Multiple Signal Classification Algorithm for super-resolution fluorescence microscopy

TL;DR

MUSICAL is a new statistical super-resolution microscopy method that achieves high resolution with fewer frames, lower power, and works with various fluorophores, outperforming some existing techniques.

Contribution

The paper introduces MUSICAL, a novel super-resolution technique that overcomes limitations of current methods by reducing acquisition time and fluorophore constraints.

Findings

Achieves at least 50 nm resolution

Operates with fewer frames and lower excitation power

Effective in live-cell imaging at 245 ms time scale

Abstract

Super-resolution microscopy is providing unprecedented insights into biology by resolving details much below the diffraction limit. State-of-the-art Single Molecule Localization Microscopy (SMLM) techniques for super-resolution are restricted by long acquisition and computational times, or the need of special fluorophores or chemical environments. Here, we propose a novel statistical super-resolution technique of wide-field fluorescence microscopy called MUltiple SIgnal Classification ALgorithm (MUSICAL) which has several advantages over SMLM techniques. MUSICAL provides resolution down to at least 50 nm, has low requirements on number of frames and excitation power and works even at high fluorophore concentrations. Further, it works with any fluorophore that exhibits blinking on the time scale of the recording. We compare imaging results of MUSICAL with SMLM and four contemporary statistical super-resolution methods for experiments of in-vitro actin filaments and datasets provided by independent research groups. Results show comparable or superior performance of MUSICAL. We also demonstrate super-resolution at time scales of 245 ms (using 49 frames at acquisition rate of 200 frames per second) in samples of live-cell microtubules and live-cell actin filaments.