<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d20116e98">This study introduces a framework for high-sensitivity analysis of molecular interactions on supported lipid bilayers (SLBs), enhancing traditional SLB biosensing applications with unsupervised detection of persistent spatio-temporal motifs and temporal super-resolution. Current SLB-based methods [ <a class="xref-link" href="#r1">1</a>] often rely on simple morphological features (e.g., average intensity, eccentricity, size, etc), which, while effective for limited experiments, have limited expressiveness when characterizing more complex spatiotemporal interactions between multiple particles. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d20116e103">Our proposed framework addresses this limitation by leveraging rotationally invariant representations developed for electron microscopy [ <a class="xref-link" href="#r2">2</a>] to autonomously quantify and classify reveal persistent, complex molecular interaction patterns, such as receptor-ligand binding or DNA hybridizations. The framework incorporates a pseudo-high-speed capture technique, which “stitches” sequential images to enhance the observation of transient molecular states and complex, higher-order interactions that would otherwise remain unresolved in real-time imaging. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d20116e108">Here is an overview of the framework. 1) Identify regions of interest (ROI) of a dynamic movie with many DNA-tagged particles interacting on an SLB (e.g., using Trackpy, a widely used particle tracking package). 2) Thereafter, we projected the image patch centered on each ROI (for each particle at each time) into its Zernike Polynomial (ZP) bases, separately for each RGB color channel. By taking the absolute values of these ZP projections, we transform each particle-centered patch into its Rotationally Invariant ZP (RIZP) representation [ <a class="xref-link" href="#r2">2</a>], ensuring orientation-independent descriptions of each particle. 3) Next, we reduce the dimensionality of the RIZP feature-vectors using t-SNE (t-distributed stochastic neighborhood embedding), which represents the wide variety of observed configurations on a lower-dimensional manifold, where clustering nearby patches reveals key patterns and features within the dataset.