Classification of T lymphocyte motility behaviors using a machine learning approach

T lymphocytes migrate into organs and interact with local cells to perform their functions. How human T lymphocytes communicate with organ-specific cells and participate in pathobiological processes remains unresolved. Brain infiltration of T lymphocytes is associated with multiple neurological disorders. Thus, to characterize the behavior of human T lymphocytes reaching the human brain, we performed time-lapse microscopy on human CD8(+) T lymphocytes co-cultured with either primary human astrocytes or neurons. Using traditional manual and visual assessment of microscopy data, we identified distinct CD8(+) T lymphocyte motility behaviors. However, such characterization is time and labor-intensive. In this work, we trained and validated a machine-learning model for the automated classification of behaviors of CD8(+) T lymphocytes interacting with astrocytes and neurons. A balanced random forest was trained for the binary classification of established classes of cell behaviors (synapse vs. kinapse) as well as visually identified behaviors (scanning, dancing, and poking). Feature selection was performed during 3-fold cross-validation using the minimum redundancy maximum relevance algorithm. Results show promising performances when tested on a held-out dataset of CD8(+) T lymphocytes interacting with astrocytes with a new experimenter and a held-out independent dataset of CD8(+) T lymphocytes interacting with neurons. When tested on the independent CD8(+) T cell-neuron dataset, the final model achieved a binary classification accuracy of 0.82 and a 3-class accuracy of 0.79. This novel automated classification approach could significantly reduce the time required to label cell motility behaviors while facilitating the identification of interactions of T lymphocytes with multiple cell types. T lymphocytes are immune cells that enter into organs and then communicate with local cells to perform their functions. The mechanisms of such interactions, especially in the case of pathobiological processes, remain unclear. Indeed, multiple neurological disorders are characterized by an infiltration of T lymphocytes into the brain and spinal cord. Time-lapse microscopy allows to observe microscopic cellular dynamics over extended periods of time. We used this technique to characterize the behavior of T lymphocytes communicating with human brain cells (neurons and astrocytes). While manual and visual assessment of the recorded videos allowed to identify distinct behaviors of T lymphocytes, it is time and labor consuming. To automate the classification of T lymphocytes' behaviors, we developed a machine learning model based on features extracted from time-lapse microscopy videos. We optimized the model by selecting the best features. The classification model was based on a balanced random forest, a combination of multiple decision trees. We trained and tested the model on 2 distinct datasets of T lymphocytes interacting with astrocytes and neurons. We found promising performances highlighting that the proposed model can automate the labeling of T lymphocyte behaviors dialoguing with human brain cells.