Biophysical Society Thematic Meeting | Riga 2026

Active and Responsive Soft Matter: From Biological to Engineered Systems

Poster Abstracts

13-POS Board 13 MOTILITY-BASED METHODS FOR MAGNETOTACTIC BACTERIA CHARACTERIZATION Mara Šmite ; Mihails Birjukovs 2 ; Andrejs Cebers 1 ; Guntars Kitenbergs 1 ; 1 University of Latvia, Laboratory of Magnetic Soft Materials, Riga, Latvia 2 University of Latvia, Institute of Numerical Modelling, Riga, Latvia

Magnetotactic bacteria (MTB) are diverse microorganisms characterized by their ability to respond to magnetic fields. Their cells typically contain chains of magnetic crystals, or magnetosomes, that allow passive alignment with the magnetic field direction. Cell size, swimming velocity, and magnetic moment are key properties for assessing culture quality and for developing potential microrobotic and biomedical applications. However, these properties remain difficult to measure at population scale, particularly in mixed species environmental samples. Here, we present motility-based methods for MTB characterization using magnetic field actuation and automated trajectory reconstruction. For cells swimming in an alternating magnetic field, a modified U-turn method fits trajectories to a theoretical shape function and estimates magnetic moment from trajectory geometry and cell properties, avoiding manual selection bias and limitations of conventional time-based methods, and enabling error estimation. Applied to Magnetospirillum gryphiswaldense, this approach reveals an approximately linear relationship between magnetic moment and cell size. We further apply automated velocimetry and U-turn analysis to an MTB rich environmental sample from a newly identified site in the Ogre River, Latvia, separating bacterial populations by velocity, size, and magnetic moment. Together with transmission electron microscopy and 16S rRNA sequencing, these methods identify multiple MTB morphotypes, magnetic response groups, and non-magnetic motile bacteria in a heterogeneous natural sample. These results demonstrate that image-based motility analysis provides a rapid, scalable, and accessible framework for MTB characterization. The presented automated data analysis pipeline can be used to rapidly characterize environmental samples containing MTB and to reliably monitor the properties of laboratory grown cultures.

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