Name: whole-brain single-cell imaging and analysis of intact neonatal mouse brains using mri, tissue clearing, and light-sheet microscopy
Uploaded: 2022-08-01T14:00:00
Description: Watch this Scientific Journal Video about Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy at JoVE.com

This work was supported by the NIH (R01MH121433, R01MH118349, and R01MH120125 to JLS and R01NS110791 to GW) and the Foundation of Hope. We thank Pablo Ariel of the Microscopy Services Laboratory for assisting in sample imaging. The Microscopy Services Laboratory in the Department of Pathology and Laboratory Medicine is supported in part by Cancer Center Core Support Grant P30 CA016086 to the University of North Carolina (UNC) Lineberger Comprehensive Cancer Center. The Neuroscience Microscopy Core is supported by grant P30 NS045892.&#160;Research reported in this publication was supported in part by the North Carolina Biotech Center Institutional Support Grant 2016-IDG-1016.

All mice were used in accordance with and approved by the Institutional Animal Care and Use Committee (IACUC) at the University of North Carolina at Chapel Hill.
1. Mouse dissection and perfusion
NOTE: The following dissections were performed on P4 and P14 mice using a syringe. The volume of perfusion fluid will vary depending on the age of the animal.
<ol>
	<li>Perfusion 
	CAUTION: Paraformaldehyde (PFA) is a hazardous chemical. Perform all ...

