Antibiotic Drug screening and Image Characterization Toolbox (A.D.I.C.T.): a robust imaging workflow to monitor antibiotic stress response in bacterial cells in vivo

Abstract

The search for novel drugs that efficiently eliminate prokaryotic pathogens is one of the most urgent health topics of our time. Robust evaluation methods for monitoring the antibiotic stress response in prokaryotes are therefore necessary for developing respective screening strategies. Besides advantages of common in vitro techniques, there is a growing demand for in vivo information based on imaging techniques that allow to screen antibiotic candidates in a dynamic manner. Gathering information from imaging data in a reproducible manner, robust data processing and analysis workflows demand advanced (semi-)automation and data management to increase reproducibility. Here we demonstrate a versatile and robust semi-automated image acquisition, processing and analysis workflow to investigate bacterial cell morphology in a quantitative manner. The presented workflow, A.D.I.C.T, covers aspects of experimental setup deployment, data acquisition and handling, image processing (e.g. ROI management, data transformation into binary images, background subtraction, filtering, projections) as well as statistical evaluation of the cellular stress response (e.g. shape measurement distributions, cell shape modeling, probability density evaluation of fluorescence imaging micrographs) towards antibiotic-induced stress, obtained from time-course experiments. The imaging workflow is based on regular brightfield images combined with live-cell imaging data gathered from bacteria, in our case from recombinant Shewanella cells, which are processed as binary images. The model organism expresses target proteins relevant for membrane-biogenesis that are functionally fused to respective fluorescent proteins. Data processing and analysis are based on customized scripts using ImageJ2/FIJI, Celltool and R packages that can be easily reproduced and adapted by users. Summing up, our approach aims at supporting life-scientists to establish their own imaging-pipeline in order to exploit their data as versatile as possible and in a reproducible manner.

A Machine Learning-Empowered Workflow to Discriminate Bacillus subtilis Motility Phenotypes

“…Nevertheless, the workflow shows how to approach these tasks systematically and dissect information, respectively. Different types of assay strategies that involve antibiotics are also possible in combination with colony formation that is monitored using time-resolved gray-scale imaging, as shown prior to this workflow [53]. Our results suggest that distinct characteristic features based on the ability to invade the surroundings are suitable to apply spatial clustering based on the DBSCAN (see Figure 7).…”

Imaging-based profiling for elucidation of antibacterial mechanisms of action

“…Another semi-automated workflow named Antibiotic Drug screening and Image Characterization Toolbox (A.D.I.C.T.) was developed [36]. Of course, the use of automation methods for analyzing a large number of cells and machine learning algorithms for solving clustering and classification problems opens up new opportunities for further successful use of morphoprofiling [27,37].…”