Morphology-based classification of mycobacteria-infected macrophages with Convolutional Neural Network: Reveal EsxA-induced morphological changes indistinguishable by naked eyes

Abstract EsxA is an essential virulence factor for Mycobacterium tuberculosis (Mtb) pathogenesis as well as an important biomarker for Mtb detection. In this study, we use light microscopy and deep learning-based image analysis to classify the morphological changes of macrophages infected by Mycobacterium marinum (Mm), a surrogate model for Mtb. Macrophages were infected either with the mCherry-expressing Mm wild type strain (Mm(WT)), or a mutant strain with deletion of the esxA-esxB operon (Mm(ΔEsxA:B)). The mCherry serves as an infection marker to train the convolution neural network (CNN) and to validate the classification results. Data show that CNN can distinguish the Mm(WT)-infected cells from uninfected cells with an accuracy of 92.4% at 2 hours post infection (hpi). However, the accuracy at 12 and 24 hpi is decreased to ∼75% and ∼83%, respectively, suggesting dynamic morphological changes through different stages of infection. The accuracy of discriminating Mm(ΔEsxA:B)-infected cells from uninfected cells is lower than 80% at all time, which is consistent to attenuated virulence of Mm(ΔEsxA:B). Interestingly, CNN distinguishes Mm(WT)-infected cells from Mm(ΔEsxA:B)-infected cells with ∼90% accuracy, implicating EsxA induces unique morphological changes in macrophages. Deconvolutional analysis successfully reconstructed the morphological features used by CNN for classification, which are indistinguishable to naked eyes and distinct from intracellular mycobacteria. This study presents a deep learning-aided imaging analytical tool that can accurately detect virulent mycobacteria-infected macrophages by cellular morphological changes. The observed morphological changes induced by EsxA warrant further studies to fill the gap from molecular actions of bacterial virulence factors to cellular morphology.