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We present software-based methods for automatic phase control and for mosaicing high-speed, Lissajous-scanned images. To achieve imaging speeds fast enough for mosaicing, we first increase the image update rate tenfold from 3 to 30 Hz, then vertically interpolate each sparse image in real-time to eliminate fixed pattern noise. We validate our methods by imaging fluorescent beads and automatically maintaining phase control over the course of one hour. We then image fixed mouse brain tissues at varying update rates and compare the resulting mosaics. Using reconstructed image data as feedback for phase control eliminates the need for phase sensors and feedback controllers, enabling long-term imaging experiments without additional hardware. Mosaicing subsampled images results in video-rate imaging speeds, nearly fully recovered spatial resolution, and millimeter-scale fields of view.

Automated cell segmentation and tracking is essential for dynamic studies of cellular morphology, movement, and interactions as well as other cellular behaviors. However, accurate, automated, and easy-to-use cell segmentation remains a challenge, especially in cases of high cell densities, where discrete boundaries are not easily discernable. Here, we present a fully automated segmentation algorithm that iteratively segments cells based on the observed distribution of optical cell volumes measured by quantitative phase microscopy. By fitting these distributions to known probability density functions, we are able to converge on volumetric thresholds that enable valid segmentation cuts. Since each threshold is determined from the observed data itself, virtually no input is needed from the user. We demonstrate the effectiveness of this approach over time using six cell types that display a range of morphologies, and evaluate these cultures over a range of confluencies. Facile dynamic measures of cell mobility and function revealed unique cellular behaviors that relate to tissue origins, state of differentiation, and real-time signaling. These will improve our understanding of multicellular communication and organization.

Human pluripotent stem cells (hPSCs) can self-renew or differentiate to diverse cell types, thus providing a platform for basic and clinical applications. However, pluripotent stem cell populations are heterogeneous and functional properties at the single cell level are poorly documented leading to inefficiencies in differentiation and concerns regarding reproducibility and safety. Here, we use non-invasive time-lapse imaging to continuously examine hPSC maintenance and differentiation and to predict cell viability and fate. We document dynamic behaviors and social interactions that prospectively distinguish hPSC survival, self-renewal and differentiation. Results highlight the molecular role of E-cadherin not only for cell-cell contact but also for clonal propagation of hPSCs. Results indicate that use of continuous time-lapse imaging can distinguish cellular heterogeneity with respect to pluripotency as well as a subset of karyotypic abnormalities whose dynamic properties were monitored.

Topical application and quantification of targeted, surface-enhanced Raman scattering (SERS) nanoparticles offer a new technique that has the potential for early detection of epithelial cancers of hollow organs. Although less toxic than intravenous delivery, the additional washing required to remove unbound nanoparticles cannot necessarily eliminate nonspecific pooling. Therefore, we developed a real-time, ratiometric imaging technique to determine the relative concentrations of at least two spectrally unique nanoparticle types, where one serves as a nontargeted control. This approach improves the specific detection of bound, targeted nanoparticles by adjusting for working distance and for any nonspecific accumulation following washing. We engineered hardware and software to acquire SERS signals and ratios in real time and display them via a graphical user interface. We report quantitative, ratiometric imaging with nanoparticles at pM and sub-pM concentrations and at varying working distances, up to 50 mm. Additionally, we discuss optimization of a Raman endoscope by evaluating the effects of lens material and fiber coating on background noise, and theoretically modeling and simulating collection efficiency at various working distances. This work will enable the development of a clinically translatable, noncontact Raman endoscope capable of rapidly scanning large, topographically complex tissue surfaces for small and otherwise hard to detect lesions.

A fluorescence confocal microscope incorporating a 1.8-mm-diam gradient-index relay lens is developed for in vivo histological guidance during resection of brain tumors. The microscope utilizes a dual-axis confocal architecture to efficiently reject out-of-focus light for high-contrast optical sectioning. A biaxial microelectromechanical system (MEMS) scanning mirror is actuated at resonance along each axis to achieve a large field of view with low-voltage waveforms. The unstable Lissajous scan, which results from actuating the orthogonal axes of the MEMS mirror at highly disparate resonance frequencies, is optimized to fully sample 500x500 pixels at two frames per second. Optically sectioned fluorescence images of brain tissues are obtained in living mice to demonstrate the utility of this microscope for image-guided resections.