<ol>
	<li>Dodt, H. U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultramicroscopy:+Three-dimensional+visualization+of+neuronal+networks+in+the+whole+mouse+brain.">Ultramicroscopy: Three-dimensional visualization of neuronal networks in the whole mouse brain.</a> Nature Methods. 4 (4), 331-336 (2007).</li><li>H&#228;gerling, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+multistep+mechanism+for+initial+lymphangiogenesis+in+mouse+embryos+based+on+ultramicroscopy.">A novel multistep mechanism for initial lymphangiogenesis in mouse embryos based on ultramicroscopy.</a> The EMBO Journal. 32 (5), 629-644 (2013).</li><li>Tomer, R., Khairy, K., Keller, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Shedding+light+on+the+system:+studying+embryonic+development+with+light+sheet+microscopy.">Shedding light on the system: studying embryonic development with light sheet microscopy.</a> Current Opinion in Genetics &amp; Development. 21 (5), 558-565 (2011).</li><li>Li, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Micro-optical+sectioning+tomography+to+obtain+a+high-resolution+atlas+of+the+mouse+brain.">Micro-optical sectioning tomography to obtain a high-resolution atlas of the mouse brain.</a> Science. 330 (6009), 1404-1408 (2010).</li><li>Stoner, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Patches+of+disorganization+in+the+neocortex+of+children+with+autism.">Patches of disorganization in the neocortex of children with autism.</a> The New England Journal of Medicine. 370 (13), 1209-1219 (2014).</li><li>Renier, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IDISCO:+A+simple,+rapid+method+to+immunolabel+large+tissue+samples+for+volume+imaging.">IDISCO: A simple, rapid method to immunolabel large tissue samples for volume imaging.</a> Cell. 159 (4), 896-910 (2014).</li><li>Susaki, E. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Whole-brain+imaging+with+single-cell+resolution+using+chemical+cocktails+and+computational+analysis.">Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis.</a> Cell. 157 (3), 726-739 (2014).</li><li>Richardson, D. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tissue+clearing.">Tissue clearing.</a> Nature Reviews. Methods Primers. 1 (1), 84 (2021).</li><li>Richardson, D. S., Lichtman, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clarifying+tissue+clearing.">Clarifying tissue clearing.</a> Cell. 162 (2), 246-257 (2015).</li><li>Renier, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+of+brain+activity+by+automated+volume+analysis+of+immediate+early+genes.">Mapping of brain activity by automated volume analysis of immediate early genes.</a> Cell. 165 (7), 1789-1802 (2016).</li><li>Santi, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Light+sheet+fluorescence+microscopy:+a+review.">Light sheet fluorescence microscopy: a review.</a> The Journal of Histochemistry and Cytochemistry: Official Journal of the Histochemistry Society. 59 (2), 129-138 (2011).</li><li>Girkin, J. M., Carvalho, M. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+light-sheet+microscopy+revolution.">The light-sheet microscopy revolution.</a> Journal of Optics. 20 (5), 053002 (2018).</li><li>Budday, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanical+properties+of+gray+and+white+matter+brain+tissue+by+indentation.">Mechanical properties of gray and white matter brain tissue by indentation.</a> Journal of the Mechanical Behavior of Biomedical Materials. 46, 318-330 (2015).</li><li>Johnson, G. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+morphologic+phenotyping+of+the+mouse+brain+with+magnetic+resonance+histology.">High-throughput morphologic phenotyping of the mouse brain with magnetic resonance histology.</a> NeuroImage. 37 (1), 82-89 (2007).</li><li>Herculano-Houzel, S., Mota, B., Lent, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cellular+scaling+rules+for+rodent+brains.">Cellular scaling rules for rodent brains.</a> Proceedings of the National Academy of Sciences of the United States of America. 103 (32), 12138-12143 (2006).</li><li>Matsumoto, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advanced+CUBIC+tissue+clearing+for+whole-organ+cell+profiling.">Advanced CUBIC tissue clearing for whole-organ cell profiling.</a> Nature Protocols. 14 (12), 3506-3537 (2019).</li><li>F&#252;rth, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+interactive+framework+for+whole-brain+maps+at+cellular+resolution.">An interactive framework for whole-brain maps at cellular resolution.</a> Nature Neuroscience. 21 (1), 139-149 (2018).</li><li>Chandrashekhar, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CloudReg:+automatic+terabyte-scale+cross-modal+brain+volume+registration.">CloudReg: automatic terabyte-scale cross-modal brain volume registration.</a> Nature Methods. 18 (8), 845-846 (2021).</li><li>Krupa, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NuMorph:+Tools+for+cortical+cellular+phenotyping+in+tissue-cleared+whole-brain+images.">NuMorph: Tools for cortical cellular phenotyping in tissue-cleared whole-brain images.</a> Cell Reports. 37 (2), 109802 (2021).</li><li>Petiet, A., Delatour, B., Dhenain, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Models+of+neurodegenerative+disease+-+Alzheimer's+anatomical+and+amyloid+plaque+imaging.">Models of neurodegenerative disease - Alzheimer's anatomical and amyloid plaque imaging.</a> Methods in Molecular Biology. 771, 293-308 (2011).</li><li>Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., Smith, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FSL.">FSL.</a> NeuroImage. 62, 782-790 (2012).</li><li>Jenkinson, M., Bannister, P., Brady, M., Smith, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+optimization+for+the+robust+and+accurate+linear+registration+and+motion+correction+of+brain+images.">Improved optimization for the robust and accurate linear registration and motion correction of brain images.</a> NeuroImage. 17 (2), 825-841 (2002).</li><li>Avants, B. B., Epstein, C. L., Grossman, M., Gee, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Symmetric+diffeomorphic+image+registration+with+cross-correlation:+evaluating+automated+labeling+of+elderly+and+neurodegenerative+brain.">Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.</a> Medical Image Analysis. 12 (1), 26-41 (2008).</li><li>. iDISCO method Available from: <a target="_blank" href="https://idisco.info/">https://idisco.info/</a> (2022)</li><li>Ariel, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=UltraMicroscope+II+-+A+User+Guide.">UltraMicroscope II - A User Guide.</a> University of North Carolina at Chapel Hill. , (2019).</li><li>. Anaconda Distribution - Anaconda documentation Available from: <a target="_blank" href="https://docs.anaconda.com/anaconda/">https://docs.anaconda.com/anaconda/</a> (2022)</li><li>Peng, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+BaSiC+tool+for+background+and+shading+correction+of+optical+microscopy+images.">A BaSiC tool for background and shading correction of optical microscopy images.</a> Nature Communications. 8, 14836 (2017).</li><li>Klein, S., Staring, M., Murphy, K., Viergever, M. A., Pluim, J. P. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=elastix:+a+toolbox+for+intensity-based+medical+image+registration.">elastix: a toolbox for intensity-based medical image registration.</a> IEEE Transactions on Medical Imaging. 29, 196-205 (2010).</li><li>Lowe, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sift-the+scale+invariant+feature+transform.">Sift-the scale invariant feature transform.</a> International Journal. 2 (91-110), 2 (2004).</li><li>Young, D. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Whole-brain+image+analysis+and+anatomical+atlas+3D+generation+using+MagellanMapper.">Whole-brain image analysis and anatomical atlas 3D generation using MagellanMapper.</a> Current Protocols in Neuroscience. 94 (1), 104 (2020).</li><li>Schindelin, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fiji:+an+open-source+platform+for+biological-image+analysis.">Fiji: an open-source platform for biological-image analysis.</a> Nature Methods. 9 (7), 676-682 (2012).</li><li>Vel&#237;&#353;ek, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Under+the+(Light)+sheet+after+the+iDISCO.">Under the (Light) sheet after the iDISCO.</a> Epilepsy Currents / American Epilepsy Society. 16 (6), 405-407 (2016).</li><li>Young, D. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Constructing+and+optimizing+3D+atlases+from+2D+data+with+application+to+the+developing+mouse+brain.">Constructing and optimizing 3D atlases from 2D data with application to the developing mouse brain.</a> eLife. 10, (2021).</li><li>Yun, D. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrafast+immunostaining+of+organ-scale+tissues+for+scalable+proteomic+phenotyping.">Ultrafast immunostaining of organ-scale tissues for scalable proteomic phenotyping.</a> bioRxiv. , (2019).</li><li>Silvestri, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Universal+autofocus+for+quantitative+volumetric+microscopy+of+whole+mouse+brains.">Universal autofocus for quantitative volumetric microscopy of whole mouse brains.</a> Nature Methods. 18 (8), 953-958 (2021).</li><li>Frasconi, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Large-scale+automated+identification+of+mouse+brain+cells+in+confocal+light+sheet+microscopy+images.">Large-scale automated identification of mouse brain cells in confocal light sheet microscopy images.</a> Bioinformatics. 30 (17), 587 (2014).</li><li>Kruskal, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+the+shortest+spanning+subtree+of+a+graph+and+the+traveling+salesman+problem.">On the shortest spanning subtree of a graph and the traveling salesman problem.</a> Proceedings of the American Mathematical Society. American Mathematical Society. 7 (1), 48-50 (1956).</li><li>Wang, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+allen+mouse+brain+common+coordinate+framework:+A+3D+reference+atlas.">The allen mouse brain common coordinate framework: A 3D reference atlas.</a> Cell. 181, 936-953 (2020).</li><li>Borland, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Segmentor:+a+tool+for+manual+refinement+of+3D+microscopy+annotations.">Segmentor: a tool for manual refinement of 3D microscopy annotations.</a> BMC Bioinformatics. 22 (1), 260 (2021).</li><li>Kumar, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dual-view+plane+illumination+microscopy+for+rapid+and+spatially+isotropic+imaging.">Dual-view plane illumination microscopy for rapid and spatially isotropic imaging.</a> Nature Protocols. 9 (11), 2555-2573 (2014).</li></ol>

Tissue clearing methods are useful techniques for measuring 3D cellular organization of the brain. There are a host of tissue clearing methods described in the literature, each with its advantages and limitations6,7,8,9. The options for computational tools to analyze the cell types in the tissue-cleared images are relatively limited. Other available tools have been implemented to sparse cell po...

