that occur either in response to CD116+ (Fig. 1 D and E). SARS-CoV-2 infection or specifically in patients with severe The MNP identification strategy allowed clear visualization of COVID-19. DC1, pre-DC and pre-DC2, cDC2, CD5+ DC2, and three sub- − − − sets of DC3 cells (CD163 CD14 , CD163 CD14+, and Major Phenotypic Alterations within Monocyte Subpopulations of CD163+CD14+) (Fig. 2A). This approach was further validated COVID-19 Patients. Next, we focused on monocytes and their by assessing DC subset-specific markers (Fig. 2B). Analysis of subsets (Fig. 4A). As expected, three major populations of monocytes were identified based on CD14 and CD16 expression absolute numbers of the identified DC subsets revealed that all − subsets were decreased in SARS-CoV-2–infected patients com- (i.e., CD14+CD16 classical monocytes [cMonos], CD14+CD16+ pared to healthy controls (Fig. 2C). Only minor differences were intermediate monocytes [iMonos], and CD14lowCD16++ nonclas- observed between the different disease severity groups with re- sical monocytes [ncMonos]) (Fig. 4A). UMAP-analysis verified the spect to the reduction in pDCs and pre-DC. If anything, it was present gating strategy and demonstrated separation between more profound in the severe COVID-19 patients (Fig. 2D), and monocytes and DCs (Fig. 4A). This was also the case for DC3s, no major differences between the different DC sublineages were which in many aspects are similar to monocytes in phenotype, es- detected (Fig. 2E). These results indicate that the number of pecially due to their expression of CD14 (Fig. 4A). In accordance circulating cDCs, their late progenitors, and pDCs are reduced with previous publications, iMonos expressed the highest levels of during SARS-CoV-2 infection. HLA-DR while cMonos displayed relatively high levels of CCR2 (Fig. 4B). Assessment of absolute counts of monocyte populations DC Response to SARS-CoV-2 Infection and Affected Developmental-Related in COVID-19 patients and controlsrevealednochangeincMono Detected in Severe COVID-19. Next, a detailed pheno- numbers, significantly increased iMono numbers in response to typic mapping of each DC subset was performed. As a starting- infection, especially in moderate patients, and declining numbers of point, Phenograph clustering was performed. The revealed ncMonos in all patients (Fig. 4C). A similar pattern was observed

2of12 | PNAS Kvedaraite et al. https://doi.org/10.1073/pnas.2018587118 Major alterations in the mononuclear phagocyte landscape associated with COVID-19 severity Downloaded by guest on September 25, 2021 A B

C D IMMUNOLOGY AND INFLAMMATION

E

Fig. 1. Clinical and experimental study design and analytical pipeline. (A) Experimental design. (B) Analytical pipeline. (C) Flow cytometry data in a UMAP plot with color-coded Phenograph clusters with cell identities established based on the displayed markers; 500,000 live cells from one representative indi- vidual from each group (healthy, moderate, severe) are shown after concatenation. (D) Representative individual from each cohort presented in a UMAP; CD116 and CD88 expression highlighted. (E) Gating strategy to identify MNPs, after exclusion steps detected in the gate defined as CD88+ and/or CD116+ (i.e., excluding CD88−, CD116− cells), and finally projected back to the UMAP.

when monocyte subset frequencies were assessed (Fig. 4D). While assessing these clusters in relation to disease severity, only one increased CD38 expression across monocyte subsets was a general cluster (#4, corresponding to ncMonos) was reduced in all pa- feature unrelated to disease severity, higher levels of CCR2 on tients compared to controls, whereas clusters 9 and 10 were iMonos, as well as lower levels of HLA-DR and CD86 with in- specifically increased in severe compared to moderately sick creased expression of CD163 in all monocyte subsets, were found in patients and healthy controls (Fig. 4G and SI Appendix, Fig. severe COVID-19 (Fig. 4E). Furthermore, CD141 expression was S2A). Next, when clusters with differential presence among the increased in severe COVID-19 in ncMonos and iMonos, while it three groups (SI Appendix, Fig. S2A) were ordered sequentially was only increased in cMonos of moderately sick patients (Fig. 4E). (controls, moderate, and severe patients), a distinct phenotypic In summary, this shows redistribution and an immature phenotype pattern emerged (Fig. 4H). In clusters enriched in moderate and of the monocyte compartment in COVID-19 that is linked to severe patients, falling into the cMono category, higher expres- disease severity. sion of the GM-CSF receptor (CD116), IL-6R (CD126), CD147, CD38, and CCR2 was found. Monocyte clusters specific to se- Dimensionality Reduction Reveals Myeloid-Derived Suppressor Cell vere COVID-19 patients (clusters 9 and 10), compared to the Phenotype in Severe COVID-19. To analyze the global monocyte ones from patients with moderate disease (clusters 3, 5, and 17), landscape in an unbiased manner and integrate the contribution had lower levels of HLA-DR and CD86 (Fig. 4H). This pheno- of all tested markers, we next reclustered all monocytes. This type may be reminiscent of a monocytic myeloid-derived sup- revealed high heterogeneity of cells that fell into the cMono pressor cell (Mo-MDSC)–like phenotype (30, 31). In addition, category (Fig. 4F). Indeed, cMonos stratified into 15 Phenograph the Mo-MDSC–like cluster 9 expressed high levels of c-KIT and clusters, whereas only one or two clusters were found for iMono CD163 (Fig. 4H). Furthermore, a positive correlation was found and ncMono (Fig. 4F and SI Appendix, Fig. S2A). When between the cMono Phenograph clusters specific to severe

Kvedaraite et al. PNAS | 3of12 Major alterations in the mononuclear phagocyte landscape associated with COVID-19 https://doi.org/10.1073/pnas.2018587118 severity Downloaded by guest on September 25, 2021 A UMAP on all DCs Algorithm for DCs 1. Live & excl CD15+ 2. Excl CD3/7/19/34+ 3. Excl basophils 4. Excl plasma cells 5. Excl CD88/116-

Excl CD88low Excl CD88+ Monos CD14+ Monos AXL CD14 CD5 CD123 HLA-DR CD141 FCER1A FCER1A

CD88 CD14 CLEC9A CD123 CD1c CD1c CD163 CD163 B

pDC DC1 CD5+ DC2 cDC2 CD163-CD14- DC3 pre-DC2 CD163+CD14- DC3 pre-DC CD163+CD14+ DC3 CD141 c-KIT CD5 CD45RA FCER1A AXL CD123 CD206 c-KIT C D 60 pDC pre-DC pre-DC2 DC1 cDC2 ** * ns ns ns ns * ns ns **** **** *** **** **** 30 2.5 ** 1.0 ** 4 40 * **** ** 40 2.0 0.8 3 30 20 1.5 0.6 20 COVID-19 2 20 1.0 0.4 Moderate 10 1 10 0 Severe 0.5 0.2 pDC DC1 cDC2

% retained relative to controls pre-DC 0 0.0 0.0 0 0 pre-DC2 CD5+ DC2

CD5+ DC2CD163-CD14- DC3 CD163+CD14- DC3 CD163+CD14+ DC3 E CD14-CD163-CD163+CD14-CD163+CD14+ DC3 DC3 DC3 60 **** **** **** **** 10 ** 15 * 15 *** 8 ***

8 40 6 * 10 10 6 4 COVID-19 COVID-19 Absolute cell count (cells/microliter) 20 4 Moderate 5 5 Healthy controls 2 2 Moderate disease Severe 0 Severe disease 0 0 0 0 DC1 pDC cDC2 pDC DC1cDC2 pre-DC pre-DC2 pre-DC2pre-DC2

% retained relative to controls CD5+ DC2 CD5+ DC2

CD14-CD163- DC3 CD163+CD14-CD163+CD14+ DC3 DC3 CD14-CD163-CD163+CD14-CD163+CD14+ DC3 DC3 DC3

Fig. 2. Circulating cDCs, their progenitors, and pDCs decline in numbers in COVID-19 irrespective of disease severity. (A) DC gating strategy (Lower) projected to the UMAP (Upper) in all concatenated samples. (B) Key marker expression in the DCs presented across the identified DC subsets. (C) Absolute DC numbers in healthy controls, and moderate and severe COVID-19 patients. (D) Absolute DC numbers, each patient related to the mean of the controls as percentage of maintained cells and compared in moderate vs. severe patients for each DC population. (E) Percentage of maintained cells, calculated as described in D, compared among DC subsets in moderate and severe patients separately. Statistical evaluation using a Kruskal–Wallis test and Dunn’s multiple comparisons test (C), Mann–Whitney U test (D), and Friedman test (E). P values: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; ns, not significant.

