Review
Tracking in cell and developmental biology

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Abstract

The past decade has seen an unprecedented data explosion in biology. It has become evident that in order to take full advantage of the potential wealth of information hidden in the data produced by even a single experiment, visual inspection and manual analysis are no longer adequate. To ensure efficiency, consistency, and completeness in data processing and analysis, computational tools are essential. Of particular importance to many modern live-cell imaging experiments is the ability to automatically track and analyze the motion of objects in time-lapse microscopy images. This article surveys the recent literature in this area. Covering all scales of microscopic observation, from cells, down to molecules, and up to entire organisms, it discusses the latest trends and successes in the development and application of computerized tracking methods in cell and developmental biology.

Introduction

It has been increasingly recognized in recent times that life is a miraculous symphony [1]. From fast metabolic pathways to the cell cycle, to the beating of the heart, all the way to annually repeating seasonal behaviors, life is composed of a multitude of interconnected oscillations, together constituting a gigantic orchestra spanning at least 10 orders of time magnitude. Recent investigations seem to suggest that careful coordination of these rhythms and their interactions is an important precondition for the maintenance of normal development and health. Conversely, a disturbance at any level of this intricate time network can be expected to result in disease. Although it is not our purpose here to discuss the interesting findings of chronobiological investigations, they do emphasize the importance of studying life's processes in both space and time [2], that is, to analyze their structure and function.

The ability to visualize cells and subcellular dynamic processes in space and time has been made possible by revolutionary developments in imaging technology in the past two decades. Advances in molecular biology, organic chemistry, and materials science have resulted in an impressive toolbox of fluorescent proteins (GFP and variants) and nanocrystals (quantum dots), and have enabled the study of protein expression, localization, conformation, diffusion, turnover, trafficking, and interaction [3], [4]. On the hardware side, advances in optical systems design have taken light microscopy from widefield to (multiphoton) confocal and spinning disk microscopy [5], [6], and more recent efforts to break the diffraction barrier have further extended the palette [7], [8]. Together, these developments have redefined biological research by enabling the switch from fixed to living cells and from qualitative to quantitative imaging [2], [9].

As was to be expected, the new possibilities offered by these developments to image (sub)cellular processes in space, time, and at multiple wavelengths, have resulted in a true data explosion. It has now become evident that in order to ensure efficiency, consistency, and completeness in handling and examining the wealth of image data acquired in even a single experiment, computational image management, processing, and analysis methods are indispensable [10], [11], [12], [13], [14], [15], [16], [17], [18]. Thus, it seems that the bottleneck in putting modern imaging technologies to high-throughput use, has shifted from the “wetware” and the hardware to the development of adequate software tools and data models. While the need for such tools has been recognized for a long time in the medical imaging communities, and advanced image processing, computer vision, and pattern recognition methods have been developed in the past 30 years to enable computer-assisted diagnosis in various clinical applications [19], [20], [21], it is only since relatively recently that similar methods are being explored to facilitate automated image analysis in biological imaging [22], [23].

This article briefly surveys the latest trends and successes in the endeavor to take full advantage of the vast amounts of image data acquired in biological imaging experiments. The emphasis is on tracking and motion analysis of objects in time-lapse microscopy images. Updating previous surveys, aimed at engineers [16], [24], [25] or biologists [17], [26], [27] from different perspectives, we cover tracking at all scales of microscopic observation, from molecules, to cells, to organisms. In view of the rapid developments in the field, and because of space limitations in the present article, we consider only (a subset of) works published since the year 2000. First, we give an overview of recent cell segmentation and tracking algorithms, which in many experiments constitute the basis for further analyses. In the subsequent sections, we shift focus in two possible directions: from cells down to molecules (capturing the trajectories of intracellular particles), and from cells up to organisms (following embryogenesis and adult locomotory behavior). The article hopefully serves as a useful source of pointers to the relevant (mostly methodological) literature on tracking for a wide variety of applications in cell and developmental biology.

Section snippets

Cell tracking

Being the fundamental units of life, cells are the key actors in many biological processes. Cell proliferation, differentiation, and migration are essential for the conception, development, and maintenance of any living organism. These processes also play a crucial role in the onset and progression of many diseases. Understanding physiological processes in health and disease and developing adequate drugs requires the imaging and analysis of the (morpho)dynamic behavior of single cells or cells

From cells to molecules

The capacity of cells to perform their fundamental roles in living organisms is the product of a complex machinery of intracellular and intranuclear processes, involving thousands of proteins and other constructs. Spurred by the technological advances mentioned in the introduction, the quest to improve medicine is therefore increasingly focussing on acquiring a deeper understanding of these processes. In turn, this has boosted the demand for powerful image processing tools able to automatically

From cells to organisms

One of the major goals of biological research in our postgenomic era is to gain full understanding of the processes by which the genome directs the development of a single-cell zygote into a multicellular organism. Complete knowledge of the gene regulatory networks giving rise to specific phenotypes will dramatically advance the discovery of drugs and, ultimately, the development of clinical therapies. Model organisms such as the nematode worm Caenorhabditis elegans and the zebrafish are now

Conclusions

In concluding this article, we summarize the most important observations and their implications for future research. First, in view of the data explosion that is currently taking place in cell and developmental biology, it is increasingly realized that powerful software tools are now essential on the road to discovery and breakthrough. The massive change in scale of biological investigations not only calls for efficient solutions for data management, but also requires computational methods for

Acknowledgments

The authors are thankful to (in alphabetic order of surname) Anna Akhmanova, Katharina Draegestein, Jeroen Essers, Ilya Grigoriev, Adriaan Houtsmuller, Niels Galjart, Akiko Inagaki, Wiro Niessen, and Martin van Royen (Erasmus MC – University Medical Center Rotterdam, The Netherlands), as well as to Rob Jelier and Ben Lehner (Centre for Genomic Regulation, Barcelona, Spain), and Marco Loog (Delft University of Technology, The Netherlands), for fruitful discussions and providing part of the

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