Skip to content
Publicly Available Published by De Gruyter February 22, 2021

A low level of CD16pos monocytes in SARS-CoV-2 infected patients is a marker of severity

Marc Vasse ORCID logo, Benjamin Zuber, Laurie Goubeau, Marie-Christine Ballester, Mathilde Roumier, Frédérique Delcominette, Florence Habarou, Emilie Jolly, Felix Ackermann, Charles Cerf, Eric Farfour ORCID logo, Tiffany Pascreau and the SARS-CoV-2 Foch Hospital Study Group



Severe forms of coronavirus disease 2019 (COVID-19) are characterized by an excessive production of inflammatory cytokines. Activated monocytes secrete high levels of cytokines. Human monocytes are divided into three major populations: conventional (CD14posCD16neg), non-classical (CD14dimCD16pos), and intermediate (CD14posCD16pos) monocytes. The aim of this study was to analyze whether the distribution of conventional (CD16neg) and CD16pos monocytes is different in patients with COVID-19 and whether the variations could be predictive of the outcome of the disease.


We performed a prospective study on 390 consecutive patients referred to the Emergency Unit, with a proven diagnosis of SARS-CoV 2 infection by RT-PCR. Using the CytoDiff™ reagent, an automated routine leukocyte differential, we quantified CD16neg and CD16pos monocytes.


In the entire population, median CD16neg and CD16pos monocyte levels (0.398 and 0.054×109/L, respectively) were in the normal range [(0.3–0.7×109/L) and (0.015–0.065×109/L), respectively], but the 35 patients in the intensive care unit (ICU) had a significantly (p<0.001) lower CD16pos monocyte count (0.018 × 109/L) in comparison to the 70 patients who were discharged (0.064 × 109/L) or were hospitalized in conventional units (0.058 × 109/L). By ROC curve analysis, the ratio [absolute neutrophil count/CD16pos monocyte count] was highly discriminant to identify patients requiring ICU hospitalization: with a cut-off 193.1, the sensitivity and the specificity were 74.3 and 81.8%, respectively (area under the curve=0.817).


Quantification of CD16pos monocytes and the ratio [absolute neutrophil count/CD16pos monocyte count] could constitute a marker of the severity of disease in COVID-19 patients.


Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2, first reported in Wuhan, China, has rapidly spread into other countries leading to a current world-wide pandemic [1]. Symptoms can range from mild, like the common cold, to life-threatening with intensive care unit admission and prolonged or extended mechanical ventilation [2]. SARS-CoV-2 infection can trigger an excessive immune response known as a cytokine storm in cases of severe COVID-19 [3]. A cytokine storm is a potentially fatal immune disease characterized by high-level activation of immune cells and excessive production of massive inflammatory cytokines and chemical mediators. This is the main cause of disease severity and death in COVID-19 patients [4], and is related to high levels of circulating cytokines, severe lymphopenia, thrombosis, and massive mononuclear cell infiltration in multiple organs [5], [6].

Monocytes are important mediators of innate immunity and, when activated, secrete high levels of cytokines. They circulate in the bloodstream and pass into tissues during the steady state and in increased quantities during inflammation. These cells play a key role in supporting tissue homeostasis by initiating and propagating immune responses to pathogens and resolving them before excessive tissue damage occurs. Human monocytes are divided into three major populations: classical (CD14posCD16neg) or M0, non-classical (CD14dimCD16pos), and intermediate (CD14posCD16pos) [7]. Each of these subsets is distinguished by the expression of distinct surface markers and by their functions in homeostasis and disease [8]. Classical monocytes express high levels of chemokine receptors, which highlight their potential to migrate to cues stemming from injured or inflamed tissues and secrete proinflammatory molecules, such as IL-6, IL-8, CCL2, CCL3, and CCL5 [9]. Intermediate monocytes express the highest levels of antigen presentation-related molecules, secrete TNF-a, IL-1b, and IL-6, upon TLR stimulation [10] and increase in patients with systemic infections, suggesting that they play an important role in the rapid defense against pathogens [11]. Non-classical monocytes have antagonizing functions to classical monocytes and promote neutrophil adhesion at the endothelial interface via the secretion of TNF-α [12] and do not reach the classical monocyte production levels of proinflammatory cytokines [13].

Different reports have studied the different subpopulations of lymphocytes in patients with severe forms of COVID-19 [14], [15], [16] but, to our knowledge, only a few studied the different sub-populations of monocytes, and only in small groups of selected patients [17], [18], [19], [20], [21], [22]. We report here the results of a prospective study, from March 26, 2020 to July 3, 2020, in patients with an infection by SARS-CoV-2 virus, proven by a positive RT-PCR, designed to assess whether monocytic subpopulations varied in SARS-CoV-2 infections and whether any variations had a prognostic value.

