1. Patent Search
  2. Details

METHOD OF EVALUATING PROGRESSION OF AN INFECTIOUS DISEASE AND/OR INFLAMMATORY DISEASE

20220187296 · 2022-06-16

    Inventors

    Cpc classification

    International classification

    Abstract

    There is provided a method of evaluating risk of severe outcome of an infectious disease and/or an inflammatory disease in a patient, the method comprising: determining/measuring the number of one or more immune cells selected from the group consisting of VD2 T cells, CD8 T cells, and immature neutrophils in a sample obtained from the patient, wherein the patient has an enhanced risk of severe infectious disease and/or inflammatory disease outcome when: (i) the ratio of immature neutrophils to VD2 T cell is at least 2:1, and/or (ii) the ratio of immature neutrophils to CD8 T cell is at least 0.5:1. Also disclosed are method of treating a patient with a severe infectious disease and/or inflammatory disease and kit for use in methods thereof.

    Claims

    1. A method of evaluating risk of severe outcome of an infectious disease and/or an inflammatory disease in a patient, the method comprising: determining/measuring the number of one or more immune cells selected from the group consisting of VD2 T cells, CD8 T cells, and immature neutrophils in a sample obtained from the patient, wherein the patient has an enhanced risk of severe infectious disease and/or inflammatory disease outcome when: (i) the ratio of immature neutrophils to VD2 T cell is at least 2:1, and/or (ii) the ratio of immature neutrophils to CD8 T cell is at least 0.5:1.

    2. The method according to claim 1, wherein the infectious disease is a coronavirus infection, optionally a SARS-COV-2 infection.

    3. The method according to claim 1, wherein the severe infectious disease outcome is pneumonia and/or hypoxia.

    4. The method according to claim 1, wherein the method further comprises measuring the number of one or more cells selected from the group consisting of CD4+ T cells, mucosal-associated invariant T cells (MAIT), VD1 T cells, plasmacytoid dendritic cells (pDCs), dendritic cells (DCs), classical and intermediate monocytes, optionally when a reduction in the number of one or more cells as compared to a control indicates an enhanced risk of severe infectious disease and/or inflammatory disease outcome.

    5. The method according to claim 1, wherein the method further comprises measuring the expression of one or more myeloid activation markers on monocytes, optionally a reduction in the expression of one or more myeloid activation markers on monocytes as compared to a control indicates an enhanced risk of severe infectious disease and/or inflammatory disease outcome.

    6. The method according to claim 1, wherein the control is a healthy subject or a sample from the patient at an earlier time point.

    7. The method according to claim 1, wherein the number of one or more immune cells are determined by measuring the number of cells expressing one or more markers selected from the group consisting of CD45, CD66b, CD15, CD16, CD10, CD3, VD2, and CD8.

    8. The method according to claim 1, wherein method further comprising performing flow cytometry and/or immunostaining to measure/determine the number of one or more immune cells.

    9. The method according to claim 1, wherein the number of immature neutrophils is determined by measuring the number of cells having the cell surface phenotype CD45.sup.+ CD3.sup.− CD66b/CD15.sup.+ CD16.sup.intermediate/− CD10.sup.−.

    10. The method according to claim 1, wherein the number of VD2 T cells is determined by measuring the number of cells having the cell surface phenotype CD45+ CD3+ VD2+.

    11. The method according to claim 1, wherein the number of CD8 T cells is determined by measuring the number of cells having the cell surface phenotype CD45+ CD3+ CD8+.

    12. The method according to claim 1, wherein the method further comprises determining the absolute number of immune cells measured in the sample.

    13. The method according to claim 1, wherein the sample is obtained from the patient between 1 to 10 days post-illness onset (pio) and/or between 1 to 40 days post-treatment.

    14. A method of treating a patient with a severe infectious disease and/or inflammatory disease, the method comprising: determining/measuring the number of one or more immune cells selected from the group consisting of VD2 T cells, CD8 T cells, and immature neutrophils in a sample obtained from the patient, wherein the patient has an enhanced risk of severe infectious disease and/or inflammatory disease outcome when: (i) the ratio of immature neutrophils to VD2 T cell is at least 2:1, and/or (ii) the ratio of immature neutrophils to CD8 T cell is at least 0.5:1 administering a care treatment for severe infectious disease and/or inflammatory disease to the patient.

    15. The method of claim 14, wherein the infectious disease is a coronavirus infection, optionally a SARS-COV-2 infection.

    16. The method of claim 14, wherein the care treatment one or more selected from the group consisting of cytokine storm inhibitors, COX inhibitors, anti-IL-17 and JAK2 inhibitor therapies.

    17. The method of claim 14, wherein the method further comprises determining the number of one or more immune cells selected from the group consisting of VD2 T cells, CD8 T cells, and immature neutrophils at a time point later than (or after) the administration of the care treatment of the disease, wherein an increase ratio of immature neutrophils to VD2 T cells and/or an increase ratio of immature neutrophils to CD8 T cells indicates a worsening of the condition in the patient.

    18. A kit comprising one or more reagent that determines and/or measures the number of VD2 T cells and immature neutrophils in a cell, optionally the kit further comprises one or more reagent that determines and/or measures the expression of CD8.

    19. The kit of claim 18, wherein the one or more reagent determines and/or measures the expression of CD45, CD66b or CD15, CD16, CD3, and VD2, optionally CD8 on a cell.

    20. The kit of claim 18, wherein the kit further comprises an agent that determines the expression of CD10, optionally the kit further comprises an agent that determines the absolute number of immune cells in a sample.

    Description

    DETAILED DESCRIPTION OF FIGURES

    [0068] Example embodiments of the disclosure will be better understood and readily apparent to one of ordinary skill in the art from the following discussions and if applicable, in conjunction with the figures. It should be appreciated that other modifications may be made without deviating from the scope of the invention. Example embodiments are not necessarily mutually exclusive as some may be combined with one or more embodiments to form new exemplary embodiments. The example embodiments should not be construed as limiting the scope of the disclosure.

    [0069] FIGS. 1A-1H SARS-CoV-2 infection induces a decrease in immune cells in peripheral blood. FIG. 1A Schematic representation of flow cytometry workflow.

    [0070] FIG. 1B Heatmap representation of row z-score of mean absolute cell counts across the groups. Individual plots are shown in FIG. 6A.

    [0071] FIG. 1C UMAP clustering of CD45+ immune cells.

    [0072] FIG. 1D Monocyte activation markers mean geometric MFI (gMFI) represented as interleaved min to max box plot with all data points shown.

    [0073] FIG. 1E Neutrophil activation markers mean geometric MFI (gMFI) represented as interleaved min to max box plot with all data points shown.

    [0074] FIG. 1F Absolute neutrophil counts.

    [0075] FIG. 1G Representative plot of mature and immature neutrophil gating strategy in healthy control or acute COVID-19 patient.

    [0076] FIG. 1H Mature (CD10+) and Immature (CD10−) Neutrophil Abs counts. Scatter dot plots are presented with mean±SD. Absolute counts were analysed by Kruskal-Wallis using Dunn correction for multiple comparison, gMFI was analysed by Brown-Forsythe and Welch ANOVA without multiple comparison. For heatmap, stars shown in acute column represent healthy vs acute comparison. Stars shown in recovered column represent acute vs recovered comparison. ns non-significant. *p<0.05, **p<0.01, ***p<0.001. Data available in source data file.

    [0077] FIGS. 2A-2D2 SARS-CoV-2 infection induces general lymphopenia and CD8, VD1 and VD2 activation. FIG. 2A Absolute counts of T-cell compartments in healthy donors, acute and recovered COVID-19 patient.

    [0078] FIG. 2B UMAP clustering of CD3+ cells.

    [0079] FIG. 2C left panel: CD45RA and CD27 gating strategy example on CD8+ T-cells; right panel: heatmap representation of mean frequencies of T-cell differentiation across the groups, individual plots given in FIG. 7A-B.

    [0080] FIGS. 2D1-2D2 Changes in CD38 gMFI in naïve, CM, EM and TEMRA f 814 or CD8, CD4, VD1 and VD2 T-cells. ND indicates not determined since frequency of these compartment was too low for accurate gMFI measurement. Absolute counts were analysed by Kruskal-Wallis using Dunn correction for multiple comparison, gMFI was analysed by Brown-Forsythe and Welch ANOVA using Dunnett T3 correction for multiple comparison. Scatter dot plots are presented with mean±SD. For heatmaps, stars shown in acute column represent healthy vs acute comparison. Stars shown in recovered column represent acute vs recovered comparison. *p<0.05, **p<0.01, ***p<0.001. Data available in source data file.

    [0081] FIGS. 3A-3E Patient symptoms are reflected in immune cell variations. FIG. 3A Schematic representation of clinical symptoms in the patient cohort.

    [0082] FIG. 3B Absolute counts of T-cells across the severity

    [0083] FIG. 3C Absolute counts of antigen presenting cells across the severity.

    [0084] FIG. 3D gMFI of activation markers on antigen presenting cells.

    [0085] FIG. 3E Absolute counts and frequency in neutrophil compartments. Scatter dot plots are presented with mean±SD. Absolute counts were analysed by Kruskal-Wallis with Dunn multiple testing correction, gMFI was analysed by Brown-Forsythe and Welch ANOVA with Dunnett T3 multiple testing correction. *p<0.05, **p<0.01, ***p<0.001. Data available in source data file.

    [0086] FIGS. 4A-4B Immature neutrophils correlate with several analytes in paired patient plasma. FIG. 4A Spearman correlations between total neutrophils or immature neutrophils and plasma analytes. Red cross represents non-significant correlations.

    [0087] FIG. 4B Individual plots of Spearman correlations between immature neutrophil counts and IL-6 and IP-10. Line was drawn using simple linear regression. Data available in source data file.

    [0088] FIGS. 5A-5B Immature neutrophil to VD2 T-cell ratio is an early prognosis marker for pneumonia and hypoxia symptoms. FIG. 5A ROC curve analysis comparison was performed for pneumonia and hypoxia symptoms between absolute counts of total neutrophils to CD8 T-cell ratio, total neutrophils to VD2 T-cell, immature neutrophils to CD8 T-cell ratio, and immature neutrophils to VD2 T-cell ratio.

    [0089] FIG. 5B Similar analysis was performed on a subset of early samples from the 54 acute patients (24 patients, 1 to 7 days pio with a median of 3 days pio). ROC curve was analysed using Wilson/Brown method. 95% confidence interval and standard error for panel B are given in Table 1. Data available in source data file.

    [0090] FIG. 6A blood collection in CPT tubes affects phenotypic markers but not cell counts (n=5 healthy donors). Paired analysis was performed either with Wilcoxon non-parametric test for cell counts or ratio of paired t-test for gMFI.

    [0091] FIG. 6B Individual plot for heatmap of cell counts presented in FIG. 1B. Scatter dot plots are presented with mean±SD. Absolute counts were analysed by Kruskal-Wallis using Dunn correction for multiple comparison, gMFI was analysed by Brown-Forsythe and Welch anova without correction for multiple comparison. *p<0.05, **p<0.01, ***p<0.001.

    [0092] FIGS. 7A-B Individual plots for heatmap of FIGS. 2D1-2D2. Individual frequencies of CD45RA vs CD27 differentiation stage of CD8, CD4, VD1 and VD2 T-cells. Scatter dot plots are presented with mean±SD. Frequencies were analysed by Kruskal-Wallis using Dunn correction for multiple comparison. *p<0.05, **p<0.01, ***p<0.001

    [0093] FIGS. 8A-8C Individual correlation plots for FIGS. 4A-B. Non-parametric Spearman test were performed for the parameters indicated in the plots for 19 patients that had paired Luminex plasma readings. FIG. 8A Individual plots of immature neutrophil counts with cytokines not presented in main FIG. 4B.

    [0094] FIG. 8B Individual plots of immature neutrophil counts with cytokines.

    [0095] FIGS. 8C-8D correlations between other immune cells and cytokines. rho and p values of the correlations are indicated on the individual plots.

    [0096] FIGS. 9A-B Gating strategy for flow cytometry panel A. 100 μL of blood was stained with the antibodies of panel A described in Table 4. Gating was performed after compensation adjustment in flowjo as presented in this figure.

    [0097] FIGS. 10A-B Gating strategy for flow cytometry panel B. 100 μL of blood was stained with the antibodies of panel B described in Table 4. Gating was performed after compensation adjustment in flowjo as presented in this figure.

    [0098] FIGS. 11A-B Gating strategy for flow cytometry panel C. 100 μL of blood was stained with the antibodies of panel C described in Table 3. Gating was performed after compensation adjustment in flowjo as presented in this figure.

    [0099] FIGS. 12A-D Correlation of analytes detected by Luminex with or without Triton-X treatment in healthy donors. Pearson correlation plots.

    [0100] FIG. 12E Pearson correlation p, r and r.sup.2 values for the analytes with readings above the detection limit.

    EXPERIMENTAL SECTION

    Example 1

    Material and Methods

    Study Design

    [0101] This was an observational cohort study of patients with PCR-confirmed COVID-19 who were admitted to the National Centre for Infectious Diseases, Singapore. All patients with COVID-19 in Singapore, regardless of the severity of infection, are admitted to isolation facilities until clinical recovery and viral clearance. Supportive therapy including supplemental oxygen and symptomatic treatment were administered as required. Pneumonia was diagnosed radiologically by interpretation of CXR or CT thorax images. Hypoxia is defined as requirement for supplemental oxygen, which was started if peripheral O.sub.2 saturations (SpO.sub.2) were <94%. Admission to ICU was reserved for those patients requiring [FiO.sub.2]≥40% or with haemodynamic instability and included invasive mechanical ventilation when necessary. Incidence of thrombo-embolic and cardiac events are indicated in Table 2.

