Cega: A Single Particle Segmentation Algorithm to Identify Moving Particles in a Noisy System

Abstract

Improvements to particle tracking algorithms are required to effectively analyze the motility of biological molecules in complex or noisy systems. A typical single particle tracking (SPT) algorithm detects particle coordinates for trajectory assembly. However, particle detection filters fail for datasets with low signal-to-noise levels. When tracking molecular motors in complex systems, standard techniques often fail to separate the fluorescent signatures of moving particles from background noise. We developed an approach to analyze the motility of kinesin motor proteins moving along the microtubule cytoskeleton of extracted neurons using the Kullback-Leibler (KL) divergence to identify regions where there are significant differences between models of moving particles and background signal. We tested our software on both simulated and experimental data and found a noticeable improvement in SPT capability and a higher identification rate of motors as compared to current methods. This algorithm, called Cega, for ‘find the object’, produces data amenable to conventional blob detection techniques that can then be used to obtain coordinates for downstream SPT processing. We anticipate that this algorithm will be useful for those interested in tracking moving particles in complex in vitro or in vivo environments.

Kinesin-4 Motor Teams Effectively Navigate Dendritic Microtubule Arrays Through Track Switching and Regulation of Microtubule Dynamics

“…We were able to directly compare the movement of either Alexa Fluor 660-labeled KIF21B-FL-Halo or KIF21B-MD-Halo to K560-GFP (Figure 1A) by mixing the differentially labeled motors and simultaneously tracking their movement on the same MT arrays (Figure 4A and B). Individual runs along extracted MT arrays were tracked using Cega (Masucci, Relich et al, 2020). Kymographs generated for the KIF21B constructs show that both the full-length and truncated motors have similar directional patterns along axonal and dendritic MT arrays, and both closely resembled the patterns seen with K560-GFP (Figure 4A and B).…”

“…Single kinesin particles moving along extracted axonal and dendritic MT arrays were segmented out with Cega (Masucci, Relich et al, 2020) and tracked with a modified version of the software used in Schwartz et al, (2017) and described in Relich, (2016), which is a tracking algorithm inspired by u-track (Jaqaman et al, 2008). Cega modified the initial candidate motor finding step to greatly enhance the quality of the downstream tracking processes when analyzing data that has heterogeneous background fluorescence and nuisance particles.…”

“…The resulting videos demonstrate the expected pattern of primarily unidirectional motility along axons and more bi-directional motility along dendrites, but the density of the native MT arrays makes unambiguous run detection more challenging. To more rigorously map the runs, we used an automated single particle segmentation and tracking program called Cega that we developed to track moving particles within a noisy environment (Masucci, Relich et al, 2020;see Methods). Due to the crossing of individual motor runs in both axons and dendrites, complete tracks could not be reliably determined.…”

Organization and dynamics of the cortical complexes controlling insulin secretion in β-cells

“…This was in contradiction with the visual inspection of acquired movies, where fast-moving particles rapidly enter and leave the imaging volume, while the slow molecules remain in the field of view for much longer periods. To overcome this analysis problem, we applied a temporal median filter with the sliding window of 15 frames (~0.5 s) to each pixel of the original movie (Hoogendoorn et al, 2014, Masucci et al, 2021. As a result, we obtained two movies with clearly separated timescales, where each of the two populations can be tracked independently (Fig.…”

“…These paired acquisitions were used both to track single molecules and quantify stoichiometry of clusters (see below). To separate fast movements of the single molecules, we applied a temporal median filter with the sliding window of 15 frames (~0.5 s) to the each pixel of the second acquisition (Hoogendoorn et al, 2014, Masucci et al, 2021. To retrieve slow movements, we subtracted the generated results of temporal median filter from all pixels/frames of the original movie using Image Calculator function of ImageJ.…”