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A light-field microscope (LFM) was first designed by Levoy et al. and the 3D information of objects was acquired by placing a microlens array (MLA) on an image plane [33]. Unlike conventional 3D microscopies such as confocal or tomographic microscopy that require mechanical scanning or multiple frames, the LFM captures spatial and angular information of an object without delay by collecting partial images through each channel of the MLA in a single frame (Fig. 1d). The 3D volumetric imaging of LFM realizes a faster frame rate compared to the other 3D microscopy because the LFM can acquire 3D imaging through a single-snapshot [34,35,36]. The LFM is available for various biomedical applications such as the imaging of neuronal activity [37, 38], single-molecule [39], and live-cell [40] owing to the fast 3D imaging of LFM. However, the limitations of LFM are spatial resolution and a low signal-to-noise ratio (SNR) due to the superimposition of spatial information through the MLA [41]. In addition, the issues of photo-bleaching and photo-toxicity still remain because the labeling process is required to observe biological structures or signals. Recent studies are being progressed to solve the resolution issues through novel LFM configurations and deep learning algorithms.

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A compressive LFM was introduced for the high-resolution and high-speed 3D imaging of the zebrafish brain [74]. Conventional LFMs had a problem with image degradation due to scattering in deep brain tissue. To solve this problem, the compressive LFM utilized high-accuracy 3D neuron localization by applying a wave-optical multi-slice model. The position and fluorescence data of neurons were quickly collected by skipping 3D image reconstruction steps. As a result, 3D neural structures of the zebrafish brain were obtained at a sampling rate of 100 Hz. A dictionary LFM technique was also introduced for observing the brain and blood vessels of zebrafish (Fig. 3a and b) [75]. The system reduces image noise and artifacts, which are chronic issues in conventional LFMs due to low laser power, by using the dictionary information trained from general biological samples. The dictionary LFM also demonstrates high contrast and artifacts-free Zebrafish calcium imaging by reducing ambiguity in blood cell counting and providing clarity of nerve observation.

Single-molecule localization microscopy (SMLM) is an imaging technique that efficiently detects molecules in biological structures with a high-spatial resolution. The device allows the observation of subcellular compartments such as neuronal synapses, lysosomes, and nuclear proteins that perform significant roles in cellular functions. The conventional 3D SMLM had some issues in that an axial resolution is reduced due to low photon throughput and extended PSFs. An LFM integrated with an SMLM was developed to improve the axial resolution (Fig. 5b) [39]. The system included the configuration of Fourier light-field microscopy, and utilized algorithms and software optimized for the conventional 2D SMLM. The analyzed results through intervals between beads show that the near-isotropic precision of the system achieves 20 nm over a DOF of 6 µm. The LFM combined with the SMLM also demonstrates sufficient resolution to observe DNA origami nanorulers and the microvilli of Jurkat T cells.

Deep learning-based LFM structure overview. Various biological models such as a worm and a zebrafish are captured through the LFM system. Each 2D light-field channel image is reconstructed into 3D depth images through a pre-trained light-field network. The light-field network is usually trained through iteratively matching light-field images and high-resolution images acquired by conventional 3D microscopes. Image artifacts reduction and image resolution improvement can be achieved by diminishing the difference between inference data and ground truths

