medRxiv preprint doi: https://doi.org/10.1101/2021.01.27.21250591; this version posted January 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

1 Title: High-dimensional profiling reveals phenotypic heterogeneity and disease-specific

2 alterations of in COVID-19

3

4 Authors: Magda Lourda1,2, Majda Dzidic1, Laura Hertwig1, Helena Bergsten1, Laura M. Palma

5 Medina1, Egle Kvedaraite1,2, Puran Chen1, Jagadeeswara R. Muvva1, Jean-Baptiste Gorin1,

6 Martin Cornillet1, Johanna Emgård1, Kirsten Moll1, Marina García1, Kimia T. Maleki1, Jonas

7 Klingström1, Jakob Michaëlsson1, Malin Flodström-Tullberg1, Susanna Brighenti1, Marcus

8 Buggert1, Jenny Mjösberg1, Karl-Johan Malmberg1, Johan K. Sandberg1, Jan-Inge Henter2,3,

9 Elin Folkesson4,5, Sara Gredmark-Russ1,4, Anders Sönnerborg4,6, Lars I. Eriksson7,8, Olav

10 Rooyackers8,9, Soo Aleman4,6, Kristoffer Strålin4,6, Hans-Gustaf Ljunggren1, Niklas K.

11 Björkström1, Mattias Svensson1, Andrea Ponzetta1, Anna Norrby-Teglund1, Benedict J.

12 Chambers1; and the Karolinska KI/K COVID-19 Study Group

13

14 Authorship note:

15 ML, MD and LH share first authorship.

16 AP, ANT and BJC share last authorship.

17 Correspondence: Magda Lourda ([email protected])

18

19 Affiliations:

20 1Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet,

21 Karolinska University Hospital, Stockholm, Sweden

22 2Childhood Cancer Research Unit, Department of Women’s and Children’s Health, Karolinska

23 Institutet, Stockholm, Sweden

24 3Theme of Children’s Health, Karolinska University Hospital, Stockholm, Sweden

25 4Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2021.01.27.21250591; this version posted January 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

26 5Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet,

27 Stockholm, Sweden

28 6Division of Infectious Diseases and Dermatology, Department of Medicine Huddinge,

29 Karolinska Institutet, Stockholm, Sweden

30 7Department of Physiology and Pharmacology, Section for Anesthesiology and Intensive Care,

31 Karolinska Institutet, Stockholm, Sweden

32 8Function Perioperative Medicine and Intensive Care, Karolinska University Hospital,

33 Stockholm, Sweden

34 9Department of Clinical Interventions and Technology CLINTEC, Division for Anesthesiology

35 and Intensive Care, Karolinska Institutet, Stockholm, Sweden.

36

37 Conflict of interest statement:

38 K-JM is a Scientific Advisor and has a research grant from Fate Therapeutics and is a member

39 of the Scientific Advisory Board of Vycellix Inc. H-GL is a member of the board of XNK

40 Therapeutics AB and Vycellix Inc. J-IH serves as consultant for Sobi AB. The remaining

41 authors have declared that no conflict of interest exists.

42

2 medRxiv preprint doi: https://doi.org/10.1101/2021.01.27.21250591; this version posted January 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

43 Abstract

44 Since the outset of the COVID-19 pandemic, increasing evidence suggests that the innate

45 immune responses play an important role in the disease development. A dysregulated

46 inflammatory state has been proposed as key driver of clinical complications in COVID-19,

47 with a potential detrimental role of granulocytes. However, a comprehensive phenotypic

48 description of circulating granulocytes in SARS-CoV-2-infected patients is lacking. In this

49 study, we used high-dimensional flow cytometry for immunophenotyping in

50 peripheral blood collected from COVID-19 patients during acute and convalescent phases.

51 Severe COVID-19 was associated with increased levels of both mature and immature

52 neutrophils, and decreased counts of eosinophils and . Distinct immunotypes were

53 evident in COVID-19 patients, with altered expression of several receptors involved in

54 activation, adhesion and migration of granulocytes (e.g. CD62L, CD11a/b, CD69, CD63,

55 CXCR4). Paired sampling revealed recovery and phenotypic restoration of the granulocytic

56 signature in the convalescent phase. The identified granulocyte immunotypes correlated with

57 distinct sets of soluble inflammatory markers supporting pathophysiologic relevance.

58 Furthermore, clinical features, including multi-organ dysfunction and respiratory function,

59 could be predicted using combined laboratory measurements and immunophenotyping. This

60 study provides a comprehensive granulocyte characterization in COVID-19 and reveals

61 specific immunotypes with potential predictive value for key clinical features associated with

62 COVID-19.

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63 Significance

64 Accumulating evidence shows that granulocytes are key modulators of the immune response

65 to SARS-CoV-2 infection and their dysregulation could significantly impact COVID-19

66 severity and patient recovery after virus clearance. In the present study, we identify selected

67 immune traits in neutrophil, eosinophil and subsets associated to severity of COVID-

68 19 and to peripheral protein profiles. Moreover, computational modeling indicates that the

69 combined use of phenotypic data and laboratory measurements can effectively predict key

70 clinical outcomes in COVID-19 patients. Finally, patient-matched longitudinal analysis shows

71 phenotypic normalization of granulocyte subsets 4 months after hospitalization. Overall, in this

72 work we extend the current understanding of the distinct contribution of granulocyte subsets

73 to COVID-19 pathogenesis.

74

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75 Introduction

76 Caused by the novel coronavirus SARS-CoV-2, the current COVID-19 pandemic poses an

77 unprecedented threat to the global health systems, with over 2 million confirmed deaths

78 reported worldwide in January 2021 (https://www.who.int/emergencies/diseases/novel-

79 coronavirus-2019). About 80% of COVID-19 patients present with a mild or moderate disease

80 course, while 15-20% develop severe complications (1, 2), including respiratory failure,

81 coagulation abnormalities and life-threatening acute respiratory distress syndrome (ARDS).

82 A dysregulated inflammatory state has been proposed as a key driver of clinical complications

83 of COVID-19 (3) and has been associated with increased mortality (4). High levels of several

84 pro-inflammatory mediators (e.g. Interleukin-1β (IL-1β), IL-6, Tumor necrosis factor ⍺

85 (TNF⍺) and CXCL8) are detected early after viral infection (5), and the resolution of this

86 inflammatory response seems impaired in patients with severe disease progression (5, 6). Many

87 of these pro-inflammatory cytokines are associated with granulocyte activation and recruitment

88 (7), and it has been reported that an increased neutrophil-to-lymphocyte ratio (NLR) in

89 peripheral blood can be used as a prognostic marker of higher disease severity in COVID-19

90 patients, similar to SARS-CoV-1 and MERS (8-10).

91 The cellular components of the polymorphonuclear granulocyte family – neutrophils,

92 eosinophils and basophils – arise from a common myeloid progenitor in humans (11) and

93 represent the very first line of defense against invading microbes. The role of granulocytes in

94 viral infection has mostly been confined to neutrophils (12, 13), though eosinophils and

95 basophils have been implicated in the host response to viruses as well (14, 15). In COVID-19,

96 an increased neutrophil abundance in circulation is mirrored by a substantial enrichment of

97 granulocytes in the inflamed lung (16, 17). Several reports highlighted the presence of

98 immature neutrophils in blood of COVID-19 patients (18, 19), implying a marked emergency

99 granulopoiesis occurring in the bone marrow. The presence of immature neutrophil populations

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100 is phenotypically reminiscent of granulocytic myeloid-derived suppressor cells (G-MDSCs),

101 and has thus been linked to a potential immunosuppressive role of neutrophils during SARS-

102 CoV-2 infection (19, 20). A confounding factor in many of the reported studies is represented

103 by the broad use of peripheral blood mononuclear cells (PBMC) (e.g. (6, 18, 21)), where only

104 the low-density granulocytic fraction can be captured, which could lead to an inherent bias in

105 the result interpretation (22, 23). Longitudinal studies performed during the course of SARS-

106 CoV-2 infection have revealed that neutrophil numbers slowly decrease during infection, and

107 the rate of such decrease is lower in more severe patients (6). Interestingly, basophil and

108 eosinophil counts were found to be inversely correlated with neutrophil levels (24).

109 In the present study we set out to delineate the phenotypic alterations within neutrophil,

110 eosinophil and basophil populations during the early phase of SARS-CoV-2 infection in

111 patients with acute COVID-19 and in the convalescent phase. We provide a detailed analysis

112 at the single-cell resolution of granulocyte diversity in fresh whole blood of COVID-19 patients

113 using 25-color flow cytometry, integrated with soluble factor detection by proteomics and

114 detailed clinical information. Overall, this study delineates the biological contribution of

115 granulocyte subsets to COVID-19 pathogenesis, with an emphasis on their phenotypical

116 heterogeneity in viral infection. The data reveals specific immunotypes with potential

117 predictive value for key clinical features associated with COVID-19 severity.

