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 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. 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 recuited to actively
pathogenic bacteria with increased T3SS activation. Our automated
microscopy workflow is useful to discover host and bacterial dynamics at
the single-cell and population level, and to fully characterise the
intracellular microenvironment controlling the S. flexneri infection
process.