    TABLE-US-00001 TABLE 2 Demographics and clinical outcomes of COVID-19 patients Acute samples Recovered samples Pneumonia Pneumonia Pneumonia Pneumonia All No without with All No without with patients pneumonia hypoxia hypoxia patients pneumonia hypoxia hypoxia Variable (n = 54) (n = 19) (n = 11) (n = 24) (n = 28) (n = 7) (n = 8) (n = 13) Demographics Mean age, 51 (15) 39 (8) 53 (10) 59 (16) 49 (13) 38 (14) 51 (8) 54 (12) years Sex, 50 (98.0) 18 (94.7) 11 (100.0) 21 (87.5) 19 (67.9)  4 (57.1)  6 (75.0)  9 (69.2) male (%) Ethnicity 21 (38.9)  6 (31.6)  4 (36.4) 11 (45.8) 21 (75.0)  5 (71.4)  7 (87.5)  9 (69.2) (Chinese) Any 24 (44.4)  5 (26.3)  2 (18.2) 15 (62.5) 17 (60.7)  3 (42.9)  2 (25.0) 12 (92.3) comorbidity (%) Diabetes  9 (16.7)  1 (5.3)  1 (9.1)  6 (25.0)  8 (28.6)  0 (0.0)  1 (12.5)  7 (53.9) Hypertension 14 (25.9)  3 (15.8)  0 (0.0) 11 (45.8) 10 (35.7)  0 (0.0)  2 (25.0)  8 (61.5) Clinical outcome Pneumonia 35 (64.8)  0 (0.0) 11 (100.0) 24 (100.0) 21 (75.0)  0 (0.0)  8 (100.0) 13 (100.0) with abnormal chest X ray (%) Required 24 (44.4)  0 (0.0)  0 (0.0) 24 (100.0) 13 (46.4)  0 (0.0)  0 (0.0) 13 (100.0) supplemental oxygen (%) ICU care (%) 15 (27.8)  0 (0.0)  0 (0.0) 15 (62.5)  9 (32.1)  0 (0.0)  0 (0.0)  9 (69.2) The values shown are based on available data. Categorical variables are shown as frequency (%). Continuous variables are shown mean (SD). ICU: intensive care unit.

    [0102] Sample Size: No power analysis was done. Sample size was based on sample availability. Randomization: No randomization was done. Blinding: Clinical parameters were made available after data analysis.

    Ethics Statement

    [0103] Written informed consent was obtained from participants in accordance with the tenets of the Declaration of Helsinki. For COVID-19 blood/plasma collection, “A Multi-centred Prospective Study to Detect Novel Pathogens and Characterize Emerging Infections (The PROTECT study group)”, a domain specific review board (DSRB) evaluated the study design and protocol, which was approved under study number 2012/00917. Healthy volunteers' samples were obtained under the following IRB “Study of blood cell subsets and their products in models of infection, inflammation and immune regulation” under the CIRB number 2017/2806 from SingHealth (Singapore).

    Donor Information

    [0104] Patients who tested PCR-positive for SARS-CoV-2 in a respiratory sample from February to April 2020 were recruited into the study.sup.73. Demographic data, days post disease onset date (unavailable for 5 asymptomatic patients), clinical score and SARS-CoV-2 RT-PCR results during the hospitalisation period were retrieved from patient clinical records. Relevant information is given in Table 2. Patients were classified in different clinical severity groups depending on the presence of pneumonia, hypoxia, and the need for ICU hospitalisation. For healthy volunteers, demographic data are provided in Table 3. Blood was collected in VACUETTE EDTA tubes (Greiner Bio, #455036) for healthy donors and acute patients, or Cell Preparation Tubes (CPT) (BD, #362761) for recovered patients. 100 μL of whole blood was extracted for each FACS staining panel (Table 4).

    TABLE-US-00002 TABLE 3 Demographics of healthy controls Healthy Controls Variable (n = 19) Demographics Mean age, years 36 (10) Sex, male (%) 10 (52.6) Ethnicity (Chinese) 12 (63.2) The values shown are based on available data. Categorical variables are shown as frequency (%). Continuous variables are shown mean (SD).

    TABLE-US-00003 TABLE 4 Flow cytometry antibodies per panels Volume No. Marker Colour (μL) Clone Cat. No. Lot number Vendor Panel A (100 ul whole blood):  1 CD45 BV786 2.5 HI30 304048 B284678 BioLegend  2 CD14 PE-CF594 1.5 MOP9 562335 9276099 BD Biosciences  3 CD16 APC Cy7 1.5 3G8 302018 B288665 BioLegend  4 CD19 SB600 2.5 SJ25C1 63-0198-42 2179717 eBioscience  5 CD11b BV510 1.5 ICRF44 563098 9346006 BD Biosciences  6 CD33 PE-Cy7 1 WM-53 25-0338-42 E10580- eBioscience 1456  7 CD169 PE 1.5 7-239 346004 B272223 Biolegend  8 HLA-DR AF700 1.5 L243 307626 B306020 Biolegend  9 CD3 APC 1.5 UCHT1 300439 B205424 Biolegend 10 CD56 FITC 5 MEM-188 304604 B291455 Biolegend 11 CD11c BV650 2.5 B-Iy6 563404 8187674 BD Biosciences 12 CD86 BV421 2 2331 562432 8337991 BD Horizon 13 CD123 BUV395 2 7G3 564195 9337379 BD Horizon 14 CD66b PerCP cy5.5 2 G10F5 305108 B204076 Biolegend Panel B (100 ul whole blood):  1 CD3 FITC 1 UCHT1 11-0038-42 2007254 eBioscience  2 CD4 BV650 2 SK3 563875 9107661 BD Horizon  3 CD8 V500 2 RPA-18 560774 4052849 BD Biosciences  4 CD45RA PerCP- 2 HI100 304122 B284187 Biolegend Cy5.5  5 CD27 APC 2 O323 17-0279-42 2168714 eBioscience  6 CD25 PE-Cy7 2 M-A251 557741 9301660 BD Biosciences  7 CD127 BUV737 2 HL-7R- 564300 9289985 BD Biosciences M21  8 CD38 BUV395 2 HB7 563811 9155743 BD Biosciences  9 CD56 PE 2 AF12-7H3 130-098- 5160830148 Miltenyi 755 Biotec 10 CD16 AF700 2 3G8 302036 B266048 Biolegend 11 Vδ1 TCR APC- 1 REA173 130-120- 5200304105 Miltenyi Vio770 438 Biotec 12 Vδ62 TCR BV711 2 B6 331412 B285901 Biolegend 13 VA7.2 TCR BV605 2 3C10 351720 B275819 Biolegend 14 CD161 BV786 2 HP-3G10 339930 B258781 Biolegend 15 CD19 PE-CF594 2 HIB19 562321 B277541 BD Biosciences 16 CD57 PB 0.5 HCD57 322316 B270598 Biolegend Panel C (100 ul whole blood):  1 CD45RA PerCP 1 HI100 304122 B284187 Biolegend Cy5.5  2 CD10 FITC 1 HI10a 312208 B270343 Biolegend  3 CD11b PE-Cy7 1 ICRF44 25-0118-42 1983204 eBioscience  4 CD49d PE-CF594 1 9F10 563645 9261644 BD Biosciences  5 Siglec8 PE 1 7C9 347104 B274554 Biolegend  6 CD8 BV786 0.5 RPA-T8 563823 9344069 BD Biosciences  7 CD4 BV650 1 RPA-T4 300536 B292888 Biolegend  8 CD16 BV605 1 3G8 563172 9179026 BD Horizon  9 CD3 V500 5 UCHT1 561416 9191445 BD Biosciences 10 CD66b BV421 1 G10F5 562940 9308264 BD Biosciences 11 HLA-DR APC-H7 0.5 G46-6 561358 9078946 BD Biosciences 12 CCR3 AF647 2 5E8 310710 B220159 Biolegend 13 CD38 BUV395 3 HB7 563811 9155743 BD Biosciences 14 CD27 BUV737 2 L128 564301 9109918 BD Biosciences

    Multiplex Microbead-Based Immunoassay

    [0105] When available, plasma fraction was harvested after 20 minutes centrifugation at 1700×g of blood collected in BD Vacutainer CPT tubes (BD, #362761). Plasma samples were treated by solvent/detergent treatment with a final concentration of 1% Triton X-100 (Thermo Fisher Scientific, #28314) for virus inactivation at RT for 2 hours in the dark under stringent Biosafety laboratory 2+ conditions (approved by Singapore Ministry of Health).sup.74. Cytokines detection in Triton-X treatment was compared with untreated samples for healthy donor and was found to be highly correlative for detected cytokines except for sCD40 (FIG. 12A-E). Immune mediator levels in COVID-19 patient plasma samples across acute samples were measured with by Luminex using the Cytokine/Chemokine/Growth Factor 45-plex Human ProcartaPlex™ Panel 1 (ThermoFisher Scientific, #EPX450-12171-901). Data acquisition was performed on FLEXMAP® 3D (Luminex) using xPONENT® 4.0 (Luminex) software. Data analysis was done on Bio-Plex Manager™ 6.1.1 (Bio-Rad). Standard curves were generated with a 5-PL (5-parameter logistic) algorithm, reporting values for both mean florescence intensity (MFI) and concentration data. Luminex data was generated from four different runs with each run having a number of samples which are common to the first run. For each subsequent run beyond the first, the mean of the common samples on each of the plates for each analyte was compared to the mean of the same samples in the first run to obtain a correction factor expressed in the following formula:


    correction_factor=mean(common_sample_concentration_in_run1)−mean(common_sample_concentration_in_subsequent_run).

    [0106] This correction factor was computed for each plate and analyte combination in the subsequent runs and added to the observed concentration to get the final normalised concentration. In the event that none of the common samples had concentration within the standard curve, no correction was done. Analyte concentrations were logarithmically transformed to ensure normality. Analytes that were not detectable in-patient samples were assigned the value of logarithmic transformation of Limit of Quantification (LOQ).

    Flow Cytometry

    [0107] Whole blood was stained with antibodies as stated in Table 4 (100 μL of whole blood per flow cytometry panel) for 20 minutes in the dark at RT. Samples were then supplemented with 0.5 mL of 1.2×BD FACS lysing solution (BD 349202). Final FACS lysing solution concentration taking into account volume in tube before addition is 1×. Samples were vortexed and incubated for 10 min at RT. 500 μL of PBS (Gibco, #10010-031) was added to wash the samples and centrifugated at 300×g for 5 min. Washing step of samples were repeated with 1 mL of PBS. Samples were then transferred to polystyrene FACS tubes containing 10 μL (10800 beads) of CountBright Absolute Counting Beads (Invitrogen, #36950). Samples were then acquired without delay, with vortexing before and every 3 min during acquisition to minimize fixed cell adherence to the tubes, using BD LSRII 5 laser configuration using automatic compensations and running BD FACS Diva Software version 8.0.1 (build 2014 07 03 11 47), Firmware version 1.14 (BDLSR II), CST version 3.0.1, PLA version 2.0. Analysis of flow cytometric data was performed with FlowJo version 10.6.1. Gating strategies for panels A, B and C are presented in FIGS. 9A-B, 10A-B and 11A-B respectively.

    Statistical Analysis

    [0108] Statistical analysis was performed using Prism 8 (Graph Pad Software, Inc). For comparisons of absolute cell counts or frequency, Kruskal-Wallis Test corrected with Dunn's method was performed. For comparisons of geometric Mean Fluorescence Intensity (gMFI) between three or more independent groups, Brown-Forsythe and Welch ANOVA using Dunnett T3 correction for multiple comparison was performed. For correlation analysis, spearman rank correlation was performed. p-values <0.05 for correlations, while adjusted p-values<0.05 for all the other comparisons were considered significant.

    Data Analysis and UMAP Visualisation

    [0109] UMAP: Gated cells were manually exported using FlowJo (Tree Star Inc.). Samples were then used for UMAP analysis using cytofkit2 R Packages with RStudio v3.5.2.sup.75. Five healthy, six acute and four recovered patients were each concatenated to its respective groups and 100000 cells were analysed using the ceil method. Custom R scripts were used to generate Z-score and correlation heatmaps.

    Results

    Circulating Myeloid Populations are Reduced in COVID-19 Patients

    [0110] A total of 54 patients with laboratory-confirmed SARS-CoV-2 infection were recruited at the National Centre for Infectious Diseases (NCID), Singapore from end March to mid-May 2020 (Table 2). Blood was collected from 54 patients upon enrollment at a median 7 days post-illness onset (pio) (Table 2), from patients who had recovered from COVID-19 disease (median 30 days pio) (Table 2) and 19 healthy donors (Table 3). Unfortunately, only 11 patients had paired acquisition between acute and recovered which prevented meaningful paired analysis (Table 2). Immunophenotyping of whole blood samples was carried out with three distinct flow cytometry panels to analyse myeloid, granulocyte and lymphoid subsets. (FIG. 1A, Table 4). Each panel was supplemented with counting beads to allow accurate assessment of cell counts. 19 of the 54 acute patients had paired plasma samples that permitted quantification of immune mediators by Luminex multiplex microbead-based immunoassay. The cohort was strongly biased towards males, of which three patients experienced a thromboembolism event (5.6%) and two patients had fatal outcomes (3.7%).

    [0111] First, the present study assessed using healthy donor samples, if the different blood collection method for recovered samples affected cell counts or activation markers. The inventors of the present disclosure observed that, while the cell count was not affected, expression of activation markers was affected on most cells but not CD38+ on T-cells (FIG. 6A), allowing direct comparison of activation markers only between healthy and acute samples. The FACS analysis revealed a declined cell count for eosinophils, basophils, total T-cells, dendritic cells (DCs), natural killer (NK) CD56 Bright, and plasmacitoid DCs (pDCs) in patients with acute COVID-19 infection (FIG. 1B, FIG. 6A). No significant changes were observed for B-cells, total monocytes, and total NK cells (FIG. 1B, FIG. 6A). Unbiased analysis by Uniform Manifold Approximation and Projection (UMAP) and graph based clustering however identified with CD169.sup.+ monocytes and CD11b.sup.high neutrophils, two additional clusters with high variation in acute patients (FIG. 1C). Further analysis showed that the monocytes presented with an increased expression of CD169 (strong type I interferon signature marker.sup.17), increased expression of CD11 b and HLA-DR, as well as CD33, a constitutive PI3K signaling inhibitor.sup.18,19 (FIG. 1D, FIG. 6B) as compared to healthy donors.

    [0112] Similar to the monocytes, neutrophils showed a significant upregulation of CD11b, CD66b, Siglec 8, CD38 and HLA-DR, suggesting that they were activated in response to SARS-CoV-2 infection (FIG. 1E,). Interestingly, despite this activation phenotype, an increase in the overall number of circulating neutrophils during acute SARS-CoV-2 infection based on conventional phenotypic markers (CD66b, CD11 b and CD16) was observed only in a small subset of our cohort (FIG. 1F). However, in-depth analysis of neutrophil subsets allows discrimination between immature (CD16.sup.low/hihh CD10.sup.−) and mature (CD16.sup.highCD10.sup.+) subsets (FIG. 1G).sup.20-22. Overall, a significant increase of immature neutrophil numbers was observed in acute patients as compared to healthy donors or recovered patients, while the number of mature neutrophils decreased (FIG. 1H).

