AbstractSingle-molecule fluorescence microscopy probes nanoscale, subcellular biology in real time. Existing methods for analyzing single-particle tracking data provide dynamical information, but can suffer from supervisory biases and high uncertainties. Here, we introduce a new approach to analyzing single-molecule trajectories: the Single-Molecule Analysis by Unsupervised Gibbs sampling (SMAUG) algorithm, which uses nonparametric Bayesian statistics to uncover the whole range of information contained within a single-particle trajectory (SPT) dataset. Even in complex systems where multiple biological states lead to a number of observed mobility states, SMAUG provides the number of mobility states, the average diffusion coefficient of single molecules in that state, the fraction of single molecules in that state, the localization noise, and the probability of transitioning between two different states. In this paper, we provide the theoretical background for the SMAUG analysis and then we validate the method using realistic simulations of SPT datasets as well as experiments on a controlled in vitro system. Finally, we demonstrate SMAUG on real experimental systems in both prokaryotes and eukaryotes to measure the motions of the regulatory protein TcpP in Vibrio cholerae and the dynamics of the B-cell receptor antigen response pathway in lymphocytes. Overall, SMAUG provides a mathematically rigorous approach to measuring the real-time dynamics of molecular interactions in living cells.Statement of SignificanceSuper-resolution microscopy allows researchers access to the motions of individual molecules inside living cells. However, due to experimental constraints and unknown interactions between molecules, rigorous conclusions cannot always be made from the resulting datasets when model fitting is used. SMAUG (Single-Molecule Analysis by Unsupervised Gibbs sampling) is an algorithm that uses Bayesian statistical methods to uncover the underlying behavior masked by noisy datasets. This paper outlines the theory behind the SMAUG approach, discusses its implementation, and then uses simulated data and simple experimental systems to show the efficacy of the SMAUG algorithm. Finally, this paper applies the SMAUG method to two model living cellular systems—one bacterial and one mammalian—and reports the dynamics of important membrane proteins to demonstrate the usefulness of SMAUG to a variety of systems.

Fluorescence microspectroscopy (FMS) with environment-sensitive probes provides information about local molecular surroundings at microscopic spatial resolution. Until recently, only probes exhibiting large spectral shifts due to local changes have been used. Herein, we show that appropriate measuring procedure and data analysis enable nanometer spectral peak position resolution, even for photosensitive fluorophores [1]. The reach of our approach is demonstrated in several examples. The first application shows how we can distinguish lipid vesicles in different lipid phases with two commonly used polarity-sensitive probes. A synthesized NBD-based fatty acid red-shifted its emission maximum by 1.5-2 nm going from gel to liquiddisordered phase in DPPC. Between these two phases Laurdan exhibits a large 50 nm red-shift. We therefore chose a more challenging combination -gel and liquid ordered phase, realized by DPPC and DPPC/Chol (40 mol%), respectively, where we were able to detect a 3 nm blue-shift with Laurdan [1]. The second example shows application of a synthesized rhodamine-based pH-activatable probe that is sensitive to aggregation. We studied a receptor-mediated internalization in dendritic cells and measured a 3 nm aggregation-induced emission spectral shift due to probe accumulation in endosomes and lysosomes [2,3]. The results show that peak position resolution, characteristic for spectrofluorimetric measurements on bulk samples, could readily be achieved at micrometer spatial scale.[1] I. Urban ci c, Z.

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glucose, mannose and galactose with water molecules to calculate spectra of water molecules. We demonstrate that the number of water molecules around typical hydroxyl is different between each monosaccharide. The result shows that the equatorial hydroxyl is more hydrated than the axial one because of steric inhabitation. However, hydroxyl next to oxygen doesn't change between equatorial and axial. Spectra of water molecules in the bulk agree with that obtained by experiments. The spectra calculated by correlation function of relative velocity include three peeks which is related to typical motions of water molecule, angular, stretching and tumbling. The difference spectra between sugar solution and bulk shows that there are two larger changes around two peaks, angular and stretching motions, and the largest peak of angular is galactose, followed by mannose and glucose in this order. These results show the difference of hydration around carbohydrates can be identified by theoretical simulation and we can also identify the spectra change of water molecules.