COVID-19 (clusters 2, 9, 10, and 18) and the soluble immuno- Altogether, this analysis revealed monocyte heterogeneity re- suppressive factors IL-10, TGF-β, and VEGFA, as well as with lated to COVID-19 severity and an expansion of monocytes with AREG (Fig. 4 I and J), the latter known to be involved in tol- a MDSC-like phenotype only observed in severe COVID-19 pa- erance and tissue repair (32). In contrast, clusters 2, 9, 10, and 18 tients. showed a negative association with stem cell factors important for myeloid cell development and differentiation (Fig. 4I). The Viremia and Seroconversion Are Associated with Recovery of Mo-MDSC–like clusters also correlated positively with hepato- Inflammatory DC3s. From a clinical point of view, all patients cyte growth factor (HGF), known to support Mo-MDSC presented with elevated inflammatory markers though clinical expansion (33) (Fig. 4I). hyperinflammation and signs of coagulation disturbances were To search for evidence of an Mo-MDSC signature at the site most pronounced in the severe COVID-19 patients (Fig. 5A and of infection, publicly available scRNA-seq data of BAL from SI Appendix, Material and Methods). Of note, the laboratory healthy controls, moderate, and severe COVID-19 patients was measurements and reference values for the clinical parameters used (22) (SI Appendix, Fig. S1 A–P). The expression of Mo- were obtained from the established clinical routine laboratory MDSC signature , calculated by comparing the transcrip- assays at the Karolinska University Hospital Laboratory (Fig. 5 A tional signatures of monocytes versus Mo-MDSC in a previous and B). Integrative correlation mapping of clinical parameters, report (34), was significantly higher in MNPs from severe pa- obtained from the routine monitoring regimen for hospitalized tients compared to controls (e.g., IFITM1, IFITM2, GRINA, COVID-19 patients—including peak measurements recorded SOCS3, and CD84) (Fig. 4K and Dataset S1). In line with the during hospitalization up until study sampling (Fig. 5B), and specific immune suppressive Mo-MDSC gene-expression pat- those taken within 24 h from MNP profiling (SI Appendix, Fig. tern, MHC class II and CD74 were found at lower levels in se- S3A)—revealed that levels of inflammatory cytokines and LDH vere COVID-19 patients compared to controls (Fig. 4K). clustered together with fatal outcome and viremia. Indeed,

4of12 | PNAS Kvedaraite et al. https://doi.org/10.1073/pnas.2018587118 Major alterations in the mononuclear phagocyte landscape associated with COVID-19 severity Downloaded by guest on September 25, 2021 Concatenated DCs from controls (n = 16) and patients with moderate COVID-19 (n = 10) and severe COVID-19 (n = 17) A CLEC9A CD141 CD123 CD45RA FCER1A CD1c 2 Phenograph clusters DC gates from Fig. 2A 15 cDC2 16 pDC cDC2 13 pDC 10 9 5 17 21 UMAP2 total pre-DC total pre-DC 20 19 CD5 CD163 CD14 CD206 AXL c-KIT 18 14 Phenograph 12 UMAP1 11 8 7 Pre-DC cluster: 2 6 4 DC1 cluster: 15 3 pDC clusters: 16,13,1 1 cDC2 clusters: 21, 20, 19, 18, 14, 12, 11, 8, 7, 6, 4, 3, 2,1 DC1 in BAL AXL+ DC1 c-KIT B C D E Interferon Signaling **** *** ns ERK/MAPK Signaling Healthy control Severe disease 30 ** ns 1500 * Activation of IRF by Cytosolic PRRs PRRs in Recognition of and Signaling by Rho Family GTPases 20 1000 MIF Regulation of Innate 0.63% 9.8% Role of Hypercytokinemia in MFI Neuroinflammation Signaling Pathway UMAP2

CLEC9A 10 500 HMGB1 Signaling CXCR4 Signaling % total of DC1 02468 UMAP1 AXL 0 0 -log10 (pvalue) CD123 CD45RA pDC in BAL LAMP5 F G ns **** H Oxidative Phosphorylation I 2000 ns ns 1500 ** ns NRF2-mediated Oxidative pDC HMGB1 Signaling CXCR4 Signaling 1500 Unfolded protein response KLF6 1000 Chemokine Signaling Sirtuin Signaling Pathway MFI

1000 MFI IL-8 Signaling

UMAP2 500 Sphingosine-1-phosphate Signaling Expression level 500 Aryl Hydrocarbon Receptor Signaling HLA-DQA2 0246 UMAP1 0 0 -log10 (pvalue)

CD5+ DC2 CD163-CD14- DC3 CD163+CD14- DC3 CD163+CD14+ DC3 ns ns ns ns J K L ns ns ns ns ns 80 ** * 80 * 80 80 cDC2 COVID-19 IMMUNOLOGY AND INFLAMMATION 60 60 60 60 Healthy controls

40 40 40 40 Moderate disease UMAP2 Severe disease 20 20 20 20 % total of DC3 %of totalDC3 %of % total of DC3 % total cDC2 of

UMAP1 0 0 0 0 M NO P pre-DC2 AXL c-KIT pre-DC *** **** pre-DC ns ns ns ** 100 1000 ** 1500 ns * 210 80 800 DC annotation 29 1000 pDC CD123 60 600 UMAP2 pre-DC2 28 pre-DC MFI

total pre-DC MFI 40 400 7 pre-DC2 500 2 r=0.5239

MFI AXL (log2) DC1 % total of pre-DC 20 200 p=0.0004 UMAP1 FCER1A 26 CD5+ DC2 7891011CD163-CD14- DC3 0 0 0 FLT3L (serum, log2) CD163+CD14- DC3 Q CD163+CD14+ DC3 HLA-DR CD86 CD38 CD200R

Controls

Moderate *** ** **** * `* * * High Severe # ***# ****### ****## ****# ****# ****## ****# ***# ** *** *** **** * ## # ******** ************# ****# CD147 CD141 CD126 (IL-6R) CD116 (GM-CSFR) Expression Controls Low Moderate * * Severe *** *** * *** *

Fig. 3. Altered activation and developmental phenotype of DCs in COVID-19. (A) All DCs from patients and controls concatenated in a UMAP, as described in Fig. 2A, with color-coded Phenograph clusters and manual DC gates; cell identity was established based on marker expression. (B) DC1s gated in a UMAP and subjected to analysis of percentage of AXL+ cells, representative healthy control and severe COVID-19 patient shown in a FACS plot. (C) Quantification of the percentage of AXL+ DC1s in the three cohorts. (D) MFI of c-KIT quantified in total DC1s, gated in DC UMAP as shown in B.(E) IPA of DEGs up-regulated in DC1s in BAL of severe COVID-19 patients compared to healthy controls; arrow indicate direction of up-regulation based on z-score; data reanalyzed from previously published report (22), as described in Material and Methods.(F) pDCs gated in a UMAP for downstream analysis. (G) MFI of CD123 and CD45RA quantified in total pDCs in the three cohorts. (H) IPA of DEGs up-regulated in pDCs in severe patients compared to moderate COVID-19; arrows indicate direction of up- regulation based on z-score; data reanalyzed from previously published report (22), as described in Material and Methods.(I) Genes in pDCs differentially expressed between severe and moderate COVID-19 patients, presented in the three cohorts; data reanalyzed from previously published report (22), as de- scribed in Material and Methods.(J) cDC2s gated in a UMAP for downstream analysis. (K) Quantification of the percentage of CD5+ DC2 among total cDC2s in the three cohorts. (L) Quantification of the percentage of the three DC3 subsets among total DC3s (as presented in Fig. 2A). DC3 subsets were defined from the cDC2 gate in J in the three cohorts. (M) Total pre-DCs gated in a UMAP subdivided in pre-DCs and pre-DC2s. (N) Quantification percentage of pre-DC2 in total pre-DCs. (O) MFI of AXL and c-KIT in total pre-DCs in the three cohorts. (P) Correlation between AXL MFI in pre-DCs and soluble FLT3L. (Q) Heatmap showing marker expression in the three cohorts, across the DC subsets, only samples with more than 10 cells included. Statistical evaluation was made separately in each indicated subset by comparing the MFI in the three cohorts; significance for healthy to moderate and healthy to severe comparisons is indicated by an asterisk (*) and for moderate to severe comparison is indicated by a pound sign (#). Statistical evaluation using Spearman test for correlation, Kruskal-Wallis test and Dunn’s multiple comparisons test for all other analysis. Samples with less than 10 cells in any of the DC subsets annotated in Q were excluded from the analyses. Significance level: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; #P < 0.05, ##P < 0.01, ###P < 0.001.

