FilamentSensor 2.0: An open-source modular toolbox for 2D/3D cytoskeletal filament tracking

TY - JOUR

T1 - FilamentSensor 2.0: An open-source modular toolbox for 2D/3D cytoskeletal filament tracking

AU - Hauke, Lara

AU - Primeßnig, Andreas

AU - Eltzner, Benjamin

AU - Radwitz, Jennifer

AU - Huckemann, Stefan F

AU - Rehfeldt, Florian

N1 - Copyright: © 2023 Hauke et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PY - 2023/2/6

Y1 - 2023/2/6

N2 - Cytoskeletal pattern formation and structural dynamics are key to a variety of biological functions and a detailed and quantitative analysis yields insight into finely tuned and well-balanced homeostasis and potential pathological alterations. High content life cell imaging of fluorescently labeled cytoskeletal elements under physiological conditions is nowadays state-of-the-art and can record time lapse data for detailed experimental studies. However, systematic quantification of structures and in particular the dynamics (i.e. frame-to-frame tracking) are essential. Here, an unbiased, quantitative, and robust analysis workflow that can be highly automatized is needed. For this purpose we upgraded and expanded our fiber detection algorithm FilamentSensor (FS) to the FilamentSensor 2.0 (FS2.0) toolbox, allowing for automatic detection and segmentation of fibrous structures and the extraction of relevant data (center of mass, length, width, orientation, curvature) in real-time as well as tracking of these objects over time and cell event monitoring.

AB - Cytoskeletal pattern formation and structural dynamics are key to a variety of biological functions and a detailed and quantitative analysis yields insight into finely tuned and well-balanced homeostasis and potential pathological alterations. High content life cell imaging of fluorescently labeled cytoskeletal elements under physiological conditions is nowadays state-of-the-art and can record time lapse data for detailed experimental studies. However, systematic quantification of structures and in particular the dynamics (i.e. frame-to-frame tracking) are essential. Here, an unbiased, quantitative, and robust analysis workflow that can be highly automatized is needed. For this purpose we upgraded and expanded our fiber detection algorithm FilamentSensor (FS) to the FilamentSensor 2.0 (FS2.0) toolbox, allowing for automatic detection and segmentation of fibrous structures and the extraction of relevant data (center of mass, length, width, orientation, curvature) in real-time as well as tracking of these objects over time and cell event monitoring.

KW - Algorithms

KW - Cytoskeleton

U2 - 10.1371/journal.pone.0279336

DO - 10.1371/journal.pone.0279336

M3 - SCORING: Journal article

C2 - 36745610

VL - 18

SP - e0279336

JO - PLOS ONE

JF - PLOS ONE

SN - 1932-6203

IS - 2

ER -