118

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119 Results

120 Altered composition of granulocyte populations is associated with disease severity in COVID-

121 19

122 To provide an in-depth characterization of the granulocyte compartment in patients with

123 moderate or severe COVID-19, we designed a strategy based on the integrated analysis of high

124 dimensional flow cytometry data from whole blood cells, extensive proteomic screening in

125 serum and plasma, and detailed clinical and laboratory information from patients included in

126 the study (Figure 1A). Twenty-six hospitalized COVID-19 patients (10 moderate and 16

127 severe) and age-matched healthy controls were recruited at Karolinska University Hospital as

128 part of the Karolinska KI/K COVID-19 Immune Atlas effort (25-28) (Figure 1B, Supplemental

129 Tables 1-3). No differences in age, sex, body-mass index (BMI) or time from symptom debut

130 until hospital admission, were present between moderate and severe patients (Figure 1B,

131 Supplemental Table 2). The severe group displayed higher concentration of the inflammatory

132 markers D-dimer and C-reactive protein (CRP), coagulation and inflammatory markers, and

133 included all patients (n=4) who subsequently died in the hospital (Figure 1B).

134 A 25-color flow cytometry panel was applied on freshly isolated whole blood cells from

135 COVID-19 patients and healthy controls (Figure 1C) to identify and characterize the main

136 granulocyte subsets. To minimize batch-effects, samples were processed and acquired during

137 a short period of time (3 weeks). A manual gating strategy for neutrophils, eosinophils and

138 basophils was used based on expression of canonical lineage-defining markers (Figure 1C).

139 Neutrophils were identified as CD15+CD193-CD66b+CD16bright/dim, eosinophils as

140 CD15bright/dimCD193bright/dimCD16-FceR1dimCD123dim/- and basophils as CD15-

141 CD193brightFceR1brightCD123brightHLA-DRdim/-. Projection of gated cells into the Uniform

142 Manifold Approximation and Projection (UMAP) space integrating all the analyzed markers

143 confirmed the efficacy of the manual gating strategy (Figure 1C).

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144 The analysis of absolute leukocyte counts in COVID-19 patients highlighted relevant

145 alterations in the granulocyte compartment in peripheral blood. In particular, neutrophils were

146 significantly increased in patients with severe disease course, while eosinophils and basophils

147 were decreased in COVID-19 patients when compared to non-infected healthy controls (Figure

148 1D). A similar trend was observed in the relative frequencies of granulocyte subsets over total

149 leukocytes. A higher proportion of neutrophils in patients with moderate and severe COVID-

150 19 (66.65 ± 11.71% and 78.96 ± 8.83 respectively) was associated with the reduction of the

151 eosinophil and basophil fractions (up to 3 and 7.5-fold, respectively, Figure 1E). The ratios of

152 neutrophils over eosinophils or basophils increased in both patient groups, while no difference

153 in the eosinophil to basophil ratios was observed between controls and patients (Supplemental

154 Figure 1A). In line with previous reports (29), we found that severe COVID-19 was associated

155 with higher neutrophil-to-lymphocyte and neutrophil-to-T cell ratios, as determined by

156 absolute cell count (Figure 1F, Supplemental Figure 1B). We observed an opposite trend for

157 basophils, which was associated with disease severity, while we did not detect any significant

158 alteration in the eosinophil to lymphocyte or eosinophil to T cell ratios (Figure 1F,

159 Supplemental Figure 1B).

160 This set of data highlights that the granulocyte expansion observed during acute SARS-CoV-

161 2 infection is dominated by neutrophils and is coupled to the reduction of the physiological

162 levels of circulating eosinophils and basophils.

163

164 Severe COVID-19 is characterized by the emergence of immature neutrophils in peripheral

165 blood

166 Neutrophils have been shown to display a high degree of phenotypical heterogeneity, which

167 has important implications on their function in several pathological contexts (30). Different

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168 neutrophil maturation states are associated with major phenotypic differences, e.g. CD16

169 upregulation during development (23, 30). In COVID-19 patients, particularly in those with

170 severe disease, we observed a robust enrichment of a neutrophil subset characterized by low

171 expression of CD16 suggesting that these were immature CD16dim neutrophils (Figure 2A).

172 A significant increase in absolute numbers was detected for both CD16bright and CD16dim

173 neutrophil subsets in COVID-19 patients, which positively correlated with disease severity

174 (Figure 2B). In addition, a significant increase in the relative fraction of CD16dim neutrophils

175 was detected, indicating an overall expansion of immature neutrophils in COVID-19 patients

176 (Figure 2C).

177 In order to analyze the phenotypic traits of the neutrophil subsets identified above in an

178 unsupervised way, 30,000 events in the neutrophil gate from each study participant were

179 concatenated and then down-sampled to 0.5 million cells. By integrating the expression of 20

180 phenotypic markers in the UMAP analysis, we observed only modest differences comparing

181 neutrophils from healthy controls and COVID-19 patients, within the UMAP space (Figure

182 2D). In addition, the CD16bright and CD16dim neutrophil subsets from controls and COVID-19

183 patients clustered separately from each other (Figure 2E), indicating substantial phenotypic

184 differences, which did not depend on the disease state.

185 Next, the expression of markers related to neutrophil development, migration and activation

186 was analyzed. The mature CD16bright neutrophil subset was characterized by higher expression

187 of CD177, CD11b, CXCR1, CXCR2 and CD62L compared to the CD16dim population. On the

188 other hand, CD16dim neutrophils were characterized by higher levels of CD66b, LOX-1 and

189 CD24, which are associated to early stages of neutrophil development (31), confirming the

190 immature phenotype of this subset (Figures 2F-G, Supplemental Figures 2A-B).

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191 We also observed significant upregulation of CD66b, CD177, CD11b, CXCR4, CD147 (the

192 receptor that binds the spike protein of SARS-CoV-2) and CD63 in mature neutrophils from

193 COVID-19 patients compared to healthy controls, as well as significant downregulation of

194 CXCR2 (Figure 2G, Supplemental Figure 2A). No differences in the expression of markers

195 related to either co-stimulatory or inhibitory functions, such as HLA-DR, CD86 and PD-L1

196 between healthy controls and COVID-19 patients within either neutrophil subset were detected

197 (Figure 2G, Supplemental Figures 2A-B).

198 While the presented analysis showed several phenotypic differences between healthy controls

199 and COVID-19 patients, we were unable to detect prominent alterations between the moderate

200 and severe patients for any of the neutrophil subsets analyzed (Figure 2G, Supplemental Figure

201 2A-B). Such phenotypic similarity between the global neutrophil compartment in moderate

202 and severe COVID-19 patients was confirmed by principal component analysis (PCA) (Figure

203 2H).

204 Collectively, our analysis highlights the accumulation of immature CD16dim neutrophils and

205 the concurrent activation of the mature neutrophil fraction in peripheral blood of COVID-19

206 patients.

207

208 The abundance of activated neutrophil immunotypes correlates with markers of anti-viral

209 immune response in moderate patients

210 To investigate further whether specific neutrophil phenotypes were associated with disease

211 progression or severity, unsupervised phenograph clustering was performed on neutrophils

212 derived from all study participants (Figure 3A). In total, 27 clusters were identified with a

213 varying prevalence in the three study groups (Figure 3B). Hierarchical clustering based on

214 marker expression was sufficient to group clusters more abundant in moderate patients (i.e.

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215 clusters 16, 22 and 27) (Figure 3C). Specifically, these clusters belonged to the CD16bright

216 subset and displayed higher expression levels of CD11b, CD177 and CD66b. On the other

217 hand, clusters that were enriched in severe COVID-19 encompassed the immature CD16dim

218 population and displayed a more heterogeneous phenotype (Figure 3C). In particular, clusters

219 18, 19 and 23, which were more enriched in severe patients, showed lower levels of several

220 activation markers including CD66b, CD11a and CD11b (Supplemental Figure 3A).

221 Correlation of cluster frequencies in each patient with the concentration of 253 serum/plasma

222 factors (Supplemental Table 4) followed by hierarchical clustering highlighted specific

223 correlation patterns associated with trends in cluster enrichment within specific disease groups

224 (Figure 3D). In particular, clusters 16, 22 and 27, which were enriched in moderate COVID-

225 19, displayed a positive correlation with several molecules of type 1 response (CXCL9,

226 CXCL10 and IL-15), anti-viral response (interferon regulatory factor 9 (IRF9), IL-12p70 and

227 interferon-ɣ (IFNɣ)) and adaptive immunity (B-cell activating factor (BAFF) and IL-2) (Figure

228 3D). In addition, key molecules involved in eosinophil/basophil homeostasis and recruitment

229 (IL-5, CCL3 and stem cell factor (SCF)) were also positively correlated with these clusters.

230 Within the clusters associated with severe patients, several members of the coagulation cascade

231 (Factor VII, VIII and IX) and molecules associated with neutrophil maturation (i.e.

232 granulocyte-colony stimulating factor (G-CSF) and myeloperoxidase (MPO)) were positively

233 correlated (Figure 3D). Shared patterns within cluster groups (e.g. 22, 27 and 5, 23) were also

234 observed when separately analyzing correlations in moderate and severe COVID-19

235 (Supplemental Figure 3B and Supplemental Table 5), suggesting that the degree of disease

236 severity could influence relationships between specific neutrophil immunotypes and soluble

237 factors.

