We present a wide-field fluorescence microscopy add-on that provides a fast, light-efficient extended depth-of-field (EDOF) using a deformable mirror with an update rate of 20 kHz. Out-of-focus contributions in the raw EDOF images are suppressed with a deconvolution algorithm derived directly from the microscope 3D optical transfer function. Demonstrations of the benefits of EDOF microscopy are shown with GCaMP-labeled mouse brain tissue.

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Real‐time feedback‐driven single‐particle tracking (RT‐FD‐SPT) is a class of techniques in the field of single‐particle tracking that uses feedback control to keep a particle of interest in a detection volume. These methods provide high spatiotemporal resolution on particle dynamics and allow for concurrent spectroscopic measurements. This review article begins with a survey of existing techniques and of applications where RT‐FD‐SPT has played an important role. Each of the core components of RT‐FD‐SPT are systematically discussed in order to develop an understanding of the trade‐offs that must be made in algorithm design and to create a clear picture of the important differences, advantages, and drawbacks of existing approaches. These components are feedback tracking and control, ranging from simple proportional‐integral‐derivative control to advanced nonlinear techniques, estimation to determine particle location from the measured data, including both online and offline algorithms, and techniques for calibrating and characterizing different RT‐FD‐SPT methods. Then a collection of metrics for RT‐FD‐SPT is introduced to help guide experimentalists in selecting a method for their particular application and to help reveal where there are gaps in the techniques that represent opportunities for further development. Finally, this review is concluded with a discussion on future perspectives in the field.

Operable under ambient light and providing chemical selectivity, stimulated Raman scattering (SRS) microscopy opens a new window for imaging molecular events on a human subject, such as filtration of topical drugs through the skin. A typical approach for volumetric SRS imaging is through piezo scanning of an objective lens, which often disturbs the sample and offers a low axial scan rate. To address these challenges, we have developed a deformable mirror-based remote-focusing SRS microscope, which not only enables high-quality volumetric chemical imaging without mechanical scanning of the objective but also corrects the system aberrations simultaneously. Using the remote-focusing SRS microscope, we performed volumetric chemical imaging of living cells and captured in real time the dynamic diffusion of topical chemicals into human sweat pores.

Single Particle Tracking (SPT) is a powerful class of methods for studying the dynamics of biomolecules inside living cells. The techniques reveal the trajectories of individual particles, with a resolution well below the diffraction limit of light, and from them the parameters defining the motion model, such as diffusion coefficients and confinement lengths. Most existing algorithms assume these parameters are constant throughout an experiment. However, it has been demonstrated that they often vary with time as the tracked particles move through different regions in the cell or as conditions inside the cell change in response to stimuli. In this work, we propose an estimation algorithm to determine time-varying parameters of systems that discretely switch between different linear models of motion with Gaussian noise statistics, covering dynamics such as diffusion, directed motion, and Ornstein–Uhlenbeck dynamics. Our algorithm consists of three stages. In the first stage, we use a sliding window approach, combined with Expectation Maximization (EM) to determine maximum likelihood estimates of the parameters as a function of time. These results are only used to roughly estimate the number of model switches that occur in the data to guide the selection of algorithm parameters in the second stage. In the second stage, we use Change Detection (CD) techniques to identify where the models switch, taking advantage of the off-line nature of the analysis of SPT data to create non-causal algorithms with better precision than a purely causal approach. Finally, we apply EM to each set of data between the change points to determine final parameter estimates. We demonstrate our approach using experimental data generated in the lab under controlled conditions.

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Fast imaging over large volumes can be obtained in a simple manner with extended-depth-of-field (EDOF) microscopy. A standard technique of Wiener deconvolution can correct for the blurring inherent in EDOF images. We compare Wiener deconvolution with an alternative, parameter-free technique based on the dual reconstruction of fluorescence and absorption layers in a sample. This alternative technique provides significantly enhanced reconstruction contrast owing to a quadratic positivity constraint that intrinsically favors sparse solutions. We demonstrate the advantages of this technique with mouse neuronal images acquired in vivo.