Equivalence of DNA-based cytometry to clinical flow cytometry and CBC for immune cell quantification in peripheral blood

Clinical flow cytometry, the current standard for immune cell typing and quantification, requires intact cell membranes, complex reagents, and substantial infrastructure, making it a costly, technically demanding process. In contrast, methylation cytometry only requires the measurement of cellular DNA methylation for accurate immune cell quantification. Patterns of DNA methylation are cell specific. The methylated or unmethylated signal at cell-type-specific CpG sites is directly proportional to a cell’s abundance within a sample. Here we evaluate the equivalence of immune cell quantification by methylation cytometry (using our extended deconvolution library) and established clinical assays to investigate methylation cytometry's potential for clinical application. Whole blood was obtained from patients undergoing routine clinical flow cytometry and CBC assays ( N = 101). We compared cell quantities obtained from methylation cytometry with quantities obtained from CLIA- and CAP-certified clinical flow cytometry and complete blood count (CBC) differential including granulocyte subsets (neutrophils, eosinophils and basophils). Methylation cytometry quantification of cell proportions showed strong concordance with clinical assays ( R 2 = 0.99; RMSE = 2.42). Bland-Altman analysis demonstrated strong agreement and minimal bias, with 95.4% of values falling within the limits of agreement (mean ± 1.96 SD). Two one-sided tests of equivalence (TOST) demonstrated that the mean differences between methylation cytometry and flow cytometry were statistically equivalent within ± 2% for all cell types. In a subset of patients with HIV ( N = 32), Bland-Altman analysis of CD4 T-cell percentages revealed a near-zero mean difference, with 97% of observations falling within the limits of agreement. Our findings demonstrate that methylation cytometry is highly accurate when compared to current clinical assays and with further development, has strong potential as a scalable option for detailed clinical immune profiling, including in resource-limited settings.