Genetic deconvolution of embryonic and maternal cell-free DNA in spent culture medium of human preimplantation embryos through deep learning

Noninvasive preimplantation genetic testing for aneuploidy based on embryonic cell-free DNA (cfDNA) released in spent embryo culture media (SECM) has brought hope in selecting embryos that are most likely to implant and grow into healthy babies during assisted reproduction. However, maternal DNA contamination in SECM significantly hampers the reliability of embryonic chromosome ploidy profiles, leading to false negative results, particularly at high contamination levels. Here, we present DECENT (deep copy number variation (CNV) reconstruction), a deep learning method to reconstruct embryonic CNVs and mitigate maternal contamination in SECM from single-cell methylation sequencing of cfDNA. DECENT integrates sequence features and methylation patterns by combining convolution modules, long-short memory, and attention mechanisms to infer the origin of cfDNA reads. The benchmarking study demonstrated DECENT's ability to estimate contamination proportions and restore embryonic chromosome aneuploidies in samples with varying contamination levels. In contaminated SECM clinical samples, including one with more than 80% maternal reads, DECENT achieved consistent CNV recovery with invasive tests. Overall, DECENT contributes to enhancing the diagnostic accuracy and effectiveness of cfDNA-based noninvasive preimplantation genetic testing, establishing a robust groundwork for its extensive clinical utilization in the field of reproductive medicine.