Abstract
Introduction
The goal of this study was to identify biomarker(s) to assign risk of mortality in COVID‐19 patients to improve intensive care unit (ICU) and coronary care unit management. A total of 100 confirmed COVID‐19 patients admitted at Imam Khomeini Hospital in Tehran, were compared to 70 control subjects. Peripheral blood leukocyte was studied using staining reagents included CD3, CD4, CD8, HLA‐DR, CD19, CD16, and CD56. The immunophenotyping analysis was evaluated using the FACSCalibur instrument. To investigate the cell density of lung infiltrating T cells, postmortem slides of needle necropsies taken from the lung tissue of 3 critical patients were evaluated by immunohistochemistry staining. The number of lymphocyte subpopulations was significantly lower in COVID‐19 patients than in the control group. Regarding the disease severity, the absolute count of T, NK, and HLA‐DR+ T cells were significantly reduced in severe patients compared to the moderate ones. The critical patients had a significantly lower count of CD8‐HLA‐DR+ T cells than the moderate cases. Regarding the disease mortality, based on univariate analysis, the count of HLA‐DR+ T, CD8− HLA‐DR+ T, and CD8+ HLA‐DR+ T cells was associated with mortality in COVID‐19 patients. Receiver operating characteristic curve analysis showed the count of CD8+ HLA‐DR+ T cells is the best candidate as a biomarker for mortality outcome. Furthermore, pulmonary infiltration of T cells in the lung tissue showed only slight infiltrations of CD3+ T cells, with an equal percentage of CD4+ and CD8+ T cell subpopulation in the lung tissue. These findings suggest that close monitoring of the value of CD8+ HLA‐DR+ T cells in COVID‐19 patients may be helpful to identify high‐risk patients. However, further studies with larger sample size are needed.
Keywords: B cells, COVID‐19, immunophenotyping, lymphocyte, NK cells, T cells
1. INTRODUCTION
Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is considered one of the most critical threats to human health worldwide. 1 SARS‐CoV‐2, like other respiratory pathogens has transmitted through aerosol, droplet, and contact. 2 Compared with other coronaviruses such as severe acute respiratory syndrome coronavirus (SARS‐CoV) and Middle East respiratory syndrome coronavirus (MERS‐CoV), the intensity of clinical manifestations of COVID‐19 patients varies considerably. 3 , 4 , 5 The results of meta‐analysis have reported statistically significant neurological manifestations in COVID‐19 patients, including fatigue, headache, myalgia, dizziness, gustatory dysfunction, arthralgia, and so forth. 6 Current evidence has shown that impaired immune response to infection and the subsequent inflammatory cytokine storm is associated with exacerbation of disease severity and increased mortality risk. 7 For humoral immunity, longevity of neutralizing antibodies is 3 years in SARS‐CoV, 2 years in MERS‐CoV, and 2−3 months (depend on disease severity) in SARS‐CoV 2 infection. 8 In the COVID‐19 illness, like SARS‐CoV and MERS‐CoV infection, lymphopenia, significant reduction of CD3+ T cells, CD4+ T cells, CD8+ T cells, B cells, and natural killer (NK) cells, as well as inflammatory cytokine storm are the essential laboratory abnormalities. 9 , 10 , 11 , 12 , 13 Previous studies showed the T cell level could be used to predict the severity and mortality of COVID‐19. 14 , 15 Xu et al. 14 in the evaluation of 145 hospitalized patients (discharge = 117 and in‐hospital death = 28), demonstrated that CD3+ T cell (<200), CD4+ T cell (<100), CD8+ T cell (<100), and B cell (<50) is positively correlated with mortality of COVID‐19. After evaluating 179 hospitalized patients with COVID‐19, Du et al. 16 suggested that CD3+CD8+ T cells ≤75 cells/μl was associated with the increased mortality rate in patients suffering from COVID‐19 pneumonia. Cantenys‐Molina et al. 7 concluded that CD4+ (≤500/µl) and CD8+ (≤100/µl) T lymphocytes have predictive value of death. Despite all the great efforts, the immune response differences between patients with a high risk of mortality and cases with low risk have not been well known. Given the high mortality rate in COVID‐19 patients, identifying a specific subpopulation of immune cells to assess the risk of disease, besides rapid monitoring of patients, can be valuable to bring down costs and workload of healthcare workers. Therefore, this study applied real‐time whole blood flow cytometry analysis to study ex vivo lymphocyte subpopulation associated with survival in hospitalized COVID‐19 patients. In addition, given that lymphopenia in COVID‐19 patients can be the consequence of the migration of these cells from the blood to other organs, in this study, we evaluated the infiltration of T lymphocytes in needle autopsies of lung tissue. It should be noted that the phenotype of cells in systemic conditions is not always the same as the local one.
2. MATERIALS AND METHODS
2.1. Study population
In this cross‐sectional study, 100 confirmed cases of COVID‐19 (based on the World Health Organization interim guidance and comprehensive national guideline for the diagnosis and treatment of COVID‐19, the sixth version) 17 who admitted at Imam Khomeini Hospital Complex in Tehran City, Iran, from February 12 to April 4, 2020, were enrolled. This period covering the first peak of Covid‐19 pandemic in Iran. 18 They were compared to 70 clinically healthy control subjects living in Tehran with no comorbidities and adverse drug history. Previously published data were used to calculate sample size. The standard deviation (SD) of CD8+ T cells is equal to 5%. A significant difference of 2.5% is needed between the healthy control group and Covid‐19 patient. Therefore, with 95% confidence and 80% power, the total sample are 64 samples in each group. 19 The inclusion criteria included positive COVID‐19 reverse transcription‐polymerase chain reaction, pulmonary infiltration on chest X‐ray, and clinical judgment by an infectious disease specialist. The exclusion criteria included loss of the sample during the experiment. Based on the oxygen therapies received, the severity of their illnesses was classified into moderate (patients receiving supportive O2 via nasal cannula or mask), severe (patients receiving noninvasive ventilation covers in the intensive care unit [ICU], and critical patients receiving mechanical ventilator [intubated] in ICU) subgroups.
