DOI: 10.5061/dryad.6wwpzgn5z
title: High-content high-resolution microscopy and deep learning assisted analysis reveals host and bacterial heterogeneity during Shigella infection

description: 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.
description: 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
description: # 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 * The provided datasets Infection.zip, Infection_DNA_synthesis.zip, Infection_protein_synthesis.zip and Infection_TSAR.zip correspond to one representative biological replicate of the experiments described in the associated pre-print, containing multiple images. Additional information contained in each file name includes: date of acquisition (6 digits), carrier number, and information of acquisition, scene, position, well and airyscan processing. As examples: 200703_plate0005-02-Scene-001-P1-B05-AiPr-01n: corresponds to an image acquired on the 3rd of July of 2020, on experiment carried on a 96 well plate (ID 0005), Acquisition 02, Scene 001, Position 1, Well B05 (96 well plate), Airyscan processed image numero 01. 210819-ibidi015-01-Scene-117-P76-AiPr-117n: corresponds to an image acquired on the 19th of August of 2021, on an ibidi carrier (ID 018), Acquisition 01, Scene 117, Position 76, Airyscan processed image numero 117. Further information on the experiment associated to each dataset is contained in the associated pre-print and the provided Dataset_information_experiments.xlsx file. * The provided datasets contained in the folders "Deep learning for single vs clump" and "Deep learning for septin vs negatives" correspond to correspond to the images used to train the CNN as described in the associated pre-print. Each folder contains two subfolders: "Data_divided_by_dataset", containing the raw images; and "Data_divided_for_training_validation_testing", containing the processed images splitted into categories used in CNN training, validation and testing. Further information on this data is contained in the associated pre-print and provided Dataset_information_experiments.xlsx file. ## Code/Software Airyscan processed tif files are provided for all datasets uploaded. In addition, processed images used to train CNN models are provided ("Deep learning for septin vs negatives" and "Deep learning for single vs clump" datasets). Processing was performed as described in [https://github.com/ATLopezJimenez/Toolset-high-content-analysis-of-Shigella-infection](https://github.com/ATLopezJimenez/Toolset-high-content-analysis-of-Shigella-infection)