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238 In summary, the phenograph-guided in-depth dissection of the neutrophil subset composition

239 allowed us to identify selected immunotypes associated with high circulating levels of

240 prototypical molecules of type-1 related anti-viral immune response.

241

242 Eosinophil activation profile in COVID-19 correlates with markers of anti-viral immune

243 response

244 Through UMAP analysis integrating the expression of 21 surface markers on eosinophils, we

245 observed that eosinophils from healthy donors clustered separately from the eosinophils of

246 patients with COVID-19 (Figure 4A), indicating significant phenotypic alterations in the

247 eosinophil populations upon SARS-CoV-2 infection. Eosinophils from COVID-19 had

248 decreased expression of the markers of the eosinophil lineage (CD15, CD66b and CD193) (32)

249 and higher expression of classical activation markers, such as CD62L, CD69, as well as CD147

250 (Figure 4B, Supplemental Figure 4A). Phenotypic analysis showed differences between

251 healthy controls and COVID-19 patients, but also between patients with moderate and severe

252 COVID-19 for some of the parameters analyzed. Patients with moderate COVID-19 expressed

253 significantly higher levels of CD66b, CD11b, CD11a and CD24 compared to patients with

254 severe COVID-19 (Figure 4C, Supplemental Figure 4B). Notably within the severe group, the

255 deceased patients had the highest CD69 expression (Figure 4C). PCA analysis recapitulated

256 these differences, highlighting CD193, CD66b, CD11a, CD24, CXCR4 and CD62L as the

257 mostly affected parameters in eosinophils from COVID-19 patients (Figure 4D).

258 We observed that patients with COVID-19 also had a significantly increased frequency of

259 CD69+ eosinophils (Figures 4E-F). Indeed, despite the strong (Figure 1D), the

260 absolute counts of CD69+ eosinophils were not significantly altered in COVID-19 patients

261 (Figure 4F). CD69+ eosinophils displayed significantly higher expression of CD66b, CD147,

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262 CD11b and CD193, compared to the corresponding CD69- counterpart, confirming their

263 activation state (Figures 4G-H, Supplemental Figure 4D). Despite their CD69 expression,

264 activated eosinophils from severe COVID-19 patients displayed lower CD11a, CD66b, and

265 CD147 expression compared to CD69+ eosinophils from the group with moderate disease

266 (Figures 4G-H, Supplemental Figure 4D), suggesting a partial functional impairment of

267 eosinophils in the more severe COVID-19 stages.

268 The absolute counts of eosinophils in blood correlated inversely with the peripheral levels of

269 SCF and molecules involved in cell response to viral infection, such as DDX58 (DExD/H-Box

270 Helicase 58, also known as RIG-I), IFNɣ and CXCL10 (Figure 4I). Conversely, the eosinophil

271 levels in blood correlated positively with the levels of Interleukin-1 receptor-associated kinase

272 1 (IRAK1), latency associated peptide-transforming growth factor β1 (LAP-TGFβ1),

273 chemoattractants CCL13, CCL17 and CXCL6 and the coagulation factor VIII (Figure 4I). The

274 frequency of CD69+ eosinophils correlated positively with the levels of molecules related to

275 IFNɣ, CCL2, CCL7, CCL8, Pentraxin-3, leukemia inhibitory factor receptor (LIFR), as well

276 as PD-L1. sCD163 correlated negatively with the frequency of CD69+ eosinophils but

277 positively with the absolute counts and frequencies of eosinophils. Representative plots of the

278 most significantly correlated parameters are shown in Supplemental Figure 4E, while the most

279 significant correlations for the moderate and severe groups separately are shown in

280 Supplemental Figure 4F. In conclusion, our results indicate that the eosinophils in the blood of

281 patients with COVID-19 are activated in response to SARS-CoV-2 infection and express high

282 levels of key receptors for lung tissue infiltration, particularly in patients with moderate

283 COVID-19.

284

285 Basophils in COVID-19 patients display an activated phenotype

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286 UMAP analyses revealed distinctive clusters in the healthy controls and COVID-19 patients,

287 suggesting alterations in basophil phenotype during the disease (Figure 5A). Specifically,

288 clusters associated with COVID-19 had higher expression of markers important for the

289 activation and recruitment of basophils, including CD11b, CD63, and CXCR4 (Figure 5B;

290 Supplemental Figure 5A). Specific upregulation of CD62L, CD147 and CD177 were observed

291 on basophils from moderate COVID-19 patients (Figure 5C, Supplemental Figure 5B). On the

292 other hand, PD-L1 expression was reduced in all patients compared to healthy controls. The

293 differences in marker expression on basophils between healthy controls and COVID-19

294 patients were further confirmed by PCA, with CXCR4, CD62L, CD193 and PD-L1 among the

295 most contributing parameters driving the separation (Figure 5D). We further examined the

296 correlation of basophils with soluble factors (Figure 5E, Supplemental Figure 5D-E). The

297 basophil frequency was negatively correlated with soluble DDX58, and with the transcription

298 factors amphiregulin (AREG), FOSB and Zinc finger and BTB domain-containing protein 16

299 (ZBTB16). Similar to eosinophils, basophil frequency was also negatively correlated with

300 CCL7. In contrast, CCL22, CCL28 and Fms-related tyrosine kinase 3 ligand (Flt3L) were

301 positively correlated to basophil frequency, particularly in COVID-19 patients with moderate

302 disease (Figure 5E, Supplemental Figure 5E). Other soluble factors that were also positively

303 associated with basophil frequency were CD83 and tumor necrosis factor-related apoptosis-

304 inducing ligand (TRAIL). In summary, the data show that basophils are depleted in COVID-

305 19 patients, a pattern associated with disease severity, and that circulating basophils from

306 patients display an activated phenotype.

307

308 Eosinophil activation and neutrophil maturation contribute to predictive models of Sequential

309 Organ Failure Assessment (SOFA) score and respiratory function

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310 Our well-defined patient cohort allowed us to compare in detail the phenotypic alterations in

311 granulocyte subsets in relation to severity of COVID-19 (Supplemental Table 1). In order to

312 elucidate the underlying relationships between immune traits and relevant clinical parameters

313 registered during hospitalization, we performed multivariate linear regression. The models

314 were based on a comprehensive data set including surface marker expression levels, absolute

315 numbers, frequencies of granulocyte subsets, available clinical information and laboratory

316 measurements. The final set of variables used in the predictive models were selected based on

317 their correlation with the tested clinical outcomes and their relevance in the specific predictive

318 models (Figure 6A).

319 The analysis revealed that the levels of CD11a expression on eosinophils contributed to the

320 linear model for prediction of SOFA score for patients included in our study (Adj. R2=0.74,

321 p=3.8x10-6, Figure 6B). Notably, the relevance of CD11a expression on eosinophils was higher

322 in predicting the SOFA score (explaining around 50% of the model) than the levels of two

323 laboratory parameters CRP and creatinine (Figure 6C).

324 We also developed models for prediction of respiratory function including PaO2/FiO2 (P/F)

325 ratio at baseline and maximum oxygen need during hospitalization. The linear model for P/F

326 ratio prediction (Adj. R2=0.60, p=1.9x10-4, Figure 6D) included contributions of CD69

327 expression on CD16dim neutrophils, of CD66b on eosinophils and of CRP. The linear model

328 for maximum level of oxygen administration encompassed absolute numbers of mature

329 neutrophils, CD66b expression on eosinophils and ferritin levels (Adj. R2=0.74, p=3.9x10-6,

330 Figure 6F). Notably, both models revealed a correlation between CD66b expression on

331 eosinophils and degree of respiratory failure during COVID-19. Moreover, the combined effect

332 of the immune parameters in both models was dominant, contributing to almost 80% of each

333 model (Figures 6C, 6E and 6G). Correction for relevant parameters (e.g. age, BMI, sex, co-

334 morbidities) did not significantly alter any of the linear models or the contribution of the

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335 immune cell populations to them, further strengthening the link between granulocyte activation

336 and clinical outcome (Supplemental Table 6). In summary, by applying a supervised machine-

337 learning method we identified eosinophil activation and mature neutrophil counts to be strongly

338 correlated to SOFA score and maximum oxygen need. These immunological signatures,

339 together with other known laboratory markers, but not alone (Supplemental Figure 6), were

340 sufficient to create predictive models.

341

342 The phenotype of granulocytes is partly restored in patients who have recovered from COVID-

343 19

344 To determine whether the phenotypical alterations observed within the granulocyte

345 compartment during acute COVID-19 were recovered after viral clearance, whole blood

346 samples were collected from the same moderate (n=8) or severe (n=7) patients approximately

347 four months (median=136 days, range=89-153 days) after hospital discharge (Figure 7A).

