Background

Single-molecule localization microscopy (SMLM) describes a family of fast-evolving, powerful imaging techniques that dramatically improve spatial resolution over standard, diffraction-limited microscopy techniques and can image biological structures at the molecular scale. SMLM can now be performed even on benchtop microscopes. In SMLM, individual fluorescent molecules are computationally localized from diffraction-limited image sequences and the localizations are used to generate a super-resolution image or a time course of super-resolution images, or to define molecular trajectories. Single molecule localization microscopy generates data in the form of Cartesian coordinates of localized fluorophores. Cluster analysis is an attractive route for extracting biologically meaningful information from such data and has been widely applied. Although powerful, these approaches have historically been computationally intensive, precluding real-time analysis.

(see Primers at https://www.nature.com/articles/s43586-021-00038-x and https://www.researchgate.net/publication/342141592_A_Review_of_Super-Resolution_Single-Molecule_Localization_Microscopy_Cluster_Analysis_and_Quantification_Methods/)

Innovation: Real-Time SMLM Analysis

OSU researchers have developed a computationally efficient software suite based on novel mathematical, statistical, and computational frameworks capable of real-time analysis of SMLM data, which can run on traditional desktop or laptop computers (not just expensive, purpose-built servers / super-computing networks). Spatial Pattern Analysis in Continuous space Enabled by Voronoi Tessellation-based Cluster Segmentation (SPACE-VorTeCS) is an algorithmic analysis pipeline that groups single molecule localization microscopy data (molecular coordinates) into clusters, discretizes the clusters into a binary image mask, and rapidly measures the spatial relationship between two species of localizations captured by the binary image masks using a form of discrete nearest neighbor point pattern analysis. This analytical software can process large datasets (tens to hundreds of millions of points) in a remarkably short time frame (seconds to minutes), exceeding the acquisition speed of current SMLM microscopes and their software by sufficient magnitude to enable high-throughput automation of SMLM experiments. (This difference in speeds as compared to existing solutions increases with the number of dimensions to be analyzed.)

Addressing a Critical Need

Traditional microscopy struggles to visualize structures smaller than 250 nanometers, hindering research in drug discovery, materials science, and other fields. SMLM overcomes this limitation (seeing structures at the 10-20 nm level) but suffers from slow and complex data analysis, impeding real-time insights and high-throughput screening.

Methods like ThunderSTORM and DeepSTORM3D analyze the raw movies from the SMLM microscopes to extract the single molecule localizations but do not offer any capabilities for then analyzing those localizations to extract biological insights. Our approach is built to take over where these let off – we perform cluster analysis of the localizations resulting from the techniques. Thus, our competitors would be tools like ClusterVisu, SR-Tesseller, and STORM-RLA. (See https://doi.org/10.1016/j.patter.2020.100038, and https://doi.org/10.1091/mbc.e16-02-0125.) Compared to these methods, we offer 1) 100-1000-fold faster analysis, 2) richer and more robust insights by using the most appropriate statistical methods, and 3) capability for automating experiments (not just image collection), which has no precedent.

Our Competitive Advantages

• Faster Analysis: Our software processes data faster than acquisition rates, enabling crucial real-time insights.

• High-Throughput Screening: Analyze large datasets rapidly based on single-molecule properties.

• Parameter-Free Analysis: Provides clear and transparent "white-box" solutions without room for bias.

• Broader Applicability: Adaptable beyond microscopy for analyzing multi-dimensional data.

Target Applications

• Drug Discovery & Development: Real-time analysis of drug-molecule interactions for faster identification of promising drug candidates.

• Diagnostic Pathology: Automated high-throughput screening for early diagnosis of disease based on subtle nanoscale remodeling.

• Materials Science: Investigate material properties at the nanoscale for advancements in batteries, solar cells, and electronics.

• Biomedical Research: Gain deeper understanding of cellular processes, protein interactions, and disease mechanisms.

Patent Protection

• United States Provisional Patent Applications have been filed.