CellVisioner: A Generalizable Cell Virtual Staining Toolbox based on Few-Shot Transfer Learning for Mechanobiological...

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.