Integration of Global Lipidomics and Gonad Histological Analysis via Multivariate Chemometrics and Machine Learning: Identification of Potential Lipid Markers of Ovarian Development in the Blue Mussel (Mytilus edulis)

Background/Objectives: Gonad histological analysis (GHA) is the traditional method for assessing the gonad maturation status of blue mussels (Mytilus edulis). GHA has some operational disadvantages, such as limited processing outputs, subjectivity in the assessment of transitional stages of gonadal maturation and the need for experienced and trained operators. Lipids could become important indicators of gonadal maturation as they cover many essential functions during such processes in mussels. In this work, blue mussel ovary (BMO) ultrastructure is integrated with liquid chromatography coupled with mass spectrometry (LC-MS) lipidomics fingerprinting to identify suitable markers for ovarian maturation through the application of chemometrics and machine learning approaches. Methods: BMOs are classified here as ripe or non-ripe by means of GHA and the gamete volume fraction (GVF). Receiving operating characteristic (ROC) curves were used to classify the results of the different statistics according to their area under the curve (AUC), and the functional role of important lipids was assessed by lipid ontology enrichment (LiOn) analysis. Results: This approach allowed for the selection of a panel of 35 lipid molecules (AUC > 0.8) that can distinguish non-ripe from ripe BMOs. Ceramide phosphoethanolamine (CerPE) 40:2 was the molecule with the highest classification ability (AUC 0.905), whereas glycerophosphoserine (PS) was the class mostly changing between the two groups. LiOn analysis indicated significant differences in the functional roles of these lipids, highlighting enrichment terms associated with membrane lipids, lysosomes and highly unsaturated triglycerides (TGs) in non-ripe ovaries, whereas terms associated with storage lipids and low-saturated TG characterised ripe BMOs.