Enabling reactive microscopy with MicroMator

Zachary R. Fox, Steven Fletcher, Achille Fraisse, Chetan Aditya, Sebastián Sosa-Carrillo, Julienne Petit, Sébastien Gilles, François Bertaux, Jakob Ruess and Gregory Batt ()
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Zachary R. Fox: Inria Paris
Steven Fletcher: Inria Paris
Achille Fraisse: Inria Paris
Chetan Aditya: Inria Paris
Sebastián Sosa-Carrillo: Inria Paris
Julienne Petit: Structural Microbiology Unit, Institut Pasteur, CNRS UMR 3528, Université de Paris
Sébastien Gilles: Inria Saclay—Ile-de-France, 1 rue H. d’Estienne d’Orves
François Bertaux: Inria Paris
Jakob Ruess: Inria Paris
Gregory Batt: Inria Paris

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract Microscopy image analysis has recently made enormous progress both in terms of accuracy and speed thanks to machine learning methods and improved computational resources. This greatly facilitates the online adaptation of microscopy experimental plans using real-time information of the observed systems and their environments. Applications in which reactiveness is needed are multifarious. Here we report MicroMator, an open and flexible software for defining and driving reactive microscopy experiments. It provides a Python software environment and an extensible set of modules that greatly facilitate the definition of events with triggers and effects interacting with the experiment. We provide a pedagogic example performing dynamic adaptation of fluorescence illumination on bacteria, and demonstrate MicroMator’s potential via two challenging case studies in yeast to single-cell control and single-cell recombination, both requiring real-time tracking and light targeting at the single-cell level.

Date: 2022
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DOI: 10.1038/s41467-022-29888-z

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