The ability to image at the single molecule scale has revolutionized research in molecular biology. This feature issue presents a collection of articles that provides new insights into the fundamental limits of single molecule imaging and reports novel techniques for image formation and analysis.
© 2016 Optical Society of America
The ability to image single molecules has revolutionized research in molecular biology. Unraveling the structure and function of the basic molecular building blocks of life is greatly aided by nanoscale imaging. This can be done in a dynamic mode by tracking sparse single molecules performing their function inside the cell as well as in a static mode by assembling a super-resolution image from single molecule localizations. The sparse distribution of single molecule images in the raw images is realized by stochastic on-off switching of the fluorescence of the molecules as pioneered by stochastic optical reconstruction microscopy (STORM)  and photoactivated localization microscopy (PALM)  techniques. The precision of localizing a single molecule is fundamentally limited by the shot noise due to the small number of collected photons per localization event, and can be as low as 10 nm. Single molecule imaging rests on three pillars: (1) the biochemistry of fluorescent labeling optimized for biological specificity, photon production, and stochastic switching, (2) the optical microscopy setup with focus on low-noise imagers such as EM-CCDs, stability during image acquisition, optical point spread function design/quality, and collection efficiency, and (3) the image processing and analysis chain to extract the single molecule position and other data from the sequence of raw camera frames.
Single molecule imaging can be viewed as an example of the paradigm of computational imaging: modifications to the physical imaging chain are coupled to image processing and analysis tools to produce a new capability, in this case nanometer precision imaging. Although many improvements and new approaches have been introduced, the potential of the computational imaging framework has not been fully explored. Further advances to alleviate the sparsity constraint, experiment design optimized for information retrieval, and point spread function engineering for extracting additional single molecule data such as axial position and orientation all rely on novel concepts that combine in-depth understanding of the optics of image formation and expert knowledge in image reconstruction and analysis.
This feature issue provides new insights into the fundamental limits of single molecule imaging and reports novel techniques for image formation and analysis. The article by Chao et al.  provides a comprehensive tutorial for analysis of fundamental limits to the single molecule localization estimation problem in terms of Cramer–Rao lower bounds that derive from Fisher information analysis. The article develops the Cramer–Rao lower bound as a benchmark of the single molecule location estimation precision based on different experimental settings and proposes it as a useful experimental design tool. Increasing the inherent density of activated fluorophores toward reducing imaging time and maintaining the single molecule localization accuracy and precision is a constant challenge in single molecule imaging. Small  in his article describes a percolation theory formulation of this multifluorophore location problem to derive a bound on the highest density of activated fluorophores without degrading the accuracy of the single molecule localization. He suggests that the current experimental work is at or near the density limit and any further increase in the density will necessarily come at the cost of reduced location accuracy and precision due to overlapping point spread functions (PSF). Yu and Prasad’s  work on three-dimensional localization with a rotating PSF presents a vector-field analysis of an improved rotating-PSF design that can jointly estimate the three-dimensional location and the polarization of the light emitted by a monochromatic point dipole emitter. The subdiffraction resolution of single molecule imaging motivates a short note by Passon and Greble-Ellis  that provides arguably an alternative perspective on classification scheme for super-resolution in far-field microscopy. Zhang et al.’s  article on frequency-domain multiphoton fluorescence lifetime imaging provides a rigorous SNR performance analysis of this imaging technique. Based on theoretical results the authors show that this imaging technique requires 50% fewer photons to reach the performance of a conventional one-photon fluorescent lifetime imaging technique. Two recently developed multiframe deconvolution algorithms for structured illumination microscopy, the pattern-illuminated Fourier ptychography algorithm (piFP) and the joint Richardson–Lucy deconvolution (jRL), are analyzed by Chakrova et al.  in their article. They report that these algorithms have an advantage in that they enable image reconstruction from images acquired under varied types of illumination.
1. M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM),” Nat. Methods 3, 793–796 (2006). [CrossRef]
2. E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313, 1642–1645 (2006). [CrossRef]
3. J. Chao, E. S. Ward, and R. J. Ober, “Fisher information theory for parameter estimation in single molecule microscopy: tutorial,” J. Opt. Soc. Am. A 33, B36–B57 (2016). [CrossRef]
4. A. Small, “Multifluorophore localization as a percolation problem: limits to density and precision,” J. Opt. Soc. Am. A 33, B21–B30 (2016). [CrossRef]
5. Z. Yu and S. Prasad, “High-numerical-aperture microscopy with a rotating point spread function,” J. Opt. Soc. Am. A 33, B58–B69 (2016). [CrossRef]
6. O. Passon and J. Grebe-Ellis, “Note on the classification of super-resolution in far-field microscopy and information theory,” J. Opt. Soc. Am. A 33, B31–B35 (2016). [CrossRef]
7. Y. Zhang, A. A. Khan, G. D. Vigil, and S. S. Howard, “Investigation of signal-to-noise ratio in frequency-domain multiphoton fluorescence lifetime imaging microscopy,” J. Opt. Soc. Am. A 33, B1–B11 (2016). [CrossRef]
8. N. Chakrova, B. Rieger, and S. Stallinga, “Deconvolution methods for structured illumination microscopy,” J. Opt. Soc. Am. A 33, B12–B20 (2016). [CrossRef]