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Newly developed scientific complementary metal-oxide semiconductor (sCMOS) cameras have the potential to dramatically accelerate data acquisition, enlarge the field of view and increase the effective quantum efficiency in single-molecule switching nanoscopy. However, sCMOS-intrinsic pixel-dependent readout noise substantially lowers the localization precision and introduces localization artifacts. We present algorithms that overcome these limitations and that provide unbiased, precise localization of single molecules at the theoretical limit. Using these in combination with a multi-emitter fitting algorithm, we demonstrate single-molecule localization super-resolution imaging at rates of up to 32 reconstructed images per second in fixed and living cells.

Total internal reflection fluorescence (TIRF) microscopy excites a thin evanescent field which theoretically decays exponentially. Each TIRF image is actually the projection of a 3-D volume and hence cannot alone produce an accurate localization of structures in the z-dimension, however, it provides greatly improved axial resolution for biological samples. Multiple angle-TIRF microscopy allows controlled variation of the incident angle of the illuminating laser beam, thus generating a set of images of different penetration depths with the potential to reconstruct the 3-D volume of the sample. With the ultimate goal to quantify important biological parameters of microtubules, we present a method to reconstruct 3-D position and orientation of microtubules based on multi-angle TIRF data, as well as experimental calibration of the actual decay function of the evanescent field at each angle. We validate our method using computer simulations, by creating a phantom simulating the curvilinear characteristics of microtubules and project the artificially constructed volume into a set of TIRF image for different penetration depth. The reconstructed depth information for the phantom data is shown to be accurate and robust to noise. We apply our method to microtubule TIRF images of PtK(2) cells in vivo. By comparing microtubule curvatures of the reconstruction results and several electron microscopy (EM) images of vertically sliced sample of microtubules, we find that the curvature statistics of our reconstruction agree well with the ground truth (EM data). Quantifying the distribution of microtubule curvature reveals an interesting discovery that microtubules can buckle and form local bendings of considerably small radius of curvature which is also visually spotted on the EM images, while microtubule bendings on a larger scale generally have a much larger radius and cannot bear the stress of a large curvature. The presented method has the potential to provide a reliable tool for 3-D reconstruction and tracking of microtubules.

With the ultimate goal to quantify important biological parameters of microtubules, we present a method to estimate the 3D positions of microtubules from multi-angle TIRF data based on the calibrated decay profiles for each angle. Total Internal Reflection Fluorescence (TIRF) Microscopy images are actually projections of 3D volumes and hence cannot alone produce an accurate localization of structures in the z-dimension, however, they provide greatly improved axial resolution for biological samples. Multiple angle-TIRF microscopy allows controlled variation of the incident angle of the illuminating laser beam, thus generating a set of images of different penetration depths with the potential to estimate the 3D volume of the sample. Our approach incorporates prior information about intensity and geometric smoothness. We validate our method using computer simulated phantom data and test its robustness to noise. We apply our method to TIRF images of microtubules in PTK2 cells and compare the distribution of the microtubule curvatures with electron microscopy (EM) images.

Microtubules are tubular biopolymers of the cytoplasm. They play numerous critical roles in a cell such as providing mechanical support and structural tracks for the anchoring and transport of chromosomes, organelles, and vesicles. They also form the microtubule assembly, which is critical for the coordination of mitosis and cell migration. The most dynamic part of the assembly are the microtubule outer, plus, tips located close to the cortex of the cell. Abnormal function of the assembly has been implicated in cell pathology such as neurodegenerative diseases and cancer. To date the study of the dynamics of the microtubule assembly is often performed qualitatively by visual inspection or quantitatively by manual annotation of the locations of the tips over time in an image sequence, which is very tedious. In this work we have developed a method to automatically track microtubule tips so as to enable a more extensive and higher throughput quantitative study of the microtubule assembly. Our approach first uses the entire image sequence to estimate the region in which a tip oscillates. In that region a tip feature is computed for all time and subsequently used to form the tip trajectory. Last, we evaluate our method with phantom as well as real data. The real data show fluorescently tagged living cells imaged with epifluorescent microscopy or confocal microscopy.

Analyzing spatio-temporal dependencies between different types of events is highly relevant to numerous biological phenomena (e.g. signalling and trafficking) especially as advances in probes and microscopy have facilitated imaging of dynamic processes in living cells. For many types of events, the segmented areas can overlap spatially and temporally forming random clumps. In this paper, we model binary image sequences of two different event types as a realization of a bivariate temporal random set and propose a non-parametric approach to quantify spatial and spatio-temporal interrelations using the pair-correlation, cross-covariance and the Ripley IK functions. Based on these summary statistics we propose a randomization procedure to test independence between event types by applying random toroidal shifts and Monte Carlo tests. A simulation study assessed the performance of the proposed estimators and showed that these statistics capture the spatio-temporal dependencies accurately. The estimation of the spatio-temporal interval of interactions was also obtained. The method was successfully applied to analyze the interdependencies of several endocytic proteins using image sequences of living cells and validated the procedure as a new way to automatically quantify dependencies between proteins in a formal and robust manner.

Counting spatially and temporally overlapping events in image sequences and estimating their shape-size and duration features are important issues in some applications. We propose a stochastic model, a particular case of the nonisotropic 3D Boolean model, for performing this analysis: the temporal Boolean model. Some probabilistic properties are derived and a methodology for parameter estimation from time-lapse image sequences is proposed using an explicit treatment of the temporal dimension. We estimate the mean number of germs per unit area and time, the mean grain size and the duration distribution. A wide simulation study in order to assess the proposed estimators showed promising results. The model was applied on biological image sequences of in-vivo cells in order to estimate new parameters such as the mean number and duration distribution of endocytic events. Our results show that the proposed temporal Boolean model is effective for obtaining information about dynamic processes which exhibit short-lived, but spatially and temporally overlapping events.