Introduction

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.

Publications

  1. Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy.
    Cite this
    Wang Q, Niemi J, Tan CM, You L, West M, 2010-01-01 - Cytometry. Part A : the journal of the International Society for Analytical Cytology

Credits

  1. Quanli Wang
    Developer

    Department of Statistical Science, Duke University, United States of America

  2. Jarad Niemi
    Developer

  3. Chee-Meng Tan
    Developer

  4. Lingchong You
    Developer

  5. Mike West
    Investigator

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Summary
AccessionBT000025
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Submitted ByMike West