This study was approved by the National Ethics Committee on Research in Medical Sciences of the Iranian Ministry of Health (Ethical Code: IR.NIMAD.REC.1398.411). Informed consent was obtained. To obtain consent from severe or critical patients, consent was obtained from patients' companions.
2.2. Blood sampling and flow cytometry
Peripheral blood samples were taken into ethylene diamine tetra‐acetic acid treated Vacutest (KIMA). Separation and preparation of blood specimens were conducted under a safety procedure. Routine blood examinations, including counting blood cells was determined using an automated system (Sysmex‐XS 500i full diff). Peripheral blood samples were stained using staining reagents, including CD3 PerCP (clone MEM‐57; Exbio), CD4‐PE (clone SK3; BD Biosciences), CD8‐FITC (clone SK1; BD Biosciences), HLA‐DR‐PE (clone L243; BD Biosciences), CD3‐FITC (clone SK7; BD Biosciences), CD19‐PerCP (clone 4G7; BD Biosciences), CD16‐PE (clone B73.1; BD Biosciences), and CD56‐APC (clone LT56; Exbio). Sample processing was done based on the manufacturer's protocol. Briefly, the blood samples were incubated for 30 min with the mentioned fluorochrome conjugated monoclonal antibodies. The red blood cells were lysed by incubation for 15 min at 4°C with lysing buffer (BD Biosciences). Then samples were washed two times at 6°C and 300×g for 5 min with 2% fetal bovine serum in phosphate‐buffered saline. The immunophenotyping analysis was evaluated using the FACSCalibur instrument (Becton Dickinson). Based on the standardized gating strategy, lymphocyte populations were identified. Briefly, the frequency of CD16+CD56+ NK cells and CD19+ cells were reported within CD3‐ out of lymphocytes, CD4+ T cells %, and CD8+ T cells % within CD3+ cells out of lymphocytes, and CD8+HLA‐DR+ T cells % within CD3+ gated cells (Supporting Information: Figure 1). Finally, the obtained data were analyzed with FlowJo software (TreeStar).
2.3. Immunohistochemical (IHC) staining
Postmortem formalin‐fixed, paraffin‐embedded (FFPE) 4 μm thickness slides of needle necropsies were taken from the lung tissues of 3 critical patients who died from COVID‐19 (IHC cases). They were provided by the Pathology Department of Imam Khomeini Hospital Complex in Tehran. To examine the cell density of lung infiltrating T cells, we evaluated these specimens by immunohistochemistry staining. Briefly, following dewaxing (with xylene), rehydration (with decreasing alcohol solution), and heat‐induced epitope retrieval, endogenous peroxidase was blocked for 10 min at room temperature (Vitro Master Diagnostic). Then, the sections were incubated with the appropriate dilution of antibodies, including anti‐CD4 (clone EP204; Vitro Master Diagnostic), anti‐CD8 (clone SP16; Vitro Master Diagnostic), and anti‐CD3 (clone EP41; Vitro Master Diagnostic). After washing with Tris‐buffered saline, the immunostaining was visualized using the Master Polymer plus Detection System (HRP) (DAB; Vitro Master Diagnostic). Finally, the slides were counterstained with hematoxylin, dehydrated (with increasing alcohol), and then cover slipped. Scanning of whole slide sections was carried out by slide scanner (CELLNAMA LS5).
2.4. Statistical analysis
The statistical analysis was carried out using SPSS (version 24.0; IBM SPSS Co.). All variables were normally distributed. The quantitative variables were presented as mean ± SD. The categorical variables were reported as No (%). Normally distributed variables were compared among the groups by Welch corrected t‐test. Gender results were compared between the studied groups using the χ 2 test. p Values less than 0.05 were considered significant. Univariate analysis was used to eliminate the impact of confounding variables, including age and gender. The diagnostic values of significant variables were assessed by receiver operating characteristic (ROC) and the area under the ROC curve (AUC).
3. RESULTS
3.1. Demographic and clinical characteristics
In a cross‐sectional study, we described 100 patients with COVID‐19 admitted to the Imam Khomeini Hospital Complex. The demographic characteristics and clinical manifestations of these patients are listed in Table 1. According to disease severity, a total COVID‐19 patient was divided into three subgroups of moderate (n = 49, 51.6%), severe (n = 12, 12.6%), and critical (n = 34, 35.8%). Also, 32 patients with COVID‐19 died, including 4 from the moderate group, 1 from severe, and 27 from critical patients. The mean ± SD age of the patients with COVID‐19 was significantly higher than the control group (p ≤ 0.001). Based on medical records, the respiratory rate on admission was significantly higher in severe patients than in moderate ones (p = 0.05). Also, the systolic blood pressure on admission was higher in critical and severe patients than in moderate ones (p = 0.03 and p = 0.06, respectively). However, the oxygen saturation on admission was significantly lower in critical patients than in moderate ones (p = 0.01). There were no significant differences in the studied groups' pulse rate, temperature, and diastolic blood pressure. In terms of gender, there was no significant difference compared with the control group. Of 100 patients with COVID‐19, 74 cases (74%) were male, and 26 (26%) were female. The common symptoms of patients were dyspnea (71%), fever (65%), dry cough (65%), myalgia (61%), chill (28%), digestive disorders (31%), fatigue (25%), headache (11%), sore throat (8%), chest pain (7%), hemoptysis (5%), rhinorrhea (2%), and sputum production (1%). Among them, only the critical patients showed significantly higher frequencies in the occurrence of a chill than moderate ones (p = 0.04). Besides, 65 patients had underlying diseases, including 32 patients in the moderate group (65.31%), 9 patients in the severe (75%), and 24 patients in the critical (70.59%) group. Patients' underlying diseases included hypertension, diabetes mellitus, coronary heart disease, chronic kidney disease, respiratory diseases, thyroid disorders, cerebrovascular accidents, and cancer. Also, 84 patients received at least one of the antiviral treatments, including oseltamivir, lopinavir/ritonavir, atazanavir, sofosbuvir, and ribavirin as follows: 43 (87.8%) in the moderate group, 12 (100%) in the severe, and 29 (85.3%) in the critical patients. Also, 32 patients were given systemic corticosteroid treatment, including 10 (20.4%) in the moderate group, 6 (50%) in the severe, and 16 (47.1%) in the critical patients. In addition, 52 patients received antibiotic treatment (16 moderate patients, 10 severe patients, and 26 critical patients). Also, 20 patients received intravenous immunoglobulin treatment (3 moderate patients, 4 severe patients, and 13 critical patients), and less than 20% received interferon.
