Abstract

This work is a contribution to the current effort in developing rapid, robust and cost-effective technologies for antimicrobial classification according to their mechanism of action, which could potentially serve as a new MoA detector. We propose an original, label-free technology based on time-lapse Digital Inline Holographic Microscopy, which will be coupled to deep-learning analysis in a later stage. This paper focuses on the physical analysis of the reconstructed time-lapse phase image at the single-cell level, and shows that time-lapse DIHM is able to capture different phenotypes linked to different antibiotic classes. PCA analysis based on 10 antibiotics for 4 inhibiting targets shows that we can discriminate 5 over 6 chemical classes in less than 2 hours.

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