Materials and methods

Study populations

From March 26, 2020 to July 3, 2020; 451 patients from the Emergency Unit of our hospital were diagnosed as SARS-CoV-2 positive by RT-PCR (Allplex 2019 nCoV Assay, Eurobio, Les Ulis, France). Simultaneously with the nasopharyngeal swab a complete blood count (CBC) with differential (Diff) was collected. Usually, serum for C-reactive protein (CRP) measurement was also collected, as well as citrated plasma for D-dimer quantification, a well-recognized marker of in-hospital mortality [23]. In addition, medical antecedents such as hypertension, diabetes, obesity (defined as a body mass index>30) recognized as risk factors for adverse outcome were recorded [24], [25] as well as usual medications. Except for acetaminophen they did not receive any specific medication for COVID-19. From this data base, it appeared than 26 patients were treated by high doses of corticoids (>1 mg/kg/D), 20 were treated by immunosuppressive agents because of a lung or kidney transplantation, and 15 were at the time of the sample under cancer chemotherapy, and were discarded from the study because these treatments modify the Diff. These 61 patients had a significantly lower absolute count of lymphocytes and monocytes than the other patients (data not shown). The 390 remaining patients were divided into four groups: 70 had limited clinical symptoms, did not require oxygen supplementation, and could go home immediately, 265 were hospitalized in “conventional” units (CoU), 20 were first hospitalized in CoU but worsened and were transferred to the intensive care unit (ICU), and 35 were immediately admitted to the ICU. The median time for the transfer from CoU to ICU was three days (range: 2–10 days). Among the 70 discharged patients, a follow-up was obtained for 23 patients, none of them had complications while they were at home. The median age of the patients was 65 years (range 21–99), with male predominance. Discharged patients were significantly younger than patients who were hospitalized, with a female predominance. The frequency of patients with either hypertension or diabetes was 14.1 and 7.7%, respectively, whereas frequency of patients with both pathologies was 21.8%. The frequency of patients with both diabetes and hypertension was higher in patients admitted to the ICU. Body mass index (BMI) was only recorded for 50.2% of the population studied, 21.9% were obese (BMI>30), with a similar distribution between the different groups of patients. The time interval since onset of symptoms could be obtained for 361 (92.6%) of the patients, and the median was nine days, with no significant differences between the different groups of patients. Sixty-six hospitalized patients died (20.6%), with a higher frequency (37.1%) for patients in the ICU. Deceased patients were significantly (p<0.001) older (median age 82 years, range: 29–99) in the CoU group than in patients in the ICU (median age 65 years, range: 50–80). These data are reported in Table 1.

Table 1:

Main demographic and clinical data of the entire population and of the different groups of patients.

AllHomeCoUCoU then ICUICU
Males, %61.844.363b85b88.6c,e
Age, years655170c62d63b,e
Hypertension, %14.111.424.2a514.3
Diabetes, %7.72.912.5a1514.3
Hypertension & diabetes, %,e
Obesity (BMI>30), %, n21.9 (196)N.A23.6 (148)15.8 (19)24 (29)
Delay since the onset of symptoms, days – n9 (361)6 (66)7 (241)9 (19)9 (35)
Deceased, %N.AN.A13.430.037.1f
Age of death, years79N.A8269e65f

  1. Values for age and delay since onset of symptoms are expressed in median and [range]. For obesity and time since onset of symptoms, data were sometimes missing, therefore the number between brackets indicate the number (n) of patients for whom the information was available. BMI, body mass index; CoU, hospitalization in conventional units; ICU, intensive care unit; N.A, not available. ap<0.05; bp<0.01; cp<0.001 vs. “Home” group; dp<0.05; ep<0.01;fp<0.001 vs. CoU group.

The study was performed in agreement with the French ethical laws (the patients and their family are informed that their biological data used for routine care may be used in an anonymous manner unless they express their opposition) and was approved by the Foch IRB (approval number IRB00012437).

The results from the patient groups were analyzed and compared with a control group composed of 56 samples of healthy blood donors (median age 50 years), provided by the Etablissement Français du Sang (EFS, Versailles, France). Reference ranges correspond to the 5th and 95th percentiles.