Kvedaraite et al. PNAS | 5of12 Major alterations in the mononuclear phagocyte landscape associated with COVID-19 https://doi.org/10.1073/pnas.2018587118 severity Downloaded by guest on September 25, 2021 Excl CD88-, CD116- Excl FCER1A+ DCs Excl CD14-, CD16- Monocytes Cell type annotation A in each sample (all concat) (all concat) (all concat) cMono iMono Algorithm for Monos ncMono pDC 1. Live & excl CD15+ CD16 CD16 CD88

2. Excl CD3/7/19/34+ FCER1A pre-DC 3. Excl basophils pre-DC2 4. Excl plasma cells

UMAP2 DC1 CD116 CD14 CD14 CD14 DC subsets CD5+ DC2 1. Downsample to 10.000 cells per sample according to 2. Concat patients and controls Fig.1A CD163-CD14- DC3 3. Dimensionality reduced (UMAP+Phenograph) in all MNPs CD163+CD14- DC3 B UMAP1 CD163+CD14+ DC3 C D

cMono iMono ncMono cMono iMono ncMono ns * ** **** ns **** ns ns ns ns ns ns ns ns ns 1500 100 ** 50 150 15 * 40 * HLA-DR CD86 CCR2 COVID-19 80 40 30 cMono 1000 100 10 Healthy controls iMono 60 30 20 Moderate disease ncMono 40 20 500 50 5 Severe disease cDC2s 10 20 10

CD88 CD14 CD16 0 0 0 0 0 0 Frequencies (% of total monos) Absolute cell count (cells/microliter)

E Key monocyte markers Stem cell-ness Metabolism Coagulation-related HLA-DR CCR2 CD86 CD88 CD163 c-KIT CD38 CD147 CD141 High cMono Controls iMono Moderate * **** **** * *** *** ** ** *** **** ncMono Expression **** **** **** ** **** **** *** **** **** * *** * ** *** **** * ** ### # ## ## ## # ## * cDC2 Severe # ### ### Low

F G Healthy (n=16) Moderate (n=10) Severe (n=16) H R 8 6 -DR 6 8 4 Phenograph in C-A 4 Significance: A KIT 1 MNP UMAP Monocyte UMAP Gates of: SS CD8CD1CD141HL CD8CD200CD116CD3CCR2CD1c- CD126CD163CD147 12 cMono H > M**, S**** (cluster 4) 3 iMono H*, M** > S (cluster 1) 5 Gating ncMono 17 H*, M* > S (cluster 12) 18 M* > S (cluster 3) 2 9 M*** > S (cluster 5) 10 re-run M** > S (cluster 17) 16 Phenograph 15 clusters M****, S*** > H (cluster 18) 14 4 S*** > M (cluster 2) Phenograph 13 9

11 Phenograph 10 S > H***, M* (cluster 9) cMono clusters: 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17 8 S > H****, M*** (cluster 10) 7 iMono cluster: 1, 18

6 ncMono cluster: 4 UMAP2 UMAP1

Cluster 9 I Immunosupressive factors Stem cell factors J 2 10 IL-10 ** **** **** ** **** ** **** * **** **** **** FLT3L *** ** * **** * *** *** * ** *** **** ** 2 5

2 0 TGFb * * *** ** * SCF * *** * **** **** * * *** **** ** 2 -5 r = 0.8291 p < 0.0001 VEGFA ** **** ** **** *** ** ** **** **** ** 0246810 CCR2 ligands Mo-MDSC expansion factor 210 Cluster 10 CCL2 ** **** **** ** **** *** *** ** **** **** **** HGF ** **** ** **** *** **** ** ** **** **** **** 2 5 % of total Monos (log2) Soluble factors (serum) CCL13 123456789101112131415161718 0 Monocyte and monocyte-like cell Phenograph clusters 2 r = 0.7484 123456 789101112131415161718 Positive Negative p < 0.0001 Monocyte and monocyte-like cell Phenograph clusters Correlation 0246810 AREG (serum, log2) K Mo-MDSC signature in COVID-19 lung IFITM1IFITM2 IFITM3 GRINA HLA-DRAHLA-DQB1 HLA-DPA1 CD74 COVID-19 Healthy controls Moderate disease Severe disease Expression level

Fig. 4. Major phenotypic alterations within monocytes and monocyte-like cells in COVID-19 patients. (A) Monocyte gating strategy projected together with DCs, gated as described in Fig. 2A, to the MNP UMAP. (B) Key monocyte markers in monocyte subsets, compared to cDC2s, in all concatenated samples. (C) Absolute numbers of monocytes in the three cohorts. (D) Monocyte frequencies in the three cohorts. (E) Heatmap showing marker expression for the three cohorts in the indicated monocyte subsets with cDC2s included as a reference, comparing expression level within each cell population individually. Statistical evaluation was made separately in each indicated subset by comparing the MFI in the three cohorts; significance for healthy to moderate and healthy to severe comparisons are indicated by an asterisk (*) and for moderate to severe comparison by a pound sign (#). (F) MNPs from patients and controls con- catenated in a UMAP with color-coded manually gated cell subsets (Left), rerun on monocytes presented in a UMAP with color-coded Phenograph clusters (Right). (G) Color-coded manual gates (Upper) and indicated Phenograph clusters (Lower) in monocyte UMAP of concatenated samples. (H) Expression of markers in selected significantly differential Phenograph clusters among the three cohorts (see also SI Appendix, Fig. S2). (I) Heatmap of correlation between Phenograph clusters 1 to 18 and levels of soluble factors in serum, a gradient between yellow and blue indicates positive (yellow) and negative (blue) correlation. (J) Correlation between soluble serum AREG and percentage of Phenograph clusters 9 and 10 in total monocytes. (K) MDSC signature genes, calculated by comparing MDSC with monocytes in a previous report (34), in lung MNP compartment differentially expressed between severe COVID-19 patients and controls, presented in the three cohorts; data reanalyzed from previously published report (22), as described in Material and Methods. Statistical evaluation using Kruskal–Wallis test and Dunn’s multiple comparisons test for comparison between the three cohorts and Spearman test for correlations, “bimod” test for DEGs. Significance level: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; #P < 0.05, ##P < 0.01, ###P < 0.001.

6of12 | PNAS Kvedaraite et al. https://doi.org/10.1073/pnas.2018587118 Major alterations in the mononuclear phagocyte landscape associated with COVID-19 severity Downloaded by guest on September 25, 2021 A Inflammatory Coagulation B

CRP Ferritin IL-6 D-Dimer PK-INR Fibrinogen ns ns 103 * 104 104 ** 8 *** 1.5 15 103 103 6 102 1.0 10 Antibiotics ALT Albumin Age Airway culture + Chronic diseases CRP Tocilizumab PK-INR Body ache Serology + Bilirubin Peak O2 need WBC Days since debut Monocytes Sex IL-10 Viremia IL-6 IL-1b Fibrinogen Steroids D-Dimer TNF BMI Myoglobin Smoking Ferritin Blood culture + Thromboembolism Troponin Creatinine Procalcitonin Remdesivir LDH Fatal outcome 102 102 4 Age 101 0.5 5 1 1 Troponin 10 10 2 Platelets 100 100 100 0 0.0 0 IL-1b Fibrinogen IL-1β IL-10 TNF Serology + 20 80 60 Biochemical Antibiotics CRP 15 60 Steroids 40 LDH Albumin Myoglobin Bilirubin 10 40 D-Dimer 25 ** 60 * 4 ** 20 10 Peak O2 need 5 20 20 WBC 40 3 15 10 Neutrophils 0 0 0 IL-10 10 20 102 LDH Hematological 5 Viremia 0 0 101 Fatal outcome TNF WBC Neutrophils Lymphocytes IL-6 Creatinine Troponin Bilirubin Tocilizumab ** *** * ns BMI 20 20 4 3 * 2 * 400 10 10 Creatinine 15 15 3 300 Myoglobin 102 Days since debut 10 10 2 200 101 Monocytes Procalcitonin 101 5 5 1 100 PK-INR ALT 0 0 0 0 100 100 Sex Smoking N/L ratio Monocytes Platelets Chronic diseases ns ns Hemoglobin 50 *** 1.5 800 Ferritin Blood culture + 40 600 Airway culture + 1.0 30 COVID-19 Normal range Thromboembolism 400 20 Remdesivir 0.5 Moderate disease Lymphocytes 10 200 Body ache 0 0.0 0 Severe disease Albumin

C D E 6 F Viremia Serology Viremia Serology 30 ns r=0.4733 p=0.0127 Serum PCR+ IgG - PCR IgG 4 + vs - - vs + 20 2

0 cMono 10 CD163+CD14- DC3 iMono 0 1500 r=0.4040 n=12 n=8 ncMono PCR+ PCR- p=0.0366 1000 30 *

pDC cMono Serum PCR- IgG + 500

pre-DC 20 IMMUNOLOGY AND INFLAMMATION 0 pre-DC2 PC2 (16.7% explained var.)

