DOI: https://doi.org/10.29363/nanoge.eimc.2021.008
Publication date: 5th July 2021
Encapsulation of single cells in monodisperse water-in-oil microdroplets offers powerful means to perform quantitative biological studies on a single cell basis, within large cell populations. For example, to understand the interaction of living cells with their environments, we need to look into what guides individual behaviour and movement. For this purpose, we have developed a versatile image-based sorter that can deterministically trap single cells in microdroplets [1]. We have used recent advances in deep learning for real-time object detection to provide rapid and acurate classifications for micro-swimmers of varying appearances. The tool was applied to the study of motility for unicellular microflagellates and compare two species of green algae, C. reinhardtii - a freshwater biflagellate, and P. octopus - a marine octoflagellate, to reveal their stereotyped behaviours and emergence of distinct motility macrostates. In such mobile single-cells, behavioural responses can be tracked with high-speed imaging, using movement as a dynamic read-out of behaviour and physiology. These comprehensive datasets will allow us to query and catalogue single-cell behavioural actions at unprecedented resolution.
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