DeepSinse: deep learning based detection of single molecules

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.

Systematic Assessment of the Accuracy of Subunit Counting in Biomolecular Complexes Using Automated Single-Molecule Brightness Analysis

Analysis of single-molecule brightness allows subunit counting of high-order oligomeric biomolecular complexes. Although the theory behind the method has been extensively assessed, systematic analysis of the experimental conditions required to accurately quantify the stoichiometry of biological complexes remains challenging. In this work, we develop a high-throughput, automated computational pipeline for single-molecule brightness analysis that requires minimal human input. We use this strategy to systematically quantify the accuracy of counting under a wide range of experimental conditions in simulated ground-truth data and then validate its use on experimentally obtained data. Our approach defines a set of conditions under which subunit counting by brightness analysis is designed to work optimally and helps in establishing the experimental limits in quantifying the number of subunits in a complex of interest. Finally, we combine these features into a powerful, yet simple, software that can be easily used for the analysis of the stoichiometry of such complexes.