Robust growth of Escherichia coli

By Ping Wang, L. Robert, James F. Pelletier, W. Dang, F. Taddei, A. Wright, S. Jun. Current Biology, 2010

1,011 citations recorded by the source corpus.

Summary The quantitative study of the cell growth [1-5] has led to many fundamental insights in our understanding of a wide range of subjects from cell cycle [6-9] to senescence [10]. Of particular importance is the growth rate, whose constancy represents a physiological steady state of an organism. Recent studies, however, suggest that the rate of elongation during exponential growth of bacterial cells decreases cumulatively with replicative age for both asymmetrically [11] and symmetrically [12,13] dividing organisms, implying that a “steady-state” population consists of individual cells that are never in a steady state of growth. To resolve this seeming paradoxical observation, we studied the long-term growth and division patterns of Escherichia coli cells by employing a microfluidic device designed to follow steady state growth and division of a large number of cells at a defined reproductive age. Our analysis of ~105 individual cells reveals a remarkable stability of growth of the mother cell inheriting the same pole for hundreds of generations. We further show that death of E. coli is not purely stochastic but is the result of accumulating damages. We conclude that E. coli, unlike all other aging model systems studied to date, has a robust mechanism of growth that is decoupled from cell death.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/15ce106f714228ae020cefe3cfa830050f169d6c

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Citations in the corpus

Deep learning for cellular image analysis (2019, background): “Although object tracking can be challenging, successful solutions have made it useful in a variety of biological analyses, including quantification of signaling dynamics 109 , efforts to understand cell motility 110 , and attempts to unravel the laws of bacterial cell growth 111 .”
High-throughput, subpixel-precision analysis of bacterial morphogenesis and intracellular spatio-temporal dynamics (2011, methodology): “…used for bacterial cells focuses on segmenting the image to separate densely packed cells at the expense of precision by using pixel-based operations, such as edge detection, watershed, and morphological erosion and dilation (Locke and Elowitz, 2009; Christen et al., 2010a; Wang et al., 2010b).”
Statistical estimation of a growth-fragmentation model observed on a genealogical tree (2012, background): “• Recent evolution of experimental technology enables to track the whole genealogy of cell populations (along prescribed lines of descendants for instance), affording the observation of other state variables such as size at division, lifetime of a single individual and so on [31].”
Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy (2011, background): “The growth of bacteria in this protocol is readily achieved with general laboratory supplies, in contrast to microfluidic growth chambers 22 that require specialized equipment.”