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Blob-B-Gone: a lightweight framework for removing blob artifacts from 2D/3D MINFLUX single-particle tracking data
Bela T L Vogler1,2, Francesco Reina1, Christian Eggeling1,2,3,4
1Leibniz Institute of Photonic Technology e.V., Member of the Leibniz Centre for Photonics in Infection Research (LPI), Jena, Germany.
Frontiers in Bioinformatics
|December 11, 2023
Summary
Blob-B-Gone computationally removes artifactual particle accumulations in MINFLUX single-particle tracking data. This lightweight framework uses geometrical features and k-means++ clustering for rapid blob separation and accurate trajectory analysis.
Area of Science:
- Biophysics
- Nanotechnology
- Computational Biology
Background:
- MINFLUX single-particle tracking (SPT) is crucial for observing molecular dynamics.
- Artifactual immobilization of particles can create dense localization accumulations (blobs), complicating data analysis.
- Distinguishing between true particle movement and artifactual blobs is essential for accurate interpretation of SPT data.
Purpose of the Study:
- To introduce Blob-B-Gone, a novel computational framework for differentiating and removing artifactual blobs in MINFLUX SPT data.
- To provide a rapid, training-free method for analyzing single-particle trajectories based on geometrical features.
- To enable accurate distinction between blob-like and elongated trajectories in various experimental conditions.
Main Methods:
- Extraction of purely geometrical features from MINFLUX-detected single-particle trajectories, treated as point clouds.
- Application of k-means++ clustering for single-shot separation of the feature space to identify blobs.
- Evaluation of results using principal component analysis (PCA) and calculation of F1 scores for performance assessment.
Main Results:
- Blob-B-Gone achieved high accuracy in distinguishing blob-like trajectories from others, with F1 scores of 0.998 (2D) and 1.0 (3D).
- The method demonstrated robust performance on both simulated data and experimental data from bead samples and quantum dots.
- Principal component analysis confirmed clear separation of blob and non-blob trajectories in the feature space.
Conclusions:
- Blob-B-Gone offers an effective and lightweight solution for artifact removal in MINFLUX SPT measurements.
- The framework's reliance on geometrical features makes it broadly applicable to generic point cloud data.
- This method significantly enhances the reliability and interpretability of single-particle tracking data by accurately identifying and removing artifacts.
