High-content high-resolution microscopy and deep learning assisted analysis reveals host and bacterial heterogeneity during Shigella infection
Creators: name: López-Jiménez, Ana Teresa
name: Brokatzky, Dominik
name: Pillay, Kamla
name: Williams, Tyrese
name: Özbaykal Güler, Gizem
name: Mostowy, Serge
Publisher: name: Zenodo
Year: 2024
Subjects: Bacterial infection
Shigella flexneri
HeLa cells
High-content microscopy
heterogeneity
Septins
Deep learning

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: Funding provided by: Swiss National Science FoundationCrossref Funder Registry ID: https://ror.org/00yjd3n13Award Number: P2GEP3_188277
Funding provided by: European CommissionCrossref Funder Registry ID: https://ror.org/00k4n6c32Award Number: H2020-MSCA-IF- 2020-895330
Funding provided by: Deutsche ForschungsgemeinschaftCrossref Funder Registry ID: https://ror.org/018mejw64Award Number: BR 6637/1-1
Funding provided by: International Human Frontier Science Program OrganizationCrossref Funder Registry ID: https://ror.org/02ebx7v45Award Number: LT000436/2021-L
Funding provided by: Wellcome TrustCrossref Funder Registry ID: https://ror.org/029chgv08Award Number: 206444/Z/17/Z
Funding provided by: European Research CouncilCrossref Funder Registry ID: https://ror.org/0472cxd90Award Number: 772853 - ENTRAPMENT
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