Modified Burst Analysis Spectroscopy for studying distributions of protein aggregates and fluorescent assemblies

The study of essential cellular functionalities and behaviors highly depends on the awareness from important parameters such as size, form, and distribution of macromolecular complexes. Furthermore, as most macromolecular complexes have heterogeneous and complex distributions, exploring their behavior is difficult. Here, we develop an extension of Burst Analysis Spectroscopy using the Genetic Algorithm and Particle Swarm Optimization. this method is a simple non-correlation-based approach that measures population distributions directly, even at very low sample concentrations. Based on this method, the highest signal-to-noise light bursts generated by single fluorescent particles are used to recursively determine the brightness and size distribution of complex mixtures of fluorescent objects. As this method neglects the non-linear behavior of reflected light by the excited nanoparticles, we propose a metaheuristic-based optimization to find the non-linear coefficients and modify the recursive procedure of particle distribution reconstruction. In order to analyze the reliability and sensitivity of the proposed algorithm, free-solution, time-resolved distribution data of assembled protein aggregates by using two fluorescently labeled proteins are generated by Monte Carlo procedure and utilized for particle distribution reconstruction. This scenario illustrates that the proposed method has superiority over the simple burst analysis method.