Whole-brain imaging at single-cell resolution is an important challenge in neuroscience. Cellular-resolution brain images allow for detailed analysis and system-level mapping of brain circuitry and how that circuitry is disrupted by genetic or environmental risk factors for neuropsychiatric disorders, cellular behavior in developing embryos, as well as neural circuits in the adult brain1,2,3. There are multiple histological methods that allow for high-resolution images of the reconstructed 3D brain; however, these techniques require expensive, specialized equipment, may not be compatible with immunolabeling, and the two-dimensional (2D) nature of some methods may lead to tissue damage and shearing during sectioning4,5.
Recent advancements have provided an alternative approach for imaging entire brains that does not require tissue sectioning; they involve using tissue clearing to make brains transparent. Transparency is achieved in most tissue clearing methods by both removing lipids, as they are a major source of light scattering, and matching the refractive index (RI) of the object with the RI of the sample immersion solution during imaging. Light can then pass through the boundary between materials without being scattered6,7,8,9.
Tissue clearing methods, such as iDISCO+, are often combined with rapid 3D imaging using single-photon excitation microscopy, such as LSFM6,7,10. Within transparent tissues labeled with a fluorophore, light-sheet fluorescence microscopy images sections by excitation with a thin plane of light11. The main advantage of LSFM is that a single optical section is illuminated at a time, with all the fluorescence from the molecules within that section being excited, which minimizes photobleaching. Moreover, imaging an entire optical slice enables camera-based detection of that excited slice, increasing speed relative to point scanning12. LSFM nondestructively produces well-registered optical sections that are suitable for 3D reconstruction.
While the iDISCO+ method allows for inexpensive tissue clearing within ~3 weeks, dehydration steps within the protocol may lead to tissue shrinkage and potential alteration of the sample morphology, thus affecting volumetric measurements6,10. Adding a secondary imaging method, such as MRI, to be used prior to the tissue clearing procedure can measure the degree of tissue clearing-induced shrinkage across the sample. During the dehydration steps, differences in mechanical properties between gray and white matter may lead to nonuniform brain matter deformations, resulting in dissimilar tissue clearing-induced volume deformations between wild-type and mutant samples and may confound interpretations of volumetric differences in these samples10,13. MRI is performed by first perfusing the animal with a contrast agent (e.g., gadolinium), followed by incubating the extracted tissue of interest in an immersion solution (e.g., fomblin) before imaging14. MRI is compatible with tissue clearing and performing LSFM on the same sample.
LSFM is often used to create large-scale microscopy images for qualitative visualization of the brain tissue of interest rather than quantitative evaluation of brain structure (Figure 1). Without quantitative evaluation, it is difficult to demonstrate structural differences resulting from genetic or environmental insults. As tissue-clearing and imaging technologies improve, along with decreased costs of storage and computing power, quantifying cell type localizations within the tissue of interest is becoming more accessible, allowing more researchers to include these data in their studies.
With over 100 million cells in the mouse brain15 and whole-brain imaging sessions that can generate terabytes of data, there is increased demand for advanced image analysis tools that allow accurate quantification of features within the images, such as cells. A host of segmentation methods exist for tissue-cleared images that apply thresholding for nuclear staining intensity and filter objects with predefined shapes, sizes, or densities10,16,17,18. However, inaccurate interpretations of results can arise from variations in parameters such as cell size, image contrast, and labeling intensity. This paper describes our established protocol to quantify cell nuclei in the mouse brain. First, we detail steps for tissue collection of the P4 mouse brain, followed by a tissue clearing and immunolabeling protocol optimized from the publicly available iDISCO+ method10. Second, we describe image acquisition using MRI and light-sheet microscopy, including the parameters used for capturing images. Finally, we describe and demonstrate NuMorph19, a set of image analysis tools our group has developed that allows cell-type specific quantification after tissue clearing, immunolabeling with nuclear markers, and light-sheet imaging of annotated regions.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Bruker 9.4T/30 cm MRI Scanner</td>
			<td>Bruker Biospec</td>
			<td></td>
			<td>Horizontal Bore Animal MRI System</td>
		</tr>
		<tr>
			<td>Dibenzyl ether</td>
			<td>Sigma-Aldrich</td>
			<td>108014-1KG</td>
			<td></td>
		</tr>
		<tr>
			<td>Dichloromethane (DCM)</td>
			<td>Sigma-Aldrich</td>
			<td>270997-1L</td>
			<td></td>
		</tr>
		<tr>
			<td>Dimethyl sulfoxide (DMSO)</td>
			<td>Fisher-Scientific</td>
			<td>ICN19605590</td>
			<td></td>
		</tr>
		<tr>
			<td>Donkey serum</td>
			<td>Sigma-Aldrich</td>
			<td>S30-100ML</td>
			<td></td>
		</tr>
		<tr>
			<td>EVO 860 4TB external SSD</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Fomblin Y</td>
			<td>Speciality Fluids Company</td>
			<td>YL-VAC-25-6</td>
			<td>perfluoropolyether lubricant</td>
		</tr>
		<tr>
			<td>gadolinium contrast agent (ProHance)</td>
			<td>Bracco Diagnostics</td>
			<td>A9576</td>
			<td></td>
		</tr>
		<tr>
			<td>gadolinium contrast agent(ProHance)</td>
			<td>Bracco Diagnostics</td>
			<td>0270-1111-03</td>
			<td></td>
		</tr>
		<tr>
			<td>GeForce GTX 1080 Ti 11GB GPU</td>
			<td>EVGA</td>
			<td>08G-P4-6286-KR</td>
			<td></td>
		</tr>
		<tr>
			<td>Glycine</td>
			<td>Sigma-Aldrich</td>
			<td>G7126-500G</td>
			<td></td>
		</tr>
		<tr>
			<td>Heparin sodium salt</td>
			<td>Sigma-Aldrich</td>
			<td>H3393-10KU</td>
			<td>Dissolved in H2O to 10 mg/mL</td>
		</tr>
		<tr>
			<td>Hydrogen peroxide solution, 30%</td>
			<td>Sigma-Aldrich</td>
			<td>H1009-100ML</td>
			<td></td>
		</tr>
		<tr>
			<td>ImSpector Pro</td>
			<td>LaVision BioTec</td>
			<td></td>
			<td>Microscope image acquisition software</td>
		</tr>
		<tr>
			<td>ITK Snap</td>
			<td></td>
			<td></td>
			<td>segmentation software</td>
		</tr>
		<tr>
			<td>Methanol</td>
			<td>Fisher-Scientific</td>
			<td>A412SK-4</td>
			<td></td>
		</tr>
		<tr>
			<td>MVPLAPO 2x/0.5 NA Objective</td>
			<td>Olympus</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Paraformaldehyde, powder, 95% (PFA)</td>
			<td>Sigma-Aldrich</td>
			<td>30525-89-4</td>
			<td>Dissolved in 1x PBS to 4%</td>
		</tr>
		<tr>
			<td>Phosphate Buffered Saline 10x (PBS)</td>
			<td>Corning</td>
			<td>46-013-CM</td>
			<td>Diluted to 1x in H2O</td>
		</tr>
		<tr>
			<td>Sodium Azide</td>
			<td>Sigma-Aldrich</td>
			<td>S2002-100G</td>
			<td>Dissolved in H2O to 10%</td>
		</tr>
		<tr>
			<td>Sodium deoxycholate</td>
			<td>Sigma-Aldrich</td>
			<td>D6750-10G</td>
			<td></td>
		</tr>
		<tr>
			<td>Tergitol type NP-40</td>
			<td>Sigma-Aldrich</td>
			<td>NP40S-100ML</td>
			<td></td>
		</tr>
		<tr>
			<td>TritonX-100</td>
			<td>Sigma-Aldrich</td>
			<td>T8787-50ML</td>
			<td></td>
		</tr>
		<tr>
			<td>Tween-20</td>
			<td>Fisher-Scientific</td>
			<td>BP337 500</td>
			<td></td>
		</tr>
		<tr>
			<td>Ultramicroscope II Light Sheet Microscope</td>
			<td>LaVision BioTec</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Xeon Processor E5-2690 v4</td>
			<td>Intel</td>
			<td>E5-2690</td>
			<td></td>
		</tr>
		<tr>
			<td>Zyla sCMOS Camera</td>
			<td>Andor</td>
			<td></td>
			<td>Complementary metal oxide semiconductor camera</td>
		</tr>
		<tr>
			<td>Antibody</td>
			<td></td>
			<td></td>
			<td>Working concentration</td>
		</tr>
		<tr>
			<td>Alexa Fluor Goat 790 Anti-Rabbit</td>
			<td>Thermofisher Scientific</td>
			<td>A11369</td>
			<td>(1:50)</td>
		</tr>
		<tr>
			<td>Alexa Fluor Goat 568 Anti-Rat</td>
			<td>Thermofisher Scientific</td>
			<td>A11077</td>
			<td>(1:200)</td>
		</tr>
		<tr>
			<td>Rat anti-Ctip2</td>
			<td>Abcam</td>
			<td>ab18465</td>
			<td>(1:400)</td>
		</tr>
		<tr>
			<td>Rabbit anti-Brn2</td>
			<td>Cell Signaling Technology</td>
			<td>12137</td>
			<td>(1:100)</td>
		</tr>
		<tr>
			<td>To-Pro 3 (TP3)</td>
			<td>Thermofisher Scientific</td>
			<td>T3605</td>
			<td>(1:400)</td>
		</tr>
	</tbody>
</table>

