SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis

Abstract The morphology of protein assemblies impacts their behavior and contributes to beneficial and aberrant cellular responses. While single-molecule localization microscopy provides the required spatial resolution to investigate these assemblies, the lack of universal robust analytical tools to extract and quantify underlying structures limits this powerful technique. Here we present SEMORE, a semi-automatic machine learning framework for universal, system and input-dependent, analysis of super-resolution data. SEMORE implements a multi-layered density-based clustering module to dissect biological assemblies and a morphology fingerprinting module for quantification by multiple geometric and kinetics-based descriptors. We demonstrate SEMORE on simulations and diverse raw super-resolution data; time-resolved insulin aggregates and imaging of nuclear pore complexes. SEMORE extracts and quantifies all protein assemblies enabling classification of heterogeneous insulin aggregation pathways and NPC geometry in minutes. SEMORE is a general analysis platform for super-resolution data, and being the first time-aware framework can also support the rise of 4D super-resolution data.