Stochastic approach to the molecular counting problem in superresolution microscopy
- aDepartment of Biochemistry and Biophysics, University of California, San Francisco, CA 94158;
- bHoward Hughes Medical Institute and
- cDepartments of Physics, Molecular and Cell Biology, and Chemistry, QB3 Institute, University of California, Berkeley, CA 94720;
- dDepartment of Physics, Indiana University–Purdue University Indianapolis, Indianapolis, IN 46202; and
- eDepartment of Cellular and Integrative Physiology, Indiana University School of Medicine, Indianapolis, IN 46202
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Edited by R. Stephen Berry, University of Chicago, Chicago, IL, and approved November 19, 2014 (received for review May 1, 2014)
Significance
Large and complex macromolecular assemblies—like RNA and DNA polymerases, the kinetochore, the ribosome, ATP synthase, and many others—are critical components of the cell. To fully characterize these molecular machines, it is not sufficient to rely solely on traditional structural methods, like cryo-EM and X-ray crystallography that provide great structural detail in vitro; we need experimental and theoretical tools that can describe the organization of these machines in their native cellular environment so as to better understand their function. Here we provide a strategy for extracting precisely this information directly from superresolution imaging data, a state-of-the-art technique for probing biological structures in living cells below the diffraction limit.
Abstract
Superresolution imaging methods—now widely used to characterize biological structures below the diffraction limit—are poised to reveal in quantitative detail the stoichiometry of protein complexes in living cells. In practice, the photophysical properties of the fluorophores used as tags in superresolution methods have posed a severe theoretical challenge toward achieving this goal. Here we develop a stochastic approach to enumerate fluorophores in a diffraction-limited area measured by superresolution microscopy. The method is a generalization of aggregated Markov methods developed in the ion channel literature for studying gating dynamics. We show that the method accurately and precisely enumerates fluorophores in simulated data while simultaneously determining the kinetic rates that govern the stochastic photophysics of the fluorophores to improve the prediction’s accuracy. This stochastic method overcomes several critical limitations of temporal thresholding methods.
Footnotes
- ↵1To whom correspondence should be addressed. Email: stevenpresse{at}gmail.com.
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Author contributions: G.C.R. and S.P. designed research; G.C.R. and S.P. performed research; G.C.R., J.Y.S., C.B., and S.P. contributed new reagents/analytic tools; G.C.R. and S.P. analyzed data; and G.C.R., J.Y.S., C.B., and S.P. wrote the paper.
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The authors declare no conflict of interest.
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This article is a PNAS Direct Submission.
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See Commentary on page 304.
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This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1408071112/-/DCSupplemental.