    CD8 and γδ T-Cell Populations are the Most Affected Lymphocyte Subsets

    [0113] To better characterise COVID-19-induced lymphopenia, levels of CD8, CD4, γδ (i.e. VD1 and VD2), and mucosal-associated invariant T-cells (MAIT, CD3.sup.+VA7.2.sup.+CD161.sup.+) were assessed during acute 146 infection. Results showed a decrease in circulating MAIT, CD8.sup.+ and VD2 T-cells (FIG. 2A). However, circulating VD1 T-cells did not vary in numbers, and CD4.sup.+ T-cells did not show a significant decrease during acute infection (FIG. 2A). Interestingly, levels of regulatory T-cells (Treg) and CD4.sup.+CD161.sup.+ T-cells increased in recovered patients as compared to acute patients (FIG. 2A).

    [0114] Next, UMAP analysis was done on CD3.sup.+ cells to visualise changes in differentiation states within the T-cell compartments (FIG. 2B). UMAP visualisation suggests that phenotypic modulation in the CD8.sup.+ cluster was the most pronounced during SARS-CoV-2 infection (FIG. 2B). In order to validate this observation, CD45RA and CD27 markers were used to analyse the frequency of naïve (CD45RA.sup.+CD27.sup.+), central memory (CM, CD45RA.sup.−CD27.sup.+), effector memory (EM, CD45RA.sup.−CD27.sup.−) and terminal effector (TEMRA, CD45RA.sup.+CD27.sup.−) amongst the T-cell populations (FIG. 2C, FIG. 7A-B). In agreement with the UMAP analysis, CD8.sup.+ T-cells showed a change in differentiation profile from naïve in favour of EM and TEMRA (FIG. 2C). Noticeably, the frequency of naïve CD4.sup.+ T-cells decreased but was not reflected in a significant increase of a specific differentiated population (FIG. 2C).

    [0115] In addition, UMAP analysis also suggested changes in VD1 and VD2 populations that were not reflected in terms of differentiation (FIG. 2B-C). Therefore, we investigated the expression of general activation marker CD38. In this context, we observed that all differentiation stages of CD8.sup.+ T-cells, VD1 and VD2, had higher expression of CD38 except VD2 TEMRA (FIG. 2D1-2D2). On the other hand, CD4.sup.+ T-cells did not show difference in the CD38 activation marker expression (FIG. 2D1-2D2). Together, our data suggest that while circulating cell counts were generally decreased for T-cells, SARS-CoV-2 differentially impacts the different T cell subsets in terms cell counts, differentiation and expression of CD38.

    Granularity of Clinical Severity is Reflected by Immune Cell Counts

    [0116] In order to associate the data with the clinical severity we separated the patients into four different groups: no pneumonia, pneumonia only, pneumonia and hypoxia, and pneumonia and hypoxia requiring ICU admission (FIG. 3A).sup.23,24. This allowed estimation of cell counts in those groups and identification of markers that potentially depict disease severity. Consistent with previous studies on CD4 and CD8 lymphopenia.sup.9,25,26 CD8.sup.+, CD4.sup.+, MAIT, VD1 and VD2 T-cells showed a gradual reduction in the peripheral blood with increasing disease severity (FIG. 3B). The effect was more pronounced for CD8.sup.+ and VD2 T-cells (FIG. 3B), suggesting a strong activation and infiltration of these cells in the lungs.

    [0117] Cell counts in various myeloid subsets showed a similar decreasing profile with severity for pDCs, DCs, classical and intermediate monocytes (FIG. 3C). In contrast to cell counts, myeloid activation markers showed differential trends with severity (FIG. 3D). CD86 expression on DCs, HLA-DR and CD33 expression on monocytes followed a gradual decrease with increasing severity (FIG. 3D). Expression of CD169 on monocytes was decreased in ICU patients, while CD86 expression on pDCs was consistent across severity groups (FIG. 3D). Together, these results suggest that the remaining circulating monocytes and DCs in severe cases have a dysregulated phenotype.

    [0118] While total circulating neutrophils showed no significant change with disease severity, neutrophilia was only observed in some patients with severe clinical complications (FIG. 3E). Particularly, there was a change in the composition of neutrophil subsets in accordance to disease severity, where an increase in the immature neutrophil cell count and frequency was accompanied with a decrease of mature neutrophils (FIG. 3E). These results suggest that immature neutrophils could reflect disease severity much more accurately than total neutrophil counts.

    Immature Neutrophil Absolute Count Correlates with Cytokines

    [0119] Neutrophil-to-Lymphocyte Ratio (NLR) or Neutrophil-to-CD8 T-cell Ratio (N8R) were proposed to be good diagnostic and prognostic markers for severe COVID-19 respiratory disease.sup.26,27. However, these studies observed increased neutrophils in severe cases which was not consistent with our observations and in another study.sup.28 (FIG. 1F and FIG. 3E). To validate that the identified populations would be good markers of disease severity, a correlation analysis between analyte levels in available paired plasma samples (from CPT sodium citrate tubes) was performed with the cell counts obtained in FACS (from EDTA vacuette tubes)(FIG. 4A, FIG. 8A-C). Interestingly, strong correlation scores were observed between analytes and immature neutrophil counts (FIG. 4A, FIG. 8A), rather than with total neutrophil counts (FIG. 4A, FIG. 8B). The strongest correlations were observed between immature neutrophil counts and IL-6 (rho=0.6747, p=0.0015), and IP-10 (rho=0.7596, p=0.0002) (FIG. 9B).

    [0120] In addition, strong correlations were also observed between mature neutrophils, monocytes and intermediate monocytes, as well as CD8 and VD2 T-cell counts (FIG. 8C). These results suggest that immature neutrophils counts can potentially be used as sensitive and reliable indicators of disease severity.

    Immature Neutrophil to VD2 T-Cell Ratio as an Improved Prognostic Marker

    [0121] The present study next assessed if an immature neutrophil-to-CD8 T-cells ratio (iN8R) or VD2 T cell counts ratio (iNVD2R) could be a better prognostic marker of disease severity as compared to the current proposed NLR and N8R.sup.26,27. To differentiate patients with and without pneumonia, iNVD2R performed better than N8R or iN8R with an area under receiver operating characteristic (AUROC) curve of 0.8451 (95% confidence interval CI: 0.7379-0.9523) vs 0.806 (95% CI: 0.6911-0.9210) and 0.7158 (95% CI: 0.5754-0.8562) respectively (FIG. 5A). In addition, to differentiate patients with and without hypoxia, an AUROC of 0.9111 (95% CI: 0.8306-0.9916) was obtained for iNVD2R as compared to 0.8931 (95% CI: 0.8044-0.9817) for iN8R and 0.7958 (95% CI: 0.6781-0.9136) for N8R. These results indicate that iNVD2R and iN8R could be good markers for severe respiratory disease.

    [0122] To assess if this analysis could have predictive prognostic value in hospitalisation settings to improve patient management, we repeated the same analysis with only samples that were acquired at or before 7 days pio amongst the 54 acute patients (24 patients, median pio=3 days, range 1 to 7 days pio). AUROC for iNVD2R showed strong prognostic value for pneumonia onset (0.9071) as well as for onset of hypoxia (0.8908) (FIG. 5B, Table 1). The present data show that immature neutrophil counts are better in predicting disease severity as compared to total neutrophil counts. Importantly, they can be used in a ratio with CD8 or VD2 lymphocyte counts to improve the current N8R predictive ratio.

    TABLE-US-00004 TABLE 1 ROC curve analysis for neutrophils to T-cell ratios in patients with pneumonia or hypoxia compared to those without as presented in FIG. 5B. Pneumonia Hypoxia AUC AUC (95% Std. (95% Std. Variable CI) Error p-value CI) Error p-value Total neutrophils/ 0.7143 0.1140 0.0790 0.8319 0.09149 0.0121 CD8 T-cells (0.4909-0.9377) (0.6526-1) Total neutrophils/ 0.8643 0.07694 0.0028 0.8824 0.08083 0.0039 VD2 T-cells (0.7135-1) (0.7239-1) Immature 0.7929 0.1043 0.0164 0.8403 0.1186 0.0101 neutrophils/CD8 (0.5884-0.9973) (0.6079-1) T-cells Immature 0.9071 0.06723 0.0008 0.8908 0.08915 0.0031 neutrophils/VD2 (0.7754-1) (0.7160-1) T-cells ROC analysis was performed on COVID-19 patients between 2 to 7 days pio (24 patients, median 3 days pio). ROC curve was built by plotting true positive rate (sensitivity) against false positive rate (100%- sensitivity) and AUC was calculated from the plot using the Wilson/Brown method. ROC, receiver operating characteristic; AUC, area under curve; CI, confidence interval; Std. Error, standard error.

    Identification of Specific Immune Cells Modulated During Disease Severity

    [0123] Three comprehensive flow cytometry panels were used on a cohort of 54 COVID-19 patients from the epidemic in Singapore. These panels allowed the identification of immature neutrophils (using CD66B or CD15 and SSC-A to gate neutrophils, followed by gating on CD16 and CD10) and VD2 T-cells (gated using CD3+VD2+) as the key immune cell populations showing changes in absolute cells count strongly associated with clinical severity such as pneumonia and hypoxia (FIG. 1A-H).

    Identification of Immature Neutrophils to VD2 Ratio as an Improved Prognostic Marker

    [0124] The present study next compared the ratio of immature neutrophil counts to VD2 T-cell counts to the main known method of severity prognosis proposed in the literature (total neutrophils to CD8 ratio) and observed that the proposed immature neutrophil to VD2 ratio is more specific and sensitive as a potential prognostic marker for clinical severity (FIG. 5A). The time frame at which this marker would be important for prognosis was assessed. In a subgroup of patients that were sampled at a median of 3 days post illness onset (pio; range 1-7 days pio), the inventors of the present disclosure observed that the immature neutrophil to VD2 ratio is still a more accurate prognostic marker of symptoms progression (FIG. 5B).

    [0125] The technology consists of a 6-colour flow cytometry panel for whole blood staining, containing anti-human CD45 (to separate immune cells from the rest of the blood products), anti-human CD3 (to identify T-cells), anti-human VD2 (to identify VD2 T-cells as the CD45.sup.+ CD3.sup.+ VD2.sup.+ population), anti-human CD66b or CD15 (to identify granulocytes), anti-human CD16 and anti-human CD10 to distinguish mature neutrophils (CD16.sup.high CD10.sup.+) from immature neutrophils (CD45.sup.+CD3.sup.− CD66b/CD15.sup.+ CD16.sup.intermediate/− CD10.sup.− population) in the granulocyte population. All these antibodies are coupled to standard fluorophores that are compatible with each other and with standard flow cytometers.

    [0126] The antibody mix is prepared as a full stain version containing the 6 antibodies and counting beads (to allow accurate absolute counts), as well as a full stain minus one (FMO) containing all the antibodies except anti-human CD10) and no counting beads. These antibodies and counting beads can be supplied either individually or premixed either as a solution or lyophilized.

    [0127] The use of the FMO mix allows for correct quantification of the CD10 marker in the full stain acquisition. It can be performed for each patient or once before each acquisition batch. The full stain mix allows for accurate counting of immature neutrophils defined as the CD45.sup.+ CD3.sup.− CD66b/CD15.sup.+ SSC-A.sup.high CD16.sup.intermediate/− and CD10.sup.− population, as well as the VD2 T-cell count defined as the CD45.sup.+ CD3.sup.+ SSC-A.sup.low VD2.sup.+ population.

    [0128] An extended version of the antibody mixes can be offered for use with 7 colour flow cytometry by also including anti-human CD8 antibody. This will allow quantification of CD8 T-cell counts of the patient (gated as CD45.sup.+ CD3.sup.+ CD8.sup.+), which would allow the additional calculation of the prognostic ratio total neutrophils to CD8, as well as the immature neutrophil to CD8 ratio. The inventors identified this immature neutrophils to CD8 ratio as a better prognostic marker than the total neutrophils to CD8 ratio but less specific or sensitive when compared to the immature neutrophil to VD2 ratio since CD8 T-cells do not vary with age (see Application).

    Example 2

    [0129] i. Prognostic Kit would Contain the Following Lyophilized Mixes: [0130] Mix 1 (full stain): counting beads and the following fluorophore tagged antibodies: antibody anti-human CD45, antibody anti-human CD66b or CD15, anti-human CD16, anti-human CD10, anti-human CD3, anti-human VD2, and the optional anti-human CD8. [0131] Mix 2 (full stain minus one): the following fluorophore tagged antibodies: antibody anti-human CD45, antibody anti-human CD66b or CD15, anti-human CD16, anti-human CD3, anti-human VD2, and the optional anti-human CD8.

    [0132] ii. Workflow Steps: [0133] 1. Patient blood is collected in EDTA (or other anti-coagulant) containing tube. [0134] 2. 100 μL of blood is pipetted into Mix 1 and 100 μL of blood is pipetted into Mix 2, and incubation is performed for 20 min at room temperature in the dark. [0135] 3. 500 μL of a fixation agent and red blood cell agent is added to the tube and incubated 10 min for inactivation. [0136] 4. Samples can then be acquired on a flow cytometer. [0137] 5. Flow cytometry analysis is performed to gate the counting beads, the true immature neutrophils (using the Mix 1 and Mix 2 difference on CD10 color to gate true CD10 signal), the VD2 T-cells and optionally the CD8 T-cells. Counting beads count acquired by flow versus the number of beads originally in the mix is used to determinate the absolute number of immature neutrophils, VD2 T-cells and optionally CD8 T-cells in 100 μL of patient blood. [0138] 6. Clinicians (or automatic analysis software) can calculate the ratio of Immature neutrophils to VD2 ratio for the patient, and optionally the immature neutrophils to CD8 ratio. [0139] 7. Clinician can refer to the patient ratio in context of the ROC data (attached—please refer to section iii & iv) to evaluate the patient risk of developing pneumonia and/or hypoxia from the coronavirus infection. [0140] 8. Hospitalization and/or pre-emptive treatment for the patient will be determined by the clinician.

