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 ..., Microscopy images were obtained using z-stack image series taking 8–16 slices. Fluorescence microscopy on infected or uninfected cells was performed using a ZEISS Plan-APOCHROMAT 20× / 0.95 Autocorr Objective or a ZEISS Plan-APOCHROMAT 50×/1.2 water immersion lens coupled to a 0.5x tubelens on a Zeiss CellDiscoverer 7 with Airyscan detectors driven by ZEN Blue software (v3.5). Microscopy images were obtained using z-stack image series taking 32 slices. Confocal images were processed using Airyscan processing (Weinerfilter) using “Auto Filter” and “3D Processing” options.
Images provided in this Dataset are unprocessed tif files. The processed datasets for CNN training as described in the associated manuscript are also provided. Processing was performed as described in https://github.com/ATLopezJimenez/Toolset-high-content-analysis-of-Shigella-infection, , # High-content high-resolution of *S. flexneri* infection in Hela cells

[https://doi.org/10.5061/dryad.6wwpzgn5z](https://doi.org/10.5061/dryad.6wwpzgn5z)

These datasets are associated to the pre-print \"High-content high-resolution microscopy and deep learning assisted analysis reveals host and bacterial heterogeneity during *Shigella *infection.\" Ana T. López-Jiménez, Dominik Brokatzky, Kamla Pillay, Tyrese Williams, Gizem Özbaykal Güler and Serge Mostowy.

Briefly, Hela (ATCC CCL-2 cells) were infected by spin inoculation with fluorescent labelled *Shigella flexneri* M90T. Samples were fixed at 3 h 40 min and immunostained for imaging with a Zeiss CellDiscoverer 7 with Airyscan detectors. For further information on the experimental procedures for each specific dataset, please refer to the Materials and Method sections of the pre-print, and to the provided Dataset_information_experiments.xlsx and Dataset_information_CNN.xlsx files.

## Description of the data and file structure

* ...