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Flexible and open-source programs for quantitative image analysis in microbial ecology

Alexis L. Pasulka, Jonathan F. Hood, Dana E. Michels, Mason D. Wright
doi: https://doi.org/10.1101/2022.09.23.509172
Alexis L. Pasulka
1Biological Sciences Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA
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  • For correspondence: apasulka@calpoly.edu
Jonathan F. Hood
2Aerospace Engineering Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA
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Dana E. Michels
1Biological Sciences Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA
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Mason D. Wright
1Biological Sciences Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA
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Abstract

Epifluorescence microscopy is an essential tool for obtaining reliable estimates of the abundance of marine microorganisms including viruses. However, computational analysis is required to gain consistent and quantitative data from digital microscopy images. Many imaging programs are proprietary and cost-prohibitive. The currently available free imaging programs are often platform specific and/or lack the flexibility to analyze microscopy images from natural samples, such as the planktonic environment, which can contain challenges such as debris and high background signals. Here we describe two MATLAB-based open-source image analysis programs that work across computer platforms and provide the tools to analyze a range of image types and cell sizes with a user-friendly interface. The Microbial Image Analysis (MiA) program aims to provide flexibility for the selection, identification, and quantification of cells that vary in size and fluorescence intensity within natural microbial communities. The Viral Image Analysis (ViA) program aims to provide an effective means for quantifying viral abundances from epifluorescence images as well as enumerating the intensity of a primary and secondary stain. In this paper, we provide an overview of the functionality of the MiA and ViA programs and highlight specific program features through several microbial image case studies.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/PECO-CP/MiA

  • https://github.com/PECO-CP/ViA

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 23, 2022.
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Flexible and open-source programs for quantitative image analysis in microbial ecology
Alexis L. Pasulka, Jonathan F. Hood, Dana E. Michels, Mason D. Wright
bioRxiv 2022.09.23.509172; doi: https://doi.org/10.1101/2022.09.23.509172
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Flexible and open-source programs for quantitative image analysis in microbial ecology
Alexis L. Pasulka, Jonathan F. Hood, Dana E. Michels, Mason D. Wright
bioRxiv 2022.09.23.509172; doi: https://doi.org/10.1101/2022.09.23.509172

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