Automated Tracking of Cell Migration with Rapid Data Analysis

DuChez, Brian J.

doi:10.1002/cpcb.28

“…ImageJ software was used to enhance contrast, subtract background and transform the images to 8-bit greyscale. Random migration trajectories were obtained from the images using the FastTracks Matlab plugin ( DuChez, 2018 ), subsequent statistical analysis and plotting were done in Python.…”

Section: Methodsmentioning

confidence: 99%

Periodic propagating waves coordinate RhoGTPase network dynamics at the leading and trailing edges during cell migration

Bolado-Carrancio

¹

,

Rukhlenko

²

,

Nikonova

³

et al. 2020

eLife

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Migrating cells need to coordinate distinct leading and trailing edge dynamics but the underlying mechanisms are unclear. Here, we combine experiments and mathematical modeling to elaborate the minimal autonomous biochemical machinery necessary and sufficient for this dynamic coordination and cell movement. RhoA activates Rac1 via DIA and inhibits Rac1 via ROCK, while Rac1 inhibits RhoA through PAK. Our data suggest that in motile, polarized cells, RhoA–ROCK interactions prevail at the rear, whereas RhoA-DIA interactions dominate at the front where Rac1/Rho oscillations drive protrusions and retractions. At the rear, high RhoA and low Rac1 activities are maintained until a wave of oscillatory GTPase activities from the cell front reaches the rear, inducing transient GTPase oscillations and RhoA activity spikes. After the rear retracts, the initial GTPase pattern resumes. Our findings show how periodic, propagating GTPase waves coordinate distinct GTPase patterns at the leading and trailing edge dynamics in moving cells.

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“…The study of cell motility typically involves recording cell behavior, using live-cell imaging, and tracking their movement over time 1 , 2 . To enable the analysis of such data, various software solutions have been developed 3 – 9 . However, despite the availability of these computational tools, manual tracking remains widely used among researchers due to the difficulty in setting up fully automated cell tracking analysis pipelines.…”

Section: Introductionmentioning

confidence: 99%

Automated cell tracking using StarDist and TrackMate

Fazeli

¹

,

Roy

²

,

Follain

³

et al. 2020

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The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

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“…The study of cell motility typically involves recording cell behavior, using live-cell imaging, and tracking their movement over time 1,2 . To enable the analysis of such data, various software solutions have been developed [3][4][5][6][7][8][9] . However, despite the availability of these computational tools, manual tracking remains widely used among researchers due to the difficulty in setting up fully automated cell tracking analysis pipelines.…”

Section: Introductionmentioning

confidence: 99%

Automated cell tracking using StarDist and TrackMate

Fazeli

¹

,

Roy

²

,

Follain

³

et al. 2020

View full text Add to dashboard Cite

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

show abstract

Automated Tracking of Cell Migration with Rapid Data Analysis

Cited by 19 publications

References 13 publications

Periodic propagating waves coordinate RhoGTPase network dynamics at the leading and trailing edges during cell migration

Periodic propagating waves coordinate RhoGTPase network dynamics at the leading and trailing edges during cell migration

Automated cell tracking using StarDist and TrackMate

Automated cell tracking using StarDist and TrackMate

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