In this review, the principles of LFM, image processing methods, and biomedical applications for exploring living organisms have been presented. The LFM is evolving into various LFM configurations through the arrangement of optical components such as an objective lens, MLA, and a relay lens. In addition, various image reconstruction algorithms have been reported to increase image resolution and reduce artifacts. The LFM has been demonstrated through various biomedical applications such as neuron activity visualization, live-cell monitoring, locomotion analysis, and single-molecule imaging. Various LFM approaches were introduced to achieve optimal performances in each application. Also, the deep learning-based LFM successfully provides images with improved spatial resolution and without artifacts. Despite the current progress of LFM, continuous advanced studies are required to realize superior performance compared to other 3D microscope imaging techniques. Improved image resolution and deep penetration performances are required in LFM imaging. The resolution of LFM is inevitably low because the MLA divides spatial information, which reduces resolution compared to other super-resolution microscopes. Sub-cellular imaging requires a high-resolution performance for a deeper understanding of mechanisms in vivo. In addition, the penetration depth of LFM has a restriction due to the scattering of tissue, and image resolution is degraded according to the depth. Overcoming these challenges requires new approaches that diversify optical arrangement, illumination, or image processing algorithms to improve image resolution and penetration depth comparable to that of advanced microscopes. Also, improvements in image-processing speed are also required. One of the LFM advantages is a fast 3D volume image acquisition speed compared to other microscopes, but the image processing time occupies the most time of volumetric imaging. The physical acquisition time of 3D information is relatively fast compared to scanning methods because the LFM acquired 3D information about an object through a single-shot. However, computational processes with time-consuming are required for relocating the mixed information. Advanced techniques for real-time 3D volumetric imaging with deep learning algorithms may continue to reduce the time. The combination of LFM with a microfluidic chip has the advantage of fixing a target model within the observation range. 3D light-field imaging of microfluidic systems such as organ-on-a chips can help to understand the functions of the body. Especially, the LFM can efficiently acquire the in vitro calcium imaging of 3D neural environments with the advantage of fast volumetric imaging. Studies for the miniaturization of LFM are also expected to be developed for expanding various applications such as endoscopy, and point-of-care testing devices. The LFM will also help to efficiently acquire various biological information in diverse animal models with fast volumetric imaging.

Tenascin-C is a large hexameric extracellular glycoprotein. The founding member of a family of four tenascins, it is unique in its distinct pattern of expression. Little or no tenascin-C is detected in healthy adult tissues. It is transiently re-expressed upon tissue injury, and down-regulated after tissue repair is complete [1]. Tenascin-C is a multimodular protein comprising four distinct domains: an assembly domain, a series of epidermal growth factor-like repeats (EGF-L), a series of fibronectin type III-like repeats (FNIII), and a C-terminal fibrinogen-like globe (FBG). Each of these domains can interact with a different subset of binding partners, including cell surface receptors and other extracellular components (Fig. 1) [2]. By virtue of this domain organization, tenascin-C is an extraordinarily pleiotropic molecule and mediates a wide range of functions during tissue injury. Here we focus on recent discoveries, areas in which significant advances have been made in our understanding of the action of tenascin-C in cardiac and arterial injury, tumor angiogenesis, and stem cell biology.

In addition to the advances in our understanding of tenascin-C in cardiac and arterial biology and pathology, investigations into the role of tenascin-C in stem cell biology are also gathering pace, particularly in the central nervous system (CNS) and in the context of tumorigenesis.

Fatty acid-binding protein 4 (FABP4) is one of ten intracellular small molecular weight proteins that make up the FABP family [28, 29] and is found in adipose tissue, peripheral macrophages, and microglia [30]. Furthermore, it is not found in normal brain blood vessels, although it has been found in certain endothelial cells or tumor cells in benign and malignant meningiomas [10, 31]. FABP4 has a role in carcinogenesis in meningiomas by stimulating cell proliferation in a cell type-independent way. In this connection, rapamycin, a well-known inhibitor of the mTOR pathway, which is a master regulator of cell growth and metabolism, inhibits FABP4 production in endothelial cells [32]. FABP4 is expressed in a significantly higher percentage of GBMs in comparison to both normal brain tissues and lower-grade glial tumors. Data suggest that FABP4 may play a role in angiogenesis associated with GBMs. Another study analyzed FABP4 expression in a cohort of paraffin-embedded meningioma specimens by immunohistochemistry and double immunofluorescence analyses [9]. These results demonstrate that FABP4 is commonly expressed in meningioma vascular endothelial cells while tumor cell expression of FABP4 is primarily observed in anaplastic meningiomas. A combination of FABP4 immunostaining with histopathologic grading might provide a more accurate prediction of the biological behavior of meningiomas than histopathologic grading alone [31, 32].

The International Mouse Phenotyping Consortium (IMPC) [6] aims to phenotype knockouts for all protein coding genes in the mouse genome and hence provide an invaluable resource to the community, given that the mouse is the premier model organism for understanding gene function in development and disease. The IMPC community has defined a core set of procedures to assess multiple biological systems and has developed a central resource ( to disseminate the data. The database contains continuous, categorical, count, and image data for over 734,284 experiments on 46,972 mice as of January 2015. 041b061a72


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