Difference between revisions of "Real-time RNA profiling within a single bacterium"

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==Summary==
 
==Summary==
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[[Image:FCS1.png|thumb|right|400px|Figure 1: Varying RNA levels in a single cell (A) and in a number of cells (B). When these levels are averaged (C), the cell cycle-dependent variations disappear, and the RNA levels seem to be constant in time.]]
 
While every cell in an organism has essentially the same genetic code, cell morphology and behavior vary due to which genes are expressed. Studying intracellular RNA or protein levels represents a common method for studying changes in cell behavior. However, classical methods for for observing temporal changes in these intracellular concentrations require lysing cells at different time points.
 
While every cell in an organism has essentially the same genetic code, cell morphology and behavior vary due to which genes are expressed. Studying intracellular RNA or protein levels represents a common method for studying changes in cell behavior. However, classical methods for for observing temporal changes in these intracellular concentrations require lysing cells at different time points.
  

Revision as of 01:08, 2 May 2009

Zach Wissner-Gross (May 1, 2009)

Information

Real-time RNA profiling within a single bacterium

Thuc T. Le, Sebastien Harlepp, Calin C. Guet, Kimberly Dittmar, Thierry Emonet, Tao Pan, Philippe Cluzel

PNAS, 2005, 102, 9160-9164

Soft matter keywords

Diffusion, fluorescence correlation spectroscopy

Summary

Figure 1: Varying RNA levels in a single cell (A) and in a number of cells (B). When these levels are averaged (C), the cell cycle-dependent variations disappear, and the RNA levels seem to be constant in time.

While every cell in an organism has essentially the same genetic code, cell morphology and behavior vary due to which genes are expressed. Studying intracellular RNA or protein levels represents a common method for studying changes in cell behavior. However, classical methods for for observing temporal changes in these intracellular concentrations require lysing cells at different time points.

Cluzel and coworkers offer a different approach that both yields single-cell resolution and is real-time (the authors estimate their acquisition time to be 2 seconds), without noticeably affecting the cell during the observation. Using a high-NA objective, the authors focus a blue laser beam to a diffraction-limited spot inside the bacterium, and then perform fluorescence correlation spectroscopy (FCS), which I explain below in more detail.

In short, the authors first show that their FCS data matches that of the current gold standard technique (260 nm UV absorption), after which they go on to demonstrate a few interesting findings. For example, they found that while the RNA levels of individual cells vary significantly over time, the averaging of several cells reduces this noise (Figure 1), explaining why such variation has gone largely unnoticed within the scientific community.