Since its emergence in Wuhan in December 2019, the virus responsible for coronavirus disease 2019 (COVID-19), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally and become a world-threatening disease (1). According to the World Health Organization (WHO) report of 7 December 2022, the pandemic has exceeded 640 million cases and 6.6 million deaths worldwide (2). Since the onset of the pandemic, the search for biomarkers to correctly classify patients has been one of the major challenges for experts in this disease. Several biomarkers such as C-reactive protein (CRP), serum ferritin, D-dimer, and interleukin-6 (IL-6) have been studied for prognostic assessment of patients with COVID-19 pneumonia or simply for patient management (3). New biomarkers such as presepsin as a soluble CD14 subtype in sepsis patients have been continuously included (4).

The role of cytokines and chemokines has been associated with disease severity and clinical COVID-19 outcomes during all pandemic, suggesting that these molecules are the most promising biomarkers for patient management. Some inflammatory biomarkers have been reported to be significantly associated with an increased risk of developing severe COVID-19, such as procalcitonin (PCT), serum ferritin, CRP, IL-6, or erythrocyte sedimentation rate (ESR) (4). In addition, technologies combining flow and mass cytometry have improved multiple single-cell immune profiling in COVID-19 patients by revealing changes in both innate and adaptive immune cell subpopulations and their correlation with disease severity. There are reports of reduced frequencies in cell populations of severe COVID-19 patients in monocytes [particularly CD14^lo CD16^hi nonclassical monocytes (NC-Mo)], dendritic cells (DCs), and natural killer (NK) cells (5).

Due to the important role of the immune system and the immunological mechanisms that correlate with disease progression, we focus on the analysis using cytometry by time-of-flight mass spectrometry (CyTOF). This is a method that allows the simultaneous analysis of more than 40 cell markers without spectral overlap (5, 6).

There are controversial data regarding the best immunologic marker for COVID-19 monitoring. Recently, it has been reported that severe disease can be distinguished from moderate disease by systemic loss and dysfunction of M1-like pro-inflammatory monocytes, conventional DCs, and plasmacytoid dendritic cells (pDCs) (7). Moreover, in pediatric patients, it has been described that serum soluble CD25 and soluble CD163 levels have been described to be upregulated in the serum of SARS-CoV-2 patients (8), as well as increased levels of CD11c⁺. A CD16^hi population has been identified during the acute phase of infection and at disease severity in non-human primates (9).

There are also proteins such as the angiotensin-converting enzyme 2 (ACE2) whose high expression pattern is associated with an increased risk of severe infection and complications in COVID-19. In addition, a positive correlation of the upregulation of CD61 and CD163 with ACE2 expression has been found (10). The SARS-CoV-2 spike (S) protein binds to ACE2, which acts as an entry receptor. Once the receptor binds, the S protein is expressed and cleaved by the transmembrane protease serine 2 (encoded by TMPRSS2) (11). Therefore, these two genes have also come into focus as COVID-19 biomarkers, and the use of single-nucleotide polymorphisms (SNPs) as noninvasive biomarkers is readily available in routine clinical practice. For example, rs2285666 (G870A) has been suggested as the best option for the ACE2 polymorphism that modulates susceptibility to SARS-CoV-2 infection (12). Similarly, the TMPRSS2 gene has been reported to have several variants associated with susceptibility to COVID-19, mainly rs2070788, rs734056, rs12329760, rs2276205, and rs3787950 (13). Furthermore, rs2070788 is highlighted as a factor affecting COVID-19 severity. MX dynamin-like GTPase (MX1) has been highlighted as a critical responder in SARS-CoV-2 infection. Its expression is increased in COVID-19 patients and is strikingly associated with the increase in viral load (14). In addition, increased basal MX1 levels have been reported to correlate with SARS-CoV-2 infection, helping to identify the patient’s predisposition to severe disease (15).

Clinically, nearly 80% of COVID-19 cases are asymptomatic or have a mild form of the disease. However, approximately 13.8% and 6.1% are severe and critical, respectively, requiring hospitalization and even intensive care in the life-threatening cases (16). Our main objective is to demonstrate the role of various noninvasive biomarkers, especially cytometric and genetic biomarkers, that could predict or anticipate the most severe outcomes among these patients.

Although COVID-19 is a recent pathology, much of the research is focused on finding biomarkers for efficient diagnosis or patient stratification. There are data on the predictive ability of mortality in hospitalized patients using a score classification combining peripheral capillary oxygen saturation, albumin, D-dimer, and age (20). Others, focusing on neuroendocrine biomarkers such as copeptin, found an increase in severe cases (21). However, there are currently no conclusive data from clinical practice.

Here, we found the role of TMPRSS2 (rs2070788) G allele carriers as an interesting and simple way to classify severe COVID-19 patients. This is not the first time that the role of this marker has been suggested, as it was previously mentioned by Akin et al. (22), who identified high levels of soluble ACE2 as well as the TMPRSS2 rs2070788 non-AA genotype and low aldosterone/renin ratio as independent factors for disease severity. TMPRSS2 is the major host protease that enables cell entry of several coronaviruses and is highly expressed in lung and bronchial tissues. SARS-CoV-2 is known to utilize ACE2 as a cleavage site for S-peak protein with the help of TMPRSS2 (23). In contrast, in the present study, we found no differences between the severity of COVID-19 and the MX1 or ACE2 genes. MX1 encodes a protein with antiviral activity against RNA and DNA viruses. Several studies have shown a high expression in COVID-19 patients, but we did not find a correlation (24). ACE2 and especially several SNPs such as rs4646994 and rs2285666 have been suggested to correlate with COVID-19 susceptibility and/or disease severity, but controversial data on these two SNPs have been reported (25). Alimoradi et al. (26) and the present data suggest that there is no association between rs2285666 and COVID-19 severity.

