A Novel Validation Algorithm Allows for Automated Cell Tracking and the Extraction of Biologically Meaningful Parameters Daniel H. Rapoport1., Tim Becker1,3*., Amir Madany Mamlouk2,3, Simone Schicktanz1, Charli Kruse1 1 Fraunhofer Institution for Marine Biotechnology, Lu¨ beck, Germany, 2 Institute for Neuro- and Bioinformatics, University of Lu¨ beck, Lu¨ beck, Germany, 3 Graduate School for Computing in Medicine and Life Science, University of Lu¨ beck, Lu¨ beck, Germany Abstract Automated microscopy is currently the only method to non-invasively and label-free observe complex multi-cellular processes, such as cell migration, cell cycle, and cell differentiation. Extracting biological information from a time-series of micrographs requires each cell to be recognized and followed through sequential microscopic snapshots. Although recent attempts to automatize this process resulted in ever improving cell detection rates, manual identification of identical cells is still the most reliable technique. However, its tedious and subjective nature prevented tracking from becoming a standardized tool for the investigation of cell cultures. Here, we present a novel method to accomplish automated cell tracking with a reliability comparable to manual tracking. Previously, automated cell tracking could not rival the reliability of manual tracking because, in contrast to the human way of solving this task, none of the algorithms had an independent quality control mechanism; they missed validation. Thus, instead of trying to improve the cell detection or tracking rates, we proceeded from the idea to automatically inspect the tracking results and accept only those of high trustworthiness, while rejecting all other results. This validation algorithm works independently of the quality of cell detection and tracking through a systematic search for tracking errors. It is based only on very general assumptions about the spatiotemporal contiguity of cell paths. While traditional tracking often aims to yield genealogic information about single cells, the natural outcome of a validated cell tracking algorithm turns out to be a set of complete, but often unconnected cell paths, i.e. records of cells from mitosis to mitosis. This is a consequence of the fact that the validation algorithm takes complete paths as the unit of rejection/acceptance. The resulting set of complete paths can be used to automatically extract important biological parameters with high reliability and statistical significance. These include the distribution of life/cycle times and cell areas, as well as of the symmetry of cell divisions and motion analyses. The new algorithm thus allows for the quantification and parameterization of cell culture with unprecedented accuracy. To evaluate our validation algorithm, two large reference data sets were manually created. These data sets comprise more than 320,000 unstained adult pancreatic stem cells from rat, including 2592 mitotic events. The reference data sets specify every cell position and shape, and assign each cell to the correct branch of its genealogic tree. We provide these reference data sets for free use by others as a benchmark for the future improvement of automated tracking methods. Citation: Rapoport DH, Becker T, Madany Mamlouk A, Schicktanz S, Kruse C (2011) A Novel Validation Algorithm Allows for Automated Cell Tracking and the Extraction of Biologically Meaningful Parameters. PLoS ONE 6(11): e27315. doi:10.1371/journal.pone.0027315 Editor: Marie Jose Goumans, Leiden University Medical Center, The Netherlands Received December 22, 2010; Accepted October 14, 2011; Published November 8, 2011 Copyright: 2011 Rapoport et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: TB was supported by the Graduate School for Computing in Medicine and Life Sciences funded by Germany’s Excellence Initiative [DFG GSC 235/1]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: tim.becker@emb.fraunhofer.de . These authors contributed equally to this work. Introduction Cell tracking comprehends all techniques to monitor the behaviour of single cells over time. This might include migration behaviour, cell divisions and lineage tracking [1,2], as well as transient cell-cell contacts, production of extracellular matrix, movements of the cell skeleton and prediction of cell fates [3]. In living organisms, such techniques have provided valuable insights in complex multi-cellular processes, such as regeneration [4] and ontogenesis [5,6]. An even broader field in which cell tracking can be applied prospectively will be the standardized and automated characterization of in vitro cell cultures. For example, the Large Scale Digital Cell Analysis System (LSDCAS) [7] is one approach to automatically create time series of cell cultures and has been utilized to explore the dynamic behaviour of in vitro cell cultures [8,9]. The basic apparative prerequisite for in vitro tracking is a computerized, automated image acquisition system with a sufficient spatiotemporal resolution (roughly 2mm lateral and 1– 15 min temporal resolution, depending on cell type and scientific question). Often, such a system is accomplished by equipping a conventional microscope with a motorized xyz-stage and a climatization chamber. This time-lapse microscope produces snapshots of the cells, normally at equidistant time points, resulting in large image stacks. These are the raw data on which cell tracking algorithms operate. To date, most tracking algorithms consist of two separate, albeit not completely independent parts: (1) Cell detection, i.e. finding PLoS ONE | www.plosone.org 1 November 2011 | Volume 6 | Issue 11 | e27315