Adaptive Optical Two-Photon Microscopy for Surface-Profiled Living Biological Specimens

Large-scale cranial window for in vivo mouse brain imaging utilizing fluoropolymer nanosheet and light-curable resin

Two-photon microscopy enables in vivo imaging of neuronal activity in mammalian brains at high resolution. However, two-photon imaging tools for stable, long-term, and simultaneous study of multiple brain regions in same mice are lacking. Here, we propose a method to create large cranial windows covering such as the whole parietal cortex and cerebellum in mice using fluoropolymer nanosheets covered with light-curable resin (termed the 'Nanosheet Incorporated into light-curable REsin' or NIRE method). NIRE method can produce cranial windows conforming the curved cortical and cerebellar surfaces, without motion artifacts in awake mice, and maintain transparency for >5 months. In addition, we demonstrate that NIRE method can be used for in vivo two-photon imaging of neuronal ensembles, individual neurons and subcellular structures such as dendritic spines. The NIRE method can facilitate in vivo large-scale analysis of heretofore inaccessible neural processes, such as the neuroplastic changes associated with maturation, learning and neural pathogenesis.

Spatial frequency-based correction of the spherical aberration in living brain imaging

Optical errors, including spherical aberrations, hinder high-resolution imaging of biological samples due to biochemical components and physical properties. We developed the Deep-C microscope system to achieve aberration-free images, employing a motorized correction collar and contrast-based calculations. However, current contrast-maximization techniques, such as the Brenner gradient method, inadequately assess specific frequency bands. The Peak-C method addresses this issue, but its arbitrary neighbor selection and susceptibility to the noise limit its effectiveness. In this paper, we emphasize the importance of a broad spatial frequency range for accurate spherical aberration correction and propose Peak-F. This spatial frequency-based system utilizes a fast Fourier transform as a bandpass filter. This approach overcomes Peak-C's limitations and comprehensively covers the low-frequency domain of image spatial frequencies.

Fluorescence radial fluctuation enables two-photon super-resolution microscopy

Despite recent improvements in microscopy, it is still difficult to apply super-resolution microscopy for deep imaging due to the deterioration of light convergence properties in thick specimens. As a strategy to avoid such optical limitations for deep super-resolution imaging, we focused on super-resolution radial fluctuation (SRRF), a super-resolution technique based on image analysis. In this study, we applied SRRF to two-photon microscopy (2P-SRRF) and characterized its spatial resolution, suitability for deep observation, and morphological reproducibility in real brain tissue. By the comparison with structured illumination microscopy (SIM), it was confirmed that 2P-SRRF exhibited two-point resolution and morphological reproducibility comparable to that of SIM. The improvement in spatial resolution was also demonstrated at depths of more than several hundred micrometers in a brain-mimetic environment. After optimizing SRRF processing parameters, we successfully demonstrated in vivo high-resolution imaging of the fifth layer of the cerebral cortex using 2P-SRRF. This is the first report on the application of SRRF to in vivo two-photon imaging. This method can be easily applied to existing two-photon microscopes and can expand the visualization range of super-resolution imaging studies.

Complex-Amplitude-Modulation Vectorial Excitation Beam for High-Resolution Observation of Deep Regions in Two-Photon Microscopy

In two-photon microscopy, aberration correction is an essential technique for realizing high resolution in deep regions. A spatial light modulator (SLM) incorporated into an optical system for two-photon microscopy performs pre-compensation on the wavefront of the excitation beam, restoring the resolution close to the diffraction limit even in the deep region of a biological sample. If a spatial resolution smaller than the diffraction limit can be achieved along with aberration correction, the importance of two-photon microscopy for deep region observation will increase further. In this study, we realize higher resolution observations in the deep region by combining two resolution-enhancement methods and an aberration correction method. Therefore, a z-polarizer is added to the aberration-correction optical system, and the SLM modulates the amplitude and phase of the excitation beam; in other words, complex-amplitude modulation is performed. The lateral resolution is found to be approximately 20% higher than the diffraction limit obtained using a circularly polarized beam. Verification was conducted by simulation and experimentation using model samples and ex vivo biological samples. The proposed method has the potential to be effective for live imaging and photostimulation of the deep region of the sample, although it requires only minor changes to the conventional optical system that performs aberration correction.

Focusing new light on brain functions: multiphoton microscopy for deep and super-resolution imaging

Multiphoton microscopy has become a powerful tool for visualizing neurobiological phenomena such as the dynamics of individual synapses and the functional activities of neurons. Owing to its near-infrared excitation laser wavelength, multiphoton microscopy achieves greater penetration depth and is less invasive than single-photon excitation. Here, we review the principles of two-photon microscopy and its technical limitations (penetration depth and spatial resolution) on brain tissue imaging. We then describe the technological improvements of two-photon microscopy that enable deeper imaging with higher spatial resolution for investigating unrevealed brain functions.

Protocol for constructing an extensive cranial window utilizing a PEO-CYTOP nanosheet for in vivo wide-field imaging of the mouse brain

Large-scale optical measurements have revealed the anatomical and functional connectivity among brain regions underlying brain functions. Here, we describe how to construct a cranial window utilizing a polyethylene-oxide-coated CYTOP (PEO-CYTOP) nanosheet that suppresses bleeding on the brain surface of mice. We demonstrate in vivo two-photon imaging through the PEO-CYTOP nanosheet at the subcellular resolution in the parietal region of the mouse brain. This protocol improves the surgical procedure and expands the optically observable regions, thereby promoting understanding of brain function.

For complete details on the use and execution of this protocol, please refer to Takahashi et al. (2020).

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Detailed protocol for constructing a vast cranial window for in vivo mouse brain imaging

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Preparation and brain-sealing method of PEO-CYTOP nanosheet

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Instruction to make a large cranial hole with a depressant for intracranial pressure

3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images

Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline, 3DeeCellTracker, by integrating multiple existing and new techniques including deep learning for tracking. With only one volume of training data, one initial correction, and a few parameter changes, 3DeeCellTracker successfully segmented and tracked ~100 cells in both semi-immobilized and 'straightened' freely moving worm's brain, in a naturally beating zebrafish heart, and ~1000 cells in a 3D cultured tumor spheroid. While these datasets were imaged with highly divergent optical systems, our method tracked 90-100% of the cells in most cases, which is comparable or superior to previous results. These results suggest that 3DeeCellTracker could pave the way for revealing dynamic cell activities in image datasets that have been difficult to analyze.