Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Pooled CRISPRi screening reveals fungal-specific drug target candidates.

Nature microbiology·2026
Same author

The evolution of azole resistance through a reduced spore dormancy pathway associated with loss of SUMOylation function in <i>Fusarium graminearum</i>.

Applied and environmental microbiology·2026
Same author

Pooled CRISPRi screening reveals fungal-specific vulnerabilities across environments and genetic backgrounds.

bioRxiv : the preprint server for biology·2026
Same author

Migration and standing variation in vaginal and rectal yeast populations in recurrent vulvovaginal candidiasis.

mSystems·2025
Same author

FungAMR: a comprehensive database for investigating fungal mutations associated with antimicrobial resistance.

Nature microbiology·2025
Same author

Fungal impacts on Earth's ecosystems.

Nature·2025

Related Experiment Video

Updated: May 30, 2025

Quantitative Live Cell Fluorescence-microscopy Analysis of Fission Yeast
06:52

Quantitative Live Cell Fluorescence-microscopy Analysis of Fission Yeast

Published on: January 23, 2012

20.2K

Quantifying Competitive Fitness in Yeast with High-Throughput Fluorescence Microscopy Imaging.

Aruni S Sumanarathne1, Aleeza C Gerstein1,2

  • 1Department of Microbiology, University of Manitoba, Winnipeg, Manitoba, Canada.

Current Protocols
|January 27, 2025
PubMed
Summary

We developed a high-throughput method using fluorescence microscopy and machine learning to accurately measure microbial competitive fitness. This automated approach replaces labor-intensive traditional assays, providing reproducible results for evolutionary biology research.

Keywords:
Image analysisOrbitR

More Related Videos

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
12:52

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Published on: April 18, 2021

4.7K
Quantification of Interbacterial Competition using Single-Cell Fluorescence Imaging
07:34

Quantification of Interbacterial Competition using Single-Cell Fluorescence Imaging

Published on: September 2, 2021

3.2K

Related Experiment Videos

Last Updated: May 30, 2025

Quantitative Live Cell Fluorescence-microscopy Analysis of Fission Yeast
06:52

Quantitative Live Cell Fluorescence-microscopy Analysis of Fission Yeast

Published on: January 23, 2012

20.2K
High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
12:52

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Published on: April 18, 2021

4.7K
Quantification of Interbacterial Competition using Single-Cell Fluorescence Imaging
07:34

Quantification of Interbacterial Competition using Single-Cell Fluorescence Imaging

Published on: September 2, 2021

3.2K

Area of Science:

  • Evolutionary Biology
  • Microbial Ecology
  • Quantitative Biology

Background:

  • Competitive fitness is crucial for understanding organismal adaptation and resource competition.
  • Traditional microbial competitive fitness assays are time-consuming and labor-intensive, involving manual colony counting.
  • Existing methods lack the throughput and automation needed for large-scale studies.

Purpose of the Study:

  • To introduce a novel, high-throughput method for quantifying microbial competitive fitness.
  • To replace traditional, labor-intensive competitive fitness assays with an automated, image-based approach.
  • To provide a comprehensive protocol for implementing this new methodology.

Main Methods:

  • Utilizing fluorescence microscopic imaging to distinguish between competing microbial populations.
  • Employing machine-learning-enabled image analysis for automated cell counting.
  • Integrating sample preparation, microscopy, image analysis, and statistical calculations in R.

Main Results:

  • The developed method provides accurate and reproducible quantitative measurements of competitive fitness.
  • This high-throughput approach significantly reduces the labor and time required compared to traditional methods.
  • The protocol includes detailed instructions and scripts for seamless implementation.

Conclusions:

  • This automated fluorescence microscopy and machine learning method offers an efficient and reliable way to assess microbial competitive fitness.
  • The technique is broadly applicable to various microbial systems and evolutionary studies.
  • This advancement facilitates high-throughput screening and detailed analysis of competitive dynamics.