    [0141] iii. ROC Data for Immature Neutrophils to VD2 Ratio:

    TABLE-US-00005 TABLE 5 Immature neutrophils to VD2 ratio early time point. No hypoxia vs hypoxia Sensi- Speci- Likelihood Ratio tivity % 95% CI ficity % 95% CI ratio >4.875 100 64.57% to 5.882 0.3017% to 1.063 100.0% 26.98% >6.394 100 64.57% to 11.76 2.090% to 1.133 100.0% 34.34% >9.408 100 64.57% to 17.65 6.191% to 1.214 100.0% 41.03% >11.94 100 64.57% to 23.53 9.555% to 1.308 100.0% 47.26% >12.42 100 64.57% to 29.41 13.28% to 1.417 100.0% 53.13% >12.85 100 64.57% to 35.29 17.31% to 1.545 100.0% 58.70% >13.00 85.71 48.69% to 35.29 17.31% to 1.325 99.27% 58.70% >13.35 85.71 48.69% to 41.18 21.61% to 1.457 99.27% 63.99% >16.95 85.71 48.69% to 47.06 26.17% to 1.619 99.27% 69.04% >21.40 85.71 48.69% to 52.94 30.96% to 1.821 99.27% 73.83% >23.33 85.71 48.69% to 58.82 36.01% to 2.082 99.27% 78.39% >26.02 85.71 48.69% to 64.71 41.30% to 2.429 99.27% 82.69% >29.87 85.71 48.69% to 70.59 46.87% to 2.914 99.27% 86.72% >37.64 85.71 48.69% to 76.47 52.74% to 3.643 99.27% 90.44% >43.83 85.71 48.69% to 82.35 58.97% to 4.857 99.27% 93.81% >79.36 85.71 48.69% to 88.24 65.66% to 7.286 99.27% 97.91% >117.4 71.43 35.89% to 88.24 65.66% to 6.071 94.92% 97.91% >138.7 71.43 35.89% to 94.12 73.02% to 12.14 94.92% 99.70% >179.3 71.43 35.89% to 100 81.57% to 94.92% 100.0% >409.5 57.14 25.05% to 100 81.57% to 84.18% 100.0% >723.3 42.86 15.82% to 100 81.57% to 74.95% 100.0% >829.2 28.57 5.077% to 100 81.57% to 64.11% 100.0% >2198 14.29 0.7328% to 100 81.57% to 51.31% 100.0%

    TABLE-US-00006 TABLE 6 Immature neutrophils to VD2 ratio early time point. No pneumonia vs pneumonia Sensi- Speci- Likelihood Ratio tivity % 95% CI ficity % 95% CI ratio >4.875 100 72.25% to 7.143 0.3664% to 1.077 100.0% 31.47% >6.394 100 72.25% to 14.29 2.538% to 1.167 100.0% 39.94% >9.408 100 72.25% to 21.43 7.571% to 1.273 100.0% 47.59% >11.94 100 72.25% to 28.57 11.72% to 1.4 100.0% 54.65% >12.42 100 72.25% to 35.71 16.34% to 1.556 100.0% 61.24% >12.85 100 72.25% to 42.86 21.38% to 1.75 100.0% 67.41% >13.00 90 59.58% to 42.86 21.38% to 1.575 99.49% 67.41% >13.35 90 59.58% to 50 26.80% to 1.8 99.49% 73.20% >16.95 90 59.58% to 57.14 32.59% to 2.1 99.49% 78.62% >21.40 90 59.58% to 64.29 38.76% to 2.52 99.49% 83.66% >23.33 80 49.02% to 64.29 38.76% to 2.24 96.45% 83.66% >26.02 80 49.02% to 71.43 45.35% to 2.8 96.45% 88.28% >29.87 80 49.02% to 78.57 52.41% to 3.733 96.45% 92.43% >37.64 80 49.02% to 85.71 60.06% to 5.6 96.45% 97.46% >43.83 80 49.02% to 92.86 68.53% to 11.2 96.45% 99.63% >79.36 80 49.02% to 100 78.47% to 96.45% 100.0% >117.4 70 39.68% to 100 78.47% to 89.22% 100.0% >138.7 60 31.27% to 100 78.47% to 83.18% 100.0% >179.3 50 23.66% to 100 78.47% to 76.34% 100.0% >409.5 40 16.82% to 100 78.47% to 68.73% 100.0% >723.3 30 10.78% to 100 78.47% to 60.32% 100.0% >829.2 20 3.554% to 100 78.47% to 50.98% 100.0% >2198 10 0.5129% to 100 78.47% to 40.42% 100.0%

    TABLE-US-00007 TABLE 7 Immature neutrophils to VD2 ratio all time points. No hypoxia vs hypoxia Sensi- Speci- Likelihood Ratio tivity % 95% CI ficity % 95% CI ratio >3.869 100 86.20% to 3.333 0.1710% to 1.034 100.0% 16.67% >4.155 100 86.20% to 6.667 1.185% to 1.071 100.0% 21.32% >4.445 100 86.20% to 10 3.460% to 1.111 100.0% 25.62% >5.164 100 86.20% to 13.33 5.310% to 1.154 100.0% 29.68% >6.120 100 86.20% to 16.67 7.337% to 1.2 100.0% 33.56% >6.609 100 86.20% to 20 9.505% to 1.25 100.0% 37.31% >6.883 100 86.20% to 23.33 11.79% to 1.304 100.0% 40.93% >7.879 100 86.20% to 26.67 14.18% to 1.364 100.0% 44.45% >10.33 100 86.20% to 30 16.66% to 1.429 100.0% 47.88% >11.94 100 86.20% to 33.33 19.23% to 1.5 100.0% 51.22% >12.09 100 86.20% to 36.67 21.87% to 1.579 100.0% 54.49% >12.48 100 86.20% to 40 24.59% to 1.667 100.0% 57.68% >12.85 100 86.20% to 43.33 27.38% to 1.765 100.0% 60.80% >13.00 95.83 79.76% to 43.33 27.38% to 1.691 99.79% 60.80% >13.35 95.83 79.76% to 46.67 30.23% to 1.797 99.79% 63.86% >15.04 95.83 79.76% to 50 33.15% to 1.917 99.79% 66.85% >18.39 95.83 79.76% to 53.33 36.14% to 2.054 99.79% 69.77% >21.40 95.83 79.76% to 56.67 39.20% to 2.212 99.79% 72.62% >23.33 95.83 79.76% to 60 42.32% to 2.396 99.79% 75.41% >25.22 95.83 79.76% to 63.33 45.51% to 2.614 99.79% 78.13% >26.80 91.67 74.15% to 63.33 45.51% to 2.5 98.52% 78.13% >27.60 91.67 74.15% to 66.67 48.78% to 2.75 98.52% 80.77% >29.87 91.67 74.15% to 70 52.12% to 3.056 98.52% 83.34% >35.46 91.67 74.15% to 73.33 55.55% to 3.438 98.52% 85.82% >41.24 87.5 69.00% to 73.33 55.55% to 3.281 95.66% 85.82% >43.83 87.5 69.00% to 76.67 59.07% to 3.75 95.66% 88.21% >48.30 87.5 69.00% to 80 62.69% to 4.375 95.66% 90.49% >56.33 83.33 64.15% to 80 62.69% to 4.167 93.32% 90.49% >61.17 83.33 64.15% to 83.33 66.44% to 5 93.32% 92.66% >62.88 83.33 64.15% to 86.67 70.32% to 6.25 93.32% 94.69% >77.21 83.33 64.15% to 90 74.38% to 8.333 93.32% 96.54% >102.6 79.17 59.53% to 90 74.38% to 7.917 90.76% 96.54% >117.4 75 55.10% to 90 74.38% to 7.5 88.00% 96.54% >122.9 75 55.10% to 93.33 78.68% to 11.25 88.00% 98.82% >141.3 70.83 50.83% to 93.33 78.68% to 10.63 85.09% 98.82% >179.3 70.83 50.83% to 96.67 83.33% to 21.25 85.09% 99.83% >232.3 66.67 46.71% to 96.67 83.33% to 20 82.03% 99.83% >273.1 62.5 42.71% to 96.67 83.33% to 18.75 78.84% 99.83% >292.8 58.33 38.83% to 96.67 83.33% to 17.5 75.53% 99.83% >304.0 54.17 35.07% to 96.67 83.33% to 16.25 72.11% 99.83% >329.6 50 31.43% to 96.67 83.33% to 15 68.57% 99.83% >359.6 45.83 27.89% to 96.67 83.33% to 13.75 64.93% 99.83% >384.4 41.67 24.47% to 96.67 83.33% to 12.5 61.17% 99.83% >443.6 37.5 21.16% to 96.67 83.33% to 11.25 57.29% 99.83% >492.5 33.33 17.97% to 96.67 83.33% to 10 53.29% 99.83% >539.0 29.17 14.91% to 96.67 83.33% to 8.75 49.17% 99.83% >597.1 25 12.00% to 96.67 83.33% to 7.5 44.90% 99.83% >723.3 20.83 9.245% to 96.67 83.33% to 6.25 40.47% 99.83% >829.2 16.67 6.679% to 96.67 83.33% to 5 35.85% 99.83% >972.0 12.5 4.344% to 96.67 83.33% to 3.75 31.00% 99.83% >1778 12.5 4.344% to 100 88.65% to 31.00% 100.0% >3004 8.333 1.481% to 100 88.65% to 25.85% 100.0% >4229 4.167 0.2137% to 100 88.65% to 20.24% 100.0%

    TABLE-US-00008 TABLE 8 Immature neutrophils to VD2 ratio all time points. No pneumonia vs pneumonia Sensi- Speci- Likelihood Ratio tivity % 95% CI ficity % 95% CI ratio >3.869 97.14 85.47% to 0 0.000% to 0.9714 99.85% 16.82% >4.155 97.14 85.47% to 5.263 0.2700% to 1.025 99.85% 24.64% >4.445 94.29 81.39% to 5.263 0.2700% to 0.9952 98.98% 24.64% >5.164 91.43 77.62% to 5.263 0.2700% to 0.9651 97.04% 24.64% >6.120 91.43 77.62% to 10.53 1.870% to 1.022 97.04% 31.39% >6.609 88.57 74.05% to 10.53 1.870% to 0.9899 95.46% 31.39% >6.883 88.57 74.05% to 15.79 5.520% to 1.052 95.46% 37.57% >7.879 88.57 74.05% to 21.05 8.508% to 1.122 95.46% 43.33% >10.33 88.57 74.05% to 26.32 11.81% to 1.202 95.46% 48.79% >11.94 88.57 74.05% to 31.58 15.36% to 1.295 95.46% 53.99% >12.09 88.57 74.05% to 36.84 19.15% to 1.402 95.46% 58.96% >12.48 88.57 74.05% to 42.11 23.14% to 1.53 95.46% 63.72% >12.85 88.57 74.05% to 47.37 27.33% to 1.683 95.46% 68.29% >13.00 85.71 70.62% to 47.37 27.33% to 1.629 93.74% 68.29% >13.35 85.71 70.62% to 52.63 31.71% to 1.81 93.74% 72.67% >15.04 85.71 70.62% to 57.89 36.28% to 2.036 93.74% 76.86% >18.39 85.71 70.62% to 63.16 41.04% to 2.327 93.74% 80.85% >21.40 85.71 70.62% to 68.42 46.01% to 2.714 93.74% 84.64% >23.33 82.86 67.32% to 68.42 46.01% to 2.624 91.90% 84.64% >25.22 82.86 67.32% to 73.68 51.21% to 3.149 91.90% 88.19% >26.80 80 64.11% to 73.68 51.21% to 3.04 89.96% 88.19% >27.60 77.14 60.98% to 73.68 51.21% to 2.931 87.93% 88.19% >29.87 77.14 60.98% to 78.95 56.67% to 3.664 87.93% 91.49% >35.46 77.14 60.98% to 84.21 62.43% to 4.886 87.93% 94.48% >41.24 74.29 57.93% to 84.21 62.43% to 4.705 85.84% 94.48% >43.83 74.29 57.93% to 89.47 68.61% to 7.057 85.84% 98.13% >48.30 74.29 57.93% to 94.74 75.36% to 14.11 85.84% 99.73% >56.33 71.43 54.95% to 94.74 75.36% to 13.57 83.67% 99.73% >61.17 68.57 52.02% to 94.74 75.36% to 13.03 81.45% 99.73% >62.88 68.57 52.02% to 100 83.18% to 81.45% 100.0% >77.21 65.71 49.15% to 100 83.18% to 79.17% 100.0% >102.6 62.86 46.34% to 100 83.18% to 76.83% 100.0% >117.4 60 43.57% to 100 83.18% to 74.45% 100.0% >122.9 57.14 40.86% to 100 83.18% to 72.02% 100.0% >141.3 54.29 38.19% to 100 83.18% to 69.53% 100.0% >179.3 51.43 35.57% to 100 83.18% to 67.01% 100.0% >232.3 48.57 32.99% to 100 83.18% to 64.43% 100.0% >273.1 45.71 30.47% to 100 83.18% to 61.81% 100.0% >292.8 42.86 27.98% to 100 83.18% to 59.14% 100.0% >304.0 40 25.55% to 100 83.18% to 56.43% 100.0% >329.6 37.14 23.17% to 100 83.18% to 53.66% 100.0% >359.6 34.29 20.83% to 100 83.18% to 50.85% 100.0% >384.4 31.43 18.55% to 100 83.18% to 47.98% 100.0% >443.6 28.57 16.33% to 100 83.18% to 45.05% 100.0% >492.5 25.71 14.16% to 100 83.18% to 42.07% 100.0% >539.0 22.86 12.07% to 100 83.18% to 39.02% 100.0% >597.1 20 10.04% to 100 83.18% to 35.89% 100.0% >723.3 17.14 8.103% to 100 83.18% to 32.68% 100.0% >829.2 14.29 6.260% to 100 83.18% to 29.38% 100.0% >972.0 11.43 4.535% to 100 83.18% to 25.95% 100.0% >1778 8.571 2.958% to 100 83.18% to 22.38% 100.0% >3004 5.714 1.015% to 100 83.18% to 18.61% 100.0% >4229 2.857 0.1466% to 100 83.18% to 14.53% 100.0%

