Non-invasive Metabolomic Profiling of Embryo Culture Medium Using Raman Spectroscopy With Deep Learning Model Predicts the Blastocyst Development Potential of Embryos

Abstract

Purpose: This study aimed to establish a non-invasive predicting model via Raman spectroscopy for evaluating the blastocyst development potential of day 3 high-quality cleavage stage embryos.Methods: Raman spectroscopy was used to detect the metabolic spectrum of spent day 3 (D3) embryo culture medium, and a classification model based on deep learning was established to differentiate between embryos that could develop into blastocysts (blastula) and that could not (non-blastula). The full-spectrum data for 80 blastula and 48 non-blastula samples with known blastocyst development potential from 34 patients were collected for this study.Results: The accuracy of the predicting method was 73.53% and the main different Raman shifts between blastula and non-blastula groups were 863.5, 959.5, 1,008, 1,104, 1,200, 1,360, 1,408, and 1,632 cm–1 from 80 blastula and 48 non-blastula samples by the linear discriminant method.Conclusion: This study demonstrated that the developing potential of D3 cleavage stage embryos to the blastocyst stage could be predicted with spent D3 embryo culture medium using Raman spectroscopy with deep learning classification models, and the overall accuracy reached at 73.53%. In the Raman spectroscopy, ribose vibration specific to RNA were found, indicating that the difference between the blastula and non-blastula samples could be due to materials that have similar structure with RNA. This result could be used as a guide for biomarker development of embryo quality assessment in the future.

Combining Machine Learning with Metabolomic and Embryologic Data Improves Embryo Implantation Prediction

“…However, there are no studies exploring the combination of ML models and NMRderived metabolite data for predicting implantation potential. Recently, a deep learning model combined with Raman profiles generated from day-3 embryos was used to predict blastocyst development [23]. In line with the earlier results utilizing ML models for patient characteristics to predict embryo implantation potential [48][49][50], certain ML models (such as nearest neighbors, RBF SVM, decision tree, random forest, and neural net) provided accuracies of 50-67% (moderate accuracies) when metabolomic data alone was used.…”

“…The combination of "omics" technology and machine learning (ML) has been suggested to be able to improve ART outcome prediction [22]. A recent study demonstrated that combining a deep learning model with day-3 metabolite profiles predicted blastocyst development [23]. However, we believe that an accurate prediction of implantation potential has a higher clinical value than that of blastulation.…”

Gold nanoparticle decorated blu‐ray digital versatile disc as a highly reproducible surface‐enhanced Raman scattering substrate for detection and analysis of rotavirus RNA in laboratory environment

“…Again, the Raman peak at 512 cm −1 is attributed to the guanine molecule ring deformation [59]. Sharp peak at 1005 cm −1 is arising due to the stretching vibration of the aromatic ring present in the sample [61, 62]. The weak peaks at 1102 and 1692 cm −1 are attributed to symmetric stretching and CO stretching of uracil, respectively [63].…”

Capillary electrophoresis–mass spectrometry as a tool for the noninvasive target metabolomic analysis of underivatized amino acids for evaluating embryo viability in assisted reproduction

“…At present, the volume has been reduced to 20-25 μL. As a consequence, highly sensitive noninvasive techniques mainly from metabolomic method portfolio able to work with limited-volume samples (on the order of tenths or units of μL) such as near infrared and Raman spectroscopy-15 μL, NMR-18 μL, HPLC-MS-20 μL, and CE-LIF-10 μL [4][5][6][7] represent the best choice. Noninvasive techniques also include the study of the metabolic profile of blastocoele fluid [8] or the monitoring of fetal DNA released into the mother's blood [9].…”