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Replicative Cell Senescence

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Related Experiment Video

Updated: Sep 11, 2025

Continuous High-resolution Microscopic Observation of Replicative Aging in Budding Yeast

Continuous High-resolution Microscopic Observation of Replicative Aging in Budding Yeast

Published on: August 20, 2013

Protocol for cellular age prediction in yeast and human single cells using transfer learning.

Subhadeep Duari¹, Vishakha Gautam¹, Gaurav Ahuja²

¹Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India.

|August 12, 2025

Summary

This study introduces a computer vision protocol to predict cellular age using microscopy images. The method analyzes cell morphology and bioactivities, adaptable across species like yeast and human fibroblasts.

Keywords:

Cell Biology Computer sciences

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Last Updated: Sep 11, 2025

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Published on: August 20, 2013

Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells

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Published on: May 6, 2009

Using Microfluidic Devices to Measure Lifespan and Cellular Phenotypes in Single Budding Yeast Cells

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Published on: March 30, 2017

Area of Science:

Biotechnology
Cell Biology
Computer Vision

Background:

Cellular aging is a complex process.
Accurate prediction of cellular age is crucial for understanding age-related diseases.
Current methods for age prediction can be labor-intensive or lack precision.

Purpose of the Study:

To present a novel protocol for predicting cellular age.
To utilize computer vision and phase contrast microscopy for age prediction.
To demonstrate the adaptability of the protocol across different cell types.

Main Methods:

Cultivation of yeast cells and induction of senescence in human dermal fibroblasts.
Phase contrast microscopy of drug-treated yeast cells and senescent fibroblasts.
Utilizing the scCamAge Docker container and model for image analysis.
Applying transfer learning to adapt a yeast-trained model for human fibroblast data.

Main Results:

The protocol enables cellular age prediction through image analysis.
Demonstrated successful application of a yeast-trained model to human fibroblasts.
Showcased the effectiveness of transfer learning for cross-species model adaptation.

Conclusions:

The developed protocol offers a robust method for cellular age prediction.
Computer vision analysis of microscopy images is a viable approach for assessing cellular age.
The scCamAge model and transfer learning provide a flexible platform for aging research.