WBC count and differential

Blood was collected in 3 mL S-Monovettes (Sarstedt, Marnay, France) containing 4.8 mg EDTA-K3. CBC were analysed within 6 h after collection on DxH 800 (Beckman Coulter, Inc., Brea, CA) and Diff was performed within 24 h, using the CytoDiff™ reagent (Beckman Coulter) according to the manufacturer’s instructions. CytoDiff™ analysis, based on the study of Faucher et al. [26], is based on a pre-mixed cocktail of six antibodies with five colours (CD36-FITC, CD2-PE, CRTH2-PE, CD19-ECD, CD16-PC5 and CD45-PC7) and allows the detection and quantification of 13 populations of WBC thanks to an exclusion gating strategy among WBC and an auto-gating software that replaces operator intervention [27]. Direct immuno-labeling is performed on 100 µL whole peripheral EDTA-anticoagulated blood using a TQ-PREP (Beckman-Coulter) according to the instructions of the manufacturer. The automatic gating software defines CD36low as monocytes. Conventional monocytes (CD16neg Mono) are identified as the CD19‐negative, CD45‐positive, and CD36‐positive cell population after exclusion of B-cells and neutrophils. The anti‐CD16 antibody is intended to identify granulocytes. Nevertheless, a bi-parametric histogram (CD16/SSC) conditioned on the monocyte cell population distinguishes CD16pos monocytes (CD16pos Mono) from CD16neg Mono. In addition, lymphocyte sub-populations [B-lymphocytes (B-Ly), CD16pos T cytotoxic lymphocytes and NK cells (cNK-Ly), noncytotoxic (CD16neg) lymphocytes (ncT-Ly)] were also identified, as previously described [26]. T-Lymphocyte count (T-Ly) was the sum of cNK-Ly and ncT-Ly, and total lymphocyte (tot-Ly) count was the sum of B-Ly and T-Ly-counts. For the different subpopulations analyzed, and calculation of ratios, only absolute counts were considered.

Other biological assays

CRP was quantified using Alinity CRP Vario Reagent on Alinity analysers (Abbott Diagnostics France, Rungis) and D-dimers were quantified on ACL 750 (D-Dimers HS 500, Werfen, Le Pré-Saint Gervais, France).

Statistical analyses

Comparisons of the different groups were performed by the ANOVA test followed by a Student-Newman-Keuls test for pairwise comparison of sub-groups when ANOVA was positive (p<0.05). Coefficients of correlation (rho) were calculated using Spearman’s rank test. Specificity, sensitivity, and cut-off were established using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) of ROC curves represents the possibility of correctly classifying a patient’s results. As proposed by Swets and Pickett, we considered that AUC values above 0.7 indicate that the parameter can be of use for diagnosis, with values >0.9 indicating high clinical accuracy of the test. Statistical analysis and ROC curves were performed with the aid of the MedCalc computer program (MedCalc Software, Mariakerke, Belgium).


Discriminant differential biomarkers for ICU admission

The median of white blood cell (WBC) count was in the normal range for the different groups of patients, but hospitalized patients, either in CoU or ICU, had higher levels of WBC than discharged patients, because of a slight increase of polymorphonuclear cells (PMN) (Supplementary Table S1). In agreement with previous reports [5], [14], [15], lymphopenia, varyingly affecting the different subpopulation identified by the CytoDiff™ analysis, was observed in hospitalized patients, which was more pronounced in patients from the ICU, whereas the median of tot-Ly lymphocyte count was in the normal range for discharged patients (Supplementary Table S1). Median levels of total monocytes (tot-Mono) were in the normal range for the different groups of patients, but patients from the ICU or patients who went to the ICU after hospitalization in CoU had monocyte levels significantly lower than patients from CoU or discharged patients (Figure 1A). CD16neg monocyte counts were not significantly different between the different groups of patients except for patients hospitalized in CoU who subsequently required ICU admission (Figure 1B). Absolute count of CD16pos Mono was in the normal range for the different groups of patients, except for patients from the ICU, who had a significant decrease of CD16pos Mono, when compared to patients who went home or to hospitalized patients in CoU (Figure 1C). Therefore, the ratio [CD16pos Mono/tot-Mono count] was significantly decreased in this group of patients (Figure 1D). We also calculated different ratios involving CD16 Mono: the ratio [CD16neg Mono/CD16pos Mono] was of course significantly higher for patients from the ICU (Figure 1E), the ratio [PMN/CD16pos Mono] was significantly increased in hospitalized patients in comparison to discharged patients, but also higher in ICU patients when compared to patients hospitalized in CoU (Figure 1F). CRP and D-dimers were significantly increased in patients requiring hospitalization, when compared to discharged patients, and higher in patients from the ICU than in patients hospitalized in CoU (Supplementary Table S1).

