Optimizing intervertebral disc cell metabolic phenotyping with machine learning and artificial neural networks

Biological phenotyping of cellular metabolism is essential for deciphering health and disease states. The Seahorse XF analyzer enables direct measurement of oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), providing insight into mitochondrial function and glycolytic activity in living cells. Accurate phenotyping requires meticulous preparation, including forming a uniform cell monolayer and carefully titrating critical reagents such as the uncoupler carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP), to ensure precise and reproducible bioenergetic measurements. Artificial neural networks (ANN) and machine learning (ML) algorithms serve as powerful tools for analyzing experimental findings, enhancing the accuracy of result predictions, and reducing the number of experimental treatments and combinations required. This study was designed to optimize seahorse bioenergetics flux analysis using ML and ANN for in vitro metabolic phenotyping attributes. The performance of the ANN and ML models was assessed using root mean square error (RMSE), mean square error (MSE), mean absolute error, and accuracy percentage. We applied supervised ML techniques in the Regression and Classification Learner App to evaluate the predictive capability of various algorithms. Our findings indicated that fine and boosted trees in the regression model, as well as fine and medium trees and linear and quadratic support vector machines (SVM) in the classification model, enhanced data prediction accuracy. Notably, we developed a rigorous biological workflow for bioenergetic phenotyping by integrating unsupervised machine learning with a supervised ANN, which closely aligned with experimental data and minimized discrepancies between predicted and observed values. This approach establishes a robust, unbiased, and data-driven framework for high-resolution metabolic phenotyping. By uniting precise experimental biology with advanced computational analysis, our study highlights a powerful synergy with significant promise for both fundamental research and therapeutic discovery.