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Noninvasive detection of macrophage activation with single-cell resolution through machine learning

Significance We developed a method enabling the noninvasive study of fine cellular responses that we applied to macrophage activation. The technique is based on a multimodal label-free microscopy system that simultaneously retrieves both morphological and molecular information based on quantitative phase imaging and Raman spectroscopy, respectively. The parameters obtained from these measurements are processed through a machine learning algorithm that makes it possible to reliably assess the macrophage activation state at single-cell level. We found that while each parameter set (morphology and Raman) can detect the activation state, they provide complementary information. Morphology is symptomatic of downstream phenotypes that make the detection dose-dependent, while Raman is indicative of upstream molecular changes that enable the detection of selective inhibition of activation pathways. We present a method enabling the noninvasive study of minute cellular changes in response to stimuli, based on the acquisition of multiple parameters through label-free microscopy. The retrieved parameters are related to different attributes of the cell. Morphological variables are extracted from quantitative phase microscopy and autofluorescence images, while molecular indicators are retrieved via Raman spectroscopy. We show that these independent parameters can be used to build a multivariate statistical model based on logistic regression, which we apply to the detection at the single-cell level of macrophage activation induced by lipopolysaccharide (LPS) exposure and compare their respective performance in assessing the individual cellular state. The models generated from either morphology or Raman can reliably and independently detect the activation state of macrophage cells, which is validated by comparison with their cytokine secretion and intracellular expression of molecules related to the immune response. The independent models agree on the degree of activation, showing that the features provide insight into the cellular response heterogeneity. We found that morphological indicators are linked to the phenotype, which is mostly related to downstream effects, making the results obtained with these variables dose-dependent. On the other hand, Raman indicators are representative of upstream intracellular molecular changes related to specific activation pathways. By partially inhibiting the LPS-induced activation using progesterone, we could identify several subpopulations, showing the ability of our approach to identify the effect of LPS activation, specific inhibition of LPS, and also the effect of progesterone alone on macrophage cells.