Table 1.
Demographic characteristics and clinical manifestation in Covid‐19 patients (categorized by disease severity)
| Moderate (n = 49) | Severe (n = 12) | Critical (n = 34) | |
|---|---|---|---|
| Mean ± SD | Mean ± SD | Mean ± SD | |
| Demographic information | |||
| Age (years) | 58.9 ± 14.9 | 58.9 ± 14.6 | 62.3 ± 12.7 |
| RR (beats/min) | 21.6 ± 5.1 | 25.4 ± 6.4 † | 22.7 ± 5.6 |
| PR (beats/min) | 96.2 ± 13.7 | 92.6 ± 18.7 | 94.9 ± 21.4 |
| T (°C) | 37.5 ± 0.9 | 37.8 ± 1.1 | 37.7 ± 1 |
| DBP (mmHg) | 78 ± 7.3 | 77.5 ± 9 | 78.1 ± 13.4 |
| SBP (mmHg) | 120.6 ± 13.6 | 129.9 ± 17.4 | 130.7 ± 24.5 § |
| SpO2 (%) | 90.1 ± 4.9 | 88 ± 4.6 | 86.9 ± 5.7 §§ |
| NO (%) | NO (%) | NO (%) | |
| Gender | |||
| Male | 41 (83.7%) | 8 (66.7%) | 23 (67.6%) |
| Female | 8 (16.3%) | 4 (33.3%) | 11 (32.4%) |
| Drugs | |||
| Antibiotics | |||
| Yes | 16 (32.7%) | 10 (83.3%) ††† | 26 (76.5%) §§§ |
| Anti virals | |||
| Yes | 43 (87.8%) | 12 (100%) | 29 (85.3%) |
| Corticosteroids | |||
| Yes | 10 (20.4%) | 6 (50%) † | 16 (47.1%) §§ |
| IVIG | |||
| Yes | 3 (6.1%) | 4 (33.3%) †† | 13 (38.2%) §§§ |
| IFN‐A | |||
| Yes | 2 (4.1%) | 1 (8.3%) | 0 (0%) |
| IFN‐B | |||
| Yes | 4 (8.2%) | 3 (25%) | 7 (20.6%) |
Note: Data are presented as mean ± SD or n (%) as appropriate.
Abbreviations: DBP, diastolic blood pressure; IFN‐A, interferon alfa; IFN‐B, interferon beta; IVIG, intravenous immunoglobulin; PR, pulse rate; RR, respiratory rate; SBP, systolic blood pressure; SpO2, peripheral oxygen saturation; T, temperature.
§ p< 0.05, §§ p< 0.01, §§§ p< 0.001. p Value stands for the comparison between critical patients and moderate patients.
† p< 0.05, †† p< 0.01, ††† p< 0.001. p Value stands for the comparison between severe patients and moderate patients.
3.2. Lymphocyte subpopulations profile in COVID‐19 patients
Evaluation of leukocyte subpopulations (Table 2) showed that COVID‐19 patients had a significantly higher WBC value than the healthy control group (p = 0.01). Among leukocytes, neutrophils had a significantly higher count, and lymphocytes had a significantly lower count in COVID‐19 patients than the healthy control group (p ≤ 0.001 and p ≤ 0.001, respectively).
Table 2.