Figure 1: Variations of total monocyte(A), CD16neg monocyte (B) and CD16pos monocyte (C) counts, and of the ratios [CD16pos monocytes/total monocyte count] (D) [CD16neg/CD16pos monocytes] (E) and [polymorphonuclear cells/CD16pos monocytes] (F) according to the different groups of patients.The grey zone corresponds to the normal range (5th–95th percentile in healthy blood donors) CoU, hospitalization in conventional units; ICU, intensive care unit. ap<0.05; bp<0.01; cp<0.001 vs. “Home” group; dp<0.01; ep<0.001 vs. CoU group.

Figure 1:

Variations of total monocyte

(A), CD16neg monocyte (B) and CD16pos monocyte (C) counts, and of the ratios [CD16pos monocytes/total monocyte count] (D) [CD16neg/CD16pos monocytes] (E) and [polymorphonuclear cells/CD16pos monocytes] (F) according to the different groups of patients.

The grey zone corresponds to the normal range (5th–95th percentile in healthy blood donors) CoU, hospitalization in conventional units; ICU, intensive care unit. ap<0.05; bp<0.01; cp<0.001 vs. “Home” group; dp<0.01; ep<0.001 vs. CoU group.

Discriminant capacity of the differential parameters to identify patients with severe forms of COVID-19

For the 11 parameters from the differential which were significantly different between patients admitted to the ICU and patients who went home or were hospitalized in CoU, we used ROC curve analysis to study whether they could be useful to identify patients with severe forms of COVID-19. Eight parameters fulfilled the criteria of an AUC>0.7 (Table 2). The ratio [PMN/CD16pos Mono] had the highest AUC (0.810), like that of CRP and D-dimers. Because hospitalized patients were older than patients who went home, we aimed to define whether differential parameters were correlated with age. In contrast to most of the differential parameters, the ratios [CD16pos Mono/tot-Mono], [CD16neg Mono/CD16pos Mono] and [PMN/CD16pos Mono] were not correlated with age. Thus, the ratio [PMN/CD16pos Mono] is the most relevant factor to predict the most severe forms of COVID-19 where the ROC curve analysis yielded a specificity of 81.8% and a sensitivity of 74.3%.

Table 2:

Receiver operating characteristic curve analysis of white blood cells (WBC) and differential parameters which identify patients requiring intensive care unit hospitalization.

ParameterCut-offSensitivity, %Specificity, %AUC, 95% CICorrelation, p-Value, (rho) with
AgeOnset of symptoms
WBC, ×109/L>8.362.9780.736 (0.69–0.779)0.007 (0.137)0.001 (0.174)
PMN, ×109/L> (0.728–0.814)0.004 (0.146)<0.001 (0.189)
T-Ly, ×109/L≤0.5954.3780.706 (0.658–0.751)0.002 (−0.154)ns
CD16pos Mono, ×109/L≤0.02974.378.80.770 (0.725–0.811)0.019 (0.12)<0.001 (−0.287)
[CD16pos Mono/tot-Mono]≤9.674.369.70.756 (0.71–0.798)ns<0.001 (−0.268)
[CD16neg Mono/CD16pos Mono]>9.2577.170.20.778 (0.733–0.819)ns<0.001 (0.281)
[PMN/CD16pos Mono]>193.174.381.80.817 (0.775–0.855)ns<0.001 (0.331)
CRP, mg/L>10783.966.80.810 (0.764–0.85)0.019 (0.127)<0.001 (0.218)
D-dimers, mg/L>1.717085.60.83 (0.766–0.883)0.005 (0.216)ns

  1. AUC, area under the curve; CI, confidence interval; CRP, C-reactive protein; cNK-Ly, cytotoxic Natural Killer cells; CRP, C-reactive protein; Mono, monocytes; PMN, polymorphonuclear cells; T-Ly, T-lymphocytes; tot-Mono, total monocyte levels; WBC, white blood cells.

For the subgroup of patients who were first hospitalized and who needed admission to the ICU some days later, only WBC, tot-Mono and CD16neg Mono counts were different from patients hospitalized only in CoU. The AUC of both parameters was below 0.7. The highest AUC was 0.691 for tot-Mono count (sensitivity and specificity were both at 70%).