Days since symptom10 debut DC1 100 r=0.4791 p=0.0057 cDC2 0 80 n=15 n=19 IgG- IgG+ 60 PC1 (30.1% explained var.) Absolute count (cells/microliter) cell

CD5+ DC2 iMono 40 Healthy controls COVID-19 CD163-CD14- DC3 20 Moderate 0 Moderate disease CD163+CD14- DC3 0102030 Severe Severe disease CD163+CD14+ DC3 Days since symptom debut

G H COVID-19 status * * * Patient clusters * CD116 ncMono Patient clusters CD141 ncMono Mono cluster 10 Cluster 1 Mono cluster 9 CD163 ncMono Cluster 2 CD163 iMono Cluster 3

CCR2 iMono * Death c-KIT total pre-DC CD163 cMono iMono count COVID-19 status Phenograph COVID-19 Outcome 23 CD141 cMono CD147 ncMono Healthy Cluster 1 Healthy controls Healthy controls Mono cluster 18 Cluster 2 Moderate disease Survivors HLA-DR ncMono Moderate Cluster 3 Severe disease Non-survivors CD86 ncMono Severe CD38 pDC Mono cluster 2 Peak O2 need LDH N/L ratio pre-DC2 in total pre-DC AXL+ DC1 CD38 CD163+CD14+ DC3 CD38 DC1 HLA-DR pDC

UMAP2 CD116 DC1 Mono cluster 5 CD163-CD14- DC3 in DC3 CD5+DC2 in DC2 HLA-DR iMono HLA-DR cMono CD200R CD163+CD14- DC3 CD200R pre-DC Ferritin D-dimer IL-6 Peak O2 need CD86 pre-DC CD86 iMono Ventilator or ECMO HLA-DR pre-DC2 10-15L/min or High flow CD126 DC1 No or <10L/min CD45RA pDC ncMono count c-KIT DC1 Others AXL total pre-DC ≤ Median Mono cluster 4 > Median CD163-CD14- DC3 count pre-DC count pDC count DC1 count UMAP1 CD141 DC1

Fig. 5. Clinical hyperinflammation, the MNP landscape, and COVID-19 outcome. (A) Levels of clinical analytes in moderate and severe COVID-19 patients, reference levels indicated as a gray shaded area; CRP, D-dimer in milligrams per liter; procalcitonin, ferritin, myoglobin in micrograms per liter; hemoglobin, albumin in grams per liter; WBC, neutrophils, lymphocytes, monocytes, platelets as 109/L; troponin in nanograms per liter; fibrinogen in grams per liter; creatinine, bilirubin in micromoles per liter; LDH in units per liter; IL-6, IL-1β, TNF, IL-10 in nanograms per liter. (B) Integrated correlation clustering map of clinical parameters; the color of the circles indicated positive (red) and negative (blue) correlations, color intensity represented correlation strength as − measured by the Pearson’s correlation coefficient. (C) Distribution of moderate and severe COVID-19 patients with respect to viremia (SARS-CoV-2 PCR+/ , − measured at sampling) and serology (SARS-CoV-2 IgG /+, measured at sampling) shown in donut charts (Left); differences in absolute numbers of MNP − − populations between SARS-CoV-2 PCR+ and SARS-CoV-2 PCR patients, and between SARS-CoV-2 IgG and SARS-CoV-2 IgG+ patients (Right), upward arrows − indicate significant increase (SI Appendix, Fig. S3B). (D) Differences in days since symptom debut until sample collection in SARS-CoV-2 PCR+/ and SARS-CoV-2 IgG−/+ patients. (E) Correlation between days since symptom debut until the sample collection and absolute numbers of cells within MNP populations (SI Appendix, Fig. S3C). (F) PCA of 108 MNP parameters in the three cohorts (SI Appendix, Fig. S3D). (G) Dimensionality reduction of 108 MNP parameters for each patient or control presented in UMAP color-coded annotated by Phenograph clusters, disease status, and outcome (above dashed line), as well as levelsof

clinical parameters; that is, peak O2 need, LDH, / (N/L) ratio, D-dimer, ferritin, and IL-6, converted to categorical variables based on a median value (below dashed line). (H) Hierarchical clustering of patients from the three Phenograph clusters and 44 selected preclinical parameters. Statistical evaluation using Mann–Whitney U for comparison between the two groups, Spearman test for correlations of nonnormally distributed data and Pearson test for correlations normally distributed data. Significance level: *P < 0.05, **P < 0.01, ***P < 0.001.

