Foundation model for efficient biological discovery in single-molecule data.

Area of Science:

• Biophysics
• Computational Biology
• Molecular Biology

Background:

• Single-molecule fluorescence microscopy (SMFM) generates complex data, often requiring manual analysis.
• Identifying rare intermediates in biological processes using SMFM is challenging and time-consuming.
• Current methods for SMFM data analysis are difficult to systematize and can be subjective.

Purpose of the Study:

• To introduce META-SiM, a transformer-based foundation model for systematic and efficient SMFM data analysis.
• To develop a tool that aids in the discovery of rare biological events and intermediates.
• To improve objectivity and accelerate biological discovery from complex single-molecule data.

Main Methods:

• Developed META-SiM, a transformer-based foundation model pre-trained on diverse SMFM analysis tasks.
• Utilized high-dimensional embedding vectors generated by META-SiM.
• Integrated the META-SiM Projector for data visualization and analysis.
• Employed Local Shannon Entropy for identifying condition-specific behaviors.

Main Results:

• META-SiM achieved high performance across various SMFM analysis tasks, including trace selection, classification, segmentation, idealization, and photobleaching analysis.
• The META-SiM Projector enabled efficient dataset visualization, labeling, comparison, and sharing.
• Combined analysis with the Projector and Local Shannon Entropy rapidly identified subtle, condition-specific behaviors.
• A previously unobserved intermediate state in pre-mRNA splicing was discovered using META-SiM on an smFRET dataset.

Conclusions:

• META-SiM effectively removes bottlenecks in SMFM data analysis.
• The model enhances objectivity and systematizes the discovery process.
• META-SiM significantly accelerates biological discovery from complex single-molecule data.
• The developed AI model facilitates the identification of novel biological insights from challenging datasets.