correlation functions quantify super-resolution images and estimate apparent clustering due to over-counting相关函数量化图像超分辨率和估计明显聚类由于重复计数.pdfVIP

Correlation Functions Quantify Super-Resolution Images and Estimate Apparent Clustering Due to Over-Counting 1,3 2 3 3 3 Sarah L. Veatch *, Benjamin B. Machta , Sarah A. Shelby , Ethan N. Chiang , David A. Holowka , Barbara A. Baird3 1 Department of Biophysics, University of Michigan, Ann Arbor, Michigan, United States of America, 2 Department of Physics, Cornell University, Ithaca, New York, United States of America, 3 Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York, United States of America Abstract We present an analytical method using correlation functions to quantify clustering in super-resolution fluorescence localization images and electron microscopy images of static surfaces in two dimensions. We use this method to quantify how over-counting of labeled molecules contributes to apparent self-clustering and to calculate the effective lateral resolution of an image. This treatment applies to distributions of proteins and lipids in cell membranes, where there is significant interest in using electron microscopy and super-resolution fluorescence localization techniques to probe membrane heterogeneity. When images are quantified using pair auto-correlation functions, the magnitude of apparent clustering arising from over-counting varies inversely with the surface density of labeled molecules and does not depend on the number of times an average molecule is counted. In contrast, we demonstrate that over-counting does not give rise to apparent co-clustering in double label experiments when pair cross-correlation functions are measured. We apply our analytical method to quantify the distribut