Specifications of qPCR based epigenetic immune cell quantification.

OBJECTIVES: Immune monitoring is an important aspect in diagnostics and clinical trials for patients with compromised immune systems. Flow cytometry is the standard method for immune cell counting but faces limitations. Best practice guidelines are available, but lack of standardization complicates compliance with e.g., in vitro diagnostic regulations. Limited sample availability forces immune monitoring to predominantly use population-based reference intervals. Epigenetic qPCR has evolved as alternative with broad applicability and low logistical demands. Analytical performance specifications (APS) have been defined for qPCR in several regulated fields including testing of genetically modified organisms or vector-shedding. METHODS: APS were characterized using five epigenetic qPCR-based assays quantifying CD3+, CD4+, CD8+ T, B and NK cells in light of regulatory requirements. RESULTS: Epigenetic qPCR meets all specifications including bias, variability, linearity, ruggedness and sample stability as suggested by pertinent guidelines and regulations. The assays were subsequently applied to capillary blood from 25 normal donors over a 28-day period. Index of individuality (IoI) and reference change values were determined to evaluate potential diagnostic gains of individual reference intervals. Analysis of the IoI suggests benefits for individual over population-based references. Reference change values (RCVs) show that changes of approx. Fifty percent from prior measurement are suggestive for clinically relevant changes in any of the 5 cell types. CONCLUSIONS: The demonstrated precision, long-term stability and obtained RCVs render epigenetic cell counting a promising tool for immune monitoring in clinical trials and diagnosis.

Epigenetic immune monitoring for COVID-19 disease course prognosis.

Background: The 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. Methods: In 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. Results: Epigenetic 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. Conclusions: Overall, 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.

Circulating Immune Cell Composition and Cancer Risk: A Prospective Study Using Epigenetic Cell Count Measures.

Although ample evidence indicates that immune cell homeostasis is an important prognostic outcome determinant in patients with cancer, few studies have examined whether it also determines cancer risk among initially healthy individuals. We performed a case-cohort study including incident cases of breast (n = 207), colorectal (n = 111), lung (n = 70), and prostate (n = 201) cancer as well as a subcohort (n = 465) within the European Prospective Investigation into Cancer and Nutrition-Heidelberg cohort. Relative counts of neutrophils, monocytes, and lymphocyte sublineages were measured by qRT-PCR. HRs and 95% confidence intervals were used to measure the associations between relative counts of immune cell and cancer risks. When relative counts of immune cell types were taken individually, a significant positive association was observed between relative counts of FOXP3+ regulatory T cells (Tregs) and lung cancer risk, and significant inverse associations were observed between relative CD8+ counts and risks of lung and breast cancer (overall and ER+ subtype). Multivariable models with mutual adjustments across immune markers showed further significant positive associations between higher relative FOXP3+ T-cell counts and increased risks of colorectal and breast cancer (overall and ER- subtype). No associations were found between immune cell composition and prostate cancer risk. These results affirm the relevance of elevated FOXP3+ Tregs and lower levels of cytotoxic (CD8+) T cells as risk factors for tumor development. SIGNIFICANCE: This epidemiologic study supports a role for both regulatory and cytotoxic T cells in determining cancer risk among healthy individuals.See related commentary by Song and Tworoger, p. 1801.

Epigenetic immune cell counting in human blood samples for immunodiagnostics.

Immune cell profiles provide valuable diagnostic information for hematologic and immunologic diseases. Although it is the most widely applied analytical approach, flow cytometry is limited to liquid blood. Moreover, either analysis must be performed with fresh samples or cell integrity needs to be guaranteed during storage and transport. We developed epigenetic real-time quantitative polymerase chain reaction (qPCR) assays for analysis of human leukocyte subpopulations. After method establishment, whole blood from 25 healthy donors and 97 HIV+ patients as well as dried spots from 250 healthy newborns and 24 newborns with primary immunodeficiencies were analyzed. Concordance between flow cytometric and epigenetic data for neutrophils and B, natural killer, CD3+ T, CD8+ T, CD4+ T, and FOXP3+ regulatory T cells was evaluated, demonstrating substantial equivalence between epigenetic qPCR analysis and flow cytometry. Epigenetic qPCR achieves both relative and absolute quantifications. Applied to dried blood spots, epigenetic immune cell quantification was shown to identify newborns suffering from various primary immunodeficiencies. Using epigenetic qPCR not only provides a precise means for immune cell counting in fresh-frozen blood but also extends applicability to dried blood spots. This method could expand the ability for screening immune defects and facilitates diagnostics of unobservantly collected samples, for example, in underdeveloped areas, where logistics are major barriers to screening.

More Specific Signal Detection in Functional Magnetic Resonance Imaging by False Discovery Rate Control for Hierarchically Structured Systems of Hypotheses.

Signal detection in functional magnetic resonance imaging (fMRI) inherently involves the problem of testing a large number of hypotheses. A popular strategy to address this multiplicity is the control of the false discovery rate (FDR). In this work we consider the case where prior knowledge is available to partition the set of all hypotheses into disjoint subsets or families, e. g., by a-priori knowledge on the functionality of certain regions of interest. If the proportion of true null hypotheses differs between families, this structural information can be used to increase statistical power. We propose a two-stage multiple test procedure which first excludes those families from the analysis for which there is no strong evidence for containing true alternatives. We show control of the family-wise error rate at this first stage of testing. Then, at the second stage, we proceed to test the hypotheses within each non-excluded family and obtain asymptotic control of the FDR within each family at this second stage. Our main mathematical result is that this two-stage strategy implies asymptotic control of the FDR with respect to all hypotheses. In simulations we demonstrate the increased power of this new procedure in comparison with established procedures in situations with highly unbalanced families. Finally, we apply the proposed method to simulated and to real fMRI data.

Simultaneous statistical inference for epigenetic data.

Epigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons. Furthermore, epigenetic analyses are often performed at various genetic loci simultaneously. Hence, in order to be able to draw valid conclusions for specific loci, an appropriate multiple testing correction is necessary. Finally, with technologies available for the simultaneous assessment of many interrelated biological parameters (such as gene arrays), statistical approaches also need to deal with a possibly unknown dependency structure in the data. Our statistical approach to the nonparametric comparison of two samples with independent multivariate observables is based on recently developed multivariate multiple permutation tests. We adapt their theory in order to cope with families of hypotheses regarding relative effects. Our results indicate that the multivariate multiple permutation test keeps the pre-assigned type I error level for the global null hypothesis. In combination with the closure principle, the family-wise error rate for the simultaneous test of the corresponding locus/parameter-specific null hypotheses can be controlled. In applications we demonstrate that group differences in epigenetic data can be detected reliably with our methodology.