Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics

We present a new, robust, computational procedure for tracking fluorescent markers in time-lapse microscopy. The algorithm is optimized for finding the time-trajectory of single particles in very noisy dynamic (two- or three-dimensional) image sequences. It proceeds in three steps. First, the images are aligned to compensate for the movement of the biological structure under investigation. Second, the particle's signature is enhanced by applying a Mexican hat filter, which we show to be the optimal detector of a Gaussian-like spot in 1//spl omega//sup 2/ noise. Finally, the optimal trajectory of the particle is extracted by applying a dynamic programming optimization procedure. We have used this software, which is implemented as a Java plug-in for the public-domain ImageJ software, to track the movement of chromosomal loci within nuclei of budding yeast cells. Besides reducing trajectory analysis time by several 100-fold, we achieve high reproducibility and accuracy of tracking. The application of the method to yeast chromatin dynamics reveals different classes of constraints on mobility of telomeres, reflecting differences in nuclear envelope association. The generic nature of the software allows application to a variety of similar biological imaging tasks that require the extraction and quantitation of a moving particle's trajectory.

I. Introduction

During the past decade, two important technological innovations have helped reshape molecular and cell biological research. One was the development of fluorescent proteins, which allow researchers to selectively label single proteins or DNA loci in vivo [1]. The second is high-resolution fluorescence imaging that was made possible by a new generation of brightfield and confocal microscopes, sensitive CCD cameras, and deconvolution algorithms [2]. Thanks to these new tools, biologists are able to observe gene expression and to study molecular dynamics within the living cell at submicron resolutions [3]. Static images can be acquired in two () or three () dimensions to localize the labeled structures of interest in a living specimen. Dynamic sequences (time-lapse series) can also be used to study the dynamic behavior of labeled molecules within a living cell. While these methods offer an enormous potential for increasing our understanding of biological events, they also constitute a challenge for quantitative analysis, which requires efficient techniques to evaluate this unprecedented flow of data. Currently, a majority of data analysis and feature extraction is done manually, which is both time consuming and susceptible to personal bias. Some commercial image analysis tools are available, but their capabilities for automatic feature extraction are limited. The analysis is complicated by the fact that the data are typically very noisy due to the weakness of the fluorescence signal and the need to work at the limit of resolution for light microscopy.