Kvedaraite et al. PNAS | 7of12 Major alterations in the mononuclear phagocyte landscape associated with COVID-19 https://doi.org/10.1073/pnas.2018587118 severity Downloaded by guest on September 25, 2021 subgroups of moderate and severe COVID-19 patients had de- A similar pattern was observed, suggesting that CD200R is di- tectable virus in serum (serum PCR+, measured at study sam- minished in the cDC2 lineage and is related not only to disease pling), while the majority of seroconverted (SARS-CoV-2 IgG+, severity but also to fatal outcome (SI Appendix, Fig. S3F). Al- measured at study sampling) patients were critically ill (74%, 14 together our results suggest lineage restricted phenotypic and of 19) (Fig. 5C). Significantly higher numbers of pre-DC2 as well as developmental shifts in MNPs and their late precursors and show − of inflammatory DC3 (CD163+CD14+ DC3 and CD163+CD14 that MNPs, alone, could identify a cluster of COVID-19 non- DC3) were found in patients without ongoing viremia (serum survivors. − PCR ) and the same DC3 populations were elevated in sero- converted patients (Fig. 5C and SI Appendix,Fig.S3B). Following a Discussion similar trend, ncMonos were also present at higher numbers in While broad efforts toward an overview of immune cell- and seroconverted patients (Fig. 5C and SI Appendix,Fig.S3B). As soluble-factor alterations in COVID-19 are under way, a deep expected, patients that had seroconverted (SARS-CoV-2 IgG+) and comprehensive understanding of the MNP system, including were sampled slightly later in their disease course (Fig. 5D). Finally, circulating progenitors, is still largely lacking. Here, we used + − absolute numbers of the inflammatory DC3 subset (CD163 CD14 high-dimensional flow cytometry to characterize the MNP DC3), as well as iMonos and cMonos, increased over time after landscape in well-defined prospective cohorts of onsetofsymptoms(Fig.5E and SI Appendix,Fig.S3C), suggesting SARS-CoV-2–infected patients with moderate and severe − that seroconverted patients had passed the time-point when their COVID-19, as well as matched SARS-CoV-2 IgG healthy DC3s were at their lowest levels. Thus, subset-specific changes in controls. This allowed a robust mapping of phenotypic and de- absolute numbers of MNPs depended on the time from symptom velopmental alterations in circulating DCs and monocytes that onset, and a time-dependent recovery of DC3 and an increase of occurred either in response to clinical infection or specifically in monocytes and monocyte-like cells were noted. severely sick patients (Fig. 6). By combining conventional gating with unsupervised dimensionality reduction, we provide a ref- Independent Assessment of the MNP Landscape Identifies Clinical erence for absolute numbers and lineage-dependent phenotypic Subgroups and Disease Outcome-Correlates in COVID-19. To get an shifts in MNP subsets in response to SARS-CoV-2. This analysis overall view of the MNP landscape in relation to COVID-19 revealed an affected developmental phenotype in the cDC2 severity, we performed a principal component analysis (PCA) of lineage and expansion of monocytic MDSC-like cells in critically the 108 MNP parameters that had been measured. COVID-19 ill patients. In addition, the results were related to data from the patients clearly separated from healthy controls, and within the site of infection and severe inflammatory impact by means of patients, those with moderate and severe disease further clus- reanalyzing BAL scRNA-seq data and relevant soluble factors in tered apart (Fig. 5F). The MNP parameters that drove the circulation. Finally, and strikingly, alterations within the MNP separation between the groups were: Higher DC counts and landscape alone were found to separate healthy controls from Mono cluster 4 (ncMonos), higher c-KIT expression in DC1, patients, and to further identify distinct patient clusters, such as higher CD45RA expression in pDCs, and higher HLA-DR, moderate and severe as well as within severe patients even those CD86, and CD200R expression in cDC2 lineage in controls; + with fatal outcome. The present high-dimensional mapping of higher CD38 expression in MNPs, higher frequency of CD5 DC2s among cDC2s in moderate patients COVID-19; and an the MNP landscape yields insights into the MNP response during expansion of Mo-MDSC-like clusters (9 and 10), higher c-KIT SARS-CoV-2 infection and in relation to COVID-19 severity levels in DC progenitors, and higher frequencies of pre-DC2s in and provides a comprehensive framework for future studies severe patients (Fig. 5F and SI Appendix, Fig. S3D). Dimen- (Fig. 6). sionality reduction using UMAP and Phenograph was performed Knowledge on alterations of specific DC subsets and their using the same MNP variables, revealing three major clusters, circulating progenitors in acute viral infection in is where cluster 1 corresponded to healthy controls, cluster 2 cor- limited. However, the depletion of all DCs and their precursors responded to all moderate and some severe COVID-19 patients, detected here is in line with previous studies showing DC dis- and cluster 3 corresponded to severe COVID-19 patients only appearance in circulation during acute viral insults (20, 35, 36). (Fig. 5G). All patients with fatal outcome were found in cluster 3 Given their central role in recognition and control of and these further had a high oxygen requirement, exhibited adaptive immunity (37), as well as their presence in COVID-19 higher levels of LDH (peak measurement), a high neutrophil/ lung (22), it is plausible that DC disappearance from the circu- lymphocyte ratio, and elevated ferritin (measured within 24 h lation is related to their influx to tissues and lymph nodes to lead from MNP profiling) (Fig. 5G and SI Appendix, Fig. S3E). and assist initiation of adaptive immune responses aiming at To visualize the key MNP profile components specific for each virus elimination. In support of this, we find recovery of absolute cluster, 44 of the most significant MNP parameters analyzed so numbers of pre-DC2s, as well as subsets of DC3s in patients far were selected and subjected to hierarchical clustering. where virus has been cleared from circulation and seroconver- Monocyte clusters 9 and 10, corresponding to Mo-MDSC–like sion occurred. Other features of host response included de- + cells, were specific to patient cluster 3 with the nonsurvivors. creased levels of c-KIT in DC1, expansion of AXL DC1 and + These parameters also clustered together with CD163 expression CD5 DC2s, similarly to monocytes increased expression of the on all monocyte subsets, CCR2 expression on iMonos, and c-KIT ecto-enzyme CD38 in most DC subsets, and decreased CD45RA in total pre-DCs. For moderate disease, higher expression of on pDC, possibly indicating activation as also detected in lung CD38 in all DC lineages, CD141 on cMono, and higher levels of tissue. It has been shown that DC1s down-regulate c-KIT upon monocyte cluster 18 (iMono, the only cluster expressing high viral stimulation in an IFN-dependent manner (38), and it is thus levels of CD200R among the patient-specific clusters), were the possible that lower c-KIT levels in DC1 found here might indi- determining parameters. A set of markers were lost in severe cate DC1 SARS-CoV-2 sensing. In support of this, pathways patients, including HLA-DR and CD86 in both monocytes and analysis of DC1s in COVID-19 lung revealed pattern-recognition DCs, and CD200R in DC subsets (Fig. 5H). Moreover, the receptor-related pathways and an IFN-response imprint. Al- CD200R decrease in nonsurvivors was restricted to the cDC2 though minor, the expansion of AXL+ DC1 in circulation could lineage, with the most significant differences seen in progenitors further support an IFN-affected phenotype, as AXL has been (pre-DCs and pre-DC2) (SI Appendix, Fig. S3F). To test whether shown to be up-regulated on myeloid cells upon type I IFN this was entirely dependent on severity status rather than out- stimulation (39). However, even if supported by transcriptional come, CD200R levels were assessed within severe patients only. data of DC1s from BAL of COVID-19 patients, our observation

8of12 | PNAS Kvedaraite et al. https://doi.org/10.1073/pnas.2018587118 Major alterations in the mononuclear phagocyte landscape associated with COVID-19 severity Downloaded by guest on September 25, 2021 Fig. 6. Summary of MNP response to SARS-CoV-2 and in relation to COVID-19 severity. Monocytes (Left) and dendritic cells (Right), respond to SARS-CoV-2 (Upper) with decreased circulating levels of all DC subsets, but higher CD5+ DC2 frequency, activated phenotype (up-regulation of ecto-enzyme CD38, ex- pansion of CD14+CD16+ iMonos, decrease of CD14lowCD16++ ncMonos, and lower CD45RA levels on pDCs), and features pointing to viral sensing and type I IFN imprint found in DC1s (e.g., lower levels of c-KIT, expansion of AXL+DC1s). Severe COVID-19 (Lower) is associated with altered developmental DC phenotype (e.g., increased frequencies of late pre-DC2 progenitors and increased stem cell marker c-KIT expression in pre-DCs, as well as immature phenotype onallDC

subsets with lower HLA-DR and CD86), expansion of Mo-MDSC–like cells and decrease in inhibitory receptor CD200R, specific to cDC2 lineage. Arrows in red IMMUNOLOGY AND INFLAMMATION boxes indicate changes in counts and frequencies of cell populations. Arrows in green areas illustrate altered expression of receptors within indicated populations.

indicating the occurrence of an IFN imprint in DC1s merits the inhibitory receptor CD200R, supporting the hypothesis that functional exploration in future studies. robust innate antiviral responses as well as efficient control of In severe COVID-19, the DC phenotype showed affected the proinflammatory state are important in order to have a maturation status with lower levels of HLA-DR and CD86 in all moderate course of COVID-19 (44). On the other hand, in line DC subsets, with an exception for DC1, and an altered devel- with previous observations (19, 27, 45), monocytic cell clusters opmental phenotype with expansion of late progenitors pre-DC2 with an immature phenotype (in particular cluster 9 and 10), and increased expression of the stem cell marker c-KIT in pre- identified as Mo-MDSC–like cells, were linked to severe disease DCs. Additional lineage-specific DC alterations in severe and a patient cluster that included all nonsurvivors. In addition, a COVID-19 included down-regulation of IL-6R in DC1 and a positive correlation between Mo-MDSC and HGF, known to drastic decrease of the inhibitory receptor CD200R in the cDC2 mediate Mo-MDSC expansion (33), was observed (Fig. 4I). As lineage, including circulating progenitors. Decreased CD200R severe dysregulation of has been reported in levels are of particular interest in development of severe COVID-19 (27), Mo-MDSC–like cells detected here could rep- COVID-19, given that CD200R signaling is known to modulate resent immature forms of MNPs with or without suppressive immune responses to pathogenic stimuli, control myeloid cell functions. Further studies are required to understand whether function, inhibit proinflammatory cytokine expression, and in- Mo-MDSC–like cells described here in fact have suppressor hibit tissue damage caused by myeloid-derived cells (40, 41). functions (e.g., through a supply of immunosuppressive cyto- Moreover, it has been suggested that CD200R signaling plays an kines) that in the present study showed a positive correlation important role in priming during viral infection (42), but with Mo-MDSC–like cell frequencies. In support of expansion of remains to be studied in further detail in COVID-19. Our find- functional Mo-MDSC in severe COVID-19, it has been pro- ings suggest that in severe COVID-19, DCs, and in particular the posed that expanded Mo-MDSC isolated from COVID-19 pa- cDC2 lineage, are immature with alterations in the develop- tients suppressed T cell proliferation and IFN-γ production mental phenotype, lack up-regulation of the activation marker partly via an arginase-1–dependent mechanism (46). In line with CD38, as seen in moderate patients, and lose the inhibitory re- previous observations in COVID-19 (20, 27, 36, 43) and other ceptor CD200R, all possibly contributing to inefficient regulation infectious and inflammatory settings (35, 47–49), loss of ncMo- of proinflammatory conditions in severe COVID-19. nos was detected among circulating monocytes, a pattern that In line with previous reports, we observed an expansion of was mostly evident in severe COVID-19 patients. intermediate CD14+CD16+ monocytes in COVID-19 patients Focusing on MNP biology in COVID-19, our study has limi- (21, 43). This appeared to be a general feature of clinical tations to be considered. First, the present study has a relatively SARS-CoV-2 infection. In addition, all monocyte subsets small sample size, and thus lacks power to draw firm conclusions responded with increased CD38 expression that, in contrast to on clinical variables, such as outcome or biomarkers predicting DCs, was independent of disease severity. It is plausible that certain clinical courses. However, it is powered and designed to iMonos, exhibiting a strong IFN signature in COVID-19 (27), interpret phenotypic changes within the MNP system in relation contribute to successful viral clearance in moderate COVID-19 to COVID-19 severity. It is also realized that the addition of patients. In addition, the Phenograph cluster we identified that a disease-control group (e.g., non–COVID-19 patients with se- corresponded to iMonos (cluster 18) (Fig. 4H) had high levels of vere infections or inflammatory conditions) could have further

