Correlation signal processing of optical threedimensional(x,y,t)data can produce super-resolution images.The second-order cross-correlation function XC2 has been documented to produce super-resolution imaging with sta...Correlation signal processing of optical threedimensional(x,y,t)data can produce super-resolution images.The second-order cross-correlation function XC2 has been documented to produce super-resolution imaging with static and blinking emitters but not for diffusing emitters.Here,we both analytically and numerically demonstrate cross-correlation analysis for diffusing particles.We then expand our fluorescence correlation spectroscopy super-resolution optical fluctuation imaging(fcsSOFI)analysis to use cross-correlation as a postprocessing computational technique to extract both dynamic and structural information on particle diffusion in nanoscale structures simultaneously.Cross-correlation maintains the same super-resolution as auto-correlation while also increasing the sampling rates to reduce aliasing for spatial information in both simulated and experimental data.Our work demonstrates how fcsSOFI with cross-correlation can be a powerful signal-processing tool to resolve the nanoscale dynamics and structure in samples relevant to biological and soft materials.展开更多
基金NIH NIGMS Grant R35GM142466 for financial support of this work.
文摘Correlation signal processing of optical threedimensional(x,y,t)data can produce super-resolution images.The second-order cross-correlation function XC2 has been documented to produce super-resolution imaging with static and blinking emitters but not for diffusing emitters.Here,we both analytically and numerically demonstrate cross-correlation analysis for diffusing particles.We then expand our fluorescence correlation spectroscopy super-resolution optical fluctuation imaging(fcsSOFI)analysis to use cross-correlation as a postprocessing computational technique to extract both dynamic and structural information on particle diffusion in nanoscale structures simultaneously.Cross-correlation maintains the same super-resolution as auto-correlation while also increasing the sampling rates to reduce aliasing for spatial information in both simulated and experimental data.Our work demonstrates how fcsSOFI with cross-correlation can be a powerful signal-processing tool to resolve the nanoscale dynamics and structure in samples relevant to biological and soft materials.
