Super-resolution radial fluctuations (SRRF): a versatile and accessible tool for live-cell nanoscopy

Authors:

Sanhua Fang, Li Liu, Dan Yang, Shuangshuang Liu, Qiong Huang

Keywords:

SRRF; local gradient convergence; radiality; NanoJ-SRRF platform; eSRRF; VeSRRF; dynamic subcellular processes; Super-resolution radial fluctuation microscopy; Computational imaging; Radial fluctuation; Live-cell imaging; Subcellular processes

Abstract:

Super-resolution radial fluctuation (SRRF) microscopy is a novel computational imaging technique that bypasses the optical diffraction limit (lateral resolutions of 200–300 nm), achieving lateral resolutions of approximately 50–100 nm while being compatible with live-cell imaging. Unlike traditional super-resolution methods such as stimulated emission depletion (STED) and single molecule localization microscopy (SMLM), SRRF minimizes phototoxicity and hardware complexity by analyzing fluorescence intensity fluctuations in standard wide-field microscopy data. This is achieved by calculating local gradient convergence (“radiality”) across time-series images, enabling the reconstruction of sub-diffraction structures without specialized fluorophores or high-intensity illumination. Implemented through the open-source NanoJ-SRRF platform, SRRF optimizes parameters like ring radius and radiality magnification to enhance resolution, suppress noise, and maintain computational efficiency. Its advantages include low phototoxicity, compatibility with conventional dyes, and integration with various imaging modalities, allowing dynamic visualization of subcellular processes (e.g., mitochondrial fission, microtubule dynamics). Despite its limitations in axial resolution and potential artifacts in high-density structures, recent advancements like enhanced SRRF (eSRRF) and variance reweighted radial fluctuations and enhanced SRRF (VeSRRF) address these challenges, facilitating real-time, multicolor imaging. Applications range from ultrastructural studies to clinical pathology, with future developments in AI processing and multimodal integration promising further enhancements in imaging capabilities. SRRF stands to significantly impact the understanding of dynamic subcellular processes and biomedical research.

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