Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy.
An increasingly common component of studies in synthetic and systems biology is analysis of dynamics of gene expression at the single-cell level, a context that is heavily dependent on the use of time-lapse movies. Extracting quantitative data on the single-cell temporal dynamics from such movies remains a major challenge. Here, we describe novel methods for automating key steps in the analysis of single-cell, fluorescent images-segmentation and lineage reconstruction-to recognize and track individual cells over time. The automated analysis iteratively combines a set of extended morphological methods for segmentation, and uses a neighborhood-based scoring method for frame-to-frame lineage linking. Our studies with bacteria, budding yeast and human cells, demonstrate the portability and usability of these methods, whether using phase, bright field or fluorescent images. These examples also demonstrate the utility of our integrated approach in facilitating analyses of engineered and natural cellular networks in diverse settings. The automated methods are implemented in freely available, open-source software.
Department of Statistical Science, Duke University, Durham, North Carolina 27708-0251, USA. quanli@stat.duke.edu
Quanli
Q
Wang
Jarad
J
Niemi
Chee-Meng
CM
Tan
Lingchong
L
You
Mike
M
West
D000465:Algorithms; D001419:Bacteria; D019070:Cell Lineage; D004926:Escherichia coli; D006801:Humans; D019044:Image Cytometry; D008856:Microscopy, Fluorescence / Q000379:methods*