Immunoprofiling of lymphocyte subpopulations in peripheral blood of COVID‐19 patients
| ×103/µl | Healthy control (n = 70) | COVID‐19 hospitalized (n = 100) | Critical (n = 34) | Severe (n = 12) | Moderate (n = 49) |
|---|---|---|---|---|---|
| Mean ± SD | Mean ± SD | Mean ± SD | Mean ± SD | Mean ± SD | |
| WBC | 6.8 ± 1.5 | 8.6 ± 5.4** | 11.2 ± 7.3 §§§ | 8.7 ± 3.8 | 6.8 ± 3 |
| Neutrophil | 3.8 ± 1.1 | 6.9 ± 5.2*** | 9.7 ± 7 §§§ | 7.57 ± 3.5 †† | 4.9 ± 2.7 |
| Lymphocyte | 2.3 ± 0.7 | 1 ± 0.7*** | 0.9 ± 0.7 | 0.7 ± 0.3 † | 1.2 ± 0.8 |
| Monocyte | 0.6 ± 0.2 | 0.6 ± 0.4 | 0.5 ± 0.4 | 0.4 ± 0.2 † | 0.6 ± 0.4 |
| Eosinophil | 0.4 ± 1.8 | 0.1 ± 0.1 | 0.1 ± 0.1 | 0 ± 0.1 | 0.1 ± 0.1 |
| CD3+ | 1.6 ± 0.6 | 0.8 ± 0.5*** | 0.6 ± 0.6 | 0.4 ± 0.2 ††† | 0.8 ± 0.5 |
| CD3+CD4+ | 1.1 ± 0.5 | 0.5 ± 0.3*** | 0.4 ± 0.4 | 0.4 ± 0.2 | 0.6 ± 0.4 |
| CD3+CD8+ | 0.5 ± 0.2 | 0.2 ± 0.2*** | 0.2 ± 0.2 | 0.1 ± 0.1 | 0.2 ± 0.2 |
| CD3+CD4+/CD3+CD8+ | 2.6 ± 1.5 | 3.9 ± 2.8*** | 4.3 ± 3.3 | 4.6 ± 2.3 | 3.4 ± 2.2 |
| CD3‐CD16+CD56+ | 0.3 ± 0.2 | 0.1 ± 0.1*** | 0.0 ± 0.1 | 0 ± 0 † | 0.1 ± 0.1 |
| CD3+CD8+HLA‐DR+ | 0.2 ± 0 | 0.1 ± 0.1* | 0.1 ± 0.1 | 0.1 ± 0.1 | 0.1 ± 0.2 |
| CD3+CD8‐HLA‐DR+ | 15 ± 6.9 | 4.9 ± 4.1*** | 3.8 ± 2.9§ | 3.6 ± 3.2 | 5.9 ± 4.8 |
| CD3+HLA‐DR+ | 39.2 ± 15.5 | 11.5 ± 11.1*** | 9.5 ± 10 | 6.6 ± 5.5 † | 13.6 ± 11.9 |
| CD19+ | 0.3 ± 0.1 | 0.2 ± 0.1*** | 0.2 ± 0.2 | 0.2 ± 0.1 | 0.1 ± 0.1 |
Note: Data are presented as mean ± SD or n (%) as appropriate.
*p < 0.05, **p < 0.01, ***p < 0.001. p Value stands for the comparison between COVID‐19 patients' group and healthy control group.
§ p < 0.05, §§§ p < 0.001. p Value stands for the comparison between critical patients and moderate patients.
† p < 0.05, †† p < 0.01, ††† p < 0.001. p Value stands for the comparison between severe patients and moderate patients.
The absolute count of different lymphocyte subsets was calculated by multiplying each relative count to the total lymphocyte count. Based on statistical analysis, as shown in Table 2, the absolute count of CD19+ cells, T cells (CD3+), CD4+ T cells, CD8+ T cells, NK cells (CD3‐CD16+CD56+), HLA‐DR +T cells, CD8‐HLA‐DR+ T cells, and CD8+HLA‐DR +T cells were significantly lower in COVID‐19 patients compared with the healthy control group (p ≤ 0.001). In contrast, a significantly higher value was detected in the CD4+ T cells to CD8+ T cell ratio (p ≤ 0.001).
3.3. Association of lymphocyte subpopulations with severity in COVID‐19 patients
We further analyzed different subsets of lymphocytes in subgroups of hospitalized COVID‐19 patients, according to the classification of disease severity. Based on statistical analysis, as shown in Table 2, the absolute count of neutrophils was significantly increased in critical and severe patients compared to the moderate ones (p ≤ 0.001 and p = 0.01, respectively). Also, the severe cases had a significantly lower lymphocyte and monocyte count value than the moderate ones (p = 0.05 and p = 0.04, respectively).
There were statistically significant lower values of the absolute count of T cells (CD3+), NK cells (CD3‐CD16+CD56+), and HLA‐DR+ T cells in severe cases compared to the moderate ones (p ≤ 0.001, p = 0.03, and p = 0.05, respectively). Also, there were significantly lower values of the absolute count of CD8‐HLA‐DR+ T cells in critical patients compared to the moderate ones (p = 0.03).
3.4. Association of lymphocyte subpopulations with mortality in COVID‐19 patients
All the lymphocyte subpopulations were compared between samples collected from COVID‐19 survivors and deceased patients. As shown in Table 3, based on statistical analysis, the value of WBC was significantly higher in non‐survivors compared with survivors (p ≤ 0.001). In addition, the absolute count of CD8‐HLA‐DR+ T cells, CD8+HLA‐DR+ T cells, and HLA‐DR+ T cells significantly were decreased in non‐survivors compared with survivors (p = 0.04, p = 0.05, and p = 0.05). Furthermore, the absolute count of T cells (CD3+) and CD4+ T cells showed a marginally significant decrease in non‐survivors compared with survivors (p = 0.08 and p = 0.08, respectively).
Table 3.
