UCLA

Super resolution Optical Fluctuation Imaging (SOFI) has been widely acknowledged and

advanced over the past years. Comparing to other extensively adopted super resolution

techniques such as PALM, STORM, STED and SIM, advantages of SOFI include compatibility

with different imaging platforms, suitability for a wide variety of probes, flexibility in imaging

conditions, and a user-controlled trade-off between spatial- and temporal- resolutions. SOFI

therefore holds great promise for ‘democratizing’ super resolution imaging for broad

applications by non-expert practitioners. The theoretical resolution enhancement of SOFI scales as the square root of the cumulant order n, and once combined with a post-processing deconvolution algorithm, the resolution enhancement factor increases up to n. In this dissertation I will discuss the fundamental challenges faced by high order SOFI applications including pixel intensity dynamic range expansion, associated artifacts, point-spread function (PSF) estimation, and deconvolution. Several approaches for solving these challenges will be presented, that together constitute what we dub as ‘SOFI-2.0’. The power of SOFI-2.0 will be demonstrated for focal-adhesion dynamics (at super resolution) in live cells.