A fully-automated, robust, and versatile algorithm for long-term budding yeast segmentation and tracking

N. Ezgi Wood, Andreas Doncic

Research output: Contribution to journalArticle

Abstract

Live cell time-lapse microscopy, a widely-used technique to study gene expression and protein dynamics in single cells, relies on segmentation and tracking of individual cells for data generation. The potential of the data that can be extracted from this technique is limited by the inability to accurately segment a large number of cells from such microscopy images and track them over long periods of time. Existing segmentation and tracking algorithms either require additional dyes or markers specific to segmentation or they are highly specific to one imaging condition and cell morphology and/or necessitate manual correction. Here we introduce a fully automated, fast and robust segmentation and tracking algorithm for budding yeast that overcomes these limitations. Full automatization is achieved through a novel automated seeding method, which first generates coarse seeds, then automatically fine-tunes cell boundaries using these seeds and automatically corrects segmentation mistakes. Our algorithm can accurately segment and track individual yeast cells without any specific dye or biomarker. Moreover, we show how existing channels devoted to a biological process of interest can be used to improve the segmentation. The algorithm is versatile in that it accurately segments not only cycling cells with smooth elliptical shapes, but also cells with arbitrary morphologies (e.g. sporulating and pheromone treated cells). In addition, the algorithm is independent of the specific imaging method (bright-field/phase) and objective used (40X/63X/100X). We validate our algorithm’s performance on 9 cases each entailing a different imaging condition, objective magnification and/or cell morphology. Taken together, our algorithm presents a powerful segmentation and tracking tool that can be adapted to numerous budding yeast single-cell studies.

Original languageEnglish (US)
Article numbere0206395
JournalPloS one
Volume14
Issue number3
DOIs
StatePublished - Mar 1 2019

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Saccharomycetales
Yeast
yeasts
cells
Imaging techniques
Seed
Microscopic examination
Coloring Agents
Cells
Microscopy
Seeds
image analysis
Pheromones
Cell Tracking
Biomarkers
Biological Phenomena
dyes
microscopy
Gene expression
Cell Shape

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

A fully-automated, robust, and versatile algorithm for long-term budding yeast segmentation and tracking. / Ezgi Wood, N.; Doncic, Andreas.

In: PloS one, Vol. 14, No. 3, e0206395, 01.03.2019.

Research output: Contribution to journalArticle

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