4.7 Article

Raman profiling of embryo culture medium to identify aneuploid and euploid embryos

期刊

FERTILITY AND STERILITY
卷 111, 期 4, 页码 753-+

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.fertnstert.2018.11.036

关键词

Raman microspectroscopy; machine learning; chromosome abnormality; IVF; preimplantation genetic testing for aneuploidy (PGT-A)

资金

  1. National Science Foundation of China [21377085, 31770070]
  2. Shanghai Jiao Tong University Med-Eng fund [YG2016MS33]
  3. High Performance Computing, (The SJTU Center for High Performance Computing) award
  4. National Key R&D Program of China, Beijing, PR China [2018YFC1003104]
  5. Engineering and Physical Sciences Research Council [EP/M002403/1, EP/M02833X/1]
  6. Natural Environment Research Council, United Kingdom [NE/M002934/1]
  7. EPSRC [EP/M02833X/1] Funding Source: UKRI
  8. NERC [NE/M002934/1] Funding Source: UKRI

向作者/读者索取更多资源

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. ((C) 2018 by American Society for Reproductive Medicine.)

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