High-content high-resolution microscopy and deep learning-assisted analysis reveals host and bacterial heterogeneity during Shigella infection.

Shigella flexneri is a Gram-negative bacterial pathogen and causative agent of bacillary dysentery. S. flexneri is closely related to Escherichia coli but harbours a virulence plasmid that encodes a type III secretion system (T3SS) required for host cell invasion. Widely recognised as a paradigm for research in cellular microbiology, S. flexneri has 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 heterogeneous S. flexneri infection 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 suggest S. flexneri can 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 recruited to bacteria with increased T3SS activation. Our automated microscopy workflow is useful to illuminate diverse host and bacterial interactions at the single-cell and population level, and to fully characterise the intracellular microenvironment controlling the S. flexneri infection process.