Method for 3D tracking behaviors of interplaying bacteria individuals

Abstract

Behaviors of platonic bacteria individuals are profoundly influenced by their interplay. However, probing such interplay still remains a challenge since identification and tracking of bacterial individuals becomes difficult as they come close and interact with each other. Herein, we report 3D tracking of the motions of multiple bacteria by using digital holographic microscopy (DHM), where the subtle 3D behaviors can be characterized as bacteria approach and run away from each other. An algorithm was developed to identify and recover the gap between 3D trajectory segments raising by the interruption from other bacteria through lateral image recognition and axial loalization utilizing cost function. We value the performance of the algorithm in terms of the statistics in trajectory length and correct rate. The study clearly shows how the interplaying Escherichia coli alter their motions.

Role of Surface Coverage of Sessile Probiotics in Their Interplay with Pathogen Bacteria Investigated by Digital Holographic Microscopy

“…Real-time 3D tracking of each planktonic ES was accomplished by the 3D reconstruction of holograms, as previously described. , Briefly, a background was subtracted from the holograms so that the swimming bacteria were retained in the images. The scattered light was reconstructed using a Rayleigh–Sommerfeld back-propagation theory , to capture the 3D coordinates of each planktonic ES in the holograms.…”

All-optical dual module platform for motility-based functional scrutiny of microencapsulated probiotic bacteria

“…It is an interferometric technique well assessed and widely applied in diagnostics and medicine for blood counting and screening [ 29 – 31 ], circulating tumor cells detection [ 32 ] and drug resistance assays [ 33 ], sperm motility analysis [ 34 , 35 ], parasites and waterborne pathogens detection [ 36 ], as well as environmental monitoring, also in the form of field portable devices [ 37 , 38 ]. Exploiting the advantageous DH numerical refocusing, 3D-tracking of bacteria freely swimming inside a volume of liquid has been performed [ 39 – 41 ]. Both low density and high-density ensembles [ 40 ] have been tracked, and the local interplay between the elements has been investigated using optimization of cost functions for localization [ 41 ] or machine learning approaches [ 42 ].…”

“…Exploiting the advantageous DH numerical refocusing, 3D-tracking of bacteria freely swimming inside a volume of liquid has been performed [ 39 – 41 ]. Both low density and high-density ensembles [ 40 ] have been tracked, and the local interplay between the elements has been investigated using optimization of cost functions for localization [ 41 ] or machine learning approaches [ 42 ].…”

Single-camera PTV within interfacially sheared drops in microgravity

“…Particle tracking velocimetry (PTV) algorithms are a class of numerical methods that determine the displacement of tracer particles in an image sequence to calculate velocity in time and space. Measurements of the velocity field is fundamental in understanding the behavior of flowing systems both natural, ranging from molecular biology (Bos et al, 2021;Faez et al, 2015;Huang and Choma, 2015;Wang et al, 2018) and cellular biology (Marin et al, 2017;Savorana et al, 2022;Wang et al, 2020) to physiology (Oeler et al, 2021;Sampath et al, 2018;Zhang et al, 2022) and the environment, (Aksamit and Pomeroy, 2016;Gaudin et al, 2014;Mujtaba and de Lima, 2018;Tagliavini et al, 2022;Tauro et al, 2019) and engineered, spanning biochemical reactors (Devasenathipathy et al, 2002;Hofmann et al, 2022;Kováts et al, 2018;Romano et al, 2023Romano et al, , 2021 and fundamental fluid dynamics (Humphrey et al, 1974;Kasagi and Nishino, 1991;Kim et al, 2016) to manufacturing (Eschner et al, 2019;Fischer et al, 2022) and civil (Bautista-Capetillo et al, 2014;Fu et al, 2015;Zhao et al, 2020) industry. The imaging system must fulfill resolution requirements relative to the physical system, however, no constraint is imposed on the overall scale of the physical system, PTV being applicable to nano, (Bos et al, 2021;Faez et al, 2015;Matsuura et al, 2018;Satake, 2022;Wang et al, 2018) micro, (Choi et al, 2012;Dehnavi et al, 2020;…”

“…However, the possibility exists for using elements native to a system as tracer particles if these system elements can be resolved by the imaging system. (Aksamit and Pomeroy, 2016;Bautista-Capetillo et al, 2014;Bos et al, 2021;Choi et al, 2022;de Cerqueira et al, 2023;Faez et al, 2015;Gaudin et al, 2014;Wang et al, 2020Wang et al, , 2018Ziegenhein et al, 2016) Another difficulty with many systems is that the optimization of PTV set-ups is often restricted, involving only a single point of view, complex geometry, or scenarios unsuitable for planar illumination or adding seeding particles as tracers. Efforts to extract accurate and minimally-invasive velocimetry data from a system form the field of PTV algorithm research.…”