Closing the multichannel gap through computational reconstruction of interaction in super-resolution microscopy

Area of Science:

• Cellular biology
• Microscopy
• Computational biology

Background:

• Cellular function relies on interactions between subcellular components, determined by physical distances.
• Multichannel super-resolution microscopy (SRM) enables nanoscale visualization and quantification of these interactions.

Purpose of the Study:

• To review novel computational methods for quantifying interactions from multichannel SRM data.
• To discuss SRM-specific challenges and specialized methods for interaction analysis.
• To present a mathematical model for evaluating reconstruction methods and identify future research trends.

Main Methods:

• Review of computational methods for analyzing point-cloud and voxel SRM data.
• Decomposition of interaction classes based on cell biology use cases and physical principles.
• Development of a mathematical model for method comparison and bottleneck quantification.

Main Results:

• Identification of SRM-specific factors affecting interaction analysis accuracy.
• Categorization of interactions with consideration of non-linear physics.
• Strategies for validating interaction analysis with incomplete data.

Conclusions:

• Computational analysis of multichannel SRM data is essential for understanding cellular interactions.
• Addressing the 'multichannel gap' in interaction analysis is crucial for advancing SRM applications.
• Standardized methods and validation strategies are needed for reliable nanoscale interaction quantification.