The ratio PMN/CD16pos monocytes is discriminating whatever the time to the onset of symptoms

There was a positive correlation between the time of onset of symptoms and different parameters of the differential (i.e., WBC, and PMN counts and the ratio [CD16neg Mono/CD16pos Mono]), whereas tot-Mono count, CD16pos Mono and the ratio [CD16pos Mono/tot-Mono] were negatively correlated. As parameters involving monocytes and CD16pos Mono are correlated with the time to onset of symptoms, for the 359 patients for whom the beginning of symptoms was recorded, we evaluated whether the different monocyte parameters previously identified were different between ICU and non-ICU patients (35 and 324, respectively). The time to the onset of symptoms was divided into three groups: less than one week, between one and two weeks (when the cytokine storm is usually observed), and more than two weeks. As can be seen in Figure 2, the different parameters were significantly different for the three periods for all the parameters, but the ratio [PMN/CD16pos Mono] was particularly discriminating.

Figure 2: Variations of CD16pos monocyte levels and of different ratios involving CD16pos monocytes according to the time of the onset of symptoms of COVID-19. Hatched columns: non–intensive care unit (ICU) patients; black columns, ICU patients. D, days; Mono, monocytes; PMN, polymorphonuclear cells. ap<0.05; bp<0.001.

Figure 2:

Variations of CD16pos monocyte levels and of different ratios involving CD16pos monocytes according to the time of the onset of symptoms of COVID-19. Hatched columns: non–intensive care unit (ICU) patients; black columns, ICU patients. D, days; Mono, monocytes; PMN, polymorphonuclear cells. ap<0.05; bp<0.001.

Monocyte subpopulations are not predictive of mortality

We compared the different parameters tested in survivors and non-survivors. WBC count, the ratios [CD16pos Mono/tot-Mono] and [CD16neg Mono/CD16pos Mono] were not significantly different between the groups, whereas PMN and the ratio [PMN/CD16pos Mono] were significantly higher in survivors. Total monocyte count, CD16pos Mono and CD16neg Mono were significantly lower in non-survivors (Supplementary Table S2, Supplementary Material). However, none of these parameters were discriminant using ROC curve analysis since the AUC was below 0.7. CRP and D-dimers were significantly increased in non-survivors, but only D-dimers were predictive of the mortality (cut-off≥1.42 mg/L, sensitivity=70%, specificity 76.1%, AUC=0.74).


If lymphopenia, affecting the different lymphocyte subsets, has rapidly been identified as a characteristic of SARS-CoV-2 infection, less has been described concerning monocyte levels. This could be because variations in monocyte total count are more subtle than the lymphocyte decrease in patients with COVID-19, and studies have included a too small number of patients to have sufficient statistical power [28], [29]. Patients with COVID-19 have usually median monocyte range within reference values, but some meta-analyses have described a lower monocyte count in the most severe forms [29], [30], [31], as we observed in this study. In addition, since the HIV infection in the early 80s, lymphocyte phenotyping has become a routine assay in medical biology laboratories, but since the identification of the three monocyte subsets was initially described in 1989 [32], their variations in diseases are not yet totally understood [33] and therefore not routinely quantified. Usually, identification of monocyte subpopulations is performed by conventional flow cytometry [18], [19], [20], [21], [22], [33], but this technology is time-consuming and needs trained technicians. For this study, we used the CytoDiff™ reagent, which is a cocktail of five labeled-antibodies, and which has the main advantage to use auto-gating software that requires no operator intervention. Therefore, this assay can be used in routine because no special skills are needed. However, it has the disadvantage that it does not allow differentiation of the intermediate and non-classical monocytes, but only segregates CD16neg from CD16pos monocytes. In addition, the gating strategy used for monocyte identification is performed through an exclusion gating strategy (positivity for CD36 and CD45 antigens and negativity for CD19), but plasmocitoid and myeloid dendritic cells are not excluded. It was recently shown that these subpopulations are decreased in COVID-19 patients [22], however, the relative concentrations of these subpopulations are low compared to the monocyte levels [34], and it is unlikely that this could create a bias in the interpretation of our results.

Different results have been reported in the few papers analyzing monocyte subsets in severe SARS-CoV-2 infection: Gatty et al. [18] described both a decrease in intermediate and unconventional monocytes, Silvin et al. [21] observed only a marked decrease of non-classical monocytes (CD14lowCD16high), whereas the percentage of intermediate monocytes (CD14highCD16high) was unaffected, and Peruzzi et al. described a more marked decrease in unconventional monocytes from non-ICU patients compared to ICU ones [22]. These discrepancies could be due to the patient selection, since these studies were performed on a limited number (40–86) of selected patients. Whatever the most affected subpopulation (intermediate or unconventional), our data, in a prospective study, are in accord with the fact that the most severe forms are characterized by a decrease in CD16pos monocytes and confirm the hypothesis of Silvin et al. suggesting that the quantification of CD16pos monocytes could constitute an interesting tool to identify severe forms of SARS-CoV-2 infection.

