Extensible Immunofluorescence (ExIF) accessibly generates high-plexity datasets by integrating standard 4-plex...

Area of Science:

• Single-cell biology
• Biotechnology
• Computational biology

Background:

• Standard immunofluorescence imaging is limited to ~4 molecular markers per cell.
• Dissecting complex cellular biology requires higher multiplexity.
• Existing multiplexed labeling methods have limited uptake.

Purpose of the Study:

• To introduce the Extensible Immunofluorescence (ExIF) framework.
• To enable theoretically unlimited marker multiplexity from standard 4-plex immunofluorescence.
• To facilitate integrated analyses of complex cell biology.

Main Methods:

• Developed a generative deep learning-based virtual labeling approach.
• Designed easily produced 4-plex immunofluorescence panels.
• Transformed 4-plex data into a unified dataset with high marker plexity.

Main Results:

• Exemplified ExIF through interrogation of the epithelial-mesenchymal transition (EMT).
• Achieved significant improvements in downstream quantitative analyses.
• Enabled classification of cell phenotypes, manifold learning of heterogeneity, and pseudotemporal inference of marker dynamics.

Conclusions:

• ExIF empowers life scientists to quantitatively interrogate complex, multimolecular single-cell processes.
• The framework approaches the performance of limited-uptake multiplexed labeling methods.
• Introduced data integration concepts from omics to microscopy for enhanced biological insights.