Comparison of the distribution of lymphocyte subpopulations as risk factors between deceased and survivor patients with COVID‐19
| ×103/µl | Survivor | Deceased | p Value | OR (95% CI) | p Value | ||
|---|---|---|---|---|---|---|---|
| N | Mean ± SD | N | Mean ± SD | ||||
| WBC | 68 | 7.4 ± 3.3 | 32 | 11.1 ± 7.7 | <0.001 | 0.105 (1.556−5.933) | <0.001 |
| Lymphocyte | 68 | 1.1 ± 0.7 | 32 | 0.8 ± 0.7 | 0.12 | ||
| CD19+ | 67 | 0.2 ± 0.1 | 32 | 0.1 ± 0.1 | 0.75 | ||
| CD3+ | 67 | 0.7 ± 0.6 | 32 | 0.5 ± 0.5 | 0.08 | 0.032 (−0.423 to 0.023) | 0.08 |
| CD3+CD4+ | 68 | 0.5 ± 0.4 | 32 | 0.4 ± 0.3 | 0.08 | 0.030 (−0.271 to 0.018) | 0.09 |
| CD3+CD8+ | 68 | 0.2 ± 0.2 | 32 | 0.1 ± 0.2 | 0.32 | ||
| CD3+CD4+/CD3+CD8+ | 68 | 3.6 ± 2.4 | 32 | 4.6 ± 3.3 | 0.1 | ||
| CD3+CD8+HLA‐DR+ | 68 | 0.1 ± 0.1 | 31 | 0 ± 0 | 0.05 | 0.037 (−0.054 to 0.001) | 0.04 |
| CD3+CD8‐HLA‐DR+ | 68 | 5.5 ± 4.4 | 32 | 3.8 ± 2.9 | 0.04 | 0.228 (2.462−5.126) | <0.001 |
| CD3+HLA‐DR+ | 68 | 12.9 ± 12 | 32 | 8.5 ± 6.8 | 0.05 | 0.168 (4.893−12.091) | <0.001 |
| CD3‐/CD16+CD56+ | 67 | 0.1 ± 0.1 | 32 | 0.1 ± 0.1 | 0.69 | ||
Note: Data are presented as mean ± SD. p ≤ 0.05 is reported in bold. Univariate analysis was used to determine mortality risk factors for Covid‐19 patients in lymphocyte subsets, which there are statistically or marginally significant between deceased and survivor patients. It should be noted that the data which presented in this table is relevant to the last 10 h of the patients' lives.
Abbreviations: CI, confidence interval; OR, odd ratio.
Based on the findings in Table 3 that were statistically or marginally significant, we evaluated the risk for mortality between COVID‐19 survivors and non‐survivors using univariate analysis. The analysis revealed that WBC, CD8+HLA‐DR+ T cells, CD8‐HLA‐DR+ T cells, and HLA‐DR+ T cells were associated with mortality (p ≤ 0.001, p = 0.04, p ≤ 0.001, and p ≤ 0.001, respectively) (Table 3). A ROC analysis was performed to evaluate the potential predictive performance of the variables that revealed a statistically significant association with mortality. The AUC and significance of this analysis (p value) were 0.656 (p = 0.01) for the WBC, 0.636 (p = 0.03) for CD8 + HLA‐DR+ T cells, 0.626 (p = 0.03) for CD8‐HLA‐DR+ T cells, and 0.605 (p = 0.08) for HLA‐DR+ T cells. The best cut‐off points were calculated from the ROC curves, with a value of 12.16 for the WBC (specificity: 91.2%, sensitivity: 40.6%), 0.028 for CD8+HLA‐DR+ T cells (specificity: 64.5%, sensitivity: 66.2%), 4.65 for CD8‐HLA‐DR+ T cells (specificity: 79.4%, sensitivity: 47.4%), and 8.60 for HLA‐DR+ T cells (specificity: 70.6%, sensitivity: 51.3%) (Supporting Information: Figure 2). Regarding the results from ROC curves, the absolute count of CD8+HLA‐DR+ T cells is the best candidate as a biomarker with a sensitivity of 66.2% and a specificity of 64.5%. Further statistical analysis showed there was no significant correlation between CD8+HLA‐DR+ T cells and related disease severity parameters including SpO2, age, blood pressure, respiratory rate, pulse rate, and temperature (Supporting Information: Table 1).
3.5. Evaluation of lymphocytic infiltration in lung tissue
Demographic and clinical features of dead covid‐19 cases involved in the IHC study (IHC cases) are presented in Table 4. Pulmonary infiltration of T cells in the lung tissue of patients who died from COVID‐19 is shown in Figure 1. Due to the lack of access to control samples, the findings are presented based on the observations of three expert pathologists on FFPE sections diagnosed as normal. 20 Based on the pathologists' reports, there were only slight infiltrations of CD3+ T cells, with an equal percentage of CD4+ and CD8+ T cell subpopulations in the lung tissue. Further studies with larger sample size are needed.
Table 4.
Demographic and clinical features of dead covid‐19 cases that have immunohistochemical results (IHC cases)
| Case 1 | Case 2 | Case 3 | |||||||
|---|---|---|---|---|---|---|---|---|---|
| 54‐year‐old man | 47‐year‐old man | 85‐year‐old woman | |||||||
| On admission | ICU | The last test before dying | On admission | ICU | The previous test before dying | On admission | ICU | The last test before dying | |
| WBC (×103/µl) | 5.5 | 12 | 8.6 | 8.4 | 12.8 | 13.6 | 9.7 | 21.4 | |
| RBC (×106/µl) | 4.18 | 4.59 | 3.18 | 6.87 | 5.81 | 5.19 | 4.45 | 4.61 | |
| Hb (g/dl) | 12.4 | 14.2 | 9.7 | 13.6 | 11.6 | 11 | 12.5 | 12.4 | |
| HCT(%) | 37.7 | 40.5 | 29.4 | 38.6 | 38.2 | 36.5 | 38.3 | 38.7 | |
| MCV (fl) | 90.2 | 88.2 | 92.5 | 19.8 | 65.7 | 70.3 | 86.1 | 83.9 | |
| MCH (pg) | 29.7 | 30.9 | 30.5 | 19.8 | 30.4 | 21.2 | 28.1 | 26.9 | |
| MCHC (g/dl) | 32.9 | 35.1 | 33 | 30.4 | 30.4 | 30.1 | 32.6 | 32 | |
| Platelet (×103/µl) | 459 | 240 | 185 | 186 | 143 | 201 | 393 | ||
| Neutrophil (%) | 82.5 | 94.9 | 89.1 | 83.7 | 76.8 | 87.2 | 74.6 | 89.1 | |
| Lymphocyte (%) | 14 | 3.6 | 9 | 20 | 15.2 | 12.8 | 15.1 | 8.7 | |
| Symptoms | Fever | ||||||||
| Fever | Shortness of breath | Fever | |||||||
| Shortness of breath | Cough | Shortness of breath | |||||||
| Cough | fatigue | Sore throat | |||||||
| fatigue | Sore throat | Myalgia | |||||||
| Myalgia | |||||||||
| Comorbidities | ‐ | Peptic ulcer disease (PUD) | hypertension | ||||||
| Diabetes | |||||||||
Note: It should be noted that the third case was admitted to the ICU upon arrival.
