PDF(Pubmed)
Abstract：
Time-lapse microscopy for embryos is a non-invasive technology used to characterize early embryo development. This study employs time-lapse microscopy and machine learning to elucidate changes in embryonic growth kinetics with maternal aging. We analyzed morphokinetic parameters of embryos from young and aged C57BL6/NJ mice via continuous imaging. Our findings show that aged embryos accelerated through cleavage stages (from 5-cells) to morula compared to younger counterparts, with no significant differences observed in later stages of blastulation. Unsupervised machine learning identified two distinct clusters comprising of embryos from aged or young donors. Moreover, in supervised learning, the extreme gradient boosting algorithm successfully predicted the age-related phenotype with 0.78 accuracy, 0.81 precision, and 0.83 recall following hyperparameter tuning. These results highlight two main scientific insights: maternal aging affects embryonic development pace, and artificial intelligence can differentiate between embryos from aged and young maternal mice by a non-invasive approach. Thus, machine learning can be used to identify morphokinetics phenotypes for further studies. This study has potential for future applications in selecting human embryos for embryo transfer, without or in complement with preimplantation genetic testing.
摘要：
胚胎延时显微镜是一种用于表征早期胚胎发育的非侵入性技术。这项研究采用延时显微镜和机器学习来阐明胚胎生长动力学随母体衰老的变化。我们通过连续成像分析了来自年轻和老年C57BL6/NJ小鼠的胚胎的形态动力学参数。我们的发现表明，与年轻的胚胎相比，衰老的胚胎通过卵裂阶段（从5个细胞）加速到桑态度。在囊胚形成的后期没有显着差异。无监督机器学习确定了两个不同的簇，包括来自老年或年轻供体的胚胎。此外，在监督学习中，XGBoost（极端梯度提升）算法成功预测了与年龄相关的表型，准确率为0.78，0.81精度，和0.83超参数调整后的召回。这些结果突出了两个主要的科学见解：母体衰老影响胚胎发育速度，AI可以通过非侵入性方法区分老年和年轻母鼠的胚胎。因此，机器学习可用于识别形态动力学表型以进行进一步研究。这项研究有可能在将来选择人类胚胎进行胚胎移植。没有或补充植入前基因检测。