    [0142] i. ROC Data for Immature Neutrophils to CD8 Ratio:

    TABLE-US-00009 TABLE 9 Immature neutrophils to CD8 ratio early time point. No hypoxia vs hypoxia Sensi- Speci- Likelihood Ratio tivity % 95% CI ficity % 95% CI ratio >0.5804 100 64.57% to 5.882 0.3017% to 1.063 100.0% 26.98% >0.8100 100 64.57% to 11.76 2.090% to 1.133 100.0% 34.34% >0.9954 85.71 48.69% to 11.76 2.090% to 0.9714 99.27% 34.34% >1.089 85.71 48.69% to 17.65 6.191% to 1.041 99.27% 41.03% >1.212 85.71 48.69% to 23.53 9.555% to 1.121 99.27% 47.26% >1.327 85.71 48.69% to 29.41 13.28% to 1.214 99.27% 53.13% >1.372 85.71 48.69% to 35.29 17.31% to 1.325 99.27% 58.70% >1.410 85.71 48.69% to 41.18 21.61% to 1.457 99.27% 63.99% >1.530 85.71 48.69% to 47.06 26.17% to 1.619 99.27% 69.04% >1.961 85.71 48.69% to 52.94 30.96% to 1.821 99.27% 73.83% >2.389 85.71 48.69% to 58.82 36.01% to 2.082 99.27% 78.39% >2.532 85.71 48.69% to 64.71 41.30% to 2.429 99.27% 82.69% >3.112 85.71 48.69% to 70.59 46.87% to 2.914 99.27% 86.72% >3.767 85.71 48.69% to 76.47 52.74% to 3.643 99.27% 90.44% >4.054 71.43 35.89% to 76.47 52.74% to 3.036 94.92% 90.44% >5.339 71.43 35.89% to 82.35 58.97% to 4.048 94.92% 93.81% >7.045 71.43 35.89% to 88.24 65.66% to 6.071 94.92% 97.91% >8.288 71.43 35.89% to 94.12 73.02% to 12.14 94.92% 99.70% >10.97 71.43 35.89% to 100 81.57% to 94.92% 100.0% >14.75 57.14 25.05% to 100 81.57% to 84.18% 100.0% >27.86 42.86 15.82% to 100 81.57% to 74.95% 100.0% >62.64 28.57 5.077% to 100 81.57% to 64.11% 100.0% >157.9 14.29 0.7328% to 100 81.57% to 51.31% 100.0%

    TABLE-US-00010 TABLE 10 Immature neutrophils to CD8 ratio early time point. No pneumonia vs pneumonia Sensi- Speci- Likelihood Ratio tivity % 95% CI ficity % 95% CI ratio >0.5804 100 72.25% to 7.143 0.3664% to 1.077 100.0% 31.47% >0.8100 100 72.25% to 14.29 2.538% to 1.167 100.0% 39.94% >0.9954 90 59.58% to 14.29 2.538% to 1.05 99.49% 39.94% >1.089 90 59.58% to 21.43 7.571% to 1.145 99.49% 47.59% >1.212 90 59.58% to 28.57 11.72% to 1.26 99.49% 54.65% >1.327 80 49.02% to 28.57 11.72% to 1.12 96.45% 54.65% >1.372 80 49.02% to 35.71 16.34% to 1.244 96.45% 61.24% >1.410 80 49.02% to 42.86 21.38% to 1.4 96.45% 67.41% >1.530 80 49.02% to 50 26.80% to 1.6 96.45% 73.20% >1.961 80 49.02% to 57.14 32.59% to 1.867 96.45% 78.62% >2.389 80 49.02% to 64.29 38.76% to 2.24 96.45% 83.66% >2.532 80 49.02% to 71.43 45.35% to 2.8 96.45% 88.28% >3.112 80 49.02% to 78.57 52.41% to 3.733 96.45% 92.43% >3.767 70 39.68% to 78.57 52.41% to 3.267 89.22% 92.43% >4.054 60 31.27% to 78.57 52.41% to 2.8 83.18% 92.43% >5.339 60 31.27% to 85.71 60.06% to 4.2 83.18% 97.46% >7.045 60 31.27% to 92.86 68.53% to 8.4 83.18% 99.63% >8.288 50 23.66% to 92.86 68.53% to 7 76.34% 99.63% >10.97 50 23.66% to 100 78.47% to 76.34% 100.0% >14.75 40 16.82% to 100 78.47% to 68.73% 100.0% >27.86 30 10.78% to 100 78.47% to 60.32% 100.0% >62.64 20 3.554% to 100 78.47% to 50.98% 100.0% >157.9 10 0.5129% to 100 78.47% to 40.42% 100.0%

    TABLE-US-00011 TABLE 11 Immature neutrophils to CD8 ratio all time points. No hypoxia vs hypoxia Sensi- Speci- Likelihood Ratio tivity % 95% CI ficity % 95% CI ratio >0.3641 100 86.20% to 3.333 0.1710% to 1.034 100.0% 16.67% >0.4641 100 86.20% to 6.667 1.185% to 1.071 100.0% 21.32% >0.5804 100 86.20% to 10 3.460% to 1.111 100.0% 25.62% >0.7651 100 86.20% to 13.33 5.310% to 1.154 100.0% 29.68% >0.8693 100 86.20% to 16.67 7.337% to 1.2 100.0% 33.56% >0.9141 100 86.20% to 20 9.505% to 1.25 100.0% 37.31% >0.9954 95.83 79.76% to 20 9.505% to 1.198 99.79% 37.31% >1.089 95.83 79.76% to 23.33 11.79% to 1.25 99.79% 40.93% >1.133 95.83 79.76% to 26.67 14.18% to 1.307 99.79% 44.45% >1.231 95.83 79.76% to 30 16.66% to 1.369 99.79% 47.88% >1.327 95.83 79.76% to 33.33 19.23% to 1.438 99.79% 51.22% >1.372 95.83 79.76% to 36.67 21.87% to 1.513 99.79% 54.49% >1.410 95.83 79.76% to 40 24.59% to 1.597 99.79% 57.68% >1.446 95.83 79.76% to 43.33 27.38% to 1.691 99.79% 60.80% >1.517 95.83 79.76% to 46.67 30.23% to 1.797 99.79% 63.86% >1.564 95.83 79.76% to 50 33.15% to 1.917 99.79% 66.85% >1.604 95.83 79.76% to 53.33 36.14% to 2.054 99.79% 69.77% >1.640 95.83 79.76% to 56.67 39.20% to 2.212 99.79% 72.62% >1.707 95.83 79.76% to 60 42.32% to 2.396 99.79% 75.41% >1.926 91.67 74.15% to 60 42.32% to 2.292 98.52% 75.41% >2.181 91.67 74.15% to 63.33 45.51% to 2.5 98.52% 78.13% >2.389 91.67 74.15% to 66.67 48.78% to 2.75 98.52% 80.77% >2.532 91.67 74.15% to 70 52.12% to 3.056 98.52% 83.34% >2.627 91.67 74.15% to 73.33 55.55% to 3.438 98.52% 85.82% >3.173 91.67 74.15% to 76.67 59.07% to 3.929 98.52% 88.21% >3.767 91.67 74.15% to 80 62.69% to 4.583 98.52% 90.49% >4.054 87.5 69.00% to 80 62.69% to 4.375 95.66% 90.49% >4.277 87.5 69.00% to 83.33 66.44% to 5.25 95.66% 92.66% >4.424 83.33 64.15% to 83.33 66.44% to 5 93.32% 92.66% >4.676 79.17 59.53% to 83.33 66.44% to 4.75 90.76% 92.66% >5.164 75 55.10% to 83.33 66.44% to 4.5 88.00% 92.66% >5.974 70.83 50.83% to 83.33 66.44% to 4.25 85.09% 92.66% >6.783 70.83 50.83% to 86.67 70.32% to 5.313 85.09% 94.69% >7.381 66.67 46.71% to 86.67 70.32% to 5 82.03% 94.69% >7.926 66.67 46.71% to 90 74.38% to 6.667 82.03% 96.54% >8.571 62.5 42.71% to 90 74.38% to 6.25 78.84% 96.54% >10.14 62.5 42.71% to 93.33 78.68% to 9.375 78.84% 98.82% >11.99 58.33 38.83% to 93.33 78.68% to 8.75 75.53% 98.82% >12.82 54.17 35.07% to 93.33 78.68% to 8.125 72.11% 98.82% >13.06 50 31.43% to 93.33 78.68% to 7.5 68.57% 98.82% >14.36 50 31.43% to 96.67 83.33% to 15 68.57% 99.83% >16.05 45.83 27.89% to 96.67 83.33% to 13.75 64.93% 99.83% >17.01 41.67 24.47% to 96.67 83.33% to 12.5 61.17% 99.83% >19.46 41.67 24.47% to 100 88.65% to 61.17% 100.0% >22.37 37.5 21.16% to 100 88.65% to 57.29% 100.0% >26.62 33.33 17.97% to 100 88.65% to 53.29% 100.0% >32.73 29.17 14.91% to 100 88.65% to 49.17% 100.0% >36.67 25 12.00% to 100 88.65% to 44.90% 100.0% >38.51 20.83 9.245% to 100 88.65% to 40.47% 100.0% >62.64 16.67 6.679% to 100 88.65% to 35.85% 100.0% >109.1 12.5 4.344% to 100 88.65% to 31.00% 100.0% >138.3 8.333 1.481% to 100 88.65% to 25.85% 100.0% >187.2 4.167 0.2137% to 100 88.65% to 20.24% 100.0%

    TABLE-US-00012 TABLE 12 Immature neutrophils to CD8 ratio all time points. No pneumonia vs pneumonia Sensi- Speci- Likelihood Ratio tivity % 95% CI ficity % 95% CI ratio >0.3641 97.14 85.47% to 0 0.000% to 0.9714 99.85% 16.82% >0.4641 97.14 85.47% to 5.263 0.2700% to 1.025 99.85% 24.64% >0.5804 97.14 85.47% to 10.53 1.870% to 1.086 99.85% 31.39% >0.7651 97.14 85.47% to 15.79 5.520% to 1.154 99.85% 37.57% >0.8693 94.29 81.39% to 15.79 5.520% to 1.12 98.98% 37.57% >0.9141 91.43 77.62% to 15.79 5.520% to 1.086 97.04% 37.57% >0.9954 88.57 74.05% to 15.79 5.520% to 1.052 95.46% 37.57% >1.089 88.57 74.05% to 21.05 8.508% to 1.122 95.46% 43.33% >1.133 88.57 74.05% to 26.32 11.81% to 1.202 95.46% 48.79% >1.231 88.57 74.05% to 31.58 15.36% to 1.295 95.46% 53.99% >1.327 85.71 70.62% to 31.58 15.36% to 1.253 93.74% 53.99% >1.372 85.71 70.62% to 36.84 19.15% to 1.357 93.74% 58.96% >1.410 85.71 70.62% to 42.11 23.14% to 1.481 93.74% 63.72% >1.446 85.71 70.62% to 47.37 27.33% to 1.629 93.74% 68.29% >1.517 82.86 67.32% to 47.37 27.33% to 1.574 91.90% 68.29% >1.564 82.86 67.32% to 52.63 31.71% to 1.749 91.90% 72.67% >1.604 82.86 67.32% to 57.89 36.28% to 1.968 91.90% 76.86% >1.640 80 64.11% to 57.89 36.28% to 1.9 89.96% 76.86% >1.707 80 64.11% to 63.16 41.04% to 2.171 89.96% 80.85% >1.926 77.14 60.98% to 63.16 41.04% to 2.094 87.93% 80.85% >2.181 74.29 57.93% to 63.16 41.04% to 2.016 85.84% 80.85% >2.389 74.29 57.93% to 68.42 46.01% to 2.352 85.84% 84.64% >2.532 74.29 57.93% to 73.68 51.21% to 2.823 85.84% 88.19% >2.627 74.29 57.93% to 78.95 56.67% to 3.529 85.84% 91.49% >3.173 74.29 57.93% to 84.21 62.43% to 4.705 85.84% 94.48% >3.767 71.43 54.95% to 84.21 62.43% to 4.524 83.67% 94.48% >4.054 68.57 52.02% to 84.21 62.43% to 4.343 81.45% 94.48% >4.277 68.57 52.02% to 89.47 68.61% to 6.514 81.45% 98.13% >4.424 65.71 49.15% to 89.47 68.61% to 6.243 79.17% 98.13% >4.676 62.86 46.34% to 89.47 68.61% to 5.971 76.83% 98.13% >5.164 60 43.57% to 89.47 68.61% to 5.7 74.45% 98.13% >5.974 57.14 40.86% to 89.47 68.61% to 5.429 72.02% 98.13% >6.783 57.14 40.86% to 94.74 75.36% to 10.86 72.02% 99.73% >7.381 54.29 38.19% to 94.74 75.36% to 10.31 69.53% 99.73% >7.926 51.43 35.57% to 94.74 75.36% to 9.771 67.01% 99.73% >8.571 48.57 32.99% to 94.74 75.36% to 9.229 64.43% 99.73% >10.14 48.57 32.99% to 100 83.18% to 64.43% 100.0% >11.99 45.71 30.47% to 100 83.18% to 61.81% 100.0% >12.82 42.86 27.98% to 100 83.18% to 59.14% 100.0% >13.06 40 25.55% to 100 83.18% to 56.43% 100.0% >14.36 37.14 23.17% to 100 83.18% to 53.66% 100.0% >16.05 34.29 20.83% to 100 83.18% to 50.85% 100.0% >17.01 31.43 18.55% to 100 83.18% to 47.98% 100.0% >19.46 28.57 16.33% to 100 83.18% to 45.05% 100.0% >22.37 25.71 14.16% to 100 83.18% to 42.07% 100.0% >26.62 22.86 12.07% to 100 83.18% to 39.02% 100.0% >32.73 20 10.04% to 100 83.18% to 35.89% 100.0% >36.67 17.14 8.103% to 100 83.18% to 32.68% 100.0% >38.51 14.29 6.260% to 100 83.18% to 29.38% 100.0% >62.64 11.43 4.535% to 100 83.18% to 25.95% 100.0% >109.1 8.571 2.958% to 100 83.18% to 22.38% 100.0% >138.3 5.714 1.015% to 100 83.18% to 18.61% 100.0% >187.2 2.857 0.1466% to 100 83.18% to 14.53% 100.0%

    TABLE-US-00013 TABLE 13 ROC curve analysis for neutrophils to T-cell ratios in patients with pneumonia or hypoxia compared to those without as presented in FIG. 5A-B. Pneumonia Hypoxia AUC AUC (95% Std. p- (95% Std. p- Variable CI) Error value CI) Error value Total 0.7158 0.07163   0.0093 0.7958 0.06008   0.0002 neutrophils/ 0.5754 0.6781 CD8 T-cells to to (n = 54) 0.8562 0.9136 Early total 0.7143 0.114    0.079 0.8319 0.09149   0.0121 neutrophils/ 0.4909 0.6526 CD8 T-cells to to 1.0 (n = 24) 0.9377 Immature 0.8451 0.05471 <0.0001 0.9111 0.04108 <0.0001 neutrophils/ 0.7379 0.8306 VD2 T-cells to to (n = 54) 0.9523 0.9916 Early immature 0.9071 0.06723   0.0008 0.8908 0.08915   0.0031 neutrophils/ 0.7754 0.7160 VD2 T-cells to 1.0 to 1.0 (n = 24) ROC analysis was performed on all 54 COVID-19 patients or a subset of 24 sampled between 2 to 7 days pio (24 patients, median 3 days pio). ROC curve was built by plotting true positive rate (sensitivity) against false positive rate (100%- sensitivity) and AUC was calculated from the plot using the Wilson/Brown method. ROC, receiver operating characteristic ; AUC, area under curve ; Ci, confidence interval ; Std. Error, standard error.