Abbreviations: Hb, hemoglobin; HCT, hematocrit; ICU, intensive care unit; IHC, immunohistochemical; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; RBCs, red blood cells.
Figure 1.
Lymphocyte infiltration in the lung tissue. Immunohistochemical staining with anti‐CD3 (A), anti‐CD4 (B), and anti‐CD8 (C) in the lung tissues of COVID‐19 patients, scale bar = 50 μm. (A) Immunohistochemistry for specific T cell marker, CD3, highlights some scattered positive lymphocytes, infiltrating lung parenchyma. (B) CD4 positive T cells are scattered throughout the lung parenchyma. Some macrophages show weak cytoplasmic reactivity. (C) Scattered CD8 positive lymphocytes infiltrate lung parenchyma.
4. DISCUSSION
Since the beginning of the Covid‐19 pandemic, numerous studies have been conducted to determine the pathogenesis of the disease. Growing studies emphasize that the dysfunction of immune responses plays a vital role. Available evidence shows that lymphopenia, significant reduction of CD3+ T cell, CD4+ T cell, CD8+ T cell, B cell, NK cell, and inflammatory cytokine storm are common in COVID‐19 patients. 21 , 22 The present study investigated the association of lymphocyte subpopulations with severity and mortality in COVID‐19 hospitalized patients using real‐time whole blood flow cytometry. The results showed a significant decrease in the lymphocyte count, but increased WBC and neutrophil count in COVID‐19 patients with increasing severity. Like our study, Huang et al. 12 reported lymphopenia and high neutrophil count in 41 hospitalized COVID‐19 patients. However, unlike our study, they found leukopenia. Also, consistent with our findings, Chen et al. 23 evaluated 99 hospitalized COVID‐19 patients and found leukocytosis, lymphopenia, and increased neutrophil count. Comparable results were reported by Wang et al. 24 in 138 hospitalized patients with COVID‐19. Consistent with our findings, Zheng et al., 25 in the evaluation of 68 hospitalized patients with COVID‐19 from China, demonstrated an increase in the neutrophil count.
Based on previous studies on other coronaviruses, lymphopenia is a significant feature of SARS‐CoV infections. A study on 271 SARS infected people reported that CD45+, CD3+, CD4+, CD8+, CD19+, and CD16+/56+ cell counts decreased significantly over time the 5 weeks. 26 In this regard, lymphopenia has been reported in 34% of MERS patients. 27 The result of the flow cytometry evaluation performed on peripheral blood lymphocytes demonstrated that the absolute count of CD19+ cells, CD3+ cells, CD4+ T cells, CD8+ T cells, and NK cells, HLA‐DR+ T cells, CD8‐HLA‐DR+ T cells, and CD8+HLA‐DR+ T cells significantly diminished in COVID‐19 patients compared with the control group. An increased ratio of CD4+ T cells to CD8+ T cells indicates more reduction of CD8+ T cells compared with CD4+ T cells in our COVID‐19 cases. These differences were more prominent among critical and severe patients versus moderated ones. In this regard, our finding demonstrated that the absolute count of CD3+ cells, NK cells, and CD3+HLA‐DR+ cells significantly reduced in severe patients compared to the moderate ones. Also, the absolute count of CD8‐HLA‐DR+ T cells was significantly lower in critical patients than in moderate ones. Wang et al., 28 in the evaluation of 60 hospitalized patients with COVID‐19, reported that the counts of total lymphocytes, CD4+ T cells, CD8+ T cells, B cells, and NK cells decreased significantly. There are several studies which in accordance with the finding of declining lymphocyte subpopulations in association with increasing disease severity. 29 , 30 , 31 , 32 , 33 Similarly, Bobcakova et al., 34 reported significant decrease in CD3+ cells, CD4+ T cells, CD8+ T cells, and CD19+ cells with increasing disease severity. Also, Tan et al. 35 reported significant reduction in CD4+ T cells, CD8+ T cells, B cells, and NK cells in severe COVID‐19 patients.
