Establishing cell motility patterns as predictors of macrophage subtypes and their relation to cell morphology

The motility of macrophages in response to microenvironment stimuli is a hallmark of innate immunity, where macrophages play pro-inflammatory or pro-reparatory roles depending on their activation status during wound healing. Cell size and shape have been informative in defining macrophage subtypes, but their link to motility properties is unknown, despite M1 and M2 macrophages exhibiting distinct migratory behaviors, in vitro, in 3D and in vivo. We apply both morphology and motility-based image processing approaches to analyze live cell images consisting of macrophage phenotypes. Macrophage subtypes are differentiated from primary murine bone marrow derived macrophages using a potent lipopolysaccharide (LPS) or cytokine interleukin-4 (IL-4). We show that morphology is tightly linked to motility, which leads to our hypothesis that motility analysis could be used alone or in conjunction with morphological features for improved prediction of macrophage subtypes. We train a support vector machine (SVM) classifier to predict macrophage subtypes based on morphology alone, motility alone, and both morphology and motility combined. We show that motility has comparable predictive capabilities as morphology. However, using both measures can enhance predictive capabilities. While motility and morphological features can be individually ambiguous identifiers, together they provide significantly improved prediction accuracies (>79%) using only phase contrast time-lapse microscopy and a small unique cell count for training (∼250). Thus, the approach combining cell motility and cell morphology information can accurately assess functionally diverse macrophage phenotypes quickly and efficiently. Our approach offers a cost efficient and high through-put method for screening biochemicals targeting macrophage polarization with small datasets. Significance Previous work has shown that macrophage phenotypes can be distinguished by their morphological characteristics. We extend this work to show that distinct motility patterns are linked to macrophage morphology. Thus, motility patterns can be used to differentiate phenotypes. This can enable high-throughput classification of cell phenotypes without regard for the high-resolution images needed to back out morphological characteristics. Furthermore, combining motility-based features with morphological information improves prediction of macrophage subtypes by a machine learning based classification model.