CLIQ-BID: A method to quantify bacteria-induced damage to eukaryotic cells by automated live-imaging of bright nuclei

Abstract

Pathogenic bacteria induce eukaryotic cell damage which range from discrete modifications of signalling pathways, to morphological alterations and even to cell death. Accurate quantitative detection of these events is necessary for studying host-pathogen interactions and for developing strategies to protect host organisms from bacterial infections. Investigation of morphological changes is cumbersome and not adapted to high-throughput and kinetics measurements. Here, we describe a simple and cost-effective method based on automated analysis of live cells with stained nuclei, which allows real-time quantification of bacteria-induced eukaryotic cell damage at single-cell resolution. We demonstrate that this automated high-throughput microscopy approach permits screening of libraries composed of interference-RNA, bacterial strains, antibodies and chemical compounds in ex vivo infection settings. The use of fluorescently-labelled bacteria enables the concomitant detection of changes in bacterial growth. Using this method named CLIQ-BID (Cell Live Imaging Quantification of Bacteria Induced Damage), we were able to distinguish the virulence profiles of different pathogenic bacterial species and clinical strains.

Induction of Necrosis in Human Macrophage Cell Lines by Corynebacterium diphtheriae and Corynebacterium ulcerans Strains Isolated from Fatal Cases of Systemic Infections

“…Fluorescence microscopy using Hoechst nuclear stain [22] also revealed bacteria-induced damage to the human macrophages. Upon contact with pathogenic corynebacteria, DNA was successively condensing over time and nuclei appeared smaller and brighter as compared with cells, which had contact with non-pathogenic C. glutamicum (Figure 2).…”

Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools

“…Network-based approaches were exploited to detect, reconstruct and study interactions among sub network modules ( 13 , 19 , 22 , 25 , 40 ); to assess functional correlation among multi-omics entities ( 12 , 14 , 20 , 55 , 61 , 62 ); to integrate and fuse networks to create comprehensive view of a disease ( 16 , 24 , 32 , 37 , 41 , 63 , 65 ). A few work leverage Bayesian methods ( 4 , 34 ) or Markov models ( 17 , 67 ).…”

Ontario Neurodegenerative Disease Research Initiative (ONDRI): Structural MRI Methods and Outcome Measures

“…Recent studies suggest that the brain utilizes the glymphatic system ( 62 , 63 ) to clear fluid and metabolic waste, using a complex series of perivascular channels surrounding the brain's veins and arteries. It has been suggested that when the perivascular channels are compromised due to aging, disease, or trauma, the perivascular space becomes enlarged and consequently, visible on structural MRI ( 64 – 67 ). MRI-visible (enlarged) perivascular spaces (PVS) on T2 appear as small (<3 mm diameter), linear, hyperintensities following the course of the vasculature ( Figure 10 ).…”