As another result, the distribution of lymphocyte subsets between COVID‐19 survivors and non‐survivors showed that the absolute count of CD8+HLA‐DR+ T cells, CD8‐HLA‐DR+ T cells, and HLA‐DR+ T cells was significantly lower in non‐survivors compared with survivors. Further analysis of statistically or marginally significant lymphocyte subpopulations to determine the potential mortality risk factors between COVID‐19 survivors and non‐survivors revealed decreased CD8+HLA‐DR+ T cells, CD8‐HLA‐DR+ T cells, and HLA‐DR+ T cells were associated with the death outcome of COVID‐19 patients. In addition, our study's ROC test demonstrated that CD8+HLA‐DR+ T cells with 0.028 cut‐off had 64.5% specificity and 66.2% sensitivity which could be proposed as a biomarker associated with mortality. Bobcakova et al., 34 reported the relative count of CD38+HLA‐DR+CD8+ T cells significantly lower in non‐survivor. In contrary, Quin et al. 36 pointed no significant differences in the proportion of HLA‐DR+CD8+ T cells between severe and non‐severe patients. Song et al. 37 showed the proportion of CD38+HLA‐DR+CD8+ T cells significantly higher in severe patients compared to mild ones. Significant reduction and functionally exhaustion of cytotoxic T cells in SARS‐CoV‐2 infected patients, inversely correlated with patient survival. 29
The result of our IHC staining of necropsy samples from the lungs of 3 patients who died from COVID‐19 indicated that there was no significant CD3+, CD4+, and CD8+ lymphocyte infiltration compared to historical studies. 20 The FFPE sections were studied to support the fact that CD8+HLA‐DR+ T cells reduction in the periphery of the 3 patients was not due to infiltration to lung tissue. Based on the absence of CD4+ and CD8+ T lymphocyte infiltration in the lung tissue, further IHC staining to include additional markers like HLA‐DR were not attempted. Consistent with our study, Barton et al., 38 in the evaluation of two complete autopsies of COVID‐19 patients who died in March 2020, demonstrated a slight infiltration of CD3+ T cell, CD4+ T cell, and CD8+ T cell in lung tissue. Inconsistent with our finding, Song et al. 37 found massive pulmonary infiltration of CD4+ T cell, CD8+ T cell, macrophages, and GZMB+ cells in the lung. More studies should be performed to understand better the role of T cells in the lung.
5. CONCLUSION
Our findings showed that CD8+HLA‐DR+ T cells can be used as an early biomarker of mortality in COVID‐19 patients. Although there was no significant correlation between CD8+HLA‐DR+ T cells and related disease severity parameters, further studies with larger sample size are needed to evaluate the clinical value of this marker and to shed light on the impact of CD8+HLA‐DR+ T cells in immunity against Covid‐19. This simple assay with fast turnaround would help risk assignment of patients to prioritize the usage of limited ICU.
AUTHOR CONTRIBUTIONS
Conception or design of the work: Tooba Ghazanfari, Mahmood Bozorgmehr, and Amina Kariminia. Data collection: Ensie Sadat Mirsharif, Maryam Rajabnia Chenary, Mahmood Bozorgmehr, Saeed Mohammadi, Mohammad‐Taghi Beigmohammadi, Alireza Abdollahi, Alireza Sadeghipour, and Tooba Ghazanfari. Data analysis and interpretation: Ensie Sadat Mirsharif, Mahmood Bozorgmehr, Saeed Mohammadi, Amina Kariminia, Fatemeh Tuserkani, and Tooba Ghazanfari. Drafting the article: Ensie Sadat Mirsharif, Seyed Mahmoud Hashemi, Sussan Kabudanian Ardestani, Alireza Sadeghipour, Amina Kariminia, and Tooba Ghazanfari. Final approval of the version to be published: Ensie Sadat Mirsharif, Maryam Rajabnia Chenary, Mahmood Bozorgmehr, Saeed Mohammadi, Seyed Mahmoud Hashemi, Sussan Kabudanian Ardestani, Mohammad‐Taghi Beigmohammadi, Alireza Abdollahi, Alireza Sadeghipour, Amina Kariminia, Fatemeh Tuserkani, and Tooba Ghazanfari.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
Supporting information
Supplementary information.
Supplementary information.
Supplementary information.
ACKNOWLEDGMENTS
We acknowledge Simorgh Clinical and Subspecialty Immunology Laboratory, and special thanks to all participants and healthcare workers involved in the diagnosis and treatment of patients in Tehran. This study was funded by the Immunoregulation Research Center of Shahed University and Iran's Ministry of Health, Treatment, and Medical Training.
Mirsharif ES, Chenary MR, Bozorgmehr M, et al. Immunophenotyping characteristics of COVID‐19 patients: peripheral blood CD8+ HLA‐DR+ T cells as a biomarker for mortality outcome. J Med Virol. 2022;95:e28192. 10.1002/jmv.28192
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
REFERENCES
- 1. Gorbalenya AE, Baker SC, Baric R, et al. The species severe acute respiratory syndrome‐related coronavirus: classifying 2019‐nCoV and naming it SARS‐CoV‐2. Nat Microbiol. 2020;5(4):536‐544. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Priyanka Choudhary OP, Singh I, Patra G. Aerosol transmission of SARS‐CoV‐2: the unresolved paradox. Travel Med Infect Dis. 2020;37:101869. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Liu J, Li S, Liu J, et al. Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS‐CoV‐2 infected patients. EBioMedicine. 2020;55:102763. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Wen W, Su W, Tang H, et al. Immune cell profiling of COVID‐19 patients in the recovery stage by single‐cell sequencing. Cell Discov. 2020;6:31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Wang Y, Zhang D, Du G, et al. Remdesivir in adults with severe COVID‐19: a randomized, ouble‐blind, placebo‐controlled, multicentre trial. Lancet. 2020;395(10236):1569‐1578. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Vitalakumar D, Sharma A, Kumar A, Flora SJS. Neurological manifestations in COVID‐19 patients: a meta‐analysis. ACS Chem Neurosci. 2021;12(15):2776‐2797. [DOI] [PubMed] [Google Scholar]
- 7. Cantenys‐Molina S, Fernández‐Cruz E, Francos P, Lopez Bernaldo de Quirós JC, Muñoz P, Gil‐Herrera J. Lymphocyte subsets early predict mortality in a large series of hospitalized COVID‐19 patients in Spain. Clin Exp Immunol. 2021;203:424‐432. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Priyanka Choudhary OP, Singh I. Protective immunity against COVID‐19: unravelling the evidences for humoral vs. cellular components. Travel Med Infect Dis. 2021;39:101911. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Chen G, Wu D, Guo W, et al. Clinical and immunological features of severe and moderate coronavirus disease 2019. J Clin Invest. 2020;130(5):2620‐2629. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Chien JY, Hsueh PR, Cheng WC, Yu CJ, Yang PC. Temporal changes in cytokine/chemokine profiles and pulmonary involvement in severe acute respiratory syndrome. Respirology. 2006;11(6):715‐722. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. De Wit E, Van Doremalen N, Falzarano D, Munster VJ. SARS, and MERS: recent insights into emerging coronaviruses. Nat Rev Microbiol. 2016;14(8):523‐534. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497‐506. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Ni M, Tian FB, Xiang DD, Yu B. Characteristics of inflammatory factors and lymphocyte subsets in patients with severe COVID‐19. J Med Viro. 2020;92(11):2600‐2606. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Xu B, Fan CY, Wang AL, et al. Suppressed T cell‐mediated immunity in patients with COVID‐19: a clinical retrospective study in Wuhan, China. J Infect. 2020;81(1):e51‐e60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. He R, Lu Z, Zhang L, et al. The clinical course and its correlated immune status in COVID‐19 pneumonia. J Clin Virol. 2020;127:104361. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Du RH, Liang LR, Yang CQ, et al. Predictors of mortality for patients with COVID‐19 pneumonia caused by SARS‐CoV‐2: a prospective cohort study. Eur Respir J. 2020;55(5):2000524. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. World Health Organization. Clinical management of severe acute respiratory infection when novel coronavirus (2019‐nCoV) infection is suspected: interim guidance. World Health Organization. 2020.
