TAPS+ 的效用：一种用于高保真表观遗传分析的阳性读出甲基化测序方法

Accurate methylation sequencing from clinical samples is challenging due to DNA-damaging chemistries that reduce sequence diversity, limiting data quality and reproducibility-particularly for fragmented or low-input materials such as cfDNA. The objective of this study was to evaluate a new positive-readout methylation method (TAPS+) in a clinically relevant cohort and compare its performance to an enzymatic methylation conversion method employing a negative-readout approach. TAPS+ is a non-damaging chemistry that converts methylated cytosines to thymines while preserving unmethylated cytosines, maintaining full sequence complexity and enabling direct methylation detection. This method was applied to a 24-sample cohort comprising invasive breast carcinoma patients, healthy donors, and control samples spanning a range of methylation states. Libraries were sequenced in parallel with those from an enzymatic negative-readout method to assess conversion efficiency, background, and biological concordance of differentially methylated regions (DMRs). The R package methylKit was used for DMR analysis, and biomarker candidates were identified based on statistically significant methylation differences within annotated genomic regions. Compared to the negative-readout method, TAPS+ achieved a shorter turnaround time and higher library yields across all sample types. In cfDNA samples, TAPS+ produced significantly higher methylation ratios than the negative-readout method (p = 8.6e-10). A methylated control showed an 88.8% 5mC rate with TAPS+, versus 76.8% with the negative-readout method, suggesting greater accuracy. CpG island methylation was strongly correlated across methods, though the negative-readout method had greater noise at low coverage sites. Both breast cancer patients and healthy individuals exhibited a characteristic dip in average DNA methylation levels around transcription start sites, consistent with promoter hypomethylation, with no significant differences observed between cohorts. DMR analysis with TAPS+ identified significant biomarker candidates, including SHH hypermethylation (q = 3.15e-5), associated with cancer development and progression. Additional biomarkers were associated with developmental pathways, cancer signaling, transcriptional regulation, and immune response. The negative-readout dataset did not demonstrate strong biomarker candidates indicative of disease status. TAPS+ enables high-fidelity methylation sequencing from diverse and challenging clinical materials, supporting robust epigenetic analysis within a streamlined workflow. The positive-readout approach demonstrated superior data quality and biological relevance compared to an enzymatic negative-readout method, highlighting its potential utility for translational research, early detection, and clinical assay development.