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CellVisioner: A Generalizable Cell Virtual Staining Toolbox based on Few-Shot Transfer Learning for Mechanobiological
Xiayu Xu1,2, Zhanfeng Xiao1,2, Fan Zhang1,2
1The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an Jiaotong University, Xi'an 710049, P.R. China.
Research (Washington, D.C.)
|March 4, 2024
Summary
CellVisioner, a new virtual staining toolbox, reduces data needs for visualizing cell structures like F-actin and nuclei. This tool aids mechanobiology research by analyzing label-free images and monitoring living cells.
Area of Science:
- Cellular biology
- Biophysics
- Computational biology
Background:
- Traditional fluorescence staining for visualizing cellular structures like the cytoskeleton and nucleus faces limitations including phototoxicity and photobleaching.
- Virtual staining offers an alternative but typically demands extensive user training data.
Purpose of the Study:
- To develop a generalizable virtual staining toolbox, CellVisioner, utilizing few-shot transfer learning to minimize user training data requirements.
- To enable virtual staining of F-actin and nuclei in diverse cell types and extract mechanobiology-relevant single-cell parameters.
Main Methods:
- Developed CellVisioner, a toolbox employing few-shot transfer learning for virtual cell staining.
- Applied CellVisioner to label-free single-cell images to predict mechanobiological status and enable long-term cell monitoring.
Main Results:
- CellVisioner requires substantially reduced user training data compared to traditional methods.
- The toolbox successfully performs virtual staining of F-actin and nuclei across various cell types.
- Enabled prediction of cell mechanobiological status, such as the Yes-associated protein nuclear/cytoplasmic ratio, from label-free images.
Conclusions:
- CellVisioner offers a powerful, data-efficient solution for virtual cell staining, overcoming limitations of traditional methods.
- Facilitates on-site mechanobiology research by providing tools for analyzing cellular structures and predicting mechanobiological status.
- Enables long-term monitoring of living cells, advancing the study of dynamic cellular processes.
