Single quantum dot tracking based on perceptual Grouping using minimal paths in a spatiotemporal volume

Semiconductor quantum dots (QDs) are new fluorescent probes with great promise for ultrasensitive biological imaging. When detected at the single-molecule level, QD-tagged molecules can be observed and tracked in the membrane of live cells over unprecedented durations. The motion of these individual molecules, recorded in sequences of fluorescence images, can reveal aspects of the dynamics of cellular processes that remain hidden in conventional ensemble imaging. Due to QD complex optical properties, such as fluorescence intermittency, the quantitative analysis of these sequences is, however, challenging and requires advanced algorithms. We present here a novel approach, which, instead of a frame by frame analysis, is based on perceptual grouping in a spatiotemporal volume. By applying a detection process based on an image fluorescence model, we first obtain an unstructured set of points. Individual molecular trajectories are then considered as minimal paths in a Riemannian metric derived from the fluorescence image stack. These paths are computed with a variant of the fast marching method and few parameters are required. We demonstrate the ability of our algorithm to track intermittent objects both in sequences of synthetic data and in experimental measurements obtained with individual QD-tagged receptors in the membrane of live neurons. While developed for tracking QDs, this method can, however, be used with any fluorescent probes.

I. Introduction

Imaging techniques have become central in the development of cell biology since they offer a tool of choice to decipher the complex mechanisms controlling the organization of live cells [1], [2]. The ability to observe biological processes in real time is essential to determine their spatial and temporal dynamics. Important mechanisms such as molecular diffusion, gene expression, protein-protein interactions or cell signaling can now be detected optically [2], [3]. The need to quantitatively analyze them has, thus, spurred a large effort which involves techniques from cell biology, biochemistry, physics, or computer science.