title: Tracking quasi-stationary flow of weak fluorescent signals by adaptive multi-frame correlation.
abstract: We have developed a novel cross-correlation technique to probe quasi-stationary flow of fluorescent signals in live cells at a spatial resolution that is close to single particle tracking. By correlating image blocks between pairs of consecutive frames and integrating their correlation scores over multiple frame pairs, uncertainty in identifying a globally significant maximum in the correlation score function has been greatly reduced as compared with conventional correlation-based tracking using the signal of only two consecutive frames. This approach proves robust and very effective in analysing images with a weak, noise-perturbed signal contrast where texture characteristics cannot be matched between only a pair of frames. It can also be applied to images that lack prominent features that could be utilized for particle tracking or feature-based template matching. Furthermore, owing to the integration of correlation scores over multiple frames, the method can handle signals with substantial frame-to-frame intensity variation where conventional correlation-based tracking fails. We tested the performance of the method by tracking polymer flow in actin and microtubule cytoskeleton structures labelled at various fluorophore densities providing imagery with a broad range of signal modulation and noise. In applications to fluorescent speckle microscopy (FSM), where the fluorophore density is sufficiently low to reveal patterns of discrete fluorescent marks referred to as speckles, we combined the multi-frame correlation approach proposed above with particle tracking. This hybrid approach allowed us to follow single speckles robustly in areas of high speckle density and fast flow, where previously published FSM analysis methods were unsuccessful. Thus, we can now probe cytoskeleton polymer dynamics in living cells at an entirely new level of complexity and with unprecedented detail.
authors.affiliation: Laboratory for Computational Cell Biology, Department of Cell Biology, CB167, The Scripps Research Institute, 10550 N. Torrey Pines Road, La Jolla, CA 92037, USA.
authors.forename: L
authors.initials: L
authors.lastname: Ji
authors.forename: G
authors.initials: G
authors.lastname: Danuser
mesh_terms: D000199:Actins / Q000378:metabolism; D000465:Algorithms; D000818:Animals; D003599:Cytoskeleton / Q000378:metabolism / Q000648:ultrastructure*; D007091:Image Processing, Computer-Assisted; D008856:Microscopy, Fluorescence / Q000379:methods*; D009474:Neurons / Q000378:metabolism
chemical_list: D000199:Actins