Visualization and Image Analysis of Yeast Cells

The kinetics and success of an industrial fermentation are dependent upon the health of the microorganism(s) responsible. Saccharomyces sp. are the most commonly used organisms in food and beverage production; consequently, many metrics of yeast health and stress have been previously correlated with morphological changes to fermentations kinetics. Many researchers and industries use machine vision to count yeast and assess health through dyes and image analysis. This study assessed known physical differences through automated image analysis taken throughout ongoing high stress fermentations at various temperatures (30 °C and 35 °C). Measured parameters included sugar consumption rate, number of yeast cells in suspension, yeast cross-sectional area, and vacuole cross-sectional area. The cell morphological properties were analyzed automatically using ImageJ software and validated using manual assessment. It was found that there were significant changes in cell area and ratio of vacuole to cell area over the fermentation. These changes were temperature dependent. The changes in morphology have implications for rates of cellular reactions and efficiency within industrial fermentation processes. The use of automated image analysis to quantify these parameters is possible using currently available systems and will provide additional tools to enhance our understanding of the fermentation process.

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Morphology and Phenotype of Peripheral Erythrocytes of Fish: A Rapid Screening of Images by Using Software

International Letters of Natural Sciences ◽

10.18052/www.scipress.com/ilns.54.27 ◽

2016 ◽

Vol 54 ◽

pp. 27-41

Author(s):

Soumendra Nath Talapatra ◽

Priyadarshini Mitra ◽

Snehasikta Swarnakar

Keyword(s):

Image Analysis ◽

Mammalian Cells ◽

Yeast Cells ◽

Rapid Screening ◽

Biological Study ◽

Biological Research ◽

Analysis Software ◽

Image Analysis Software ◽

Comparative Results ◽

Cellular Phenotypes

Many information of biological study as stained cells analysis under microscope cannot be obtained rich information like detail morphology, shape, size, proper intensity etc. but image analysis software can easily be detected all these parameters within short duration. The cells types can be yeast cells to mammalian cells. An attempt has been made to detect cellular abnormalities from an image of metronidazole (MTZ) treated compared to control images of peripheral erythrocytes of fish by using non-commercial, open-source, CellProfiler (CP) image analysis software (Ver. 2.1.0). The comparative results were obtained after analysis the software. In conclusion, this image based screening of Giemsa stained fish erythrocytes can be a suitable tool in biological research for primary toxicity prediction at DNA level alongwith cellular phenotypes. Moreover, still suggestions are needed in relation to accuracy of present analysis for Giemsa stained fish erythrocytes because previous works have been carried out images of cells with fluorescence dye.

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STUDY OF SACCHAROMYCES CEREVISIAE YEAST CELLS BY FIELD FLOW FRACTIONATION AND IMAGE ANALYSIS

Computer Applications in Biotechnology ◽

10.1016/b978-0-08-042377-7.50042-8 ◽

1995 ◽

pp. 217-220

Author(s):

A. Litzén ◽

G.M. Kresbach ◽

M.N. Pons ◽

M. Ehrat ◽

H. Vivier

Keyword(s):

Saccharomyces Cerevisiae ◽

Image Analysis ◽

Yeast Cells ◽

Field Flow Fractionation ◽

Flow Fractionation

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YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells

Bioinformatics ◽

10.1093/bioinformatics/btz402 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4525-4527 ◽

Cited By ~ 10

Author(s):

Alex X Lu ◽

Taraneh Zarin ◽

Ian S Hsu ◽

Alan M Moses

Keyword(s):

Image Analysis ◽

Web Application ◽

Single Cells ◽

Yeast Cells ◽

Supplementary Information ◽

Supplementary Data ◽

User Friendly ◽

Microscopy Images

Abstract Summary We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines. Availability and implementation YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation. Supplementary information Supplementary data are available at Bioinformatics online.

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