whole-brain single-cell imaging and analysis of intact neonatal mouse brains using mri, tissue clearing, and light-sheet microscopy

Tissue clearing followed by light-sheet microscopy (LSFM) enables cellular-resolution imaging of intact brain structure, allowing quantitative analysis of structural changes caused by genetic or environmental perturbations. Whole-brain imaging results in more accurate quantification of cells and the study of region-specific differences that may be missed with commonly used microscopy of physically sectioned tissue. Using light-sheet microscopy to image cleared brains greatly increases acquisition speed as compared to confocal microscopy. Although these images produce very large amounts of brain structural data, most computational tools that perform feature quantification in images of cleared tissue are limited to counting sparse cell populations, rather than all nuclei.
Here, we demonstrate NuMorph (Nuclear-Based Morphometry), a group of analysis tools, to quantify all nuclei and nuclear markers within annotated regions of a postnatal day 4 (P4) mouse brain after clearing and imaging on a light-sheet microscope. We describe magnetic resonance imaging (MRI) to measure brain volume prior to shrinkage caused by tissue clearing dehydration steps, tissue clearing using the iDISCO+ method, including immunolabeling, followed by light-sheet microscopy using a commercially available platform to image mouse brains at cellular resolution. We then demonstrate this image analysis pipeline using NuMorph, which is used to correct intensity differences, stitch image tiles, align multiple channels, count nuclei, and annotate brain regions through registration to publicly available atlases.
We designed this approach using publicly available protocols and software, allowing any researcher with the necessary microscope and computational resources to perform these techniques. These tissue clearing, imaging, and computational tools allow measurement and quantification of the three-dimensional (3D) organization of cell-types in the cortex and should be widely applicable to any wild-type/knockout mouse study design.

