Leveraging mRNA cMH and machine learning for the deconvolution of mixed body fluid stains in East Asian populations

Body fluid identification methods have become increasingly well-established, but deconvoluting multi-component mixtures remains a significant challenge. When dealing with complex mixtures, methods based on mRNA coding-region SNPs (cSNPs) still face limitations such as limited polymorphisms of markers and insufficient population specificity. mRNA coding-region microhaplotypes (cMHs), combining tissue specificity with high discrimination power, offer a promising alternative for donor attribution in body fluid stains. In this study, we developed a targeted RNA sequencing panel for East Asian populations, comprising 36 body fluid-specific genes containing 48 cMHs (including 108 cSNPs), along with five genes lacking cMHs. This panel enables both tissue source identification and donor attribution for five types of body fluids and their mixtures. Through specificity evaluation of single-source body fluid stains, we identified six novel body fluid-specific genes. Furthermore, by integrating machine learning algorithms with an in silico mixing strategy, we developed a body fluid classifier that achieved accurate inference for both single-source fluids and complex mixtures. The panel's cumulative discrimination power across the five body fluid types ranged from 0.99568126 (peripheral blood) to 0.99999999 (vaginal secretion). In conclusion, the dual-purpose panel demonstrates robust performance in both tissue source identification and donor attribution for single-source body fluids and complex mixtures.