Kvedaraite et al. PNAS | 9of12 Major alterations in the mononuclear phagocyte landscape associated with COVID-19 https://doi.org/10.1073/pnas.2018587118 severity Downloaded by guest on September 25, 2021 strengthened the conclusions drawn. Thus, the alterations noted medical care; 5, hospitalized, on oxygen; 6, hospitalized, on high-flow oxy- in the MNP landscape of COVID-19 patients might at varying gen or noninvasive mechanical ventilation; 7, hospitalized, on mechanical degrees be shared with other febrile illnesses. Finally, since we ventilation or extracorporeal membrane oxygenation; 8, death. In addition, largely excluded the oldest patients, those with ongoing malig- a group of healthy volunteers matched for age and sex, no history of COVID- 19, no signs or symptoms of ongoing COVID-19, and SARS-CoV-2 IgG sero- nancies, and patients receiving immune-suppressive treatment negative, was recruited in parallel as a control group. The research was prior to admission in order to study a more homogeneous patient approved by the Swedish Ethical Review Authority, and all participants cohort, future larger studies should also assess the impact of age provided informed consent. and underlying conditions on the MNP response in COVID-19. Hyperinflammation in COVID-19 is characterized by high Serology. SARS-CoV-2 IgG titers were measured at the day of sampling in systemic cytokine levels combined with coagulation abnormali- serum in all the patients and controls according to the routinely used clinical ties and thromboembolism (2, 50, 51), also indicated in our study pipeline at the Clinical Microbiology Department, Karolinska University by increased levels of fibrinogen and D-dimer. Increased levels Hospital, and finally interpreted by a clinical microbiologist as positive or of thrombomodulin (CD141), a cofactor for thrombin reducing negative, according to clinical practice. blood coagulation, possibly possessing an anticoagulant poten- tial, were observed on cMonos in both moderate and severe Viral Titers and Clinical Blood Tests. Viral load was assessed at the day of disease, and on iMonos and ncMonos in severe COVID-19. sampling as serum SARS-CoV-2 PCR positivity, examined in serum from all the patients following the pipeline at the Clinical Microbiology Department, While the role of the extracellular metalloprotease inducer Karolinska University Hospital. Briefly, two independent tests were used and CD147 that was discussed to be a viral spike protein receptor finally interpreted by a clinical microbiologist as positive or negative, remains to be determined, it is also a potential host factor in according to the clinical routine. Clinical laboratory blood tests were per- infection-mediated coagulation (52). In the present study, ele- formed at the Karolinska University Laboratory as a part of clinical routine vated levels of CD147 on iMonos and ncMonos was a general monitoring of COVID-19 patients. feature of all patients, while increased levels of CD147 on pDCs, pre-DCs, and pre-DC2s were specific to severe COVID-19. In Trucount Staining. In order to determine the absolute leukocyte numbers, this context, it is interesting to note that DC progenitors can trucount staining was performed. Briefly, 50 μL EDTA blood and 20 μLof themselves be susceptible to viral insult (53). It remains to be mix (6-color TBNK Reagent and CD123 BUV395 from BD, CD15 PB, addressed what causes CD147 up-regulation in COVID-19 and CD193 BV605 and HLA-DR BV785 from Biolegend, and CD14 PE-Cy5 from how the MNP system may participate in dysregulated coagula- eBioscience) were added in BD trucount tubes and incubated for 15 min at room temperature in the dark. The samples were then fixed using 430 μL1× tion, but it has previously been shown that CD147 supports BD FACS lysing solution (BD Biosciences). –monocyte interactions promoting vascular inflammation γ (54). In addition, CD147 is up-regulated upon TNF and IFN- Sample Processing and Flow Cytometry. Peripheral blood mononuclear cells stimulation, an effect strongly potentiated by bacterial stimuli (PBMCs) were isolated from patient and control blood samples after Ficoll (55). Of note, the two pulmonary embolism cases in our cohort separation using Lymphoprep (Stemcell Technologies) and FACS staining was occurred in severe COVID-19 patients suffering from superin- performed on freshly isolated cells in three independent experiments during fection with positive blood cultures. three subsequent weeks to minimize batch effects. To assess batch-related In summary, we here provide a comprehensive mapping of the effects, internal control of frozen healthy PBMCs were used in all experi- MNP landscape in COVID-19. Lineage-restricted changes were ments and revealed minimal variability. Briefly, PBMCs were resuspended in observed with loss of the inhibitory receptor CD200R in cDC2s, PBS containing 2% FCS and 2 mM EDTA and a mixture of , sup- plemented with BD Horizon Brilliant Stain Buffer Plus (BD Biosciences) at 1:5 including the progenitors, associated with severe disease and – and FcR Blocking Reagent (Miltenyi Biotec) at 1:25, and were incubated for fatal outcome as well as expansion of immature Mo-MDSC like 30 min. Next, cells were washed twice and fixed in PBS containing 1% PFA monocytes in the critically ill. These findings suggest dysregula- for 2 h in order to inactivate the virus. Cells were acquired on a FACSym- tion of myeloid development in severe COVID-19, involving phony A5 instrument (BD Biosciences), equipped with UV (355 nm), violet both monocyte and DC lineages, and changes detected in cir- (405 nm), blue (488 nm), yellow/green (561 nm), and red laser (637 nm). culation were mirrored in lung tissue. Unsupervised clustering Details of antibodies used as well as information on filters for cytometer analysis revealed that MNPs, alone, could identify a cluster with configuration are provided in SI Appendix, Table S3. nonsurvivors. Further research is needed to understand if and how immature myeloid derived cells in COVID-19 contribute to Flow Cytometry Analysis. Acquired data were analyzed using DIVA (BD Bio- sciences), FlowJo v.10.5.3 (BD Biosciences), and Prism v8.0.2 (GraphPad perpetuation and silencing of the inflammatory storm, and how + the altered MNP development affects monocyte and DC ability Software). Absolute numbers of MNPs were determined relating to CD14 cells in the trucount staining. Algorithms used for dimensionality reduction to contribute to viral clearance. The knowledge gained from were UMAP (58) (https://github.com/lmcinnes/umap) and Phenograph (59) investigating MNP biology provides a framework that has the (https://github.com/JinmiaoChenLab/Rphenograph). UMAP coordinates and potential to influence vaccine development, where accurate Phenograph cluster annotation were assigned to each cell in each sample in MNP host responses are needed, and improvement of treatment the concatenated sample file, and from there, subset-specific phenotypic strategies aiming at controlling the proinflammatory storm changes of mean fluorescence intensity (MFI) were analyzed by gating di- without affecting the ’s ability to clear the virus. rectly in the UMAP space on the concatenated samples using FlowJo v.10.5.3 (BD Biosciences). Materials and Methods Patient and Healthy Control Cohorts. COVID-19 patients were recruited at the Soluble Factor Analysis. Serum levels of soluble factors were analyzed at Karolinska University Hospital in Stockholm. All patients were SARS-CoV-2 the day of the sampling using proximity extension assay, based on real-time PCR+ in nasopharynx or , had clinical COVID-19, and were sampled PCR quantification of pair-wise binding of oligonucleotide-labeled target within 8 d from hospitalization. Moderate COVID-19 patients and an oxygen antibodies (Olink Proteomics; sensitivity, specificity, dynamic range, repeat-