In addition, the present analysis also exploited the power of CyTOF technology, which is currently one of the most powerful tools for immune phenotyping, allowing simultaneous and high-throughput quantification of more than 40 parameters at the single-cell level (5, 6). There are still few studies using this technology to characterize immune cell responses against SARS-CoV-2, as we do here (27). We focus on the role of monocytes, particularly the CD163^-/CD206^- and CD163⁺/CD206^- populations, as the main markers for discriminating between severe and mild patients. A study conducted by Trombetta et al. (7) also suggests increased expression patterns of CD163 and CD206 as immunoregulatory markers in COVID-19. Elevated CD163 levels were positively correlated with ACE2 expression, and CD163 indirectly contributes to the anti-inflammatory response. In addition, higher ACE2 protein expression was found in severe COVID-19 disease, correlating with disease severity (10). CD163, due to its high expression in macrophages formed in response to tissue damage, is a potential inflammation biomarker and a therapeutic target (9). Here, we confirm its position in severe COVID-19 patients.

Here, we also reported the role of CD33 C-Mo associated with severe patients when compared with age. Previous data have shown that (CD33^- HLA-DMA^- CD14⁺) C-Mo and (CLEC10A^- S100A9^lo) pDCs are involved in viral persistence and in the innate immune response against SARS-CoV-2 infection. Wang et al. (28) suggested that the enrichment of (CD33^- HLA-DMA^- CD14⁺) C-Mo may attenuate antigen presentation and antiviral immune response, which may also explain the present data in severe patients. Alberca et al. (29) also proposed a combination of the cell markers CD11b⁺ CD33⁺ HLA-DR^-CD14⁺ and CD11b⁺ CD33⁺ HLA-DR^- CD66b⁺ as novel severity biomarkers for COVID-19. In the present study, we confirmed the presence of CD33⁺ CD11b⁺ cell markers in the blood of patients with severe disease. In addition, CD11b⁺ (macrophages and neutrophils) has also been described with high levels of cell infiltration in the lungs (9), which is common in severe patients. In addition, we found that CD14^dim/CD33⁺ are good options as biomarkers for stratification of mild/severe COVID-19. Similarly, it has been previously reported that upregulation of C-Mo and, in particular, higher numbers of CD14⁺ CD33⁺ HLA-DR cells and S100A8/9/12 expressing C-Mo are present in severe/critical COVID-19 and sepsis (30). In the acute phase, Fahlberg et al. (9) showed a robust migration of CD16-expressing monocytes into the lung and described two subsets of interstitial macrophages (HLA-DR⁺CD206^-) directly associated with plasma IL-6 levels. Furthermore, alveolar macrophages in acute lung injury with alveolar type II cell hyperplasia showed a characteristic phenotype (CD68, CD11c, CD14, CD205, CD206, CD123/IL3AR, and PD-L1) (31). As in our analysis in the severe population, we also found CD206 cell markers, in addition to those previously mentioned, in combination with CD80, which has been classified as an inflammatory monocyte subset not typically seen in healthy controls (32).

Moreover, CD45^- seems to be a good marker between mild and severe patients. Similar results were previously reported by Jin et al. (33) who suggested CD45 as a useful tool to discriminate between severe and non-severe cases. Furthermore, recent publications performed in healthy vs. COVID-19 patients reported that CD45 expression on leukocytes is altered in COVID-19 patients. This event is explained by the changes in signal transduction from binding to Toll-like receptor 4, changes in leukocyte subtypes, or maturation of cells during the infection process (34). It is reinforced by the important role that CD45 plays in autoimmune and oncological events, but also in viral infections (33).

According to gender, we found differences in NK and monocyte populations (C-Mo, T-Mo, and NC-Mo). In several studies, patient characteristics such as male sex, advanced age, and the presence of comorbidities have been associated with an increased risk of severe COVID-19 and ICU admission (35). However, this is the first time that differences in immunologic markers between men and women have been reported.

This analysis indicates the relevant role of several markers such as TMPRSS2, CD45^-, CD163/CD206, and CD33 for COVID-19 aggressiveness. The optimal classification of severe or more aggressive COVID-19 patients could help clinicians to offer different stratifications and follow-up. This is relevant considering that the opposite has been described in other infectious diseases, where CD163 is decreased in C-Mo and T-Mo. Here, for the first time, we report a combination of markers that can be performed in blood and will help in this classification, although we must consider that one of the challenges of the present project is due to the limited size of the population. However, the use of high-throughput analysis such as CyTOF provides a lot of novel information in the present data.

The data presented in the study are deposited in the European Genome-Phenome Archive, accession number EGAD00010002445.

The studies involving human participants were reviewed and approved by 1329-N-21. The patients/participants provided their written informed consent to participate in this study.

VA-R, SC-L, FM-B and PP-Q performed the experiments and data analysis. OS and JD-C performed cytometry analysis. LM-G and MA-C collected the related papers and drafted the manuscript. LM-G and MA-C participated in the design of the article. LM-G and MA-C wrote, and final proofed the manuscript. AA-M and MR-R performed the immunological revision and support of the article. SM-D, GL-T, SM-E and AC-V collected all the samples and updated clinical recording. LM-G and MA-C designed and supervised the study. SC-L and FM-B have statistical support. SM-D, CE-B, LM-G and MA-C obtained financial support. All authors contributed to the article and approved the submitted version.