genealogical trees [28,36,37,38]. Most of these measures require rather large, highly branched trees (at least 7 or 8 generations; order of hundred or more leaves). However, cell culture often deals with a large number of smaller trees, spanning a few generations only; typically, a confluent cell layer is splitted into 2 to 10 new dishes to overcome contact inhibition and to give the cells new area to grow. This ratio of 1:2 to 1:10 corresponds to 2–4 generations only (one doubling per generation). It would be therefore inappropriate to apply the aforementioned topological measures to our data. Instead, we use a simple measure, which we term "sibling symmetry"; it is defined as the life time difference of two sibling cells. Figure 12c displays the sibling symmetry distributions of both, reference data sets A and B; the closer to zero the value, the more symmetrical the siblings behave. Contrary, to what might be expected from stem cell cultures, sibling cells divide in a surprisingly symmetrical manner (figs. 12b and 12c). This behaviour can be observed irrespective of the cell passage, as can be inferred from comparing the sibling symmetry figures of both data sets (set A is in passage 48; set B in passage 39, fig. 12c). We take this as a hint, that asymmetrical cell division, as accomplished e.g. in ontogenesis and tissue homeostasis, is not maintained in adult stem cell in vitro cultures. Together with the reported spontaneous differentiation behaviour of pancreatic stem cells [23,24], these observations somewhat challenge the idea, that the state of differentiation is necessarily reflected in cell cycle times. However, answering the question how, and to which degree in vitro differentiation of adult stem cells is tied to proliferation, will require many additional experiments. The results presented in this paper are meant to substantiate that validated cell tracking will be among the most powerful techniques to answer such questions. Discussion In recent cell tracking work, the main emphasis was put on the different methods of cell detection and cell tracking. This work Automated Tracking Turns Cell Culture into Numbers addresses dedicatedly two other equally important aspects of cell tracking: validation and evaluation. Validation refers to the independent automated determination of the reliability of the tracking results. We will argue, that validation is an imperative part of every tracking method. Evaluation, on the other hand, refers to methods for scoring different tracking algorithms. Here, we propose various performance benchmarks and provide two large, manually tracked data sets to facilitate and standardize future evaluation procedures. I) Validation To achieve fully automated cell tracking, validation is mandatory. Our argument can be derived from the fact that no measurement or detection strategy is error-free. The particular difficulty in cell tracking is the fatal error propagation mechanism; errors do not remain confined to a particular spatiotemporal vicinity but effect the entire genealogy. A small local error, like e.g. a merged cell event, may lead to large deviations, e.g. of life time determinations, tree topologies and single cell genealogy. The particularly severe error propagation in cell tracking can be illustrated by a simple estimation: Assume a fairly good cell detection rate of 95%; assume further, that the detection errors are evenly distributed in space and time. A complete path of 50 frames length could then be tracked with a probability of 7.7% only (0.95‘50). Though in reality, the errors are not evenly distributed and other than detection errors may occur as well, it is for this specific reason that even ideally high detection rates of nearly 100% will lead to many erroneous paths when tracking is performed without validation. Indeed, it can be noted from table 2 that our detection rate of 95% yields 1400 paths, 44% of which are wrong, when using a "naive", i.e. unvalidated tracking algorithm. Tracking results of such a low trustworthiness must be regarded as meaningless. Biologically meaningful information and method-related artefacts appear in equal amounts and are virtually indistinguishable. Figure 12. Genealogic trees and their symmetry. Reference sets A/B contain 386/85 trees with an average size of 3.5/3.8 generations, i.e. 11.3/ 14.2 connected paths. (A) shows a 3D-xyt-representation of the largest tree encountered; it consists of 65 paths (tree number 7 in data set B). (B) displays the same genealogy as a binary tree; the tree appears very balanced. The high symmetry is confirmed statistically by the histograms in (C), which depicts the distribution of the sibling symmetry for both data sets A and B. doi:10.1371/journal.pone.0027315.g012 PLoS ONE | www.plosone.org 12 November 2011 | Volume 6 | Issue 11 | e27315