This decrease in CD16pos monocytes seems to be specific to SARS-CoV-2 infection. Indeed, CD16pos monocytes were found to be increased in patients with viral or bacterial diseases [35]. In a small series of patients with flu, using conventional flow cytometry, a significant increase of CD16pos monocytes was observed [36]. Using the CytoDiff™ reagent for a series of 56 patients with flu, proven by RT-PCR, we observed a median value of 0.188 × 109/L of CD16pos monocytes (unpublished observation). However, none of these patients with flu were in the ICU, and therefore it could be interesting to analyze whether the most severe forms of flu are also characterized by a low CD16pos monocyte level. In severe sepsis, levels of CD16pos monocytes were increased, but patients with a poor outcome had lower levels of CD16pos monocytes [37]. In the case of COVID-19, the decrease of CD16pos monocytes could be a marker for a severe lung aggression, since a study found that CD16pos monocytes are enriched in the lungs from critical COVID-19 patient [19]. Lastly, CD16pos monocytes have been shown to recognize and clear dying endothelial cells in a TLR7-dependent manner to maintain vascular homeostasis [38]. This decrease in CD16pos monocytes could explain in part why patients with COVID-19 have increased levels of circulating endothelial cells [39].

As previously described, patients with severe COVID-19 have increased levels of PMN [5], [22], therefore we calculated the ratio [PMN/CD16pos Mono]. This ratio has the advantage of being independent of the age of the patients, and even if it was correlated with the time of onset of symptoms, it was highly discriminant for the different periods tested.

Patients who required admission to the ICU after hospitalization in CoU had significant lower monocyte and CD16neg Mono levels than patients who did not need the ICU, suggesting that monocytopenia could constitute a marker of severity of the disease, but CD16pos monocyte levels were not significantly decreased. However, samples at admission were obtained three days (median) before the transfer to ICU, but unfortunately, we did not perform quantification of CD16pos monocytes at the admission of these patients to the ICU. It would therefore be interesting, in future studies, to analyze whether the follow-up of CD16pos monocytes could be predictive of an unfavorable evolution.

Previous reports have suggested that the [PMN/lymphocyte] ratio could be predictive of the severity of COVID-19. In our series, this ratio was significantly higher in ICU patients than in the other groups of patients (data not shown), but AUC was lower (0.797) than the [PMN/CD16pos Mono] ratio, because of a lower specificity (72.1%) than that of the [PMN/CD16pos Mono] ratio.

In conclusion, the determination of CD16pos monocyte count and of [PMN/CD16pos Mono] ratio is related to the severity of COVID-19. The interest of the follow-up of these parameters to predict a favorable or unfavorable evolution remains to be established in new prospective studies.

Corresponding author: Prof. Marc Vasse, Biology Department, Foch Hospital, 40 rue Worth, 92150Suresnes, France; and UMRS-1176, Le Kremlin-Bicêtre, France, Phone: +33 (0)1 46 25 22 96, E-mail:


The authors thank Elena Suckhacheva for helpful discussions, the team of technicians of the hematology unit of Foch Hospital, for their additional work to the routine during the acute phase of the pandemic, Pauline Touche (Direction à la Recherche Clinique et Innovation – Foch Hospital) for her administrative assistance and Polly Gobin for her editorial assistance.

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: The study was performed in agreement with the French ethical laws (the patients and their family are informed that their biological data used for routine care may be used in an anonymous manner unless they express their opposition) and was approved by the Foch IRB (approval number IRB00012437).


1. Chen, N, Zhou, M, Dong, X, Qu, J, Gong, F, Han, Y, et al.. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395:507–13. in Google Scholar

2. Guan, WJ, Ni, ZY, Hu, Y, Liang, WH, Ou, CQ, He, JX, et al.. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020;382:1708–20. in Google Scholar

3. Feng, Y, Ling, Y, Bai, T, Xie, Y, Huang, J, Li, J, et al.. COVID-19 with different severities: a multicenter study of clinical features. Am J Respir Crit Care Med 2020;201:1380–8. in Google Scholar

4. Mehta, P, McAuley, DF, Brown, M, Sanchez, E, Tattersall, RS, Manson, JJ, et al.. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet 2020;395:1033–4. in Google Scholar

5. Huang, C, Wang, Y, Li, X, Ren, L, Zhao, J, Hu, Y, et al.. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395:497–506. in Google Scholar

6. Merad, M, Martin, JC. Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages. Nat Rev Immunol 2020;20:355–62. in Google Scholar