- 18. Pourmalek F, Rezaei Hemami M, Janani L, Moradi‐Lakeh M. Rapid review of COVID‐19 epidemic estimation studies for Iran. BMC Public Health. 2021;21(1):257. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Taghiloo S, Aliyali M, Abedi S, et al. Apoptosis and immunophenotyping of peripheral blood lymphocytes in Iranian COVID‐19 patients: clinical and laboratory characteristics. J Med Virol. 2021;93(3):1589‐1598. [DOI] [PubMed] [Google Scholar]
- 20. Purwar R, Campbell J, Murphy G, Richards WG, Clark RA, Kupper TS. Resident memory T cells (T(RM)) are abundant in human lung: diversity, function, and antigen specificity. PLoS One. 2011;6(1):e16245. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Liu Y, Tan W, Chen H, et al. Dynamic changes in lymphocyte subsets and similar cytokine levels in patients with severe and critical COVID‐19. BMC Infect Dis. 2021;21(1):1‐10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Luo XH, Zhu Y, Mao J, Du RC. T cell immunobiology and cytokine storm of COVID‐19. Scand J Immunol. 2021;93(3):e12989. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395(10223):507‐513. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus infected pneumonia in Wuhan, China. JAMA. 2020;323(11):1061‐1069. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Zheng M, Gao Y, Wang G, et al. Functional exhaustion of antiviral lymphocytes in COVID‐19 patients. Cell Mol Immunol. 2020;17(5):533‐535. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. He Z, Zhao C, Dong Q, et al. Effects of severe acute respiratory syndrome (SARS) coronavirus infection on peripheral blood lymphocytes and their subsets. Int J Infect Dis. 2005;9(6):323‐330. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Assiri A, Al‐Tawfiq JA, Al‐Rabeeah AA, et al. Epidemiological, demographic, and clinical characteristics of 47 cases of Middle East respiratory syndrome coronavirus disease from Saudi Arabia: a descriptive study. Lancet Infect Dis. 2013;13(9):752‐761. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Wang F, Nie J, Wang H, et al. Characteristics of peripheral lymphocyte subset alteration in COVID‐19 pneumonia. J Infect Dis. 2020;221(11):1762‐1769. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Diao B, Wang C, Tan Y, et al. Reduction and functional exhaustion of T cells in patients with coronavirus disease 2019 (COVID‐19). Front Immunol. 2020;11:827. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Chen R, Sang L, Jiang M, et al. Longitudinal hematologic and immunologic variations associated with the progression of COVID‐19 patients in China. J Allergy Clin Immunol. 2020;146(1):89‐100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Jiang M, Guo Y, Luo Q, et al. T‐cell subset counts in peripheral blood can be used as discriminatory biomarkers for diagnosis and severity prediction of coronavirus disease 2019. J Infect Dis. 2020;222(2):198‐202. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Liu R, Wang Y, Li J, et al. Decreased T cell populations contribute to the increased severity of COVID‐19. Clin Chim Acta. 2020;508:110‐114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Wang F, Hou H, Luo Y, et al. The laboratory tests and host immunity of COVID‐19 patients with different severity of illness. JCI Insight. 2020;5(10):e137799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Bobcakova A, Petriskova J, Vysehradsky R, et al. Immune profile in patients with COVID‐19: lymphocytes exhaustion markers in relationship to clinical outcome. Front Cell Infect Microbiol. 2021;11:646688. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Tan M, Liu Y, Zhou R, et al. Immunopathological characteristics of coronavirus disease 2019 cases in Guangzhou, China. Immunology. 2020;160(3):261‐268. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Qin C, Zhou L, Hu Z, et al. Dysregulation of immune response in patients with coronavirus 2019 (COVID‐19) in Wuhan, China. Clin Infect Dis. 2020;71(15):762‐768. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Song JW, Zhang C, Fan X, et al. Immunological and inflammatory profiles in mild and severe cases of COVID‐19. Nat Commun. 2020;11(1):3410. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Barton LM, Duval EJ, Stroberg E, Ghosh S, Mukhopadhyay S. COVID‐19 autopsies, Oklahoma, USA. Am J Clin Pathol. 2020;153(6):725‐733. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary information.
Supplementary information.
Supplementary information.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.