Automated Model-Free Sorting of Single-Molecule Fluorescence Events Using a Deep Learning Based Hidden-State Model
This provides a scalable and reproducible tool for researchers studying biomolecular dynamics at the single-molecule level, though it appears incremental as it builds on existing deep learning automation efforts.
The paper tackles the labor-intensive and non-scalable analysis of single-molecule fluorescence data by introducing DASH, a fully automated deep learning model that achieves robust performance in classifying and sorting events without user input, as demonstrated in Cas12a-mediated DNA cleavage systems.
Single-molecule fluorescence assays enable high-resolution analysis of biomolecular dynamics, but traditional analysis pipelines are labor-intensive and rely on users' experience, limiting scalability and reproducibility. Recent deep learning models have automated aspects of data processing, yet many still require manual thresholds, complex architectures, or extensive labeled data. Therefore, we present DASH, a fully streamlined architecture for trace classification, state assignment, and automatic sorting that requires no user input. DASH demonstrates robust performance across users and experimental conditions both in equilibrium and non-equilibrium systems such as Cas12a-mediated DNA cleavage. This paper proposes a novel strategy for the automatic and detailed sorting of single-molecule fluorescence events. The dynamic cleavage process of Cas12a is used as an example to provide a comprehensive analysis. This approach is crucial for studying biokinetic structural changes at the single-molecule level.