Cells in the brain act as components of extended networks. Therefore, to understand neurobiological processes in a physiological context, it is essential to study them in vivo. Super-resolution microscopy has spatial resolution beyond the diffraction limit, thus promising to provide structural and functional insights that are not accessible with conventional microscopy. However, to apply it to in vivo brain imaging, we must address the challenges of 3D imaging in an optically heterogeneous tissue that is constantly in motion. We optimized image acquisition and reconstruction to combat sample motion and applied adaptive optics to correcting sample-induced optical aberrations in super-resolution structured illumination microscopy (SIM) in vivo. We imaged the brains of live zebrafish larvae and mice and observed the dynamics of dendrites and dendritic spines at nanoscale resolution.
While calcium imaging has become a mainstay of modern neuroscience, the spectral properties of current fluorescent calcium indicators limit deep tissue imaging as well as simultaneous use with other probes. Using two monomeric near-infrared fluorescent proteins, we engineered a near-infrared FRET-based genetically encoded calcium indicator (iGECI). iGECI exhibits high brightness, high photostability, and up to 600% increase in fluorescence response to calcium. In dissociated neurons, iGECI detects spontaneous neuronal activity, and electrically and optogenetically induced firing. We validated iGECI performance up to a depth of almost 400 μm in acute brain slices using one-photon light-sheet imaging. Applying hybrid photoacoustic and fluorescence microscopy, we simultaneously monitored neuronal and hemodynamic activities in the mouse brain through an intact skull, with ~3 μm lateral and ~25–50 μm axial resolution. Using two-photon imaging, we detected evoked and spontaneous neuronal activity in the mouse visual cortex, with fluorescence changes of up to 25%. iGECI allows biosensors and optogenetic actuators to be multiplexed without spectral crosstalk.
Optical microscopy, owing to its noninvasiveness and subcellular resolution, enables in vivo visualization of neuronal structure and function in the physiological context. Optical-sectioning structured illumination microscopy (OS-SIM) is a widefield fluorescence imaging technique that uses structured illumination patterns to encode in-focus structures and optically sections 3D samples. However, its application to in vivo imaging has been limited. In this study, we optimized OS-SIM for in vivo neural imaging. We modified OS-SIM reconstruction algorithms to improve signal-to-noise ratio and correct motion-induced artifacts in live samples. Incorporating an adaptive optics (AO) module to OS-SIM, we found that correcting sample-induced optical aberrations was essential for achieving accurate structural and functional characterizations in vivo. With AO OS-SIM, we demonstrated fast, high-resolution in vivo imaging with optical sectioning for structural imaging of mouse cortical neurons and zebrafish larval motor neurons, and functional imaging of quantal synaptic transmission at Drosophila larval neuromuscular junctions.
Based on a molecular-mechanism-based anticancer drug discovery program enabled by an innovative femtomedicine approach, we have found a previously unknown class of non-platinum-based halogenated molecules (called FMD compounds) as potent antitumor agents for effective treatment of cancers. Here, we present in vitro and in vivo studies of the compounds for targeted chemotherapy of cervical, breast, ovarian, and lung cancers. Our results show that these FMD agents led to DNA damage, cell cycle arrest in the S phase, and apoptosis in cancer cells. We also observed that such a FMD compound caused an increase of reduced glutathione (GSH, an endogenous antioxidant) levels in human normal cells, while it largely depleted GSH in cancer cells. We correspondingly found that these FMD agents exhibited no or little toxicity toward normal cells/tissues, while causing significant cytotoxicity against cancer cells, as well as suppression and delay in tumor growth in mouse xenograft models of cervical, ovarian, breast and lung cancers. These compounds are therefore a previously undiscovered class of potent antitumor agents that can be translated into clinical trials for natural targeted chemotherapy of multiple cancers.
Estimation of optical aberrations from volumetric intensity images is a key step in sensorless adaptive optics for 3D microscopy. Recent approaches based on deep learning promise accurate results at fast processing speeds. However, collecting ground truth microscopy data for training the network is typically very difficult or even impossible thereby limiting this approach in practice. Here, we demonstrate that neural networks trained only on simulated data yield accurate predictions for real experimental images. We validate our approach on simulated and experimental datasets acquired with two different microscopy modalities and also compare the results to non-learned methods. Additionally, we study the predictability of individual aberrations with respect to their data requirements and find that the symmetry of the wavefront plays a crucial role. Finally, we make our implementation freely available as open source software in Python.
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