Bo Liang1, Yuan Gao2, Jiabao Xu3, Yizhi Song3, Liming Xuan4, Ting Shi1, Ning Wang5, Zhaoxu Hou5, Yi-Lei Zhao6, Wei E Huang3, Zi-Jiang Chen2. 1. State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. 2. Center for Reproductive Medicine, Provincial Hospital Affiliated with Shandong University, Jinan, Shandong, China; Key Laboratory for Reproductive Endocrinology of Ministry of Education, Jinan, Shandong, China. 3. Department of Engineering Science, University of Oxford, Oxford, United Kingdom. 4. Basecare Medical Device Co., Suzhou, Jiangsu, China. 5. Mathematical Institute, University of Oxford, Oxford, United Kingdom. 6. State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. Electronic address: yileizhao@sjtu.edu.cn.
Abstract
OBJECTIVE: To develop and validate Raman metabolic footprint analysis to determine chromosome euploidy and aneuploidy in embryos fertilized in vitro. DESIGN: Retrospective study. SETTING: Academic hospital. PATIENT(S): Unselected assisted reproductive technology population. INTERVENTION(S): To establish the analysis protocol, spent embryo culture medium samples with known genetic outcomes from 87 human embryos were collected and measured with the use of Raman spectroscopy. Individual Raman spectra were analyzed to find biologic components contributing to either euploidy or aneuploidy. To validate the protocol via machine-learning algorithms, additional 1,107 Raman spectra from 123 embryo culture media (61 euploidy and 62 aneuploidy) were analyzed. MAIN OUTCOME MEASURE(S): Raman-based footprint profiling of spent culture media and preimplantation genetic testing for aneuploidy (PGT-A). RESULT(S): Mean-centered Raman spectra and principal component analysis showed differences in the footprints of euploid and aneuploid embryos growing in culture medium. Significant differences in Raman bands associated with small RNAs and lipids were also observed. Stacking classification based on k-nearest-neighbor, random forests, and extreme-gradient-boosting algorithms achieved an overall accuracy of 95.9% in correctly assigning either euploidy or aneuploidy based on Raman spectra, which was validated by PGT-A sequencing results. CONCLUSION(S): This study suggests that chromosomal abnormalities in embryos should lead to changes of metabolic footprints in embryo growth medium that can be detected by Raman spectroscopy. The ploidy status of embryos was analyzed by means of Raman-based footprint profiling of spent culture media and was consistent with PGT-A testing performed by next-generation sequencing.
OBJECTIVE: To develop and validate Raman metabolic footprint analysis to determine chromosome euploidy and aneuploidy in embryos fertilized in vitro. DESIGN: Retrospective study. SETTING: Academic hospital. PATIENT(S): Unselected assisted reproductive technology population. INTERVENTION(S): To establish the analysis protocol, spent embryo culture medium samples with known genetic outcomes from 87 human embryos were collected and measured with the use of Raman spectroscopy. Individual Raman spectra were analyzed to find biologic components contributing to either euploidy or aneuploidy. To validate the protocol via machine-learning algorithms, additional 1,107 Raman spectra from 123 embryo culture media (61 euploidy and 62 aneuploidy) were analyzed. MAIN OUTCOME MEASURE(S): Raman-based footprint profiling of spent culture media and preimplantation genetic testing for aneuploidy (PGT-A). RESULT(S): Mean-centered Raman spectra and principal component analysis showed differences in the footprints of euploid and aneuploid embryos growing in culture medium. Significant differences in Raman bands associated with small RNAs and lipids were also observed. Stacking classification based on k-nearest-neighbor, random forests, and extreme-gradient-boosting algorithms achieved an overall accuracy of 95.9% in correctly assigning either euploidy or aneuploidy based on Raman spectra, which was validated by PGT-A sequencing results. CONCLUSION(S): This study suggests that chromosomal abnormalities in embryos should lead to changes of metabolic footprints in embryo growth medium that can be detected by Raman spectroscopy. The ploidy status of embryos was analyzed by means of Raman-based footprint profiling of spent culture media and was consistent with PGT-A testing performed by next-generation sequencing.
Authors: Ferdinand Dhombres; Jules Bonnard; Kévin Bailly; Paul Maurice; Aris T Papageorghiou; Jean-Marie Jouannic Journal: J Med Internet Res Date: 2022-04-20 Impact factor: 7.076