An automatic system for in vitro cell migration studies.
This paper describes a system for in vitro cell migration analysis. Adult neural stem/progenitor cells are studied using time-lapse bright-field microscopy and thereafter stained immunohistochemically to find and distinguish undifferentiated glial progenitor cells and cells having differentiated into type-1 or type-2 astrocytes. The cells are automatically segmented and tracked through the time-lapse sequence. An extension to the Chan-Vese Level Set segmentation algorithm, including two new terms for specialized growing and pruning, made it possible to resolve clustered cells, and reduced the tracking error by 65%. We used a custom-built manual correction module to form a ground truth used as a reference for tracked cells that could be identified from the fluorescence staining. On average, the tracks were correct 95% of the time, using our new segmentation. The tracking, or association of segmented cells, was performed using a 2-state Hidden Markov Model describing the random behaviour of the cells. By re-estimating the motion model to conform with the segmented data we managed to reduce the number of tracking parameters to essentially only one. Upon characterization of the cell migration by the HMM state occupation function, it was found that glial progenitor cells were moving randomly 2/3 of the time, while the type-2 astrocytes showed a directed movement 2/3 of the time. This finding indicates possibilities for cell-type specific identification and cell sorting of live cells based on specific movement patterns in individual cell populations, which would have valuable applications in neurobiological research.
Degerman
J
J
Department of Signals and System, Chalmers University of Technology, S-412 96 Gothenburg, Sweden. johan@degerman.net
Thorlin
T
T
Faijerson
J
J
Althoff
K
K
Eriksson
P S
PS
Put
R V D
RV
Gustavsson
T
T
D000818:Animals; D002465:Cell Movement; D002478:Cells, Cultured; D018715:Microscopy, Video / Q000379:methods*; D051381:Rats; D013234:Stem Cells / Q000502:physiology*