saturation (SO2) between 90% and 94% or had a 0.5 to 3 L/min oxygen ability, reproducibility, and scalability validation documents are available at requirement at screening. Severe COVID-19 patients were treated at the https://www.olink.com/resources-support/document-download-center/). intensive care unit or a high-dependency unit. Patients with an ongoing malignancy or ongoing immune-suppressive medication prior to hospitali- Single Cell Analysis. Seurat v3 was used to reanalyze single cell data. In brief, zation were excluded from the study. The patients were further described by scRNA-seq from a previously published report (22) was used and 10X Ge- Sequential Organ Failure Assessment score (56) and the NIH ordinal scale nomics filtered_feature_bc_matrix files were acquired from Gene Expression (57). The NIH ordinal scale scores are as follows: 1, not hospitalized, no Omnibus (GEO; accession no. GSE145926). Quality control, such as gene limitations; 2, not hospitalized, with limitations; 3, hospitalized, no active number between 200 and 6,000, UMI count above 1,000, and mitochondrial medical problems; 4, hospitalized not on oxygen but requiring ongoing gene percentage below 0.1 were applied, and “LogNormalize” method in

10 of 12 | PNAS Kvedaraite et al. https://doi.org/10.1073/pnas.2018587118 Major alterations in the mononuclear phagocyte landscape associated with COVID-19 severity Downloaded by guest on September 25, 2021 Seurat v3 was used to normalize the data matrix followed by PCA using the Statistical Analysis. Differences between two groups were evaluated using a top 2,000 most variable genes. The top 50 principal components were used Mann–Whitney U test, and Kruskal–Wallis test with Dunn’s multiple com- to visualize the cells in UMAP, and a “bimod” test was used to detect dif- parisons test were used to evaluate differences among the three groups in ferentially expressed genes (DEGs). First, we selected myeloid DC clusters 22, all the analysis. Friedman test was used in Fig. 2E to evaluate differences 25, 27 (CLEC9A, CADM1, CD1c, FCER1A), pDC cluster 28 (LILRA4, IL3RA), and among paired variables. A Spearman test was used for correlations of other myeloid clusters 0, 1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 21, 23, 29 (CD14, nonnormally distributed data and Pearson test was used for normally dis- CD68), after exclusion of B cells, plasma cells, , NK cells, and T cells tributed data. Significance for pathways analyses in Fig. 3 E and H was de- (SI Appendix, Fig. S1 A–F). After rerunning total myeloid cells through the fined by the IPA software (Ingenuity Systems). Significance level: *P < 0.05, dimensionality reduction pipeline (SI Appendix, Fig. S1 G–K), neutrophils **P < 0.01, ***P < 0.001, ****P < 0.0001. were excluded (SI Appendix, Fig. S1 F and G). To identify DCs, DC-containing clusters 11, 27, and 29 from the myeloid cell UMAP were reclustered (SI Data Availability. Curated flow cytometry data will be made available for – Appendix, Fig. S1 L P). As cluster 4 contained a comparable number of cells exploration via the Karolinska KI/K COVID-19 Immune Atlas homepage in severe and moderate cohorts, and cluster 7 in severe and healthy, the (https://covid19cellatlas.com) or from the corresponding author (E.K.) DEGs in those clusters were calculated between the severe patients and the upon request. respective cohort and subjected to pathway analysis. Ingenuity Pathway Analysis (IPA) was performed to study pathways (Content version: 51963813; ACKNOWLEDGMENTS. We thank the patients for their generous participa- Release date: 2020-03-11; Ingenuity Systems). Predicted up-regulation and tion; nurses, research nurses, physiotherapists, nurse assistants, dieticians, down-regulation of pathways was based on z-score, where a positive score occupational therapists, physicians, psychologists, counselors, clinical labo- implied up-regulation and a negative score implied down-regulation. ratory analysis personnel, and all other medical care personnel in Sweden and worldwide for their contributions during the COVID-19 pandemic; PCA and Correlation Plots. PCA plots were created using “prcomp” function figures were created with BioRender.com and we are thankful to the team and “ggbiplot” package in R and correlation mapping was performed using at the SciLifeLab Plasma Profiling Facility in Stockholm for generating the Olink data; and Dr. Samuel Myers for insightful discussion. The study was the “corrplot” package in R. The color of the circles indicated positive (red) supported by the Knut and Alice Wallenberg Foundation, Nordstjernan AB, and negative (blue) correlations; color intensity represented correlation the Swedish Children’s Cancer Foundation, Swedish Cancer Foundation, the ’ strength as measured by the Pearson s correlation coefficient. The correla- Swedish Governmental Agency for Innovation Systems (VINNOVA) under the tion matrix was reordered using “hclust” for hierarchical clustering order. frame of NordForsk (Project no. 90456, PerAID), the Swedish Research Coun- Significance tests were performed to produce P values and confidence in- cil under the frame of ERA PerMed (Project 2018-151, PerMIT), Mary Béves tervals for each pair of input features. Foundation, and the Erik och Edith Fernströms Foundation.

