A Novel Validation Algorithm Allows for Automated Cell Tracking and the Extraction of Biologically Meaningful Parameters

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.

Beyond genealogies: Mutual information of causal paths to analyse single cell tracking data

“…A number of works focused on the analysis of cell cycling dynamics based on cellular genealogies [6,7,8]. Furthermore the synchronicity of cycling between related cells (e.g.…”

Global Linking of Cell Tracks Using the Viterbi Algorithm

“…The biological questions can be extremely different from one another, but time-lapse microscopy is often a very important and powerful technique to study and analyze the cells [1]. Time-lapse microscopy can be used to characterize and quantify many different aspects of cell behavior, such as proliferation [2], mitosis (cell division) [3], [4], apoptosis (cell death) [5], migration [6], and morphology [7], that are important in the study of cancer [8], [9], embryogenesis [10], [11], stem cells [12]–[14], and many other topics in the fields of cell and developmental biology. In early works like [5], [10] cells were observed using transmission microscopy, and the images were sketched by hand at appropriate time intervals, or recorded on video tape in cases where all cells of interest were in the same focal plane.…”

“…In particle tracking, the tracked objects are usually small enough to be represented as point objects, and they usually interact less with each other than cells do, but the tracking problem is similar to cell tracking in many other aspects. Cell tracking algorithms are normally classified into model evolution algorithms, where mathematical models of the cells are propagated in time [22]–[24], and tracking by detection algorithms, where the tracking problem is separated into finding the outlines of the cells (segmentation) and linking the detected outlines into tracks (track linking, data association, or tracking) [2], [25]–[27]. …”

“…Joint segmentation of multiple cells has been identified as the most problematic segmentation error for cell tracking [2], and is seldom treated explicitly by track linking algorithms. An example of an algorithm that can handle joint segmentation is [31], where outlines can be split at branching points where two outlines in one image overlap with the same outline in the prior or following image.…”

Generating semi-synthetic validation benchmarks for embryomics

“…In recent years, various benchmarks for bioimage analysis have been presented for tasks such as seed detection [1,2], segmentation [2,3] or tracking [4,5]. A general problem with manually created benchmark datasets, however, is caused by the interand intra-expert variability, which means that ambiguous image content may be rated differently by different investigators or even by the same investigator during multiple labeling…”