348 Convalescent samples displayed normalized cell counts for neutrophils, eosinophils and

349 basophils, as well as neutrophil-to-lymphocyte ratio, compared to the samples collected during

350 acute viral infection (Figure 7B, Supplemental Figure 7A), while the relative abundance of the

351 granulocyte subsets over total leukocytes was only partially restored (Figure 7C), suggesting

352 that circulating non-granulocytic immune cells might still be quantitatively affected.

353 Nevertheless, both absolute numbers and frequencies of granulocyte subsets in convalescent

354 patients were very similar to those observed in healthy controls (Figures 7B-C). Additionally,

355 PCA highlighted that convalescent patients were markedly more similar to healthy controls

356 than to acute patients (Figure 7D). In particular, CD69 and CD193 expression on eosinophils,

357 as well as CD147 expression on neutrophils and eosinophils, and basophil counts were among

358 the variables contributing to the observed clustering (Figures 7E-F, Supplemental Figure 7B).

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359 Immature CD16dim neutrophils, elevated in acute COVID-19, were almost absent in

360 convalescence (Figure 7G). Neutrophils from convalescent patients showed normalized levels

361 of CXCR1, CXCR4 and CD147 compared to acute infection (Figure 7H, Supplemental Figure

362 7C). Eosinophil activation was also reduced in convalescent patients, as shown by the

363 normalized expression of CD69, CD62L, CD63, and CD147, as well as by the increased PD-

364 L1 expression (Figures. 7I-J, Supplemental Figure 7D). However, the expression of CD66b,

365 CD15 and CD193 on eosinophils was not completely recovered in convalescent samples when

366 compared to healthy controls (Figure 7J, Supplemental Figure 7D), suggesting that a partial

367 phenotypic impairment might still be present in the eosinophil population. Similarly, basophils

368 in the convalescent samples showed an overall recovery of several phenotypic traits altered in

369 acute COVID-19. In particular, the expression levels of CD11b, CD63, CXCR4, and PD-L1

370 (Figures 7K-L, Supplemental Figure 7E) were in line with the levels found in healthy controls.

371

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372 Discussion

373 The reported clinical relevance of the inflammatory state in the more severe forms of COVID-

374 19 has raised an unprecedented awareness on the potential detrimental role of

375 polymorphonuclear cells in acute viral infections (19). However, a comprehensive phenotypic

376 description performed at the protein level on circulating granulocytes in SARS-CoV-2-infected

377 patients is still lacking. In this report, we show that immature CD16dim neutrophils accumulate

378 in peripheral blood of COVID-19 patients and confirm that general can be

379 considered a hallmark of severe COVID-19. However, in contrast to the tenet linking

380 neutrophil activation with more severe clinical conditions, our approach based on high-

381 dimensional flow cytometry was able to identify activated neutrophil immunotypes detected

382 preferentially in moderate patients showing a better clinical outcome. Furthermore, we

383 extended our analysis to other granulocyte subsets, reporting the substantial ablation of both

384 eosinophils and basophils in peripheral blood of COVID-19 patients. Such depletion could

385 possibly result from two, non-mutually exclusive phenomena: first, both cell types might be

386 recruited to inflamed tissues, and in particular to the lung. This hypothesis is supported by the

387 relevant expression changes of several adhesion/migration molecules (e.g. CD62L, CD11a/b

388 and CXCR4) on both eosinophils and basophils. Moreover, concentrations of key soluble

389 factors involved in the recruitment of both granulocyte subsets (e.g. CCL13, CCL17, CCL22

390 and CCL28) (33) correlated with their levels in circulation. A second explanation for the

391 detected basopenia/eosinopenia in favor of an expanded neutrophil compartment might imply

392 the re-programming of the granulocyte precursor in the bone marrow which typically occurs

393 during emergency hematopoiesis (34).

394 The release of immature neutrophils in circulation has been observed in previous studies

395 investigating a variety of viral infections, including HIV, dengue and SARS-CoV-2 (18, 19,

396 35-38) and is strictly associated with emergency granulopoiesis. Recent studies included CD63

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397 mRNA levels in an immature pro/pre-neutrophil signature (31), which was found significantly

398 enriched in COVID-19 patients (19). In this regard, we found a robust CD63 upregulation on

399 CD16dim neutrophils and a further increased expression in COVID-19 patients. Both moderate

400 and severe COVID-19 patients were characterized by the progressive expansion of CD16dim

401 neutrophils, which also expressed lower levels of CD177, CD11b and CD62L and higher levels

402 of CD66b and LOX-1, a phenotype compatible with neutrophil immaturity (31).

403 Our in-depth phenograph-guided analysis of the neutrophil compartment revealed the

404 enrichment of activated neutrophil immunotypes in moderate COVID-19 patients, associated

405 with higher circulating levels of molecules involved in the IFN-mediated anti-viral response

406 (i.e. IRF9, IL-12b, IFNɣ). Neutrophils are known to be highly responsive to type I IFN in

407 different contexts (39, 40) and recent studies described the existence of an IFN-responsive,

408 developmentally distinct, neutrophil subset (41). Intriguingly, we observed a positive

409 correlation between type 1 inflammatory mediators (e.g. IFNɣ and CXCL10) and eosinophil

410 activation, suggesting that, particularly in moderately affected patients, part of the granulocyte

411 compartment could be actively participating in the efficient viral clearance, similarly to what

412 occurs upon influenza infection (42, 43). Interestingly, CD62L expression on eosinophils can

413 be triggered by IFNɣ, and CD62L-expressing eosinophils have been suggested to contribute to

414 dysregulated inflammation and ARDS in acute COVID-19 (24, 44).

415 Eosinophils have been reported to express receptors that allow the recognition and

416 orchestration of anti-viral responses to respiratory viruses (43, 45, 46). Eosinophil-associated

417 lung pathology has been reported in other viral infections and in SARS-CoV-1 vaccination

418 studies (summarized in (47)). On the other hand, the role of human basophils in viral infections

419 is poorly understood and mainly focused on basophil response to HIV (48, 49). CXCR4 is one

420 of the most highly expressed basophil receptors in COVID-19 patients in our cohort and might

421 be implicated in basophil trans-endothelial migration (49). CD63 expression on basophils can

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422 be induced by cross-linking of CD62L and CD11b, amongst other stimuli (50). Therefore, the

423 upregulation of CD62L, CD63, CD11b, and CXCR4 on basophils observed during the acute

424 phase of COVID-19 and their normalization after viral clearance might imply a role of this

425 phenotype in COVID-19 pathophysiology.

426 In contrast to the neutrophil phenotypes observed in moderate COVID-19, the neutrophil

427 clusters enriched in patients with severe disease correlated with several coagulation factors and

428 with molecules involved in neutrophil maturation (G-CSF and MPO), but not with any of the

429 screened anti-viral response-related molecules. This suggests an impaired neutrophil response

430 during the more severe phases of the disease. Notably, we did not detect evident signs of

431 immune-suppressive activity (e.g. PD-L1 expression) in our phenotypic analysis, and direct

432 functional evidence will be crucial to assess whether increased suppressive functions are

433 triggered in neutrophils in severe COVID-19 patients. In addition, despite their CD69

434 expression, activated eosinophils in severe COVID-19 patients displayed lower levels of

435 CD11a, CD63, and CD66b compared to the CD69+ eosinophils from patients with moderate

436 COVID-19. This phenotype is indicative of activated eosinophils suggesting that they might

437 have recently degranulated (32, 51). Overall, our analyses showed that the robust activation

438 observed in the granulocyte compartment in moderate COVID-19 declines in the more severe

439 stages of the disease.

440 Combination of eosinopenia with elevated CRP could effectively triage suspected patients with

441 COVID-19 from other patients with fever (52). A relevant finding emerging from our study is

442 that selected granulocytic phenotypical traits could, in concert with other known laboratory

443 markers such as CRP or creatinine, predict to a significant extent, key clinical outcomes,

444 including respiratory functionality and SOFA score. Although this finding needs to be taken

445 with caution considering the limited size of our patient cohort, we provide the proof-of-

446 principle that the combination of immunological, and specifically granulocyte-related,

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447 measurements with standard clinical data could be used in generating algorithms for patient

448 classification and, thus, tailored therapeutic regimens. Notably, the immunological parameters

449 were predominant in driving the prediction models in comparison to standard laboratory

450 measurements. On the same lines, we connected clinical manifestations, such multi-organ

451 failure and pulmonary function, with specific phenotypes, primarily the eosinophil activation

452 markers CD11a, CD66b, and CD69. Future studies will be important to assess and validate

453 whether there is a mechanistic link between granulocyte activation and specific clinical features

454 of COVID-19 patients.

455 Finally, by taking advantage of paired longitudinal sampling, we report the almost complete

456 phenotypic recovery upon viral clearance of the phenotypic alterations observed in

457 granulocytes from acute SARS-CoV-2 infection. Our findings are in agreement with previous

458 studies showing the replenishment of the eosinophil and basophil pool and the normalization

459 of neutrophil numbers in circulation (24, 53). The current study, however, is the first to

460 characterize in depth the eosinophil and basophil profile in patients with COVID-19 and their

461 phenotypic alterations linked to disease. This is emphasized by the finding that six out of the

462 ten most significant immunological parameters that drive the separate clustering of patients

463 with acute COVID-19 from matched convalescent samples or healthy controls are eosinophil

464 traits. However, the expression of a set of markers (CD15, CD66b, and CD193) was not

465 completely recovered in convalescent samples when compared to non-infected healthy

466 controls, suggesting a partial remaining phenotypic impairment in the granulocyte population

467 in convalescent samples, at least four months after hospital discharge.

