High-content high-resolution microscopy and deep learning assisted analysis reveals host and bacterial heterogeneity duringShigellainfection

Abstract

Shigella flexneriis a Gram-negative bacterial pathogen and causative agent of bacillary dysentery.S. flexneriis closely related toEscherichia colibut harbors a virulence plasmid that encodes a Type III Secretion System (T3SS) required for host cell invasion. Widely recognized as a paradigm for research in cellular microbiology,S. flexnerihas emerged as important to study mechanisms of cell-autonomous immunity, including septin cage entrapment. Here we use high-content high-resolution microscopy to monitor the dynamic and heterogeneousS. flexneriinfection process by assessing multiple host and bacterial parameters (DNA replication, protein translation, T3SS activity). In the case of infected host cells, we report a reduction in DNA and protein synthesis together with morphological changes that suggestS. flexnerican induce cell-cycle arrest. We developed an artificial intelligence image analysis approach using Convolutional Neural Networks to reliably quantify, in an automated and unbiased manner, the recruitment of SEPT7 to intracellular bacteria. We discover that heterogeneous SEPT7 assemblies are recuited to actively pathogenic bacteria with increased T3SS activation. Our automated microscopy workflow is useful to illuminate host and bacterial dynamics at the single-cell and population level, and to fully characterise the intracellular microenvironment controlling theS. flexneriinfection process.