As the iDISCO+ protocol introduces significant tissue shrinkage, which is easily noticeable by eye (Figure 2B), we added an MRI step to this pipeline prior to tissue clearing to quantify the shrinkage induced by tissue clearing. The workflow starts with removal of the non-brain tissue from the MR image (Figure 2C). Next, a rigid transformation (3 translation and 3 rotation angles) is applied to align the MR image to the light-sheet image (Fi...

Watch this Scientific Journal Video about Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy at JoVE.com

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

This protocol describes methods for conducting magnetic resonance imaging, clearing, and immunolabeling of intact mouse brains using iDISCO+, followed by a detailed description of imaging using light-sheet microscopy, and downstream analyses using NuMorph.

Tissue clearing followed by light-sheet microscopy (LSFM) enables cellular-resolution imaging of intact brain structure, allowing quantitative analysis of ...

With around 100 million cells in the mouse brain and sizes of whole-brain cellular resolution images approaching the terabyte scale, advanced image analysis tools are needed to accurately quantify cells. Our computational pipeline can preprocess images and quantify nuclei within the mouse cortex while maintaining a reasonable compromise between cell-detection accuracy, imaging time, and computational resources. Demonstrating the procedure will be Felix Kyere and Ian Curtin, graduate students from my lab.To begin, mount the sample in the correct sample size holder such that the sample is oriented with the z-dimension no more than 5.2 millimeters in depth due to the rated working distance of the UltraMicroscope II microscope. Then insert the holder into the sample cradle such that the screw of the holder is at 45 degrees angle to the supports of the cradle. Next, place the cradle into a position such that the sample is oriented perpendicular to the light path.Afterward, set the zoom body on the microscope to 4x magnification or higher, yielding 0.75 micrometers per pixel. In the Inspector Pro software, select a single light sheet with a numerical aperture value of approximately 0.08. To ensure axial resolution is maintained along the width of the image, select Horizontal Dynamic Focusing and apply the recommended number of steps depending on the laser wavelength.Then adjust the fine focus for each channel with respect to the registration channel and laser power per channel with respect to the channel properties. Next, adjust the light-sheet width to about 50%to ensure sheet power is distributed optimally in the y-dimension for the sample size. Afterward, set the number of tiles with respect to the size of the sample with a recommended overlap of 15%between tiles, and capture images for each channel sequentially for each stack at a given tile position.First, download and install Conda environment manager for Linux and NuMorph image-processing tools. On the command line, run matlab and NM_setup. m from NuMorph to download and install image analysis software packages needed for analysis.Then specify sample names, input and output directories, channel information, and light-sheet imaging parameters by editing the file NM_samples.m. For intensity adjustment, in NMp_template, set intense the adjustment to true and use_processed_images as false when working with a new set of images. Next, set save_images and save_samples as true.Next, set tile shading to basic to apply shading correction using the basic algorithm, or manual to apply tile shading correction using measurements from the UltraMicroscope II at specific light-sheet widths. For image channel alignment, in NMp_template, set channel_alignment to true and channel alignment_method to either translation or elastics. Next, set sift_refinement as true and overlap value of 0.15 to match tile overlaps during imaging.To run preprocessing steps in MATLAB, specify the sample name and set the configuration to NM_config process sample. Then run any of the preprocessing steps by specifying NM_process config stitch while specifying the stage using intensity, align, or stitch, and check the output directory for output files for each of the stages. Start with a 3D Atlas image and an associated annotation image that assigns each voxel to a particular structure.Align both the Atlas image and annotation file to ensure they match correctly in the right orientation. After alignment, process the files in NuMorph to specify the inputs as described in the manuscript by executing the command. In NMa_template, set resample_images to true and resample_resolution to match the Atlas.Then specify the channel number to be resampled using resample_channels. Afterward, set register_images to true, specify the atlas_file to match the file in the Atlas directory, and set registration_parameters as default. Then set save_registered_images to true.For nuclei detection, cell counting, and classification, set both count_nuclei and classify_cells as true. Then set the count_method to 3dunet and min_intensity to define a minimum intensity threshold for detected objects. Next, set classify_method to either threshold which is based on a non-supervised fluorescence intensity at centroid positions, or SVM which models a supervised linear support vector machine classifier.To perform analysis steps in MATLAB, specify the sample name and set the configuration to NM_config analyze sample. Next, run any of the analysis steps by specifying NM_analyze config stage while specifying the stage using resample, register, count, or classify and check the output directory for output files for each of the stages. In NMe_template, set update to true and compare_structures_by to either index.Then set the template file and structure table which specifies all possible structure indexes and structures to evaluate while specifying cell counting and cell type classification. Tissue clearing using iDISCO+protocol and neuronal layer-specific nuclei markers resulted in clearly-defined cell groups of upper and lower-layer neurons in the isocortex. Cell counting using NuMorph was dependent on successful preprocessing steps involving intensity adjustment, channel alignment, and stitching.However, errors in preprocessing steps could result in improper stitching, leading to improper alignment and stitching, and thus, result in images with in-focus and out-of-focus pattern. In order to count nuclei from specific brain regions, the stitched images will be annotated using Atlas, allowing annotations to be overlaid on brain regions. The centroids of nuclei were detected with a trained 3D U-Net model in NuMorph with around 12 million total nuclei that were TO-PRO-3-positive in the isocortex with about 2.6 million brain-two-positive and 1.6 million CTIP2-positive nuclei.About 3.7 and 2.9 million TO-PRO-3-positive total nuclei in the basal ganglia and hippocampal allocortex were detected, respectively. However, the brain-two-positive cells detected in these two brain regions were negligible, and only about 1.5 in less than one million CTIP2-positive cells were detected each in the basal ganglia and hippocampal allocortex. Perform visual quality checks during acquisition to achieve good segmentation.And properly set up the Conda environment to ensure that no errors are encountered in a downstream analysis. In addition to cell counting, this pipeline allow for integration with other segmentation tools that measure cell size and shape which can then be compared across genotype groups. With our pipeline, we can identify how brain anatomy changes at cellular resolution, leading to identification of cell types and brain regions important for disease risk.