468 The inference of mechanisms driving COVID-19 immunopathology from the analysis of

469 circulating granulocytes might have limitations when considering the pulmonary damage

470 displayed in COVID-19 patients. However, several studies have shown that the combined use

471 of peripheral immune signatures, soluble factors and patient metadata retain the capacity to

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472 predict clinical outcome to a notable extent (6, 53). Moreover, accumulating evidence indicates

473 that other tissues (e.g. kidney, gut, brain) are affected by COVID-19-related immune

474 alterations, thus underscoring the systemic nature of this disease and emphasizing the relevance

475 of studying alterations in peripheral immune cells (54). Our findings highlight the significant

476 alterations of granulocyte subpopulations in frequency and function in the blood of patients

477 with COVID-19. Moreover, our data indicate the potential contribution of granulocytes to

478 SARS-CoV-2 immunopathology and point towards the combined use of granulocyte-related

479 immunological parameters and basic clinical laboratory tests as better prognostic biomarkers

480 of disease severity and disease course.

481

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482 Materials and Methods

483 Study cohort

484 SARS-CoV-2 infected patients with COVID-19 admitted at the intensive care or high

485 dependency unit (n=16, severe COVID-19) or the infectious disease clinic (n=10, moderate

486 COVID-19) at Karolinska University Hospital, Stockholm, Sweden, were recruited to this

487 study. Serum and EDTA blood samples were collected and analyzed. Inclusion and exclusion

488 criteria for patient enrollment and collected clinical information and laboratory values of the

489 patients are provided in Supplemental Tables 1-3. Convalescent serum and EDTA samples

490 were later collected from 15 patients (8 and 7, from the moderate and severe groups,

491 respectively) approximately 4 months after hospital discharge (median=136 days, range=89-

492 153 days). As controls, serum and blood samples from age- and sex-matched SARS-CoV-2

493 IgG seronegative healthy volunteers (Supplemental Table 1) were collected on the same days

494 as the acute COVID-19 (n=17) and convalescent (n=11) patients. All samples were processed

495 using the same standardized operating procedures. Informed consent was obtained from all

496 study participants and the study was approved by the regional Ethics Committee in Stockholm,

497 Sweden, and performed in accordance with the Declaration of Helsinki.

498

499 Absolute counts of leukocytes in peripheral blood

500 Absolute counts of peripheral blood leukocytes were determined for all study participants using

501 BD Trucount tubes (6-color TBNK Reagent and, in addition, CD123 BUV395 (BD

502 Biosciences), CD15 PB, CD193 BV605 and HLA-DR BV785 (Biolegend) and CD14 PE-Cy5

503 (eBioscience) according to the manufacturer’s instructions. Samples were fixed in 1X BD

504 FACS lysing solution (BD Biosciences) for 2h and acquired on a BD FACSymphony A5

505 instrument, equipped with UV (355nm), violet (405 nm), blue (488 nm), yellow/green (561

506 nm) and red (637 nm) lasers. For absolute cell count calculations, the number of events for

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507 populations of interest was divided by the number of bead events and multiplied by the BD

508 Trucount bead count.

509

510 Proximity extension assay

511 The proximity extension assay technology (PEA), based on Q-PCR quantification of pair-wise

512 binding of oligonucleotide-labeled target antibodies (OLINK AB, Uppsala, Sweden), was used

513 for the quantification of 276 selected soluble factors in serum from the study participants

514 (Supplemental Table 4).

515

516 Luminex assays

517 Soluble factors in the serum of the study participants were measured using 4 customized

518 multiplex panels including 36, 12, 15 and 12 analytes covering a range of human inflammatory

519 factors (R&D Systems, UK; Supplemental Table 4). Coagulation factors were measured in

520 plasma using 3 different multiplex panels, including the 6-, 4-, and 3-plex human ProcartaPlex

521 panels (ThermoFisher). Assays were performed according to the manufacturer’s guidelines and

522 samples were acquired on a Luminex MAGPIX instrument using xPonent 4.0 software

523 (Luminex).

524

525 Cell preparation and staining for multicolor flow cytometry

526 For flow cytometric analysis of polymorphonuclear leukocytes, EDTA whole blood samples

527 from all patients and controls were stained with fluorescently labeled antibodies (Supplemental

528 Table 7). In brief, plasma fractions were removed by centrifugation and cells were washed with

529 FACS buffer (PBS, 5% FCS, 0.05 mM EDTA), followed by 15-minute incubation with a

530 cocktail containing Fc-block (Miltenyi) and fluorescent-labeled antibodies for extracellular

531 staining. Dead cells were excluded using the fixable LIVE/DEAD Yellow Dead Cell Stain Kit

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532 (Life Technologies). Red blood cells (RBC) were lysed with fixation/permeabilization working

533 solution (BD Cytofix/Cytoperm™). For SARS-CoV-2 inactivation, samples were fixed for two

534 additional hours in ultra-pure, methanol-free, 1% formaldehyde (Polyscience). Due to heavy

535 RBC coagulation in COVID-19 patient samples, an additional RBC lysis for 10 min was

536 performed. Samples were acquired on a BD FACSymphony equipped with five lasers (BD

537 Biosciences).

538

539 Flow cytometry data analysis

540 Standard flow cytometry data analysis was performed using FlowJo version 10. A

541 compensation matrix for the 25-color flow cytometry panel was generated using AutoSpill

542 (55), optimized and applied to all fcs files. All data was pre-processed using the time gate.

543 Following exclusion of doublets and dead cells, neutrophils were defined as CD15+CD193-

544 CD66b+CD16bright/dim, eosinophils were defined as CD15bright/dimCD193bright/dimCD16-

545 FcER1dimCD123dim/- and basophils were defined as CD15-

546 CD193brightFcER1brightCD123brightHLA-DRdim/-. In addition, exclusion of potential

547 contamination by non-granulocytes was done using lineage exclusion markers (CD3, CD14,

548 CD19, CD56, CD304). Samples containing less than 30 events in the final granulocyte subset

549 gate were not considered for further phenotypical analysis. The Cytonorm FlowJo plugin v1.0

550 was applied on each granulocyte population to correct for potential batch effects and was based

551 on an internal control that was sampled on all sampling occasions. Dimensionality reduction

552 was performed with the UMAP FlowJo plugin v3.1. Neutrophil populations from each

553 individual were down-sampled for comparability (FlowJo Downsample plugin v3.3), barcoded

554 and concatenated. FlowJo Phenograph v3 was used for unsupervised clustering.

555

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556 Statistical analysis

557 GraphPad Prism version 9 (GraphPad Software) and R v4.0.1 (56) were used to conduct

558 statistical analyses, where p-values < 0.05 were considered significant. Two-tailed and non-

559 parametric Mann-Whitney U test was used for two-group comparisons. Non-parametric

560 Kruskal-Wallis test, in combination with two-stage step-up method of Benjamini, Krieger and

561 Yekutieli for controlling False Discovery rate (FDR), was used for multiple-group

562 comparisons. Paired samples were analyzed with Wilcoxon matched-pairs signed rank test. For

563 correlations, the two-tailed non-parametric Spearman test was applied. Where indicated, �-

564 score of median fluorescence intensity (MFI) was calculated as follows: z = (x-μ)/σ, being x =

565 raw score, µ = mean of sample distribution and σ = standard deviation. Correlation, hierarchical

566 clustering and multivariate analysis of flow cytometry data with clinical parameters and

567 proteomic data was performed and visualized in GraphPad Software and R v4.0.1 and v1.3.959

568 (56), using the packages factoextra (v1.0.7) (57), FactoMineR (v2.3) (58),

569 PerfomanceAnalytics (v2.0.4) (59), ggplot2 (v3.3.1) (60), gplots (v3.0.4) (61), pheatmap

570 (v1.0.12) (62), vegan (v2.5-6) (63), corrplot (v0.84) (64), lattice (v0.20-41) (65) and

571 latticeExtra (v0.6-29) (66), stats (v4.0.1) and complexheatmap (v2.5.6) (67).

