Automatic Bayesian single molecule identification for localization microscopy

Machine Learning for Single-Molecule Localization Microscopy: From Data Analysis to Quantification

Single-molecule localization microscopy (SMLM) is a versatile tool for realizing nanoscale imaging with visible light and providing unprecedented opportunities to observe bioprocesses. The integration of machine learning with SMLM enhances data analysis by improving efficiency and accuracy. This tutorial aims to provide a comprehensive overview of the data analysis process and theoretical aspects of SMLM, while also highlighting the typical applications of machine learning in this field. By leveraging advanced analytical techniques, SMLM is becoming a powerful quantitative analysis tool for biological research.

Real-time 3D single-molecule localization microscopy analysis using lookup tables

Herein, we present a new algorithm for real-time analysis of 3D single molecule localization microscopy images with a small impact on fitting accuracy using lookup-tables with discrete xyz-positions. The algorithm realizes real-time visualization during acquisition. We demonstrate its performance on simulated and measured data. Additionally, combining real-time fitting with a feedback loop controlling the activation laser pulse keeps the number of emitters per image frame constant.

Machine learning approach for single molecule localisation microscopy

Single molecule localisation (SML) microscopy is a fundamental tool for biological discoveries; it provides sub-diffraction spatial resolution images by detecting and localizing "all" the fluorescent molecules labeling the structure of interest. For this reason, the effective resolution of SML microscopy strictly depends on the algorithm used to detect and localize the single molecules from the series of microscopy frames. To adapt to the different imaging conditions that can occur in a SML experiment, all current localisation algorithms request, from the microscopy users, the choice of different parameters. This choice is not always easy and their wrong selection can lead to poor performance. Here we overcome this weakness with the use of machine learning. We propose a parameter-free pipeline for SML learning based on support vector machine (SVM). This strategy requires a short supervised training that consists in selecting by the user few fluorescent molecules (∼ 10-20) from the frames under analysis. The algorithm has been extensively tested on both synthetic and real acquisitions. Results are qualitatively and quantitatively consistent with the state of the art in SML microscopy and demonstrate that the introduction of machine learning can lead to a new class of algorithms competitive and conceived from the user point of view.

Navigating challenges in the application of superresolution microscopy

In 2014, the Nobel Prize in Chemistry was awarded to three scientists who have made groundbreaking contributions to the field of superresolution (SR) microscopy (SRM). The first commercial SR microscope came to market a decade earlier, and many other commercial options have followed. As commercialization has lowered the barrier to using SRM and the awarding of the Nobel Prize has drawn attention to these methods, biologists have begun adopting SRM to address a wide range of questions in many types of specimens. There is no shortage of reviews on the fundamental principles of SRM and the remarkable achievements made with these methods. We approach SRM from another direction: we focus on the current practical limitations and compromises that must be made when designing an SRM experiment. We provide information and resources to help biologists navigate through common pitfalls in SRM specimen preparation and optimization of image acquisition as well as errors and artifacts that may compromise the reproducibility of SRM data.