whole-brain-single-cell-imaging-analysis-intact-neonatal-mouse-brains

Research

JoVE Journal

Neuroscience

Multiple-mouse Neuroanatomical Magnetic Resonance Imaging

The field of mouse phenotyping with magnetic resonance imaging (MRI) is rapidly growing, motivated by the need for improved tools for characterizing and evaluating mouse models of human disease. MRI is an excellent modality for investigating genetically altered animals. It is capable of whole brain coverage, can be used in vivo, and provides multiple contrast mechanisms for investigating different aspects of neuranatomy and physiology. The advent of high-field scanners along with the ability to scan multiple mice simultaneously allows for rapid phenotyping of novel mutations.
Effective mouse MRI studies require attention to many aspects of experiment design. In this article, we will describe general methods to acquire quality images for mouse phenotyping using a system that images mice concurrently in shielded transmit/receive radio frequency (RF) coils in a common magnet (Bock et al., 2003). We focus particularly on anatomical phenotyping, an important and accessible application that has shown a high potential for impact in many mouse models at our imaging centre. Before we can provide the detailed steps to acquire such images, there are important practical considerations for both in vivo brain imaging (Dazai et al., 2004) and ex vivo brain imaging (Spring et al., 2007) that should be noted. These are discussed below.

Magnetic resonance imaging (MRI) has become an increasingly popular tool for examining the phenotype of genetically altered mice. This article illustrates the methods necessary to achieve high-throughput phenotyping of genetically altered mice using multiple-mouse MRI.

The field of mouse phenotyping with magnetic resonance imaging (MRI) is rapidly growing, motivated by the need for improved tools for characterizing and ...

Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains technically challenging. One approach, Activation-Induced Manganese-enhanced MRI (AIM MRI), has been used successfully to map neuronal activity in rodents 1-5. In AIM MRI, Mn2+ acts a calcium analog and accumulates in depolarized neurons 6,7. Because Mn2+ shortens the T1 tissue property, regions of elevated neuronal activity will enhance in MRI. Furthermore, Mn2+ clears slowly from the activated regions; therefore, stimulation can be performed outside the magnet prior to imaging, enabling greater experimental flexibility. However, because Mn2+ does not readily cross the blood-brain barrier (BBB), the need to open the BBB has limited the use of AIM MRI, especially in mice.
One tool for opening the BBB is ultrasound. Though potentially damaging, if ultrasound is administered in combination with gas-filled microbubbles (i.e., ultrasound contrast agents), the acoustic pressure required for BBB opening is considerably lower. This combination of ultrasound and microbubbles can be used to reliably open the BBB without causing tissue damage 8-11.
Here, a method is presented for performing AIM MRI by using microbubbles and ultrasound to open the BBB. After an intravenous injection of perflutren microbubbles, an unfocused pulsed ultrasound beam is applied to the shaved mouse head for 3 minutes. For simplicity, we refer to this technique of BBB Opening with Microbubbles and UltraSound as BOMUS 12. Using BOMUS to open the BBB throughout both cerebral hemispheres, manganese is administered to the whole mouse brain. After experimental stimulation of the lightly sedated mice, AIM MRI is used to map the neuronal response.
To demonstrate this approach, herein BOMUS and AIM MRI are used to map unilateral mechanical stimulation of the vibrissae in lightly sedated mice 13. Because BOMUS can open the BBB throughout both hemispheres, the unstimulated side of the brain is used to control for nonspecific background stimulation. The resultant 3D activation map agrees well with published representations of the vibrissae regions of the barrel field cortex 14. The ultrasonic opening of the BBB is fast, noninvasive, and reversible; and thus this approach is suitable for high-throughput and/or longitudinal studies in awake mice.

A technique is described for broadly opening the blood-brain barrier in the mouse using microbubbles and ultrasound. Using this technique, manganese can be administered to the mouse brain. Because manganese is an MRI contrast agent that accumulates in depolarized neurons, this approach enables imaging of neuronal activity.

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains ...

Micron-scale Resolution Optical Tomography of Entire Mouse Brains with Confocal Light Sheet Microscopy

Understanding the architecture of mammalian brain at single-cell resolution is one of the key issues of neuroscience. However, mapping neuronal soma and projections throughout the whole brain is still challenging for imaging and data management technologies. Indeed, macroscopic volumes need to be reconstructed with high resolution and contrast in a reasonable time, producing datasets in the TeraByte range. We recently demonstrated an optical method (confocal light sheet microscopy, CLSM) capable of obtaining micron-scale reconstruction of entire mouse brains labeled with enhanced green fluorescent protein (EGFP). Combining light sheet illumination and confocal detection, CLSM allows deep imaging inside macroscopic cleared specimens with high contrast and speed. Here we describe the complete experimental pipeline to obtain comprehensive and human-readable images of entire mouse brains labeled with fluorescent proteins. The clearing and the mounting procedures are described, together with the steps to perform an optical tomography on its whole volume by acquiring many parallel adjacent stacks. We showed the usage of open-source custom-made software tools enabling stitching of the multiple stacks and multi-resolution data navigation. Finally, we illustrated some example of brain maps: the cerebellum from an L7-GFP transgenic mouse, in which all Purkinje cells are selectively labeled, and the whole brain from a thy1-GFP-M mouse, characterized by a random sparse neuronal labeling.