572

573 Multivariable linear regression

574 Predictive models for clinical outcomes were built based on a data set of 167 immune traits

575 that included surface marker expression levels, absolute numbers, frequencies of granulocyte

576 subsets, available clinical information and laboratory measured parameters. The creation of the

577 models was performed in R v. 3.6.0 (56). Variable redundancy was avoided by identification

578 of variables with pair-wise spearman correlation coefficient higher than 0.99 (function cor from

579 package stats), and removal of the variable with the largest mean absolute correlation. The

580 remaining variables were pair-wise correlated with the clinical outcome, and those with p-

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581 values < 0.05 were selected for further evaluation. All possible linear combinations of variables

582 were evaluated with the function regsubsets from the package leaps (68) using an ‘exhaustive’

583 method and evaluating 3 subsets per model size. The models with highest adjusted R2, lowest

584 bayesian information criterion and a Mallow’s Cp lower and closer to the number of

585 coefficients, were selected for further evaluation. The variance inflation factor for each model

586 was calculated to avoid redundancy in the independent variables with the function vir from

587 package car (69). The selected final model had no redundant variables, and all must be

588 significant (p-value < 0.05). The contribution of each parameter to each model was calculated

589 with the functions cal.relimp from package relaimpo (70) with the lgm metric. Differences in

590 the model after addition of factors for correction (sex, age, BMI, smoking, days from symptom

591 to sampling and from admission to sampling and co-morbidities) were evaluated by computing

592 an analysis of variance for the two linear models.

593

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594 Author contributions

595 Conceptualization: ML, MD, LH, AP, ANT, BJC. Methodology: ML, MD, LH, LMPM.

596 Investigation: ML, MD, LH, LMPM, PC, JRM, J-BG, JE, KMo, MG, KMa. Data analysis: ML,

597 MD, LH, HB, LMPM, EK, MC, AP. Patient recruitment and clinical information: EF, SGR,

598 AS, LIE, OR, SA, KS, H-GL, NKB. Organization of sample handling and other resources: PC,

599 JK, JMi, MFT, SB, MB, JMj, K-JM, JS, J-IH and the Karolinska KI/K COVID-19 Study Group.

600 Writing (original draft): ML, MD, LH, HB, LMPM, AP, BJC, ANT. Writing (review and

601 editing): All authors. Supervision of experiments: ML.

602

603 Acknowledgements

604 We thank all patients and healthy donors for their generous participation.

605 See Supplemental Acknowledgments for Karolinska KI/K COVID-19 consortium details. The

606 study was supported by grants from Nordstjernan AB and Knut and Alice Wallenberg

607 Foundation to H-GL, the Swedish Governmental Agency for Innovation Systems (VINNOVA)

608 under the frame of NordForsk (Project no. 90456, PerAID) and the Swedish Research Council

609 under the frame of ERA PerMed (Project 2018-151, PerMIT) to ANT, the Swedish Research

610 Council to BJC and AP, the Swedish Cancer Foundation to BJC, Clas Groschinsky Foundation,

611 Centrum for Innovative Medicine to AP and ANT, the Swedish Children’s Cancer Foundation

612 (TJ2018-0128 and PR2019-0100) to ML, Åke Olsson Foundation to ML and AP and KI

613 Research Foundation to ML, LH and AP.

614

615 Data and material availability

616 Curated flow cytometry data will be made available for exploration via the KI/K COVID-19

617 Immune Atlas, http://covid19cellatlas.com/. All other data needed to evaluate the conclusions

618 of the paper are presented in the paper or in the Supplemental Material.

28 medRxiv preprint doi: https://doi.org/10.1101/2021.01.27.21250591; this version posted January 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

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Figures and figure legends

Figure 1

A Clinical data collection Soluble factors and clinical data SARS-CoV-2 Absolute counts/ 25-color flow cytometry frequencies N=17 N=10 N=16 Granulocytes Activation Chemotaxis Fluorescence Function Neutrophil Basophil Apoptosis intensities Degranulation Eosinophil Immune checkpoint Dimensionality reduction: UMAP 5

4

3

2

1

0 Luminex assays and OLINK 10 20 30 40 50 Unsupervised Healthy Moderate Severe clustering: Phenograph controls COVID-19 COVID-19 Flow cytometry data

Study cohort Study design Flow cytometry analysis Correlation & multivariate analysis

B Timeline Characteristics Inflammation Coagulation Disease severity Viremia Outcome Oxygen Days from sampling to: Sex 12 60 28 20 700 5 No Alive 15 M 50 21 500 8 2 40 14 10 BMI

WBC 300 4 30 7 5 Yes 4 F 100

Thrombocytes Deceased 1

0 0 Total SOFA score 20 Symptoms debut 0 3 0 20 40 60 80 0 7 14 21 0 100 200 300 400 0 2 4 6 8 1 20 3 4 0 200 400 600

Age Admission CRP D-dimer SOFA resp PaO2/FiO2 ratio Moderate COVID-19 Severe COVID-19 Deceased Neutrophils Healthy controls C Eosinophils Moderate COVID-19 Leukocytes Basophils Severe COVID-19 Neutrophils Eosinophils Basophils Neutrophils Basophils FceR1 CD1 5 CD12 3

Excl. doublets CD1 5 SS C CD12 3 CD1 5 and dead cells CD12 3 UM AP -Y UM AP -Y Eosinophils FSC CD193 CD193 Lineage UMAP-X UMAP-X CD193 Lineage D E F 0.1 *** 2.5 107 **** 107 * 106 **** 100 **** ** 2.0 **** 102 **** 1.00 ** * * ** ** ** * 10 ** ** * * 7 6 0.08

2.0 10 10 8 1.5 0.75 80 1 104 10 0.06 (cells /ml) (cells /ml) 1.5 107 105

(cells /ml) 6 rophil s

inophil s 1.0 0.50 60 0.04 1.0 107 104 4 102 100 B asophil s Neu t

E os 0.5 0.25 rophils 0.02 6 3 ophil s 5.0 10 10 40 2 % % % % as / lymphs ratio os / lymphs ratio B E Neu / lymphs ratio osinophils -1 0 2 Bas 0 0.0 0.00 Neu t 0.0 E 10 10 0 10 Healthy controls Moderate COVID-19 Severe COVID-19 Deceased

Figure 1. Disease severity-dependent neutrophilia and marked decrease of eosinophils and basophils in COVID-19. (A) Experimental and analytical workflow of the study performed on samples from moderate and severe COVID-19 patients and age-matched healthy controls. (B) Dot plots (left) and alluvial diagram (right) describing demographics and the clinical characteristics of the patients included in the study cohort. Severity groups (moderate=blue; severe=purple; deceased=gray) are indicated. (C) Gating strategy for the identification of granulocyte subsets and their UMAP projection is shown on the left. On the right, analogue results are obtained through unsupervised subset identification based on UMAP projection of total granulocytes. (D-E) Absolute cell counts (D) and frequencies (E) among total leukocytes for neutrophils, eosinophils and basophils in healthy controls (n=17), moderate COVID-19 patients (n=10) and severe COVID-19 patients (n=16). (F) Ratios of granulocyte absolute counts over lymphocyte absolute counts in healthy controls (n=17), moderate COVID-19 patients (n=10) and severe COVID-19 patients (n=16). In (D–F), Kruskall-Wallis test and two-stage Benjamini, Krieger and Yekutieli test. Bars represent median. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001. Neu, neutrophils; eos, eosinophils; bas, basophils; lymphs, lymphocytes. medRxiv preprint doi: https://doi.org/10.1101/2021.01.27.21250591; this version posted January 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Figure 2 A Eosinophil & basophil exclusion Healthy control Moderate COVID-19 Severe COVID-19

bright CD16 CD16bright CD16bright CD16 Count CD15 CD66b dim dim dim CD15-CD193- cells CD16 CD16 CD16 CD193 CD15+CD193- cells CD66b B C **** **** ** ** Healthy controls 7 * * 2.0 107 * 10 * 100 20 Moderate COVID-19 Severe COVID-19 106 1.5 107 95 15 Deceased 5

10 neutrophils 1.0 107 90 neutrophils 10

ml neutrophils/ ml 4 ml neutrophils/ ml 10 di m bright 6 5.0 10 di m 3 85 5

bright 10

0.0 102 80 0 % CD1 6 CD1 6 % CD1 6 D CD1 6 E All groups Healthy controls Moderate COVID-19 Severe COVID-19 CD16bright CD16dim UMAP-Y UMAP-Y

F UMAP-X UMAP-X CD66b CD177 CD11b CXCR1 CXCR2 CXCR4 CD62L UMAP-Y

UMAP-X CD16bright CD16 dim Low High UMAP scale G H CD16 bright CD16dim 1.0 5 0.5 brigh t *** * *** * ** 0 4 Z score -0.5 2.5

CD1 6 *** 2

0 0 di m PC2 (23.5%) *** ** PC2 (18.4%) -2.5 CD1 6 ** **** -2

-6 -3 0 3 -2-4 0 2 4 6 CD86 PD-L1 LOX-1 CD11b CD177 CD62L CD66b

CXCR1 CXCR4 PC1 (39.4%) PC1 (28.4%) CXCR2 HLA-DR

Healthy controls Moderate COVID-19 Severe COVID-19 Healthy controls Moderate COVID-19 Severe COVID-19

