On characterizing membrane protein clusters withmodel-freespatial correlation approaches

Spatial aggregation or clustering of membrane proteins could be important for their functionality, e.g., in signaling, and nanoscale imaging can be used to study its origins, structure and function. Such studies require accurate characterization of clusters, both for absolute quantification and hypothesis testing. A set ofmodel-freequantification approaches -freeof specific cluster models- including the radius of maximal aggregation raobtained from the maxima of the L(r)-r function as an estimator of cluster size, and the estimation of various cluster parameters based on an exponential approximation for the Pair Correlation Function (PCF), have been proposed for this purpose. However, the parameter identifiability and bias due to their model-free nature are not clear. In practice, the clusters might exhibit specific patterns, and the behavior of these estimators in such cases must be studied. Here, we theoretically analyze these approaches for a set of cluster models, and obtain information about their identifiability and bias. We find that the ratio between raand true size depends on both the true size as well as the number of clusters per unit area, or other corresponding parameters, in a model-dependent manner. In particular, rascales with respect to the true size by a factor that can be arbitrarily large. For the method using PCF approximation, for most models we analyzed, the ratios between approximate and true model parameters were found to be constants that depend only on models and independent of other parameters, and the ratios were found to be within 100% of the true parameters. We also discuss some general issues in inference using second-order spatial properties. While precision could also be key, information on identifiability and accuracy provides clarity on estimation, can lead to better inference and also fuel more accurate method development.