title: Imaging, quantification and visualization of spatio-temporal patterning in mESC colonies under different culture conditions.
abstract: BACKGROUND
  Mouse embryonic stem cells (mESCs) have developed into a prime system to study the regulation of pluripotency in stable cell lines. It is well recognized that different, established protocols for the maintenance of mESC pluripotency support morphologically and functionally different cell cultures. However, it is unclear how characteristic properties of cell colonies develop over time and how they are re-established after cell passage depending on the culture conditions. Furthermore, it appears that cell colonies have an internal structure with respect to cell size, marker expression or biomechanical properties, which is not sufficiently understood. The analysis of these phenotypic properties is essential for a comprehensive understanding of mESC development and ultimately requires a bioinformatics approach to guarantee reproducibility and high-throughput data analysis.
  
  
  RESULTS
  We developed an automated image analysis and colony tracking framework to obtain an objective and reproducible quantification of structural properties of cell colonies as they evolve in space and time. In particular, we established a method that quantifies changes in colony shape and (internal) motion using fluid image registration and image segmentation. The methodology also allows to robustly track motion, splitting and merging of colonies over a sequence of images. Our results provide a first quantitative assessment of temporal mESC colony formation and estimates of structural differences between colony growth under different culture conditions. Furthermore, we provide a stream-based visualization of structural features of individual colonies over time for the whole experiment, facilitating visual comprehension of differences between experimental conditions. Thus, the presented method establishes the basis for the model-based analysis of mESC colony development. It can be easily extended to integrate further functional information using fluorescence signals and differentiation markers.
  
  
  BACKGROUND
  The analysis tool is implemented C++ and Mathematica 8.0 (Wolfram Research Inc., Champaign, IL, USA). The tool is freely available from the authors. We will also provide the source code upon request.
  
  
  BACKGROUND
  nico.scherf@tu-dresden.de.
authors.affiliation: Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Dresden University of Technology, Fetscherstrasse 74, D-01307 Dresden, Germany. nico.scherf@tu-dresden.de
authors.forename: N
authors.initials: N
authors.lastname: Scherf
authors.forename: M
authors.initials: M
authors.lastname: Herberg
authors.forename: K
authors.initials: K
authors.lastname: Thierbach
authors.forename: T
authors.initials: T
authors.lastname: Zerjatke
authors.forename: T
authors.initials: T
authors.lastname: Kalkan
authors.forename: P
authors.initials: P
authors.lastname: Humphreys
authors.forename: A
authors.initials: A
authors.lastname: Smith
authors.forename: I
authors.initials: I
authors.lastname: Glauche
authors.forename: I
authors.initials: I
authors.lastname: Roeder
mesh_terms: D000465:Algorithms; D000818:Animals; D018929:Cell Culture Techniques; D002478:Cells, Cultured; D053595:Embryonic Stem Cells / Q000166:cytology*; D007091:Image Processing, Computer-Assisted; D051379:Mice; D008858:Microscopy, Phase-Contrast; D059008:Time-Lapse Imaging