Figure 2. Emergence of immature neutrophils in severe COVID-19. (A) Representative plots showing neutrophil identification and CD16bright and CD16dim subset discrimination in different study groups. Absolute cell counts (B) and frequencies (C) among total neutrophils of CD16bright and CD16dim cells in healthy controls (n=17), moderate COVID-19 patients (n=10) and severe COVID-19 patients (n=16). (D) UMAPs on concatenated files (see Methods) of total neutrophils in healthy controls, moderate and severe COVID-19 patients. (E) UMAPs of CD16bright and CD16dim cells within pooled neutrophils from all study groups (30,000 down-sampled cells/individual). (F) Median fluorescence intensity (MFI) of selected surface markers in the UMAP projection described in (D). (G) Heatmap showing the mean normalized marker expression (z-score) in CD16bright and CD16dim neutrophils in healthy medRxiv preprint doi: https://doi.org/10.1101/2021.01.27.21250591; this version posted January 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

controls (n=17), moderate (n=10) and severe COVID-19 patients (n=16). (H) PCA based on marker expression (MFI) on CD16bright and CD16dim neutrophils. (B) and (C) Kruskall-Wallis test and two-stage Benjamini, Krieger and Yekutieli test. Bars represent median. * p < 0.05; ** p < 0.01; **** p < 0.0001. medRxiv preprint doi: https://doi.org/10.1101/2021.01.27.21250591; this version posted January 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Figure 3

A B 35 All groups Healthy controls Moderate COVID-19 Severe COVID-19 Healthy controls Moderate COVID-19 Severe COVID-19 30

(% ) 25

20 * size * *** **

er 15 *** * 10 **

UMAP-Y *** Clus t *** * * *** *** ***/# 5 ** ** **** ** 0 ** 1 2 4 6 3 7 8 5 9 11 UMAP-X Phenograph clusters Phenograph10 cluster13 14 21 16 22 27 15 19 18 23 12 17 20 24 25 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 C D

24 Factor VIII 16 27 Factor VII 22 25 Factor IX 2 5 26 11 10 23 15 17 Soluble Factors Soluble 8 MPO

Phenograph cluster 7 12 20 14 4 13 9 6 IFN 21 SCF 1 BAFF 18 19 3

5 2 8 1 9 3 6 4 7 19 23 20 25 15 17 11 10 24 13 21 18 14 27 22 16 12 26 CD88 CD63 CD24 CD69 CD86 CD15 CD16CD177 CD11a CD147CD11b CD66b O Phenograph cluster Markers Mod to Sev Normalized expression Spearman r 1 0 3 2 1 0 1 0 3 2 1 0

Figure 3. Phenograph analysis identifies activated neutrophil subsets correlating with markers of anti-viral immune response in moderate COVID-19 patients. (A) Distribution of the 27 identified phenograph clusters overlaid on the UMAP projection. (B) Frequency of the phenograph clusters within healthy controls (n=17), moderate (n=10) and severe COVID- 19 patients (n=16). *, statistically significant differences in comparisons with healthy controls. #, statistically significant differences comparing moderate and severe patients. (C) Heatmap showing normalized expression (MFI z-score) of 20 markers analyzed by flow cytometry in the 27 neutrophil clusters identified in (A). Hierarchical clustering was performed and rows were split by k-means=4. Right annotation shows the log10-ratio of cluster enrichment in moderate (blue) versus severe (purple) COVID-19 patients. (D) Heatmap showing Spearman correlation between phenograph clusters and soluble factors detected in all COVID-19 patients (n=26). Hierarchical clustering was performed, and columns were split by k-means=4. Bottom annotation shows the log10-ratio of cluster enrichment in moderate (blue) versus severe (purple) COVID-19 patients. In (B) Kruskall-Wallis test and two-stage Benjamini, Krieger and Yekutieli test. * p < 0.05; ** p < 0.01; **** p < 0.0001. medRxiv preprint doi: https://doi.org/10.1101/2021.01.27.21250591; this version posted January 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Figure 4

A B UMAP scale All groups Healthy control Moderate COVID-19 Severe COVID-19 Low High CD15 CD66b CD62L CD69 CD147 UM AP -Y UM AP -Y

UMAP-X UMAP-X CD193 C D *** *** *** *** *** CD15 CD11a **** ** ** 2500 ** 10000 *** 15000 *** 1000 ** 3000 4000 2.5 12500 2500 0 2000 8000 800 0.0 3000 10000 2000 CD88 1500 6000 (MFl ) 600 CXCR1 7500 1500 2000 1b (MFl ) 1000 4000 400 5000 1000 CD15 (MFI )

CD 1 1000 CD69 (MFl ) CD147 (MFl ) CD62 L PC2 (22.8%) PC2 (22.8%) CD66b (MFI ) 500 2000 2500 200 500 0 0 0 0 0 0 0 5 0 5 Healthy controls Moderate COVID-19 Severe COVID-19 Deceased PC1 (30.3%) PC1 (30.3%) Healthy controls Moderate COVID-19 Severe COVID-19

E F **** G CD69- Healthy control Moderate COVID-19 Severe COVID-19 100 **** 10 6 Healthy controls

+ CD69 Moderate COVID-19 80 5 Severe COVID-19 10 CD69- Deceased 60 + 1 CD69 10 4

eosinophils 40 eosinophils 0 - + + CD69 (cells/ml ) 3 Z score + CD193 10 20 -1 CD69 CD6 9 0 CD6 9 10 2 (% of total eosinophils ) CD63 IL-6R CD88 CD24

CD15 CD88 CD24CD63 CD86 CD15 CD86

PD-L1 FceR1 PD-L1 CD66bCD193 CD11aCD11bCD62L CD147CD126 FceR1 CD11b CD147 CD11a CD62L

CXCR1CXCR2CXCR4 CD66b CD193 CD69 HLA-DR CXCR1 CXCR2 CXCR4 HLA-DR

H ** ** I ** ** * * * 10000 *** * * 4000 * * * 5000 **** ** ** ********** **************** Eosinophil count 8000 4000 3000 ******** **** ********** % Eosinophils

(M FI ) 6000 3000 2000 ***** *** *** % CD69+ eosinophils 4000 1b (MFI ) 2000

1000 SCF SIT1 Flt3L IL-10 LIF R LIF CD 1 DFFA CCL7 CCL8 CD14 2000 1000 CCL2

NPPC CD66b CD147 (MFI ) SIRT2 PD-L1 IRAK1

CCL17 CCL13 CD147 TRAF2 CXCL6 DDX58 sgp130 NUCB2 0 IL-18R1 Resistin sCD163 CX3CL1 CXCL10 SLAMF1 SH2D1A ENTPD6 ST3GAL1 PPP1R9B 0 0 0 VIII Factor 0.6 -0.6 Pentraxin-3 CD69 - + - + - + - + - + - + - + - + - + Healthy controls Moderate COVID-19 Severe COVID-19 Deceased Spearman correlation

Figure 4: Activation of eosinophils in patients with COVID-19. (A) Overall and individual UMAPs on concatenated files (see Methods) of eosinophils in healthy controls, moderate and severe COVID-19 patients. (B) UMAP displaying the MFI of selected markers. (C) MFI of selected eosinophil markers in healthy controls (n=17), patients with moderate (n=8) and severe (n=14) COVID-19. Median values for each group are indicated. (D) PCA based on MFI expression of eosinophil markers. (E) CD69+ eosinophils from a representative healthy control and COVID-19 patients. (F) Frequencies and absolute counts of CD69+ eosinophils in healthy controls (n=17), moderate (n=8) and severe (n=14) COVID-19 patients. (G) Marker expression (z-score) on CD69- and CD69+ subsets. (H) MFI of selected markers in eosinophil subsets in healthy controls (n=15), in moderate (n=8) and severe (n=11) COVID-19 patients. (I) Heatmaps demonstrating correlations (Spearman non-parametric, r < -0.4 or r > 0.4, p < 0.05) between absolute eosinophil counts, frequencies and percent of CD69+ eosinophils and soluble factors in COVID-19 patients. Statistical analysis by Kruskall-Wallis test and two-stage Benjamini, Krieger and Yekutieli test (C, F and H) and Wilcoxon matched-pairs signed rank test (H). FDR adjusted p-values are indicated. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001. medRxiv preprint doi: https://doi.org/10.1101/2021.01.27.21250591; this version posted January 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Figure 5 A All groups Healthy control Moderate COVID-19 Severe COVID-19

UMAP-Y

UMAP-X B CD63 CD177 CD11b CD62L

CD147 CXCR4 PD-L1 Low High UMAP scale

UMAP-Y

UMAP-X C 800 *** 2000 ** 4000 * 30000 * *** ** ** *** 600 1500 3000 20000 (MFI ) 400 1000 2000 10000

CD63 (MFI ) 200 CD177 (MFI ) 500 CD11b (MFI ) 1000 CD62 L

0 0 0 0

8000 ** * 200 3000 *** Healthy controls ** *** Moderate COVID-19 6000 150 Severe COVID-19 2000 Deceased 4000 100 1000 2000 50 PD-L1 (MFI ) CD147 (MFI ) CXCR4 (MFI )

