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Advanced Optical Imaging in the Study of Acute and Chronic Response to Implanted Neural Interfaces
Published in Yu Chen, Babak Kateb, Neurophotonics and Brain Mapping, 2017
Cristin G. Welle, Daniel X. Hammer
It should be clear that many imaging methods have been demonstrated for use in neuroscience, each with advantages and disadvantages and each suited to different applications. Other emerging in vivo optical imaging techniques that have been applied to neural applications include intrinsic signal optical imaging (ISOI) (Frostig and Chen-Bee 2009), TPLSM with longer wavelengths (1300 nm) for deep tissue probing (Kobat et al. 2009), three-photon microscopy (Horton et al. 2013), and light sheet microscopy (Keller et al. 2015; Prevedel et al. 2014).
Complex Light Beams
Published in Lingyan Shi, Robert R. Alfano, Deep Imaging in Tissue and Biomedical Materials, 2017
Beyond zmax the mode disappears, as there is no longer beam overlap or interference. Because these can be thin and non-expanding, they are particularly suited for imaging via scanning light-sheet microscopy [23, 44]. Bessel beams are “self-healing” [38]. That is, an obstacle placed at a point along the beam does not cast a lasting shadow. Using simple geometry, we get that when a Bessel beam encounters an object of transverse side a, the mode repairs itself after a distance
Intravital imaging of megakaryocytes
Published in Platelets, 2020
David Stegner, Katrin G. Heinze
A different approach is light sheet fluorescence microscopy (LSFM), which is a wide-field method that allows to image large specimen with high resolution. Samples are excited by a focused [93] or scanned [94] “sheet of light” while the fluorescence light is detected by a sensitive camera perpendicular to the illumination plane. Camera-based modalities are typically fast, and here, photodamage and fluorophore bleaching are additionally kept at a minimum as only the detected plane is illuminated at a time. 3D image stacks are generated by moving the sample through the light sheet. The disadvantage here is that mammalian model organisms are opaque for visible light so that light-sheet microscopy is usually performed on optically cleared samples, which excludes intravital imaging. Nevertheless, this technique offers the desired large FOV and is compatible with labeling strategies used for intravital microscopy. Thus, it is a complementary technique that can give important information on megakaryocyte distributions over the whole (intact, but not living) bone, the respective vasculature and inner bone structure [12] (Figure 4). Moreover, such the segmented objects in the whole bone image can serve as biological templates. Together with dynamics derived from intravital imaging, it is possible to perform meaningful computational simulations of bone marrow dynamics involving various cell types of interest [22]. Such realistic simulations with “true” objects derived from imaging could even interrogate situations that are not assessable by animal experiments for ethical or technical reasons.
System level analysis of motor-related neural activities in larval Drosophila
Published in Journal of Neurogenetics, 2019
Youngteak Yoon, Jeonghyuk Park, Atsushi Taniguchi, Hiroshi Kohsaka, Ken Nakae, Shigenori Nonaka, Shin Ishii, Akinao Nose
While this work was in progress, a similar study on Drosophila larval functional imaging was published by Lemon et al. (2015). The authors used state-of-the-art multi-view light-sheet microscopy with one- or two-photon excitation to achieve superior temporal and spatial resolution in the 1st and 3rd instar CNS. In the study by Lemon et al. (2015), GCaMP alone was expressed in neurons, and thus the functional unit of the statistical analyses of neuronal activity was voxels but not cells. Instead, in this study, a nuclear marker was co-expressed to allow activity profiling at the cell level. Our study also identified BW-biased neurons that are not described by Lemon et al. (2015). A difficulty common to our study and that of Lemon et al. (2015) was that since all neurons are visualized, it is difficult to know the identity of the neurons that show characteristic activity patterns. Our previous study showed that when GCaMP is expressed in fewer than ∼10 cells in each neuromere, correlation analyses may be used to reveal the outline of the neuron showing specific activity (Park et al., 2018). Thus, applying a functional imaging technique, such as the one described in this study to a large number of relatively sparse Gal4 lines may enable system-level analyses of motor activity while retaining the ability to identify the neurons of interest. Once candidate neurons are identified, their roles may be studied by the use of optogenetics. Furthermore, by combining functional imaging with optogenetical perturbations, system-level analyses of the dynamics of the motor circuits would be possible in the future.