Abstract
Digital fluorescence microscopy is commonly used to track individual proteins and their dynamics in living cells. However, extracting molecule-specific information from fluorescence images is often limited by the noise and blur intrinsic to the cell and the imaging system. Here we discuss a method called "model-convolution," which uses experimentally measured noise and blur to simulate the process of imaging fluorescent proteins whose spatial distribution can-not be resolved. We then compare model-convolution to the more standard approach of experimental deconvolution. In some circumstances, standard experimental deconvolution approaches fail to yield the correct underlying fluorophore distribution. In these situations, model-convolution removes the uncertainty associated with deconvolution and therefore allows direct statistical comparison of experimental and theoretical data. Thus, if there are structural constraints on molecular organization, the model-convolution method better utilizes information gathered via fluorescence microscopy, and naturally integrates experiment and theory.
Original language | English (US) |
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Pages (from-to) | 163-170 |
Number of pages | 8 |
Journal | Cellular and Molecular Bioengineering |
Volume | 3 |
Issue number | 2 |
DOIs | |
State | Published - Jun 2010 |
Bibliographical note
Funding Information:This work was supported by the Whitaker Foundation, the National Science Foundation, and the National Institutes of Health. The authors thank John Condeelis and James McNally for stimulating discussions.
Keywords
- Deconvolution
- Fluorescence
- Microscopy
- Model-convolution
- Modeling