0 0 0 D E Basophil% count Basophils

CXCR2 * * 4 4 CD193 CCL22 * PD-L1 Flt3L CD83 *

2 2 CXCR1 * CCL28 CD177 * * * * CD11b TNFB CD62L CCL3 0 0 CD88 FGF19 CD63 TRAIL * PC2 (17.3%) PC2 (17.3%) -2 -2 CCL7 1 ** DDX58 CXCR4 ** AREG

-4 -4 * FOSB

-2.5 0.0 5.0 7.5 * 2.5 -2.5 0.0 2.5 5.0 7.5 ZBTB16 PC1 (33.5%) PC1 (33.5%) CCL23

Healthy controls Moderate COVID-19 Severe COVID-19 Spearman correlation CXCL6 -1

Figure 5. Basophils in COVID-19 patients display an activated phenotype. (A) UMAPs on concatenated files (see Methods) showing basophil overview together with representative plots

Dim1 (24.6%) medRxiv preprint doi: https://doi.org/10.1101/2021.01.27.21250591; this version posted January 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

heathy controls, moderate and severe COVID-19 patients. (B) UMAP showing the level of expression of selected markers on basophils. (C) MFI for selected markers on basophils in heathy controls (n=17), moderate (n=10) and severe (n=12) COVID-19 patients. Median values for each group are indicated. (D) PCA and biplot based on the MFI expression of representative basophil markers in healthy controls, moderate and severe COVID-19 patients. (E) Heatmap demonstrating correlation (Spearman non-parametric, r < -0.4 or r > 0.4, p < 0.05) between absolute basophil counts/basophil frequencies of total leukocytes and basophil-associated soluble factors, in COVID-19 patients. Significant differences between healthy controls and patient groups in (C) were evaluated with Kruskall-Wallis test and two-stage Benjamini, Krieger and Yekutieli test. FDR adjusted p-values are indicated. * p < 0.05; ** p < 0.01; *** p < 0.001. medRxiv preprint doi: https://doi.org/10.1101/2021.01.27.21250591; this version posted January 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Figure 6 A

dim

bright

Peak oxygen needP/F ratio CRP Creatinine Ferritin Eos CD11a (MFI) Eos CD66b (MFI)% Neu CD16 CD69Neu (MFI)CD16 count 1 SOFA score 0.8

Peak oxygen need 0.6

P/F ratio 0.4

CRP 0.2

Creatinine 0

0.2 Ferritin

0.4 Eos CD11a (MFI)

0.6 Eos CD66b (MFI)

0.8 % Neu CD16 dim CD69 (MFI) 1

B C 0.6 Adj. R2: 0.74 RMSE: 1.45 p-value: 3.88x10-6 10 0.4

5 0.2 Predicted Contribution 0 0.0 0 5 10 SOFA score CRP Creatinine Moderate COVID-19 Severe COVID-19 Eos CD11a (MFI) D E Adj. R2: 0.60 0.6 500 RMSE: 78.33 -4 p-value: 1.92x10 0.4 400 0.3

300 0.2

Predicted 200 0.1 Contribution

0.0 100

CRP

100 200 300 400 500 CD69 (MFI) PaO2/FiO2 ratio dim Eos CD66b (MFI) Moderate COVID-19 Severe COVID-19

% Neu CD16 F G Adj. R2: 0.74 6 RMSE: 0.70 -6 p-value: 3.97x10 0.4 4

0.2 2 Contribution

Predicted

0 0.0 0 2 4 6

Peak oxygen need Ferritin count

bright Moderate COVID-19 Severe COVID-19

Eos CD66b (MFI) Neu CD16

Figure 6. Eosinophil activation and neutrophil maturation are relevant for prediction of SOFA score and respiratory function. (A) Diagonal correlation matrix including predicted clinical outcomes (bold) and the explanatory variables included in the final models. (B-G) Multivariate linear regressions for prediction of SOFA score (B-C), PaO2/FiO2 (P/F) ratio (D- E) and peak oxygen need (F-G) were modeled based on immune traits and laboratory markers. (B, D, F) Scatter plots of actual values versus predicted values of the linear models. (C, E, G) Individual contribution of the variables included in the final models. Adj R2, Adjusted R2; RMSE, Root-mean-square error; Eos, Eosinophils; Neu, Neutrophils. medRxiv preprint doi: https://doi.org/10.1101/2021.01.27.21250591; this version posted January 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Figure 7 A B C N=8 ns ns ns ns ns *** 8 **ns ** ns ns ** 25-color flow cytometry 10 107 ns* 106 **ns 14 1.0 * N=7 * 80 4 months Granulocytes 12 6 0.8 after hospital 10 105 ell s/ ml) ells /ml) 60 10 discharge 107 ell s/ ml) (c 105 8 0.6 4 rophil s Neutrophil 10 40 inophil s 104 6 0.4 Moderate 6 B asophil s Basophil 10 3 Neu t 4 10 E os

(c rophils 20 COVID-19 3 ophil s (c

10 % 0.2 Eosinophil inophil s % % % 2 2 Severe COVID-19 105 102 Bas 10 0 0 0.0 Neu t Ac Cv Eos Ac Cv Ac Cv Ac Cv Ac Cv Ac Cv Acute patients Convalescent patients Moderate COVID-19 Severe COVID-19 Moderate COVID-19 Severe COVID-19

Contributions (%) D E Contribution F 0 1 2 3 r v Eos CD147 (MFI) r %Bas v %Eos Neu CXCR2 Counts Bas Eos CD193 (MFI) Neu CXCR1 HD Eos CD193 Neu CD147 (MFI) Eos CD11a Neu CD11a Eos %CD69+ Neu CD177 Bas CD62L Eos CD11a (MFI)

PC2 (13%) Eos CD88 PC2 (13%) Neu CD147 Bas Counts Eos CD62L Eos CD147 Neu CXCR2 (MFI) Eos %CD69+ Neu CD177 (MFI)

Eos CD88 (MFI) PC1 (14.9%) PC1 (14.9%) %Eos

G H ns ns Acute COVID-19 Convalescent 20 *** ns ** *** ** * ns ns ns ns bright 3000 * 2500 15000 ** 1500 * 2500 * CD16 CD16bright * * 15 2000 2000 2000 10000 1000 1500 1500

neutrophils 10

dim 1000 1000 1000 5000 500 5 500 500 CD147 (MFI) CD66 b (MFI) CXCR4 (MFI) CXCR1 (MFI) CD11 b (MFI) CD16

dim dim CD1 6 CD16 CD16 0 0 0 0 0 0

% % Ac Cv Ac Cv Ac Cv Ac Cv Ac Cv Ac Cv CD66b Moderate COVID-19 Severe COVID-19

I Acute COVID-19 Convalescent **** J ns ns ** ns ** ** *** ns 12500 20000 **ns ns* ** ns 100 * * 1000 8000 * 1200 10000 800 1000 80 15000 6000 800 (M Fl) (M Fl) 7500 600 (M Fl)

60 (M Fl) 10000 4000 (M FI ) 600 eosinophils + 40 5000 400 400 5000 2000 CD63 CD66b 20 2500 CD62 L 200 CD15 CD147 200 CD193 CD6 9 0 0 0 0 0 0 CD69 % Ac Cv Ac Cv Ac Cv Ac Cv Ac Cv Ac Cv Moderate COVID-19 Severe COVID-19

K L ns Acute COVID-19 Convalescent ** ** ns *** *** ** 800 ns* 2000 **ns 25000 ns 400 2500 6000 *** *** **ns 20000 2000 600 1500 300 4000 (M Fl) (M Fl) (M Fl) 1500 (M Fl) 15000 (M Fl)

(M Fl) 400 1000 200 10000 1000 2000 200 500 100 5000 500 CD147 CXCR4 PD-L1 CD177 L CD62 L CD63 CD63 0 0 0 0 0 0 Ac Cv Ac Cv Ac Cv Ac Cv Ac Cv Ac Cv CXCR4 Moderate COVID-19 Severe COVID-19

Figure 7. Granulocyte phenotype in convalescent patients is largely restored. (A) Schematic illustration of paired acute (Ac) and convalescent (Cv) patient samples included in the study. Absolute cell counts (B) and granulocyte frequencies over total leukocytes (C) in paired Ac and Cv patients (n=15). (D) PCA based on cell counts, frequencies and normalized MFI (z-score) of all measured markers. (E) Variable correlation plot. (F) Relative contribution of variables to PC1 and PC2. (G) CD16dim neutrophil frequencies in paired Ac and Cv patient samples (n=15). (H) Expression of selected neutrophil markers in paired Ac and Cv patients (n=15). (I) CD69 eosinophil expression in paired Ac and Cv patients (n=15). (J) Expression of selected eosinophil markers in paired Ac and Cv patient samples (n=15). (K) CXCR4 basophil expression in Ac and Cv patients. (L) Expression of selected basophil markers in paired Ac and Cv patients (n=15). Wilcoxon matched-pairs rank test; ns, not significant; * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001. Healthy controls (n=28, median±IQR) ranges are shown in green. Bars indicate statistical significance considering all sampled patients (black), only moderate (blue) or only severe (purple) patients. Bas, Basophils; Eos, Eosinophils; Neu, Neutrophils.