    [0143] In this study, immunophenotyping of peripheral blood from COVID-19 patients revealed a significant shift in the ratio between mature and immature neutrophils associating with severity. The increased numbers of immature neutrophils and the disappearance of mature neutrophils likely reflect gradual and sustained mobilisation of these cells into the lungs in response to an ongoing inflammation, leading to premature release of immature neutrophils from the bone marrow.sup.22. Supporting this hypothesis, a recent study, investigating several myeloid populations between circulating PBMCs and the lung lavage of COVID-19 patients showed that granulocytes represent up to 80% of total CD45+ lung infiltrates.sup.29. In addition, autopsies of COVID-19 fatalities showed typical lesions associated with toxic neutrophil effects.sup.30,31. In line with this observation, marked morphological abnormalities of the circulating neutrophils were reported in COVID-19 patients.sup.28. These cells present typical hallmarks of immature neutrophils and their precursors such as band shaped nuclei and a lower expression of CD10 and CD16.sup.32. Consistent with our data, a recent non peer reviewed study on a small number of patients reported that the presence of “low density inflammatory neutrophils” was strongly associated with disease severity and IL-6 levels 33. Functionally these low density neutrophils showed spontaneous extracellular trap formation, enhanced cytokine production and associated with D-dimer and systemic IL-6 and TNF-α levels 33. We hypothesise that the CD11b.sup.intCD44.sup.lowCD16.sup.int low density neutrophil population identified in that study is likely constituted primarily of CD10− immature neutrophils. More recently, two studies used flow cytometry, single cell sequencing and mass cytometry to confirm the immature and dysfunctional phenotype in the myeloid populations, including these neutrophils.sup.34,35. Interestingly, the diagnostic value of a neutrophil “left shift” (banded versus segmented neutrophils) had previously been explored in order to predict infectious diseases in addition to inflammatory diseases.sup.36 and is therefore not limited to COVID-19 severity. Similarly, the presence of immature low density neutrophils have been reported in the literature for various infectious and inflammatory diseases 37-39 as well as induced by LPS in healthy subjects.sup.40, highlighting the necessity of future studies to compare the role and function of these COVID-19 immature neutrophils with the circulating immature neutrophils present in other diseases.

    [0144] During SARS-CoV-2 infection, immature neutrophil numbers strongly correlated with IL-6 and IP-10. IL-6 and IP-10 are consistently upregulated during a cytokine storm and are associated with severe ARDS.sup.12,13,41,42 While some studies report inflammatory monocytes as the source of IL-6.sup.12,43,44, our results suggest that immature neutrophils could also be a non-negligible source of IL-6 during COVID-19-induced cytokine storm. Indeed, neutrophils have been found to produce biologically relevant amounts of IL-6 after engagement of TLR8, a toll like receptor recognising single strand RNAs of viral or bacterial origin.sup.45,46. Since IL-17 operates upstream of IL-1 and IL-6, and is a major orchestrator of sustained neutrophils mobilisation 47, it is plausible that IL-17 could significantly affect the neutrophils compartment in COVID-19 patients. Consistent with this hypothesis, CD4 T-cells in COVID-19 patients are skewed towards a Th17 phenotype.sup.16, and we also observed increased CD4+CD161+ T-cells in recovered patients. These CD4+CD161+ T-cells are known to be either IL-17 producer cells or their precursors.sup.48. Thus, our results could reflect the re-circulation of these cells from the lung or secondary lymphoid organs after infection and support the possibility of IL-17 in mediating neutrophil damage to the lungs. Together, this would support proposed anti-IL-17 or JAK2 inhibitor therapies for severe COVID-19 disease.sup.49-51.

    [0145] In addition to the changes in the heterogeneity of neutrophils, a strong decrease in T-cells was observed, especially in subsets that possess cytolytic activity such as CD8, VD1 and VD2 T-cells. These results are consistent with other studies showing a decrease of CD8+ during COVID-19 disease.sup.15,16 As for VD2 T cells, which are not MHC-restricted T-cells.sup.52,53 we showed a general decrease in the periphery with disease severity. This is in line with other inflammatory disease such as psoriasis 54 and Crohn's disease 55. However, in the lungs, during chronic obstructive pulmonary disease, γδ T-cell counts have been reported to be significantly lower in induced sputum (IS) and bronchoalveolar lavage (BAL) but not in peripheral blood, suggesting unclear inflammatory mechanisms that could influence γδ T-cells counts in the periphery 56 Interestingly, γδ T-cells, in particular VD2, are known to participate in influenza immune response 57, and actively recruit and activate neutrophils to the site of infection or inflammation.sup.58,59. Activated, neutrophils have also been found to inhibit γδ T-cells functional capacity, promoting the resolution of inflammation.sup.60,61 Therefore, it will be essential to investigate the neutrophil to γδ T-cells relationship present in lungs of SARS-CoV-2 infected patients.

    [0146] During aging, VD2 T-cell counts in the periphery have been shown to decrease with age for both males and females.sup.62-65. Interestingly, VD2 counts between males and females can be quite variable depending on the population sampled. Higher VD2 counts in males were observed in a Japanese population, while a similar study in Germany and Italy observed higher VD2 counts in females.sup.62,65 Additionally, elderly individuals generally have systemic chronic low-grade inflammation, which was previously termed “inflamm-aging”.sup.66, with higher basal levels of molecules such as CRP, TNF-α and IL-6.sup.67-69. These similarities in modulation of VD2 T-cell counts and cytokines between COVID-19 severity and aging could explain why elderly individuals are more susceptible to severe disease, since they have a higher basal level of inflammation and lower level of VD2 T-cells as compared to the young. In any case, the lower VD2 counts in elderly populations will influence the immature neutrophil to VD2 ratio by overestimating their risk to severe COVID-19 as compared to a younger population. However, since age is a very well established risk factor for severe COVID-19 disease.sup.70-72, we postulate that the immature neutrophil to VD2 ratio takes into account the immunological age (measured by VD2 T-cell counts of the patient) which contributes to the improved sensitivity and specificity observed here with area under receiver operating characteristic analysis (FIG. 5A-B).

    [0147] Our results indicate that an early post illness onset iNVD2R, accessible through a simple 5 colours flow cytometry panel (CD3; VD2; CD66b/CD15; CD10; CD45), would be an excellent prognostic screening tool for predicting probable patient progression to pneumonia or hypoxia. This prognostic possibility needs to be validated in a prospective cohort. Moreover, CD8 could also be included in the flow cytometry panel as a fallback option since VD2 counts could be decreased by medication, such as Azathioprine, as well as underlying conditions, such as inflammatory bowel disease, aging or psoriasis, which could be risk factors for COVID-19.sup.55. Analysis of the proposed parameter would allow for a more accurate and earlier prognosis due to the interconnection between neutrophils and V62 T cells, which can then be utilised for early therapeutic interventions, improve patient triage and better healthcare resource management.

