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

Analysis of Short-Term Subjective Well-Being/Comfort and Its Correlation to Different EEG Metrics.

Sensors (Basel, Switzerland)·2026
Same author

Adverse effects of posaconazole on adrenal steroid biosynthesis: An integrative approach using FAERS-based pharmacovigilance and systematic review with meta-analysis of randomised controlled trials.

International journal of antimicrobial agents·2025
Same author

Unveiling and stratifying cell cycle-dependent drug efficacy using a single-cell PLOM-CON approach with correlation anomaly and presage protein signals.

Communications biology·2025
Same author

Accurate and simple measurement of power generation efficiency and figure of merit of thermoelectric modules based on optical heating and non-contact temperature detection methods.

Science and technology of advanced materials·2025
Same author

Clinical impact of rapid test for macrolide-resistance gene mutation among cases with Mycoplasma pneumoniae in Japan.

Journal of infection and public health·2025
Same author

Quantitative measurements of transverse thermoelectric generation and cooling performances in SmCo<sub>5</sub>/Bi<sub>0.2</sub>Sb<sub>1.8</sub>Te<sub>3</sub>-based artificially tilted multilayer module.

Science and technology of advanced materials·2025

Related Experiment Video

Updated: Aug 17, 2025

Differentiation and Imaging of Brown Adipocytes from the Stromal Vascular Fraction of Interscapular Adipose Tissue from Newborn Mice
04:46

Differentiation and Imaging of Brown Adipocytes from the Stromal Vascular Fraction of Interscapular Adipose Tissue from Newborn Mice

Published on: February 3, 2023

1.5K

Microscopic image-based classification of adipocyte differentiation by machine learning.

Yoshiyuki Noguchi1, Masataka Murakami2, Masayuki Murata3

  • 1International Research Center for Neurointelligence, Institutes for Advanced Study, The University of Tokyo, 7-3-1, Hongo, Bunkyo-Ku, Tokyo, 113-8654, Japan.

Histochemistry and Cell Biology
|December 12, 2022
PubMed
Summary

This study introduces a machine learning classifier to determine single-cell adipocyte differentiation stages using microscopic images. The model accurately identifies differentiation stages and drug effects, overcoming limitations of traditional averaged data analysis.

Keywords:
Adipocyte differentiationImage analysisMachine learningObesity

More Related Videos

Visualization and Quantification of Mesenchymal Cell Adipogenic Differentiation Potential with a Lineage Specific Marker
13:26

Visualization and Quantification of Mesenchymal Cell Adipogenic Differentiation Potential with a Lineage Specific Marker

Published on: March 31, 2018

9.9K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

660

Related Experiment Videos

Last Updated: Aug 17, 2025

Differentiation and Imaging of Brown Adipocytes from the Stromal Vascular Fraction of Interscapular Adipose Tissue from Newborn Mice
04:46

Differentiation and Imaging of Brown Adipocytes from the Stromal Vascular Fraction of Interscapular Adipose Tissue from Newborn Mice

Published on: February 3, 2023

1.5K
Visualization and Quantification of Mesenchymal Cell Adipogenic Differentiation Potential with a Lineage Specific Marker
13:26

Visualization and Quantification of Mesenchymal Cell Adipogenic Differentiation Potential with a Lineage Specific Marker

Published on: March 31, 2018

9.9K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

660

Area of Science:

  • Cell Biology
  • Biochemistry
  • Computational Biology

Background:

  • Adipocyte differentiation is a complex, sequential process crucial for metabolic health.
  • Current methods like Western blotting and qPCR analyze pooled cell lysates, obscuring single-cell heterogeneity.
  • Distinguishing precise differentiation stages at the individual cell level is challenging with averaged data.

Purpose of the Study:

  • To develop a machine learning classifier for determining adipocyte differentiation stages at the single-cell level.
  • To utilize microscopic images of cells stained for peroxisome proliferator-activated receptor gamma (PPARγ) and lipid droplets as input data.
  • To evaluate the classifier's accuracy in distinguishing differentiation stages and assessing drug actions.

Main Methods:

  • Development of a machine learning classifier using microscopic images of cells.
  • Input data included immunofluorescence images stained for PPARγ and lipid droplets.
  • Classification of single cells into differentiation stages and fitting with a sequential reaction model.

Main Results:

  • The classifier successfully determined the precise stage of adipocyte differentiation for individual cells.
  • Pioglitazone and rosiglitazone were identified as PPARγ agonists, promoting transition to the next differentiation stage.
  • The model accurately estimated drug action points and is suitable for evaluating cell states during differentiation or disease.

Conclusions:

  • A machine learning approach integrating biochemical and morphological data enables single-cell adipocyte differentiation stage classification.
  • This method overcomes the limitations of traditional analyses based on averaged data.
  • The classifier and model provide a valuable tool for studying adipogenesis and evaluating therapeutic interventions.