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

Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

2.3K
Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
2.3K
Bioreactor Controls-I01:28

Bioreactor Controls-I

94
Maintaining optimal conditions within fermenters is essential for maximizing microbial productivity and ensuring process efficiency. This lesson focuses on key parameters—temperature, foam, pH, carbon dioxide, oxygen, and pressure—and their precise measurement and control strategies in fermentation systems.Temperature ControlTemperature regulation is critical due to the exothermic nature of many fermentation processes. In small laboratory fermenters, temperature is commonly...
94
Methods of Medium Optimization01:28

Methods of Medium Optimization

70
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
70
Scale-Up Processes01:14

Scale-Up Processes

105
The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...
105

You might also read

Related Articles

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

Sort by
Same author

Sti1 participates in the dynamics of protein aggregation triggered by glucose signaling in <i>Saccharomyces cerevisiae</i>.

Acta biochimica et biophysica Sinica·2026
Same author

Cross-subject generalization for EEG decoding: a survey of deep learning methods.

Progress in biomedical engineering (Bristol, England)·2026
Same author

Lipid nanocomposites for precisely triggered Ttc3 gene silencing in pulmonary fibrosis treatment.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

A novel nanotherapeutic strategy: rescuing nucleus pulposus cells from fatty acid metabolic disorder and pyroptosis through ACOT13 by Chinese herbal formula nanoparticles.

Journal of nanobiotechnology·2026
Same author

Cadmium accumulation and translocation in maize cultivars on contaminated soils in southern China.

BMC plant biology·2025
Same author

Self-Supervised Representation Learning and Temporal-Spectral Feature Fusion for Bed Occupancy Detection.

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies·2025

Related Experiment Video

Updated: May 1, 2026

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS
17:01

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Published on: July 18, 2013

12.9K

Machine learning driven semi-automated framework for yeast sporulation efficiency quantification using ilastik

Xuan Shang1, Zhenwei Yang2, Guanzu Peng3

  • 1State Key Laboratory of Cognitive Neuroscience and Learning and Beijing Key Laboratory of Genetic Engineering Drugs & Biotechnology, College of Life Sciences, Beijing Normal University, Beijing 100875, PR China; Key Laboratory of Cellular Physiology, Ministry of Education, Shanxi Medical University, Taiyuan 030001, PR China; Department of Physiology, Shanxi Medical University, Taiyuan 030001, PR China.

Fungal Genetics and Biology : FG & B
|August 28, 2025
PubMed
Summary

This study presents a new automated method for quantifying yeast sporulation efficiency, reducing manual counting time by 68% while maintaining high accuracy. The pipeline reliably classifies spore numbers across diverse yeast strains and genetic backgrounds.

Keywords:
Batch processingComputational biology workflowIlastik-Fiji integrationMother cell segmentationYeast sporulation quantification

More Related Videos

Analysis of Lipid Droplet Content in Fission and Budding Yeasts using Automated Image Processing
08:43

Analysis of Lipid Droplet Content in Fission and Budding Yeasts using Automated Image Processing

Published on: July 17, 2019

8.1K
Author Spotlight: Exploring Cytoskeletal Dynamics to Unveil Novel Antibiotics Through Innovative Cell-Based Assays
05:57

Author Spotlight: Exploring Cytoskeletal Dynamics to Unveil Novel Antibiotics Through Innovative Cell-Based Assays

Published on: April 26, 2024

921

Related Experiment Videos

Last Updated: May 1, 2026

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS
17:01

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Published on: July 18, 2013

12.9K
Analysis of Lipid Droplet Content in Fission and Budding Yeasts using Automated Image Processing
08:43

Analysis of Lipid Droplet Content in Fission and Budding Yeasts using Automated Image Processing

Published on: July 17, 2019

8.1K
Author Spotlight: Exploring Cytoskeletal Dynamics to Unveil Novel Antibiotics Through Innovative Cell-Based Assays
05:57

Author Spotlight: Exploring Cytoskeletal Dynamics to Unveil Novel Antibiotics Through Innovative Cell-Based Assays

Published on: April 26, 2024

921

Area of Science:

  • * Microbiology and Genetics
  • * Bioimage Analysis

Background:

  • * Accurate quantification of yeast sporulation efficiency is crucial for genetic studies.
  • * Manual counting is labor-intensive and prone to subjective errors.
  • * Existing deep learning tools may not be universally applicable or adaptable.

Purpose of the Study:

  • * To develop an automated, robust, and accessible pipeline for yeast sporulation efficiency quantification.
  • * To reduce processing time and subjective bias associated with manual counting.
  • * To provide a reliable alternative for diverse genetic backgrounds and spore morphologies.

Main Methods:

  • * Utilized ilastik for texture-feature optimization to segment sporulating yeast cells.
  • * Employed Fiji for optimized image processing and spore quantification within segmented cells.
  • * Implemented automated classification of dyads, triads, and tetrads with manual quality control checkpoints.

Main Results:

  • * Achieved 93.4% agreement with manual counting (ICC = 0.94).
  • * Reduced processing time by 68% (P < 0.001).
  • * Demonstrated consistent performance across Hsp82 phosphorylation mutants and diverse genetic backgrounds.

Conclusions:

  • * The developed pipeline offers a reproducible and precise alternative to manual yeast sporulation quantification.
  • * The modular design allows for adjustable parameters and compatibility with various imaging datasets and markers.
  • * This method balances throughput and accuracy, making it suitable for standard laboratory microscopy.