Shivaani Manickavasagan, Madan Pavneesh
Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada.
Front Vet Sci. 2024 May 7;11:1364570. doi: 10.3389/fvets.2024.1364570. eCollection 2024.
Although embryo transfers have grown considerably in the cattle industry, the selection of embryos required for successful pregnancies remains a challenging task. Visual inspection of 7th-day embryos using a stereomicroscope, followed by classification based on morphological features is the most commonly practiced procedure. However, there are inaccuracies and inconsistencies in the manual grading of bovine embryos. The objective of this review was to evaluate the potential of imaging and spectroscopic techniques in the selection of bovine embryos. Digital analysis of microscopic images through extracting visual features in the embryo region, and classification using machine learning methods have yielded about 88-96% success in pregnancies. The Raman spectral pattern provides valuable information regarding developmental stages and quality of the embryo. The Raman spectroscopy approach has also been successfully used to determine various parameters of bovine oocytes. Besides, Fourier Transform Infrared (FTIR) spectroscopy has the ability to assess embryo quality through analyzing embryo composition, including nucleic acid and amides present. Hyperspectral Imaging has also been used to characterize metabolite production during embryo growth. Although the time-lapse imaging approach is beneficial for morphokinetics evaluation of embryo development, optimized protocols are required for successful implementation in bovine embryo transfers. Most imaging and spectroscopic findings are still only at an experimental stage. Further research is warranted to improve the repeatability and practicality to implement in commercial facilities.
尽管胚胎移植在养牛业中已有显著增长,但选择能成功受孕所需的胚胎仍然是一项具有挑战性的任务。使用体视显微镜对第7天的胚胎进行目视检查,然后根据形态特征进行分类是最常用的方法。然而,牛胚胎的人工分级存在不准确和不一致的情况。本综述的目的是评估成像和光谱技术在牛胚胎选择中的潜力。通过提取胚胎区域的视觉特征对显微图像进行数字分析,并使用机器学习方法进行分类,已在妊娠方面取得了约88%-96%的成功率。拉曼光谱模式提供了有关胚胎发育阶段和质量的有价值信息。拉曼光谱方法也已成功用于确定牛卵母细胞的各种参数。此外,傅里叶变换红外(FTIR)光谱能够通过分析胚胎组成,包括其中存在的核酸和酰胺来评估胚胎质量。高光谱成像也已用于表征胚胎生长过程中的代谢产物产生。尽管延时成像方法有利于胚胎发育的形态动力学评估,但在牛胚胎移植中成功实施需要优化方案。大多数成像和光谱研究结果仍仅处于实验阶段。有必要进行进一步研究以提高其在商业设施中实施的可重复性和实用性。