Data-Driven Analysis of Fluorescence Lifetime Imaging Experiments: Unraveling the Signal/Stress Relationship of Polluted Microalgae Cells with Machine Learning

Chlorophyll <i>a</i> fluorescence decay profiles of biological cells may be used as indicators of the ability of a plant to tolerate environmental stress and the extent of the associated damage to its photosynthetic apparatus. However, the interpretation of data remains often complex and sometimes controversial. Based on previously recorded experimental data from fluorescence lifetime imaging microscopy (FLIM) on the freshwater microalga Coccomyxa cimbrica exposed to the Cu-(II) toxic agent, in this work, we set out to investigate the relationship between FLIM measurements and cell stress conditions based on a data-driven approach. In particular, we analyze the changes induced by Cu-(II) in the photosynthetic cycle of the microalga by monitoring the decay profiles of single cells exposed to different concentrations of Cu-(II) (0, 30, 100, 300, 500, and 700 μg mL^-1) as a function of time (0, 24, 48, 72, and 96 h) and use Machine Learning to train predictive models mapping the signal shape to Cu-(II) dosage (defined here as the product of the concentration of Cu-(II) and the time of exposure to it) and to gain insights into the signal features that are more deeply connected with the cell health status. Results show that a good tabularization of the data can lead to acceptable predictions with several standard models, with random forest and ridge regressors showing the best performances. Feature-importance analysis of the forest model reveals that a few statistical features of the fluorescence signal, in combination with its decay rate, are the most relevant descriptors. A final analysis of the predictive performances of more sophisticated models, including fully connected and convolutional neural networks, confirms that careful feature engineering coupled with simpler ML models can lead to equally good performances in shorter training times.