In this article we describe the full experimental procedure to reconstruct, with high resolution, the fine brain anatomy of fluorescently labeled mouse brains. The described protocol includes sample preparation and clearing, specimen mounting for imaging, data post-processing and multi-scale visualization.

Understanding the architecture of mammalian  brain at single-cell resolution is one of the key issues of neuroscience.  However, mapping neuronal soma and ...

Stereotaxic Surgery for Genetic Manipulation in Striatal Cells of Neonatal Mouse Brains

Many genes are expressed in embryonic brains, and some of them are continuously expressed in the brain after birth. For such persistently expressed genes, they may function to regulate the developmental process and/or physiological function in neonatal brains. To investigate neurobiological functions of specific genes in the brain, it is essential to inactivate genes in the brain. Here, we describe a simple stereotaxic method to inactivate gene expression in the striatum of transgenic mice at neonatal time windows. AAV-eGFP-Cre viruses were microinjected into the striatum of Ai14 reporter gene mice at postnatal day (P) 2 by stereotaxic brain surgery. The tdTomato reporter gene expression was detected in P14 striatum, suggesting a successful Cre-loxP mediated DNA recombination in AAV-transduced striatal cells. We further validated this technique by microinjecting AAV-eGFP-Cre viruses into P2Foxp2fl/fl mice. Double labeling of GFP and Foxp2 showed that GFP-positive cells lacked Foxp2 immunoreactivity in P9 striatum, suggesting the loss of Foxp2 protein in AAV-eGFP-Cre transduced striatal cells. Taken together, these results demonstrate an effective genetic deletion by stereotaxically microinjected AAV-eGFP-Cre viruses in specific neuronal populations in the neonatal brains of floxed transgenic mice. In conclusion, our stereotaxic technique provides an easy and simple platform for genetic manipulation in neonatal mouse brains. The technique can not only be used to delete genes in specific regions of neonatal brains, but it also can be used to inject pharmacological drugs, neuronal tracers, genetically modified optogenetics and chemogenetics proteins, neuronal activity indicators and other reagents into the striatum of neonatal mouse brains.

We describe a protocol of stereotaxic surgery with a homemade head-fixed device for microinjecting reagents into the striatum of neonatal mouse brains. This technique allows genetic manipulation in neuronal cells of specific regions of neonatal mouse brains.

Many genes are expressed in embryonic brains, and some of them are continuously expressed in the brain after birth. For such persistently expressed genes, ...

Visualization of Motor Axon Navigation and Quantification of Axon Arborization In Mouse Embryos Using Light Sheet Fluorescence Microscopy

Spinal motor neurons (MNs) extend their axons to communicate with their innervating targets, thereby controlling movement and complex tasks in vertebrates. Thus, it is critical to uncover the molecular mechanisms of how motor axons navigate to, arborize, and innervate their peripheral muscle targets during development and degeneration. Although transgenic Hb9::GFP mouse lines have long served to visualize motor axon trajectories during embryonic development, detailed descriptions of the full spectrum of axon terminal arborization remain incomplete due to the pattern complexity and limitations of current optical microscopy. Here, we describe an improved protocol that combines light sheet fluorescence microscopy (LSFM) and robust image analysis to qualitatively and quantitatively visualize developing motor axons. This system can be easily adopted to cross genetic mutants or MN disease models with Hb9::GFP lines, revealing novel molecular mechanisms that lead to defects in motor axon navigation and arborization.

Here, we describe a protocol for visualizing motor neuron projection and axon arborization in transgenic Hb9::GFP mouse embryos. After immunostaining for motor neurons, we used light sheet fluorescence microscopy to image embryos for subsequent quantitative analysis. This protocol is applicable to other neuron navigation processes in the central nervous system.

Spinal motor neurons (MNs) extend their axons to communicate with their innervating targets, thereby controlling movement and complex tasks in ...

Imaging and Quantification of Intact Neuronal Dendrites via CLARITY Tissue Clearing

Brain activity, the electrochemical signals passed between neurons, is determined by the connectivity patterns of neuronal networks, and from the morphology of processes and substructures within these neurons. As such, much of what is known about brain function has arisen alongside developments in imaging technologies that allow further insight into how neurons are organized and connected in the brain. Improvements in tissue clearing have allowed for high-resolution imaging of thick brain slices, facilitating morphological reconstruction and analyses of neuronal substructures, such as dendritic arbors and spines. In tandem, advances in image processing software provide methods of quickly analyzing large imaging datasets. This work presents a relatively rapid method of processing, visualizing, and analyzing thick slices of labeled neural tissue at high-resolution using CLARITY tissue clearing, confocal microscopy, and image analysis. This protocol will facilitate efforts toward understanding the connectivity patterns and neuronal morphologies that characterize healthy brains, and the changes in these characteristics that arise in diseased brain states.

Neuronal dendritic morphology often underlies function. Indeed, many disease processes that affect the development of neurons manifest with a morphological phenotype. This protocol describes a simple and powerful method for analyzing intact dendritic arbors and their associated spines.

