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LAMMP Seminar Video
Imaging live cells with nanometer resolution: Theoretical, computational, and statistical limits
Alex Small, PhD

Superresolution microscopy techniques enable imaging of live cells with subwavelength resolution. In these techniques, fluorescent molecules are switched on and off, with only a small fraction of them emitting light at any given time. Consequently, the molecules form non-overlapping blurs in the image plane, enabling localization of molecules with subwavelength resolution limited only by noise in photon detection. This work holds the promise of revealing very high-resolution details of biological processes in live cells. The question that we ask is, given that diffraction no longer limits the amount of information that can be obtained in fluorescence microscopy, what is the new theory that predicts the limits of performance? We have taken several steps towards a new theory of optical imaging. First, we have developed a formalism for benchmarking algorithms that correct errors in image reconstruction by removing “overlap” images. Only a handful of algorithm performance parameters are shown to matter, suggesting the possibility of fast error correction based on simple principles, while allowing quantitative comparisons between different approaches to subwavelength microscopy. Second, using a kinetic model of the fluorophores, we proved the existence of optimal image acquisition schemes that maximize the number of single-molecule images (i.e. no over-lapping blurs). When bleaching and activation processes are controlled by different wavelengths of light, the optimal scheme is simple, independent of the details of the molecules being studied, and very robust. When all processes are controlled by the same wavelength, however, the acquisition scheme and its robustness depend very sensitively on details of the bleaching and activation processes. Finally, we have tested a new image analysis algorithm that enables a “square root speedup” of the analysis process, enabling fast processing of high-throughput data.

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