7. Vlacil, AK, Schuett, J, Schieffer, B, Grote, K. Variety matters: diverse functions of monocyte subtypes in vascular inflammation and atherogenesis. Vasc Pharmacol 2019;113:9–19. in Google Scholar

8. Kapellos, TS, Bonaguro, L, Gemünd, I, Reusch, N, Saglam, A, Hinkley, ER, et al.. Human monocyte subsets and phenotypes in major chronic inflammatory diseases. Front Immunol 2019;10:2035. in Google Scholar

9. Wong, KL, Tai, JJ-Y, Wong, WC, Han, H, Sem, X, Yeap, WH, et al.. Gene expression profiling reveals the defining features of the classical, intermediate, and nonclassical human monocyte subsets. Blood 2011;118:e16–31. in Google Scholar

10. Belge, KU, Dayyani, F, Horelt, A, Siedlar, M, Frankenberger, M, Frankenberger, B, et al.. The proinflammatory CD14+CD16+DR++ monocytes are a major source of TNF. J Immunol 2002;168:3536–42. in Google Scholar

11. Fingerle, G, Pforte, A, Passlick, B, Blumenstein, M, Ströbel, M, Ziegler-Heitbrock, HW. The novel subset of CD14+/CD16+ blood monocytes is expanded in sepsis patients. Blood 1993;82:3170–6. in Google Scholar

12. Chimen, M, Yates, CM, McGettrick, HM, Ward, LSC, Harrison, MJ, Apta, B, et al.. Monocyte subsets coregulate inflammatory responses by integrated signaling through TNF and IL-6 at the endothelial cell interface. J Immunol 2017;198:2834–43. in Google Scholar

13. Boyette, LB, Macedo, C, Hadi, K, Elinoff, BD, Walters, JT, Ramaswami, B, et al.. Phenotype, function, and differentiation potential of human monocyte subsets. PloS One 2017;12:e0176460. in Google Scholar

14. Shao, L, Li, X, Zhou, Y, Yu, Y, Liu, Y, Liu, M, et al.. Novel insights into illness progression and risk profiles for mortality in non-survivors of COVID-19. Front Med 2020;7:246. in Google Scholar

15. Feng, X, Li, P, Ma, L, Liang, H, Lei, J, Li, W, et al.. Clinical characteristics and short-term outcomes of severe patients with COVID-19 in Wuhan, China. Front Med 2020;7:491. in Google Scholar

16. Kang, CK, Han, GC, Kim, M, Kim, G, Shin, HM, Song, KH, et al.. Aberrant hyperactivation of cytotoxic T-cell as a potential determinant of COVID-19 severity. Int J Infect Dis 2020;97:313–21. in Google Scholar

17. Jurado, A, Martín, MC, Abad-Molina, C, Orduña, A, Martínez, A, Ocaña, E, et al.. COVID-19: age, Interleukin-6, C-reactive protein, and lymphocytes as key clues from a multicentre retrospective study. Immun Ageing 2020;17:22. in Google Scholar

18. Gatti, A, Radrizzani, D, Viganò, P, Mazzone, A, Brando, B. Decrease of non-classical and intermediate monocyte subsets in severe acute SARS-CoV-2 infection. Cytometry 2020;97:887–90. in Google Scholar

19. Sánchez-Cerrillo, I, Landete, P, Aldave, B, Sánchez-Alonso, S, Sánchez-Azofra, A, Marcos-Jiménez, A, et al.. COVID-19 severity associates with pulmonary redistribution of CD1c+ DC and inflammatory transitional and nonclassical monocytes. J Clin Invest 2020;130:6290–300. in Google Scholar

20. Alzaid, F, Julla, JB, Diedisheim, M, Potier, C, Potier, L, Velho, G, et al.. Monocytopenia, monocyte morphological anomalies and hyperinflammation characterise severe COVID-19 in type 2 diabetes. EMBO Mol Med 2020;12:e13038. in Google Scholar

21. Silvin, A, Chapuis, N, Dunsmore, G, Goubet, AG, Dubuisson, A, Derosa, L, et al.. Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19. Cell 2020;182:1401–18.e18. in Google Scholar

22. Peruzzi, B, Bencini, S, Capone, M, Mazzoni, A, Maggi, L, Salvati, L, et al.. Quantitative and qualitative alterations of circulating myeloid cells and plasmacytoid DC in SARS-CoV-2 infection. Immunology 2020;161:345–53. in Google Scholar

23. Zhang, L, Yan, X, Fan, Q, Liu, H, Liu, X, Liu, Z, et al.. D-dimer levels on admission to predict in-hospital mortality in patients with Covid-19. J Thromb Haemostasis 2020;18:1324–9. in Google Scholar

