Author:
Joshi Prakash,Mondal Partha Pratim
Abstract
Molecular assembly in a complex cellular environment is vital for understanding underlying biological mechanisms. Biophysical parameters (such as single-molecule cluster density, cluster-area, pairwise distance, and number of molecules per cluster) related to molecular clusters directly associate with the physiological state (healthy/diseased) of a cell. Using super-resolution imaging along with powerful clustering methods (K-means, Gaussian mixture, and point clustering), we estimated these critical biophysical parameters associated with dense and sparse molecular clusters. We investigated Hemaglutinin (HA) molecules in an Influenza type A disease model. Subsequently, clustering parameters were estimated for transfected NIH3T3 cells. Investigations on test sample (randomly generated clusters) and NIH3T3 cells (expressing Dendra2-Hemaglutinin (Dendra2-HA) photoactivable molecules) show a significant disparity among the existing clustering techniques. It is observed that a single method is inadequate for estimating all relevant biophysical parameters accurately. Thus, a multimodel approach is necessary in order to characterize molecular clusters and determine critical parameters. The proposed study involving optical system development, photoactivable sample synthesis, and advanced clustering methods may facilitate a better understanding of single molecular clusters. Potential applications are in the emerging field of cell biology, biophysics, and fluorescence imaging.
Subject
Radiology, Nuclear Medicine and imaging,Instrumentation,Atomic and Molecular Physics, and Optics
Cited by
3 articles.
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