    REFERENCES

    [0148] 1 Cohen, J. & Normile, D. New SARS-like virus in China triggers alarm. Science 367, 234-235, doi:10.1126/science.367.6475.234 (2020). [0149] 2 Guan, W. J., Ni, Z. Y., Hu, Y., Liang, W. H., Ou, C. Q., He, J. X., Liu, L., Shan, H., Lei, C. L., Hui, D. S. C., Du, B., Li, L. J., Zeng, G., Yuen, K. Y., Chen, R. C., Tang, C. L., Wang, T., Chen, P. Y., Xiang, J., Li, S. Y., Wang, J. L., Liang, Z. J., Peng, Y. X., Wei, L., Liu, Y., Hu, Y. H., Peng, P., Wang, J. M., Liu, J. Y., Chen, Z., Li, G., Zheng, Z. J., Qiu, S. Q., Luo, J., Ye, C. J., Zhu, S. Y., Zhong, N. S. & China Medical Treatment Expert Group for, C. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med, doi:10.1056/NEJMoa2002032 (2020). [0150] 3 Wu, Z. & McGoogan, J. M. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72314 Cases From the Chinese Center for Disease Control and Prevention. JAMA, doi:10.1001/jama.2020.2648 (2020). [0151] 4 Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., Zhao, Y., Li, Y., Wang, X. & Peng, Z. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA, doi:10.1001/jama.2020.1585 (2020). [0152] 5 Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., Xia, J., Yu, T., Zhang, X. & Zhang, L. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395, 507-513, doi:10.1016/S0140-6736(20)30211-7 (2020). [0153] 6 Connors, J. M. & Levy, J. H. COVID-19 and its implications for thrombosis and anticoagulation. Blood 135, 2033-2040, doi:10.1182/blood.2020006000 (2020). [0154] 7 Magro, C., Mulvey, J. J., Berlin, D., Nuovo, G., Salvatore, S., Harp, J., Baxter-Stoltzfus, A. & Laurence, J. Complement associated microvascular injury and thrombosis in the pathogenesis of severe COVID-19 infection: A report of five cases. Transl Res 220, 1-13, doi:10.1016/j.trsl.2020.04.007 (2020). [0155] 8 Jose, R. J. & Manuel, A. COVID-19 cytokine storm: the interplay between inflammation and coagulation. Lancet Respir Med 8, e46-e47, doi:10.1016/S2213-2600(20)30216-2 (2020). [0156] 9 Chen, G., Wu, D., Guo, W., Cao, Y., Huang, D., Wang, H., Wang, T., Zhang, X., Chen, H., Yu, H., Zhang, M., Wu, S., Song, J., Chen, T., Han, M., Li, S., Luo, X., Zhao, J. & Ning, Q. Clinical and immunological features of severe and moderate coronavirus disease 2019. J Clin Invest 130, 2620-2629, doi:10.1172/JC1137244 (2020). [0157] 10 Zheng, H. Y., Zhang, M., Yang, C. X., Zhang, N., Wang, X. C., Yang, X. P., Dong, X. Q. & Zheng, Y. T. Elevated exhaustion levels and reduced functional diversity of T cells in peripheral blood may predict severe progression in COVID-19 patients. Cell Mol Immunol, doi:10.1038/s41423-020-0401-3 (2020). [0158] 11 Qin, C., Zhou, L., Hu, Z., Zhang, S., Yang, S., Tao, Y., Xie, C., Ma, K., Shang, K., Wang, W. & Tian, D. S. Dysregulation of immune response in patients with COVID-19 in Wuhan, China. Clin Infect Dis, doi:10.1093/cid/ciaa248 (2020). [0159] 12 Zhou, Y., Fu, B., Zheng, X., Wang, D., Zhao, C., qi, Y., Sun, R., Tian, Z., Xu, X. & Wei, H. Pathogenic T cells and inflammatory monocytes incite inflammatory storm in severe COVID-19 patients. National Science Review, doi:10.1093/nsr/nwaa041 (2020). [0160] 13 Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., Xiao, Y., Gao, H., Guo, L., Xie, J., Wang, G., Jiang, R., Gao, Z., Jin, Q., Wang, J. & Cao, B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497-506, doi:10.1016/S0140-6736(20)30183-5 (2020). [0161] 14 Hadjadj, J., Yatim, N., Barnabei, L., Corneau, A., Boussier, J., Smith, N., Pere, H., Charbit, B., Bondet, V., Chenevier-Gobeaux, C., Breillat, P., Carlier, N., Gauzit, R., Morbieu, C., Pene, F., Marin, N., Roche, N., Szwebel, T. A., Merkling, S. H., Treluyer, J. M., Veyer, D., Mouthon, L., Blanc, C., Tharaux, P. L., Rozenberg, F., Fischer, A., Duffy, D., Rieux-Laucat, F., Kerneis, S. & Terrier, B. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science 369, 718-724, doi:10.1126/science.abc6027 (2020). [0162] 15 Xu, Z., Shi, L., Wang, Y., Zhang, J., Huang, L., Zhang, C., Liu, S., Zhao, P., Liu, H., Zhu, L., Tai, Y., Bai, C., Gao, T., Song, J., Xia, P., Dong, J., Zhao, J. & Wang, F. S. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med 8, 420-422, doi:10.1016/S2213-2600(20)30076-X (2020). [0163] 16 De Biasi, S., Meschiari, M., Gibellini, L., Bellinazzi, 539 C., Borella, R., Fidanza, L., Gozzi, L., Iannone, A., Lo Tartaro, D., Mattioli, M., Paolini, A., Menozzi, M., Milic, J., Franceschi, G., Fantini, R., Tonelli, R., Sita, M., Sarti, M., Trenti, T., Brugioni, L., Cicchetti, L., Facchinetti, F., Pietrangelo, A., Clini, E., Girardis, M., Guaraldi, G., Mussini, C. & Cossarizza, A. Marked T cell activation, senescence, exhaustion and skewing towards TH17 in patients with COVID-19 pneumonia. Nat Commun 11, 3434, doi:10.1038/s41467-020-17292-4 (2020). [0164] 17 Bourgoin, P., Biechele, G., Ait Belkacem, I., Morange, P. E. & Malergue, F. Role of the interferons in CD64 and CD169 expressions in whole blood: Relevance in the balance between viral- or bacterial-oriented immune responses. Immun Inflamm Dis 8, 106-123, doi:10.1002/iid3.289 (2020). [0165] 18 Lajaunias, F., Dayer, J. M. & Chizzolini, C. Constitutive repressor activity of CD33 on human monocytes requires sialic acid recognition and phosphoinositide 3-kinase-mediated intracellular signaling. Eur J Immunol 35, 243-251, doi:10.1002/eji.200425273 (2005). [0166] 19 Lubbers, J., Rodriguez, E. & van Kooyk, Y. Modulation of Immune Tolerance via Siglec-Sialic Acid Interactions. Front Immunol 9, 2807, doi:10.3389/fimmu.2018.02807 (2018). [0167] 20 Marini, O., Costa, S., Bevilacqua, D., Calzetti, F., Tamassia, N., Spina, C., De Sabata, D., Tinazzi, E., Lunardi, C., Scupoli, M. T., Cavallini, C., Zoratti, E., Tinazzi, I., Marchetta, A., Vassanelli, A., Cantini, M., Gandini, G., Ruzzenente, A., Guglielmi, A., Missale, F., Vermi, W., Tecchio, C., Cassatella, M. A. & Scapini, P. Mature CD10(+) and immature CD10(−) neutrophils present in G-CSF-treated donors display opposite effects on T cells. Blood 129, 1343-1356, doi:10.1182/blood-2016-04-713206 (2017). [0168] 21 Evrard, M., Kwok, I. W. H., Chong, S. Z., Teng, K. W. W., Becht, E., Chen, J., Sieow, J. L., Penny, H. L., Ching, G. C., Devi, S., Adrover, J. M., Li, J. L. Y., Liong, K. H., Tan, L., Poon, Z., Foo, S., Chua, J. W., Su, I. H., Balabanian, K., Bachelerie, F., Biswas, S. K., Larbi, A., Hwang, W. Y. K., Madan, V., Koeffler, H. P., Wong, S. C., Newell, E. W., Hidalgo, A., Ginhoux, F. & Ng, L. G. Developmental Analysis of Bone Marrow Neutrophils Reveals Populations Specialized in Expansion, Trafficking, and Effector Functions. Immunity 48, 364-379 e368, doi:10.1016/j.immuni.2018.02.002 (2018). [0169] 22 Ng, L. G., Ostuni, R. & Hidalgo, A. Heterogeneity of neutrophils. Nat Rev Immunol 19, 255-265, doi:10.1038/s41577-019-0141-8 (2019). [0170] 23 Young, B. E., Ong, S. W. X., Kalimuddin, S., Low, J. G., Tan, S. Y., Loh, J., Ng, O. T., Marimuthu, K., Ang, L. W., Mak, T. M., Lau, S. K., Anderson, D. E., Chan, K. S., Tan, T. Y., Ng, T. Y., Cui, L., Said, Z., Kurupatham, L., Chen, M. I., Chan, M., Vasoo, S., Wang, L. F., Tan, B. H., Lin, R. T. P., Lee, V. J. M., Leo, Y. S., Lye, D. C. & Singapore Novel Coronavirus Outbreak Research, T. Epidemiologic Features and Clinical Course of Patients Infected With SARS-CoV-2 in Singapore. JAMA, doi:10.1001/jama.2020.3204 (2020). [0171] 24 Young, B. E. a. O., Sean Wei Xiang and Ng, Lisa F P and Anderson, Danielle E. and Chia, Wan Ni and Chia, Po Ying and Ang, Li Wei and Mak, Tze-Minn and Kalimuddin, Shirin and Chai, Louis Yi Ann and Pada, Surinder and Tan, Seow Yen and Sun, Louisa and Parthasarathy, Purmina and Fong, Siew-Wai and Chan, Yi-Hao and Tan, Chee Wah and Lee, Bernett and Rotzschke, Olaf and Ding, Ying and Tambyah, Paul and Low, Jenny G H and Cui, Lin and Barkham, Timothy and Lin, Raymond Tzer Pin and Leo, Yee-Sin and Renia, Laurent and Wang, Lin-Fa and Lye, David Chien and Team, Singapore 2019 Novel Coronavirus Outbreak Research. Immunological and Viral Correlates of COVID-19 Disease Severity: A Prospective Cohort Study of the First 100 Patients in Singapore (Apr. 15, 2020). Available at SSRN: https://ssrn.com/abstract=3576846 orhttp://dx.doi.org/10.2139/ssrn.3576846 (2020). [0172] 25 Cossarizza, A., De Biasi, S., Guaraldi, G., Girardis, M., Mussini, C. & (MoCol9)#, M. C.-W. G.SARS-CoV-2, the Virus that Causes COVID-19: Cytometry and the New Challenge for Global Health. Cytometry A 97, 340-343, doi:10.1002/cyto.a.24002 (2020). [0173] 26 Liu, J., Li, S., Liu, J., Liang, B., Wang, X., Wang, H., Li, W., Tong, Q., Yi, J., Zhao, L., Xiong, L., Guo, C., Tian, J., Luo, J., Yao, J., Pang, R., Shen, H., Peng, C., Liu, T., Zhang, Q., Wu, J., Xu, L., Lu, S., Wang, B., Weng, Z., Han, C., Zhu, H., Zhou, R., Zhou, H., Chen, X., Ye, P., Zhu, B., Wang, L., Zhou, W., He, S., He, Y., Jie, S., Wei, P., Zhang, J., Lu, Y., Wang, W., Zhang, L., Li, L., Zhou, F., Wang, J., Dittmer, U., Lu, M., Hu, Y., Yang, D. & Zheng, X. Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients. EBioMedicine 55, 102763, doi:10.1016/j.ebiom.2020.102763 (2020). [0174] 27 Lagunas-Rangel, F. A. Neutrophil-to-lymphocyte ratio and lymphocyte-to-C-reactive protein ratio in patients with severe coronavirus disease 2019 (COVID-19): A meta-analysis. J Med Virol, doi:10.1002/jmv.25819 (2020). [0175] 28 Zini, G., Bellesi, S., Ramundo, F. & d'Onofrio, G. Morphological a 599 nomalies of circulating blood cells in COVID-19. Am J Hematol, doi:10.1002/ajh.25824 (2020). [0176] 29 Sanchez-Cerrillo, I., Landete, P., Aldave, B., Sanchez-Alonso, S., Sanchez-Azofra, A., Marcos-Jimenez, A., Avalos, E., Alcaraz-Serna, A., de Los Santos, I., Mateu-Albero, T., Esparcia, L., Lopez-Sanz, C., Martinez-Fleta, P., Gabrie, L., Del Campo Guerola, L., de la Fuente, H., Calzada, M. J., Gonzalez-Alvaro, I., Alfranca, A., Sanchez-Madrid, F., Munoz-Calleja, C., Soriano, J. B., Ancochea, J. & Martin-Gayo, E. COVID-19 severity associates with pulmonary redistribution of CD1c+DC and inflammatory transitional and nonclassical monocytes. J Clin Invest, 2020.2005.2013.20100925, doi:10.1172/JC1140335 (2020). [0177] 30 Yao, X. H., Li, T. Y., He, Z. C., Ping, Y. F., Liu, H. W., Yu, S. C., Mou, H. M., Wang, L. H., Zhang, H. R., Fu, W. J., Luo, T., Liu, F., Guo, Q. N., Chen, C., Xiao, H. L., Guo, H. T., Lin, S., Xiang, D. F., Shi, Y., Pan, G. Q., Li, Q. R., Huang, X., Cui, Y., Liu, X. Z., Tang, W., Pan, P. F., Huang, X. Q., Ding, Y. Q. & Bian, X. W. [A pathological report of three COVID-19 cases by minimal invasive autopsies]. Zhonghua Bing LiXue Za Zhi 49, 411-417, doi:10.3760/cma.j.cn112151-20200312-00193 (2020). [0178] 31 Barnes, B. J., Adrover, J. M., Baxter-Stoltzfus, A., Borczuk, A., Cools-Lartigue, J., Crawford, J. M., Dassler-Plenker, J., Guerci, P., Huynh, C., Knight, J. S., Loda, M., Looney, M. R., McAllister, F., Rayes, R., Renaud, S., Rousseau, S., Salvatore, S., Schwartz, R. E., Spicer, J. D., Yost, C. C., Weber, A., Zuo, Y. & Egeblad, M. Targeting potential drivers of COVID-19: Neutrophil extracellular traps. J Exp Med 217, doi:10.1084/jem.20200652 (2020). [0179] 32 Hidalgo, A., Chilvers, E. R., Summers, C. & Koenderman, L. The Neutrophil Life Cycle. Trends Immunol 40, 584-597, doi:10.1016/j.it.2019.04.013 (2019). [0180] 33 Morrissey, S. M., Geller, A. E., Hu, X., Tieri, D., Cooke, E. A., Ding, C., Woeste, M., Zhange, H.-g., Cavallazi, R., Clifford, S. P., Chen, J., Kong, M., Watson, C. T., Huang, J. & Yan, J. Emergence of Low-density Inflammatory Neutrophils Correlates with Hypercoagulable State and Disease Severity in COVID-19 Patients. Preprint at https://www.medrxiv.org/content/medrxiv/early/2020/05/26/2020.05.22.20106724.full.pdf, 2020.2005.2022.20106724, doi:10.1101/2020.05.22.20106724 (2020). [0181] 34 Schulte-Schrepping, J., Reusch, N., Paclik, D., Baßler, K., Schlickeiser, S., Zhang, B., Kramer, B., Krammer, T., Brumhard, S., Bonaguro, L., De Domenico, E., Wendisch, D., Grasshoff, M., Kapellos, T. S., Beckstette, M., Pecht, T., Saglam, A., Dietrich, O., Mei, H. E., Schulz, A. R., Conrad, C., Kunkel, D., Vafadarnejad, E., Xu, C.-J., Horne, A., Herbert, M., Drews, A., Thibeault, C., Pfeiffer, M., Hippenstiel, S., Hocke, A., Müller-Redetzky, H., Heim, K.-M., Machleidt, F., Uhrig, A., Bosquillon de Jarcy, L., Jürgens, L., Stegemann, M., Glösenkamp, C. R., Volk, H.-D., Goffinet, C., Landthaler, M., Wyler, E., Georg, P., Schneider, M., Dang-Heine, C., Neuwinger, N., Kappert, K., Tauber, R., Corman, V., Raabe, J., Kaiser, K. M., Vinh, M. T., Rieke, G., Meisel, C., Ulas, T., Becker, M., Geffers, R., Witzenrath, M., Drosten, C., Suttorp, N., von Kalle, C., Kurth, F., Händler, K., Schultze, J. L., Aschenbrenner, A. C., Li, Y., Nattermann, J., Sawitzki, B., Saliba, A.-E. & Sander, L. E. Severe COVID-19 is marked by a dysregulated myeloid cell compartment. Cell, doi:https://doi.org/10.1016/j.cell.2020.08.001 (2020). [0182] 35 Silvin, A., Chapuis, N., Dunsmore, G., Goubet, A.-G., Dubuisson, A., Derosa, L., Almire, C., Hénon, C., Kosmider, O., Droin, N., Rameau, P., Catelain, C., Alfaro, A., Dussiau, C., Friedrich, C., Sourdeau, E., Marin, N., Szwebel, T.-A., Cantin, D., Mouthon, L., Borderie, D., Deloger, M., Bredel, D., Mouraud, S., Drubay, D., Andrieu, M., Lhonneur, A.-S., Saada, V., Stoclin, A., Willekens, C., Pommeret, F., Griscelli, F., Ng, L. G., Zhang, Z., Bost, P., Amit, I., Barlesi, F., Marabelle, A., Pène, F., Gachot, B., Andre, F., Zitvogel, L., Ginhoux, F., Fontenay, M. & Solary, E. Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19. Cell, doi:https://doi.org/10.1016/j.cell.2020.08.002 (2020). [0183] 36 Seebach, J. D., Morant, R., Ruegg, R., Seifert, B. & Fehr, J. The diagnostic value of the neutrophil left shift in predicting inflammatory and infectious disease. Am J Clin Pathol 107, 582-591, doi:10.1093/ajcp/107.5.582 (1997). [0184] 37 Carmona-Rivera, C. & Kaplan, M. J. Low-density granulocytes: a distinct class of neutrophils in systemic autoimmunity. Semin Immunopathol 35, 455-463, doi:10.1007/s00281-013-0375-7 (2013). [0185] 38 Silvestre-Roig, C., Fridlender, Z. G., Glogauer, M. & Scapini, P. Neutrophil Diversity in Health and Disease. Trends Immunol 40, 565-583, doi:10.1016/j.it.2019.04.012 (2019). [0186] 39 Mortaz, E., Alipoor, S. D., Adcock, I. M., Mumby, S. & Koenderman, L. Update on Neutrophil Function in Severe Inflammation. Front Immunol 9, 2171, doi:10.3389/fimmu.2018.02171 (2018). [0187] 40 van Grinsven, E., Textor, J., Hustin, 659 L. S. P., Wolf, K., Koenderman, L. & Vrisekoop, N. Immature Neutrophils Released in Acute Inflammation Exhibit Efficient Migration despite Incomplete Segmentation of the Nucleus. J Immunol 202, 207-217, doi:10.4049/jimmunol.1801255 (2019). [0188] 41 Aziz, M., Fatima, R. & Assaly, R. Elevated Interleukin-6 and Severe COVID-19: A Meta-Analysis. J Med Virol, doi:10.1002/jmv.25948 (2020). [0189] 42 Yang, Y., Shen, C., Li, J., Yuan, J., Wei, J., Huang, F., Wang, F., Li, G., Li, Y., Xing, L., Peng, L., Yang, M., Cao, M., Zheng, H., Wu, W., Zou, R., Li, D., Xu, Z., Wang, H., Zhang, M., Zhang, Z., Gao, G. F., Jiang, C., Liu, L. & Liu, Y. Plasma IP-10 and MCP-3 levels are highly associated with disease severity and predict the progression of COVID-19. J Allergy Clin Immunol, doi:10.1016/j.jaci.2020.04.027 (2020). [0190] 43 Wen, W., Su, W., Tang, H., Le, W., Zhang, X., Zheng, Y., Liu, X., Xie, L., Li, J., Ye, J., Dong, L., Cui, X., Miao, Y., Wang, D., Dong, J., Xiao, C., Chen, W. & Wang, H. Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing. Cell Discov 6, 31, doi:10.1038/s41421-020-0168-9 (2020). [0191] 44 Zhang, D., Guo, R., Lei, L., Liu, H., Wang, Y., Wang, Y., Dai, T., Zhang, T., Lai, Y., Wang, J., Liu, Z., He, A., O'Dwyer, M. & Hu, J. COVID-19 infection induces readily detectable morphological and inflammation-related phenotypic changes in peripheral blood monocytes, the severity of which correlate with patient outcome. Preprint at https://www.medrxiv.org/content/medrxiv/early/2020/03/26/2020.03.24.20042655.full.pdf, 2020.2003.2024.20042655, doi:10.1101/2020.03.24.20042655 (2020). [0192] 45 Zimmermann, M., Aguilera, F. B., Castellucci, M., Rossato, M., Costa, S., Lunardi, C., Ostuni, R., Girolomoni, G., Natoli, G., Bazzoni, F., Tamassia, N. & Cassatella, M. A. Chromatin remodelling and autocrine TNFalpha are required for optimal interleukin-6 expression in activated human neutrophils. Nat Commun 6, 6061, doi:10.1038/ncomms7061 (2015). [0193] 46 Cervantes, J. L., Weinerman, B., Basole, C. & Salazar, J. C. TLR8: the forgotten relative revindicated. Cell Mol Immunol 9, 434-438, doi:10.1038/cmi.2012.38 (2012). [0194] 47 Linden, A., Laan, M. & Anderson, G. P. Neutrophils, interleukin-17A and lung disease. Eur Respir J 25, 159-172, doi:10.1183/09031936.04.00032904 (2005). [0195] 48 Maggi, L., Santarlasci, V., Capone, M., Peired, A., Frosali, F., Crome, S. Q., Querci, V., Fambrini, M., Liotta, F., Levings, M. K., Maggi, E., Cosmi, L., Romagnani, S. & Annunziato, F. CD161 is a marker of all human IL-17-producing T-cell subsets and is induced by RORC. Eur J Immunol 40, 2174-2181, doi:10.1002/eji.200940257 (2010). [0196] 49 Pacha, O., Sallman, M. A. & Evans, S. E. COVID-19: a case for inhibiting IL-17? Nat Rev Immunol, doi:10.1038/s41577-020-0328-z (2020). [0197] 50 Megna, M., Napolitano, M. & Fabbrocini, G. May IL-17 have a role in COVID-19 infection? Med Hypotheses 140, 109749, doi:10.1016/j.mehy.2020.109749 (2020). [0198] 51 Wu, D. & Yang, X. O. TH17 responses in cytokine storm of COVID-19: An emerging target of JAK2 inhibitor Fedratinib. J Microbiol Immunol Infect, doi:10.1016/j.jmii.2020.03.005 (2020). [0199] 52 Tanaka, Y., Morita, C. T., Tanaka, Y., Nieves, E., Brenner, M. B. & Bloom, B. R. Natural and synthetic non-peptide antigens recognized by human gamma delta T cells. Nature 375, 155-158, doi:10.1038/375155a0 (1995). [0200] 53 Poupot, M. & Fournie, J. J. Non-peptide antigens activating human Vgamma9/Vdelta2 T lymphocytes. Immunol Lett 95, 129-138, doi:10.1016/j.imlet.2004.06.013 (2004). [0201] 54 Laggner, U., Di Meglio, P., Perera, G. K., Hundhausen, C., Lacy, K. E., Ali, N., Smith, C. H., Hayday, A. C., Nickoloff, B. J. & Nestle, F. O. Identification of a novel proinflammatory human skin-homing Vgamma9Vdelta2 T cell subset with a potential role in psoriasis. J Immunol 187, 2783-2793, doi:10.4049/jimmunol.1100804 (2011). [0202] 55 McCarthy, N. E., Hedin, C. R., Sanders, T. J., Amon, P., Hoti, I., Ayada, I., Baji, V., Giles, E. M., Wildemann, M., Bashir, Z., Whelan, K., Sanderson, I., Lindsay, J. O. & Stagg, A. J. Azathioprine therapy selectively ablates human Vdelta2(+) T cells in Crohn's disease. J Clin Invest 125, 3215-3225, doi:10.1172/JC180840 (2015). [0203] 56 Urboniene, D., Babusyte, A., Lotvall, J., Sakalauskas, R. & Sitkauskiene, B. Distribution of gammadelta and other T-lymphocyte subsets in patients with chronic obstructive pulmonary disease and asthma. Respir Med 107, 413-423, doi:10.1016/j.rmed.2012.11.012 (2013). [0204] 57 Sant, S., Jenkins, M. R., Dash, P., Watson, K. A., Wang, Z., Pizzolla, A., Koutsakos, M., Nguyen, T. H., Lappas, M., Crowe, J., Loudovaris, T., Mannering, S. I., Westall, G. P., Kotsimbos, T. C., Cheng, A. C., Wakim, L., Doherty, P. C., Thomas, P. G., Loh, L. & Kedzierska, K. Human gammadelta T-cell receptor repertoire is shaped by influenza viruses, age and tissue compartmentalisation. Clin Transl Immunology 8, e1079, doi:10.1002/cti2.1079 (2019). [0205] 58 Bonneville, M., O'Brien, R. L. & Born, W. K. Gammadelta T cell effector functions: a blend of innate programming and acquired plasticity. Nat Rev Immunol 10, 467-478, doi:10.1038/nri2781 (2010). [0206] 59 Davey, M. S., Lin, C. Y., Roberts, G. W., Heuston, S., Brown, A. C., Chess, J. A., Toleman, M. A., Gahan, C. G., Hill, C., Parish, T., Williams, J. D., Davies, S. J., Johnson, D. W., Topley, N., Moser, B. & Eberl, M. Human neutrophil clearance of bacterial pathogens triggers antimicrobial gammadelta T cell responses in early infection. PLoS Pathog 7, e1002040, doi:10.1371/journal.ppat.1002040 (2011). [0207] 60 Fazio, J., Kalyan, S., Wesch, D. & Kabelitz, D. Inhibition of human gammadelta T cell proliferation and effector functions by neutrophil serine proteases. Scand J Immunol 80, 381-389, doi:10.1111/sji.12221 (2014). [0208] 61 Sabbione, F., Gabelloni, M. L., Ernst, G., Gori, M. S., Salamone, G., Oleastro, M., Trevani, A., Geffner, J. & Jancic, C. C. Neutrophils suppress gammadelta T-cell function. Eur J Immunol 44, 819-830, doi:10.1002/eji.201343664 (2014). [0209] 62 Caccamo, N., Meraviglia, S., Ferlazzo, V., Angelini, D., Borsellino, G., Poccia, F., Battistini, L., Dieli, F. & Salerno, A. Differential requirements for antigen or homeostatic cytokines for proliferation and differentiation of human Vgamma9Vdelta2 naive, memory and effector T cell subsets. Eur J Immunol 35, 1764-1772, doi:10.1002/eji.200525983 (2005). [0210] 63 Tan, C. T., Wistuba-Hamprecht, K., Xu, W., Nyunt, M. S., Vasudev, A., Lee, B. T., Pawelec, G., Puan, K. J., Rotzschke, O., Ng, T. P. & Larbi, A. Vdelta2+ and alpha/ss T cells show divergent trajectories during human aging. Oncotarget 7, 44906-44918, doi:10.18632/oncotarget.10096 (2016). [0211] 64 Vasudev, A., Ying, C. T., Ayyadhury, S., Puan, K. J., Andiappan, A. K., Nyunt, M. S., Shadan, N. B., Mustafa, S., Low, I., Rotzschke, O., Fulop, T., Ng, T. P. & Larbi, A. gamma/delta T cell subsets in human aging using the classical alpha/beta T cell model. J Leukoc Biol 96, 647-655, doi:10.1189/jlb.5A1213-650RR (2014). [0212] 65 Michishita, Y., Hirokawa, M., Guo, Y. M., Abe, Y., Liu, J., Ubukawa, K., Fujishima, N., Fujishima, M., Yoshioka, T., Kameoka, Y., Saito, H., Tagawa, H., Takahashi, N. & Sawada, K. Age-associated alteration of gammadelta T-cell repertoire and different profiles of activation induced death of Vdelta1 and Vdelta2 T cells. Int J Hematol 94, 230-240, doi:10.1007/si2185-011-0907-7 (2011). [0213] 66 Franceschi, C., Bonafe, M., Valensin, S., Olivieri, F., De Luca, M., Ottaviani, E. & De Benedictis, G. Inflamm-aging. An evolutionary perspective on immunosenescence. Ann N Y Acad Sci 908, 244-254, doi:10.1111/j.1749-6632.2000.tb06651.x (2000). [0214] 67 Fulop, T., Larbi, A., Dupuis, G., Le Page, A., Frost, E. H., Cohen, A. A., Witkowski, J. M. & Franceschi, C. Immunosenescence and Inflamm-Aging As Two Sides of the Same Coin: Friends or Foes? Front Immunol 8, 1960, doi:10.3389/fimmu.2017.01960 (2017). [0215] 68 Xu, W., Lau, Z. W. X., Fulop, T. & Larbi, A. The Aging of gammadelta T Cells. Cells 9, doi:10.3390/cells9051181 (2020). [0216] 69 Bonafe, M., Prattichizzo, F., Giuliani, A., Storci, G., Sabbatinelli, J. & Olivieri, F. Inflamm aging: Why older men are the most susceptible to SARS-CoV-2 complicated outcomes. Cytokine Growth Factor Rev 53, 33-37, doi:10.1016/j.cytogfr.2020.04.005 (2020). [0217] 70 Liu, Y., Mao, B., Liang, S., Yang, J. W., Lu, H. W., Chai, Y. H., Wang, L., Zhang, L., Li, Q. H., Zhao, L., He, Y., Gu, X. L., Ji, X. B., Li, L., Jie, Z. J., Li, Q., Li, X. Y., Lu, H. Z., Zhang, W. H., Song, Y. L., Qu, J. M., Xu, J. F. & Shanghai Clinical Treatment Experts Group for, C. Association between age and clinical characteristics and outcomes of COVID-19. Eur Respir J 55, doi:10.1183/13993003.01112-2020 (2020). [0218] 71 Li, J., Huang, D. Q., Zou, B., Yang, H., Hui, W. Z., Rui, F., Yee, N. T. S., Liu, C., Nerurkar, S. N., Kai, J. C. Y., Teng, M. L. P., Li, X., Zeng, H., Borghi, J. A., Henry, L., Cheung, R. & Nguyen, M. H. Epidemiology of COVID-19: A Systematic Review and Meta-analysis of Clinical Characteristics, Risk factors and Outcomes. J Med Virol, doi:10.1002/jmv.26424 (2020). [0219] 72 Vahidy, F. S., Nicolas, J. C., Meeks, J. R., Khan, O., Pan, A., Jones, S. L., Masud, F., Sostman, H. D., Phillips, R., Andrieni, J. D., Kash, B. A. & Nasir, K. Racial and ethnic disparities in SARS-CoV-2 pandemic: analysis of a COVID-19 observational registry for a diverse US metropolitan population. BMJ Open 10, e039849, doi:10.1136/bmjopen-2020-039849 (2020). [0220] 73 Pung, R., Chiew, C. J., Young, B. E., Chin, S., 777 Chen, M. I., Clapham, H. E., Cook, A. R., Maurer-Stroh, S., Toh, M., Poh, C., Low, M., Lum, J., Koh, V. T. J., Mak, T. M., Cui, L., Lin, R., Heng, D., Leo, Y. S., Lye, D. C. & Lee, V. J. M. Investigation of three clusters of COVID-19 in Singapore: implications for surveillance and response measures. Lancet 395, 1039-1046, doi:10.1016/s0140-6736(20)30528-6 (2020). [0221] 74 Darnell, M. E. & Taylor, D. R. Evaluation of inactivation methods for severe acute respiratory syndrome coronavirus in noncellular blood products. Transfusion 46, 1770-1777, doi:10.1111/j.1537-2995.2006.00976.x (2006). [0222] 75 Chen, H., Lau, M. C., Wong, M. T., Newell, E. W., Poidinger, M. & Chen, J. Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline. PLoS Comput Biol 12, e1005112, doi:10.1371/journal.pcbi.1005112 (2016).