24. Tian, W, Jiang, W, Yao, J, Nicholson, CJ, Li, RH, Sigurslid, HH, et al.. Predictors of mortality in hospitalized COVID-19 patients: a systematic review and meta-analysis. J Med Virol 2020 May 22. [Epub ahead of print].Search in Google Scholar

25. Simonnet, A, Chetboun, M, Poissy, J, Raverdy, V, Noulette, J, Duhamel, A, et al.. High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation. Obesity 2020;28:1195–9. in Google Scholar

26. Faucher, JL, Lacronique-Gazaille, C, Frébet, E, Trimoreau, F, Donnard, M, Bordessoule, D, et al.. “6 markers/5 colors” extended white blood cell differential by flow cytometry. Cytometry 2007;71:934–44. in Google Scholar

27. Cottard, A, Wagner-Ballon, O, Le Priol, J, Azzaoui, I, Ly-Sunarram, B, Fest, T, et al.. Improvement of the leukocyte differential performed by flow cytometry using the advanced 2.0 version of the CytoDiff CXP software. Cytometry 2014;85:653–7. in Google Scholar

28. Zhang, H, Cao, X, Kong, M, Mao, X, Huang, L, He, P, et al.. Clinical and hematological characteristics of 88 patients with COVID-19. Int J Lab Hematol 2020;42:780–7. in Google Scholar

29. Sun, S, Cai, X, Wang, H, He, G, Lin, Y, Lu, B, et al.. Abnormalities of peripheral blood system in patients with COVID-19 in Wenzhou, China. Clin Chim Acta 2020;507:174–80. in Google Scholar

30. Bao, J, Li, C, Zhang, K, Kang, H, Chen, W, Gu, B. Comparative analysis of laboratory indexes of severe and non-severe patients infected with COVID-19. Clin Chim Acta 2020;509:180–94. in Google Scholar

31. Ghahramani, S, Tabrizi, R, Lankarani, KB, Kashani, SMA, Rezaei, S, Zeidi, N, et al.. Laboratory features of severe vs. non-severe COVID-19 patients in Asian populations: a systematic review and meta-analysis. Eur J Med Res 2020;25:30. in Google Scholar

32. Auffray, C, Fogg, D, Garfa, M, Elain, G, Join-Lambert, O, Kayal, S, et al.. Monitoring of blood vessels and tissues by a population of monocytes with patrolling behavior. Science 2007;317:666–70. in Google Scholar

33. Narasimhan, PB, Marcovecchio, P, Hamers, AAJ, Hedrick, CC. Nonclassical monocytes in health and disease. Annu Rev Immunol 2019;37:439–56. in Google Scholar

34. Orsini, G, Legitimo, A, Failli, A, Massei, F, Biver, P, Consolini, R. Enumeration of human peripheral blood dendritic cells throughout the life. Int Immunol 2012;24:347–56. in Google Scholar

35. Ożańska, A, Szymczak, D, Rybka, J. Pattern of human monocyte subpopulations in health and disease. Scand J Immunol 2020;92:e12883. in Google Scholar

36. Janols, H, Bredberg, A, Thuvesson, I, Janciauskiene, S, Grip, O, Wullt, M. Lymphocyte and monocyte flow cytometry immunophenotyping as a diagnostic tool in uncharacteristic inflammatory disorders. BMC Infect Dis 2010;10:205. in Google Scholar

37. Gainaru, G, Papadopoulos, A, Tsangaris, I, Lada, M, Giamarellos-Bourboulis, EJ, Pistiki, A. Increases in inflammatory and CD14dim/CD16pos/CD45pos patrolling monocytes in sepsis: correlation with final outcome. Crit Care 2018;22:56. in Google Scholar

38. Carlin, LM, Stamatiades, EG, Auffray, C, Hanna, RN, Glover, L, Vizcay-Barrena, G, et al.. Nr4a1-dependent Ly6C(low) monocytes monitor endothelial cells and orchestrate their disposal. Cell 2013;153:362–75. in Google Scholar

39. Khider, L, Gendron, N, Goudot, G, Chocron, R, Hauw-Berlemont, C, Cheng, C, et al.. Curative anticoagulation prevents endothelial lesion in COVID-19 patients. J Thromb Haemostasis 2020;18:2391–9. in Google Scholar

Supplementary Material

The online version of this article offers supplementary material (

Received: 2020-10-03
Accepted: 2021-02-08
Published Online: 2021-02-22
Published in Print: 2021-06-25

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Scroll Up Arrow