Machine Learning Elucidates Population Density‐Dependent Morphological Phenotypic Changes of Macrophages

Macrophages play a central role in modulating different biological and physiological events. The behaviors and functions of macrophages may be regulated by a host of factors, including their viability, proliferation rate, and population density. Specifically, the population density of macrophages has been increasingly reported to be correlated with their activities. It is, however, still unclear if changes in macrophage population density will alter the biophysical attributes of these cells, notably their morphology. Herein, label‐free phase‐contrast microscopy is coupled with machine learning to interrogate the relationship between the population density and morphological features of macrophages. Through a systematic approach, variations in the morphological phenotypes of macrophages, which are dependent on their population density, are revealed. In parallel, through unsupervised clustering, the presence of single‐cell morphological heterogeneity within each macrophage population and subpopulation is elucidated. Next, discriminative morphological attributes which can be leveraged to distinguish between macrophages from different groups are identified through feature scoring. Finally, high‐performing explainable supervised machine learning algorithms that can be employed to predict the population density of macrophages based on their size and shape features are identified. This work is anticipated to offer a deeper understanding of the association between macrophage population density and morphologyas well as the potential use of morphological attributes as predictive metrics for analyzing cell populations.