    Applications

    [0223] Embodiments of the methods disclosed herein provide a rapid and efficient care management of patients by identifying patients that may have a high or enhanced risk of developing severe infectious disease and/or inflammatory disease onset. Embodiments of the disclosed methods also seek to overcome the problems of identifying patients who may have an enhanced or increased risk of developing pneumonia and/or hypoxia symptoms of SAR-COV2.

    [0224] Advantageously, immature neutrophils to VD2 T cell ratio shows very high sensitivity and specificity with pneumonia and hypoxia symptoms in SAR-CoV2-infected patients early after infection allowing for rapid and efficient care management of patients identified as high risk.

    [0225] In some examples, the use of VD2 T-cells as the denominator of the ratio instead of CD8 T-cells may be used. In such examples, VD2 T-cells show a lower spread (standard deviation) as compared to CD8 T-cells in the different severity groups ( ), and decreases in the periphery with age. This advantageously allows the parameter to be automatically adjusted to the immunological age of the patient.

    [0226] Even more advantageously, the technology is compatible with most standard colour cytometers available in hospitals and harnesses their rapid diagnostic capacities to perform assessment of the said parameters as a prognostic marker for infected patients (such as SAR-CoV2-infected patients). In addition, the methods as disclosed herein may be performed using antibodies and/or counting beads known in the art. Advantageously, antibodies and counting beads are easily packaged premixed and lyophilized allowing easy shipment and long shelf life. Usage of counting beads also advantageously allows for the comparison of the number of beads acquired to the theoretical number of beads in the mix. This allows correction of the acquired and detected cell population counts to the theoretical counts in the fixed volume of blood mixed with the full stain mix.

    [0227] The present disclosure also provides for the possibility of having a standardized identification of immature neutrophil population using the combination of the highly specific markers in a kit (for example, as exemplified in Example 2, the full stain mix and/or the FMO mix).