Epigenetic memory ensures stable inheritance of gene expression patterns critical for embryonic development. Environmental exposures can disrupt this memory, yet the mechanisms remain unclear. Here we demonstrate that chronic morphine exposure induces a persistent transcriptomic and epigenetic memory by repressing Smchd1, a key chromatin regulator, in mouse embryonic stem cells, preimplantation embryos, and human induced pluripotent stem cells. This repression compromises maintenance of X-chromosome inactivation and genomic imprinting, leading to sustained dysregulation of developmentally important gene clusters. Morphine-induced epigenetic alterations also involve changes in DNA methylation and histone modifications along the X chromosome and notably increased H3K27me3 at the Smchd1 locus. These findings reveal a conserved mechanism by which opioid exposure disrupts higher-order chromatin architecture and epigenetic memory during early development, potentially contributing to long-term developmental and clinical outcomes.

1. Background: Human infertility affects approximately 17.5% of the global population, with male factors accounting for nearly half of all cases. The identification of reliable molecular biomarkers is crucial for improving the diagnosis and assessment of male fertility. In this study, we developed and optimized an untargeted high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (HPLC-ESI-MS/MS) workflow for comprehensive lipidomic and metabolomic profiling of human spermatozoa using only 1.25 million cells per sample. Results: Compared to previous reports, our optimized method achieved unprecedented analytical depth, identifying 473 lipid species and 955 structurally annotated metabolites, corresponding to nearly 7.600-fold improvements in detection efficiency per cell over published approaches. Lipidomic analysis revealed cholesterol, fatty acids, phosphatidylcholines, and phosphatidylethanolamine plasmalogens as the most abundant lipid classes, consistent with the structural complexity of the sperm plasma membrane. Metabolomic profiling showed strong enrichment of lipid-related and steroidogenic pathways, including phospholipid biosynthesis, glycerolipid metabolism and androgen and estrogen metabolism. The integration of lipidomic and metabolomic data highlighted functionally interconnected pathways related to membrane dynamics, energy metabolism, and hormone biosynthesis. Conclusions: Overall, this work establishes a robust, sensitive, and scalable analytical framework enabling high-coverage molecular characterization of spermatozoa from limited sample material, laying the groundwork for future biomarker discovery and clinical applications in male infertility research. One Sentence Summary: Development of a highly sensitive untargeted HPLC-ESI–MS/MS lipidomic and metabolomic workflow that achieves unprecedented molecular coverage from only 1.25 million human spermatozoa, revealing interconnected lipid and metabolic pathways and providing a robust foundation for biomarker discovery in male infertility. Graphical

Human infertility affects approximately 17.5% of the global population, with male factors accounting for nearly half of all cases. Identifying reliable molecular biomarkers is crucial for improving the diagnosis and assessment of male fertility. This study established and refined an untargeted high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (HPLC-ESI-MS/MS) protocol for a comprehensive lipidomic and metabolomic analysis of human spermatozoa, using only 1.25 million cells per sample. Compared with previous reports, our optimized method achieved an unparalleled level of analytical depth, identifying 473 lipid species and 955 structurally annotated metabolites. This corresponds to nearly a 7600-fold improvement in detection efficiency per cell compared with previously published approaches. Lipidomic analysis revealed that the most abundant lipid classes were glycerophospholipids (39%), cholesterol (20%) and fatty acids (19%), with cholesterol representing the single most abundant compound. This observation is consistent with the structural complexity of the sperm plasma membrane. Metabolomic profiling similarly identified glycerophospholipids (44%), eicosanoids (14%) and N-acyl amino acids (12%) as the major metabolite classes. The integration of lipidomic and metabolomic data highlighted functionally interconnected pathways related to membrane dynamics, energy metabolism, and hormone biosynthesis. Overall, this work establishes a robust, sensitive, and scalable analytical framework that enables the high-coverage molecular characterization of spermatozoa from limited sample material, laying the groundwork for future biomarker discovery and clinical applications in male infertility research.

Despite the improvements in tool development for DNA methylation analysis, there is a lack of a consensus on computational and statistical models used for differentially methylated cytosine (DMC) identification. This variability complicates the interpretation of findings and raises concerns about the reproducibility and biological significance of the detected results. In this regard, here we conducted a comparative evaluation of edgeR and methylKit tools to assess their performance, concordance, and biological relevance in detecting DMCs following a morphine exposure model in mouse embryonic stem cells (mESCs). Both pipelines were applied to the same WGBS dataset (GEO accession number: GSE292082), and concordance was calculated at both single-base and gene levels. Although the total number of DMCs identified differed between tools, both pipelines detected a global hypomethylation pattern. Genomic distribution analysis revealed that DMCs predominantly localized to intergenic and intronic regions, as well as to open sea regions. Despite differences in sensitivity, both pipelines demonstrated moderate concordance at the DMC level (~56%) and high concordance at the gene level (~90%), identifying largely overlapping sets of differentially methylated genes (DMGs). Comparative assessments further showed that the choice of statistical metric can influence the perceived magnitude of biological effects. Sensitivity analyses indicated that threshold selection and normalization methods influence DMC detection, whereas aggregation at gene level reduces discrepancies. Overall, our findings underscore the complementary strengths of methylKit and edgeR and highlight the importance of careful tool selection for epigenetic studies. As a conclusion, we recommend integrating both pipelines to ensure a balanced interpretation of effect sizes, particularly in studies with complex experimental designs.