The authors declare no conflict of interest Word count: 3994 Total number of figures and tables: 2 figures, 2 tables Supplemental Data and Methods: 1 figure, 5 tables Research.

BackgroundThe course of COVID-19 is associated with severe dysbalance of the immune system, causing both leukocytosis and lymphopenia. Immune cell monitoring may be a powerful tool to prognosticate disease outcome. However, SARS-CoV-2 positive subjects are isolated upon initial diagnosis, thus barring standard immune monitoring using fresh blood. This dilemma may be solved by epigenetic immune cell counting.MethodsIn this study, we used epigenetic immune cell counting by qPCR as an alternative way of quantitative immune monitoring for venous blood, capillary blood dried on filter paper (dried blood spots, DBS) and nasopharyngeal swabs, potentially allowing a home-based monitoring approach.ResultsEpigenetic immune cell counting in venous blood showed equivalence with dried blood spots and with flow cytometrically determined cell counts of venous blood in healthy subjects. In venous blood, we detected relative lymphopenia, neutrophilia, and a decreased lymphocyte-to-neutrophil ratio for COVID-19 patients (n =103) when compared with healthy donors (n = 113). Along with reported sex-related differences in survival we observed dramatically lower regulatory T cell counts in male patients. In nasopharyngeal swabs, T and B cell counts were significantly lower in patients compared to healthy subjects, mirroring the lymphopenia in blood. Naïve B cell frequency was lower in severely ill patients than in patients with milder stages.ConclusionsOverall, the analysis of immune cell counts is a strong predictor of clinical disease course and the use of epigenetic immune cell counting by qPCR may provide a tool that can be used even for home-isolated patients.

<p>Supplementary Figure S1. Stacked bar charts of normalized immune cell counts across strata of baseline characteristics in the sub-cohort (n = 465).</p>

<p>Table S1 shows partial Spearman correlations between relative immune cell count adjusted for age at blood collection and sex within the subcohort. Table S2 shows cross-sectional associations of circulating immune cell composition and various covariates within the subcohort overall as well as separately in men and in women using Dirichlet regression. Table S3 shows partial spearman's rank correlation coefficients of relative counts of immune markers measured one and 15 years apart. Table S4 shows the hazard ratios and confidence intervals for the association between relative counts of circulating immune cell and cancer risk adjusted for age and sex as well as factors that showed cross-sectional relationships to immune composition in the Dirichlet regression. Table S5 shows the results from the sensitivity analyses which meant to exclude subjects with follow-up shorter than two years. Hazard ratios and confidence intervals are presented for the association between relative counts of circulating immune cell and cancer risk adjusted for age and sex. Figure S1 shows stacked bar chart of normalized immune cell counts across strata of baseline characteristics that were found significant in the Dirichlet regression in the sub-cohort overall, and separately in men and in women.</p>

<p>Table S1 shows partial Spearman correlations between relative immune cell count adjusted for age at blood collection and sex within the subcohort. Table S2 shows cross-sectional associations of circulating immune cell composition and various covariates within the subcohort overall as well as separately in men and in women using Dirichlet regression. Table S3 shows partial spearman's rank correlation coefficients of relative counts of immune markers measured one and 15 years apart. Table S4 shows the hazard ratios and confidence intervals for the association between relative counts of circulating immune cell and cancer risk adjusted for age and sex as well as factors that showed cross-sectional relationships to immune composition in the Dirichlet regression. Table S5 shows the results from the sensitivity analyses which meant to exclude subjects with follow-up shorter than two years. Hazard ratios and confidence intervals are presented for the association between relative counts of circulating immune cell and cancer risk adjusted for age and sex. Figure S1 shows stacked bar chart of normalized immune cell counts across strata of baseline characteristics that were found significant in the Dirichlet regression in the sub-cohort overall, and separately in men and in women.</p>