1. P. Mehta et al.; HLH Across Speciality Collaboration, UK, COVID-19: Consider cytokine 24. I. Sánchez-Cerrillo et al.; REINMUN-COVID and EDEPIMIC groups, COVID-19 severity storm syndromes and immunosuppression. Lancet 395, 1033–1034 (2020). associates with pulmonary redistribution of CD1c+ DCs and inflammatory transitional IMMUNOLOGY AND INFLAMMATION 2. C. Huang et al., Clinical features of patients infected with 2019 novel coronavirus in and nonclassical monocytes. J. Clin. Invest. 130, 6290–6300 (2020). Wuhan, China. Lancet 395, 497–506 (2020). 25. W. Wen et al., Immune cell profiling of COVID-19 patients in the recovery stage by 3. M. Guilliams et al., Dendritic cells, monocytes and : A unified nomen- single-cell sequencing. Cell Discov. 6, 31 (2020). clature based on ontogeny. Nat. Rev. Immunol. 14, 571–578 (2014). 26. A. Silvin et al., Elevated and abnormal myeloid cell subsets discriminate – 4. M. Rescigno, functions: Learning from microbial evasion strategies. severe from mild COVID-19. Cell 182, 1401 1418.e18 (2020). Semin. Immunol. 27, 119–124 (2015). 27. J. Schulte-Schrepping et al., Suppressive myeloid cells are a hallmark of severe COVID- 5. K. L. Wong et al., Gene expression profiling reveals the defining features of the 19. medRxiv:10.1101/2020.06.03.20119818 (5 June 2020). 28. A. Combes et al., BAD-LAMP controls TLR9 trafficking and signalling in plas- classical, intermediate, and nonclassical human monocyte subsets. Blood 118, e16–e31 macytoid dendritic cells. Nat. Commun. 8, 913–918 (2017). (2011). 29. P. Felker et al., TGF-beta1 accelerates dendritic cell differentiation from common 6. M. A. Ingersoll et al., Comparison of gene expression profiles between human and dendritic cell progenitors and directs subset specification toward conventional den- mouse monocyte subsets. Blood 115, e10–e19 (2010). dritic cells. J. Immunol. 185, 5326–5335 (2010). 7. F. Geissmann, S. Jung, D. R. Littman, Blood monocytes consist of two principal subsets 30. D. I. Gabrilovich, S. Nagaraj, Myeloid-derived suppressor cells as regulators of the with distinct migratory properties. Immunity 19,71–82 (2003). immune system. Nat. Rev. Immunol. 9, 162–174 (2009). 8. A. Mildner, S. Yona, S. Jung, A close encounter of the third kind: Monocyte-derived 31. L. Cassetta et al., Deciphering myeloid-derived suppressor cells: Isolation and markers – cells. Adv. Immunol. 120,69 103 (2013). in humans, mice and non-human primates. Cancer Immunol. Immunother. 68, 9. K.-L. Chu, N. V. Batista, M. Girard, T. H. Watts, Monocyte-derived cells in tissue- 687–697 (2019). – resident memory T cell formation. J. Immunol. 204, 477 485 (2020). 32. D. M. W. Zaiss, W. C. Gause, L. C. Osborne, D. Artis, Emerging functions of amphir- 10. P. F. Rodrigues et al., Distinct progenitor lineages contribute to the heterogeneity of egulin in orchestrating immunity, inflammation, and tissue repair. Immunity 42, plasmacytoid dendritic cells. Nat. Immunol. 19, 711–722 (2018). 216–226 (2015). + 11. R. J. Dress et al., Plasmacytoid dendritic cells develop from Ly6D lymphoid progen- 33. B. L. Yen et al., Multipotent human mesenchymal stromal cells mediate expansion of itors distinct from the myeloid lineage. Nat. Immunol. 20, 852–864 (2019). myeloid-derived suppressor cells via hepatocyte growth factor/c-met and STAT3. Stem 12. M. Collin, V. Bigley, Human dendritic cell subsets: An update. Immunology 154,3–20 Cell Reports 1, 139–151 (2013). (2018). 34. H. Alshetaiwi et al., Defining the emergence of myeloid-derived suppressor cells in 13. E. Segura et al., Human inflammatory dendritic cells induce Th17 cell differentiation. breast cancer using single-cell transcriptomics. Sci. Immunol. 5, eaay6017 (2020). Immunity 38, 336–348 (2013). 35. S. Scholz et al., Human hantavirus infection elicits pronounced redistribution of 14. C.-A. Dutertre et al., Single-cell analysis of human mononuclear phagocytes reveals mononuclear phagocytes in peripheral blood and airways. PLoS Pathog. 13, e1006462 subset-defining markers and identifies circulating inflammatory dendritic cells. Im- (2017). munity 51, 573–589.e8 (2019). 36. J. Hadjadj et al., Impaired type I interferon activity and exacerbated inflammatory 15. P. Bourdely et al., Transcriptional and functional analysis of CD1c+ human dendritic responses in severe Covid-19 patients. medRxiv:10.1101/2020.04.19.20068015 (23 cells identifies a CD163+ subset priming CD8+CD103+ T cells. Immunity 53, 335–352.e8 April 2020). 37. J. Banchereau, R. M. Steinman, Dendritic cells and the control of immunity. Nature (2020). 392, 245–252 (1998). 16. U. Cytlak et al., Differential IRF8 transcription factor requirement defines two path- 38. S. Simonetti et al., Dendritic cells modulate c- expression on the edge between ways of dendritic cell development in humans. Immunity 53, 353–370.e8 (2020). activation and death. Eur. J. Immunol. 49, 534–545 (2019). 17. P. See et al., Mapping the human DC lineage through the integration of high- 39. S. Scutera et al., Survival and migration of human dendritic cells are regulated by an dimensional techniques. Science 356, eaag3009 (2017). IFN-alpha-inducible Axl/Gas6 pathway. J. Immunol. 183, 3004–3013 (2009). 18. A.-C. Villani et al., Single-cell RNA-seq reveals new types of human blood dendritic 40. R. M. Hoek et al., Down-regulation of the lineage through interaction cells, monocytes, and progenitors. Science 356, eaah4573 (2017). with OX2 (CD200). Science 290, 1768–1771 (2000). 19. E. J. Giamarellos-Bourboulis et al., Complex in COVID-19 pa- 41. C. A. Vaine, R. J. Soberman, The CD200-CD200R1 inhibitory signaling pathway: Im- – tients with severe respiratory failure. Cell Host Microbe 27, 992 1000.e3 (2020). mune regulation and host-pathogen interactions. Adv. Immunol. 121, 191–211 (2014). 20. A. J. Wilk et al., A single-cell atlas of the peripheral immune response in patients with 42. C. M. Cameron, J. W. Barrett, L. Liu, A. R. Lucas, G. McFadden, Myxoma virus M141R – severe COVID-19. Nat. Med. 26, 1070 1076 (2020). expresses a viral CD200 (vOX-2) that is responsible for down-regulation of macro- 21. M. Merad, J. C. Martin, Pathological inflammation in patients with COVID-19: A key phage and T-cell activation in vivo. J. Virol. 79, 6052–6067 (2005). role for monocytes and macrophages. Nat. Rev. Immunol. 20, 355–362 (2020). 43. A. Gatti, D. Radrizzani, P. Viganò, A. Mazzone, B. Brando, Decrease of non-classical 22. M. Liao et al., Single-cell landscape of bronchoalveolar immune cells in patients with and intermediate monocyte subsets in severe acute SARS-CoV-2 infection. Cytometry COVID-19. Nat. Med. 26, 842–844 (2020). A 97, 887–890 (2020). 23. Z. Zhou et al., Heightened innate immune responses in the respiratory tract of COVID- 44. D. Blanco-Melo et al., Imbalanced host response to SARS-CoV-2 drives development of 19 patients. Cell Host Microbe 27, 883–890.e2 (2020). COVID-19. Cell 181, 1036–1045.e9 (2020).

Kvedaraite et al. PNAS | 11 of 12 Major alterations in the mononuclear phagocyte landscape associated with COVID-19 https://doi.org/10.1073/pnas.2018587118 severity Downloaded by guest on September 25, 2021 45. C. Agrati et al., Expansion of myeloid-derived suppressor cells in patients with severe 52. A. Schulz et al., Protective vascular coagulation in response to bacterial infection of coronavirus disease (COVID-19). Cell Death Differ. 27, 3196–3207 (2020). the is regulated by bacterial and host CD147. Pathog. Dis. 76, fty087 46. S. Falck-Jones et al., Functional myeloid-derived suppressor cells expand in blood but (2018). not airways of COVID-19 patients and predict disease severity. medRxiv:10.1101/ 53. N. Ruffin et al., Constitutive Siglec-1 expression confers susceptibility to HIV-1 infec- 2020.09.08.20190272 (9 September 2020). tion of human dendritic cell precursors. Proc. Natl. Acad. Sci. U.S.A. 116, 21685–21693 47. J. S. Naranjo-Gómez et al., Different phenotypes of non-classical monocytes associ- (2019). ated with , endothelial alteration and hepatic compromise in 54. C. Schulz et al., EMMPRIN (CD147/) mediates platelet-monocyte interactions patients with dengue. Immunology 156, 147–163 (2019). in vivo and augments monocyte recruitment to the vascular wall. J. Thromb. Haemost. 48. H. Poehlmann, J. C. Schefold, H. Zuckermann-Becker, H.-D. Volk, C. Meisel, Phenotype 9, 1007–1019 (2011). changes and impaired function of dendritic cell subsets in patients with : A 55. A. Till et al., A role for membrane-bound CD147 in NOD2-mediated recognition of prospective observational analysis. Crit. Care 13, R119–R12 (2009). – 49. C. Burbano, G. Vasquez, M. Rojas, Modulatory effects of CD14+CD16++ monocytes on bacterial cytoinvasion. J. Cell Sci. 121, 487 495 (2008). CD14++CD16- monocytes: A possible explanation of monocyte alterations in systemic 56. M. Singer et al., The Third International Consensus definitions for sepsis and septic – erythematosus. Arthritis Rheumatol. 66, 3371–3381 (2014). shock (Sepsis-3). JAMA 315, 801 810 (2016). — 50. P. Toniati et al., Tocilizumab for the treatment of severe COVID-19 pneumonia with 57. J. H. Beigel et al., Remdesivir for the treatment of Covid-19 Preliminary report. N. hyperinflammatory syndrome and acute respiratory failure: A single center study of Engl. J. Med. 383, 1813–1826 (2020). 100 patients in Brescia, Italy. Autoimmun. Rev. 19, 102568 (2020). 58. E. Becht et al., Demensionality reduction for visualizing single-cell data using UMAP. 51. G. Magro, COVID-19: Review on latest available drugs and therapies against Nat. Biotechnol. 37,38–44 (2019). SARS-CoV-2. Coagulation and inflammation cross-talking. Virus Res. 286, 198070 59. J. H. Levine et al., Data-driven phenotypic dissection of AML reveals progenitor-like (2020). cells that correlate with prognosis. Cell 162, 184–197 (2015).

12 of 12 | PNAS Kvedaraite et al. https://doi.org/10.1073/pnas.2018587118 Major alterations in the mononuclear phagocyte landscape associated with COVID-19 severity Downloaded by guest on September 25, 2021