Low levels of mouse sperm chromatin fragmentation delay embryo development.

We previously demonstrated that MnCl2 induces double-stranded DNA breaks in sperm in a process that we term as sperm chromatin fragmentation. Here, we tested if the levels of double-stranded DNA breaks were corelated to the concentration of MnCl2, and we compared this to another agent that causes single-stranded DNA breaks, H2O2. We found that both methods have the advantage of inducing DNA breaks in a concentration-dependent manner. Mouse sperm were treated with varying concentrations of either H2O2 or MnCl2, and the DNA damage was assessed by pulse-field gel electrophoresis, and the alkaline and neutral comet assays. Oocytes were injected with either treated sperm and the resulting embryos analyzed with an embryoscope to detect subtle changes in embryonic development. We confirmed that H2O2 treatment induced primarily single-stranded DNA breaks and MnCl2 induced primarily double-stranded DNA breaks, indicating different mechanisms of damage. These sperm were injected into oocytes, and the development of the resulting embryos followed with an embryoscope equipped with time lapse recording. We found that aberrations in early embryonic development by day 2 with even the lowest levels of DNA damage and that the levels of embryonic aberrations correlated to the concentration of either H2O2 or MnCl2. Low levels of H2O2 caused significantly more aberrations in embryonic development than low levels of MnCl2 even though the levels of DNA damage as measured by comet assays were similar. These data demonstrate that even low levels of sperm DNA damage cause delays and arrests in embryonic development.

Comparison of euploid blastocyst expansion with subgroups of single chromosome, multiple chromosome, and segmental aneuploids using an AI platform from donor egg embryos.

PURPOSE: This retrospective observational study compares how different classes of blastocyst genotypes from egg donor cycles differentially blastulate and expand using a standard assay. METHODS: Quantitative measurements of expansion utilized a customized neural network that segments all sequential time-lapse images during the first 10 h of expansion. RESULTS: Analyses were performed using two developmental time perspectives using time-lapse imaging. The first was the time to blastocyst formation (tB), which broadly reflects variations in developmental rate. Euploidy peaked at 100-115 h from fertilization. In contrast, aneuploidy peaks flanked this interval bi-modally. These distributions limit ploidy discrimination based upon traditional standard grading features when assessed in real time. In contrast, from the second perspective of progressive blastocyst expansion that is normalized to each individual blastocyst's tB time, euploidy was significantly increased at expansion values > 20,000µ2 across all tB intervals studied. A Cartesian coordinate plot graphically summarizes information useful to rank order blastocysts within cohorts for transfer. Defined aneuploidy subgroups, distinguished by the number and complexity of chromosomes involved, also showed distributive differences from both euploids and from each other. A small subset of clinically significant trisomies did not show discriminating features separating them from other euploids. CONCLUSION: A standard assay of blastocyst expansion normalized to each individual blastocyst's time of blastocyst formation more usefully discriminates euploidy from aneuploidy than real-time expansion comparisons using absolute developmental time from fertilization.

Deep learning neural network analysis of human blastocyst expansion from time-lapse image files.

RESEARCH QUESTION: Can artificial intelligence (AI) discriminate a blastocyst's cellular area from unedited time-lapse image files using semantic segmentation and a deep learning optimized U-Net architecture for use in selecting single blastocysts for transfer? DESIGN: This platform was retrospectively applied to time-lapse files from 101 sequentially transferred single blastocysts that were prospectively selected for transfer by their highest expansion ranking within cohorts using a 10 h expansion assay rather than standard grading. RESULTS: The AI platform provides expansion curves and raw data files to classify and compare blastocyst phenotypes within both cohorts and populations. Of 35 sequential unbiopsied single blastocyst transfers, 23 (65.7%) resulted in a live birth. Of 66 sequential single euploid blastocyst transfers, also selected for their most robust expansion, 49 (74.2%) resulted in live birth. The AI platform revealed that the averaged expansion rate was significantly (P = 0.007) greater in euploid blastocysts that resulted in live births compared with those resulting in failure to give a live birth. The platform further provides a framework to analyse fragmentation phenotypes that can test new hypotheses for developmental regulation during the preimplantation period. CONCLUSIONS: AI can be used to quantitatively describe blastocyst expansion from unedited time-lapse image files and can be used to quantitatively rank-order blastocysts for transfer. Early clinical results from such single blastocyst selection suggests that live birth rates without biopsy may be comparable to those found using single euploid blastocysts in younger, good responder patients.