Brain activity, the electrochemical signals passed between neurons, is determined by the connectivity patterns of neuronal networks, and from the ...

Deep-Tissue Three-Photon Fluorescence Microscopy in Intact Mouse and Zebrafish Brain

Multiphoton microscopy techniques, such as two-photon microscopy (2PM) and three-photon microscopy (3PM), are powerful tools for deep-tissue in vivo imaging with subcellular resolution. 3PM has two major advantages for deep-tissue imaging over 2PM that has been widely used in biology laboratories: (i) longer attenuation length in scattering tissues by employing ~1,300 nm or ~1,700 nm excitation laser; (ii) less background fluorescence generation due to higher-order nonlinear excitation. As a result, 3PM allows high-contrast structural and functional imaging deep within scattering tissues such as intact mouse brain from the cortical layers to the hippocampus and the entire forebrain of adult zebrafish.
Today, laser sources suitable for 3PM are commercially available, enabling the conversion of an existing two-photon (2P) imaging system to a three-photon (3P) system. Additionally, multiple commercial 3P microscopes are available, which makes this technique readily available to biology research laboratories. This paper shows the optimization of a typical 3PM setup, particularly targeting biology groups that already have a 2P setup, and demonstrates intravital 3D imaging in intact mouse and adult zebrafish brains. This protocol covers the full experimental procedure of 3P imaging, including microscope alignment, prechirping of ~1,300 and ~1,700 nm laser pulses, animal preparation, and intravital 3P fluorescence imaging deep in adult zebrafish and mouse brains.

Three-photon microscopy enables high-contrast fluorescence imaging deep in living biological tissues, such as mouse and zebrafish brains, with high spatiotemporal resolution.

Multiphoton microscopy techniques, such as two-photon microscopy (2PM) and three-photon microscopy (3PM), are powerful tools for deep-tissue in vivo ...

Imaging the Aging Cochlea with Light-Sheet Fluorescence Microscopy

Deafness is the most common sensory impairment, affecting approximately 5% or 430 million people worldwide as per the World Health Organization1. Aging or presbycusis is a primary cause of sensorineural hearing loss and is characterized by damage to hair cells, spiral ganglion neurons (SGNs), and the stria vascularis. These structures reside within the cochlea, which has a complex, spiral-shaped anatomy of membranous tissues suspended in fluid and surrounded by bone. These properties make it technically difficult to investigate and quantify histopathological changes. To address this need, we developed a light-sheet microscope (TSLIM) that can image and digitize the whole cochlea to facilitate the study of structure-function relationships in the inner ear. Well-aligned serial sections of the whole cochlea result in a stack of images for three-dimensional (3D) volume rendering and segmentation of individual structures for 3D visualization and quantitative analysis (i.e., length, width, surface, volume, and number). Cochleae require minimal processing steps (fixation, decalcification, dehydration, staining, and optical clearing), all of which are compatible with subsequent high-resolution imaging by scanning and transmission electron microscopy. Since all the tissues are present in the stacks, each structure can be assessed individually or relative to other structures. In addition, since imaging uses fluorescent probes, immunohistochemistry and ligand binding can be used to identify specific structures and their 3D volume or distribution within the cochlea. Here we used TSLIM to examine cochleae from aged mice to quantify the loss of hair cells and spiral ganglion neurons. In addition, advanced analyses (e.g., cluster analysis) were used to visualize local reductions of spiral ganglion neurons in Rosenthal&#39;s canal along its 3D volume. These approaches demonstrate TSLIM microscopy&#39;s ability to quantify structure-function relationships within and between cochleae.

A light-sheet microscope was developed to image and digitize whole cochlea.

Deafness is the most common sensory impairment, affecting approximately 5% or 430 million people worldwide as per the World Health Organization1. Aging or ...

&lt;em&gt;In Vivo&lt;/em&gt; Whole-Brain Imaging of Zebrafish Larvae

In Vivo Whole-Brain Imaging of Zebrafish Larvae Using Three-Dimensional Fluorescence Microscopy

As a vertebrate model animal, larval zebrafish are widely used in neuroscience and provide a unique opportunity to monitor whole-brain activity at the cellular resolution. Here, we provide an optimized protocol for performing whole-brain imaging of larval zebrafish using three-dimensional fluorescence microscopy, including sample preparation and immobilization, sample embedding, image acquisition, and visualization after imaging. The current protocol enables in vivo imaging of the structure and neuronal activity of a larval zebrafish brain at a cellular resolution for over 1 h using confocal microscopy and custom-designed fluorescence microscopy. The critical steps in the protocol are also discussed, including sample mounting and positioning, preventing bubble formation and dust in the agarose gel, and avoiding motion in images caused by incomplete solidification of the agarose gel and paralyzation of the fish. The protocol has been validated and confirmed in multiple settings. This protocol can be easily adapted for imaging other organs of a larval zebrafish.

Author Spotlight: In Vivo Whole-Brain Imaging of Zebrafish Larvae Using Three-Dimensional Fluorescence Microscopy  

Presented here is a protocol for in vivo whole-brain imaging of larval zebrafish using three-dimensional fluorescence microscopy. The experimental procedure includes sample preparation, image acquisition, and visualization.

As a vertebrate model animal, larval zebrafish are widely used in neuroscience and provide a unique opportunity to monitor whole-brain activity at the ...

Mouse Neonatal Brain Micro-CT Scanning and Image Processing

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains

Neuroimages are a valuable tool for studying brain morphology in experiments using animal models. Magnetic resonance imaging (MRI) has become the standard method for soft tissues, although its low spatial resolution poses some limits for small animals. Here, we describe a protocol for obtaining high-resolution three-dimensional (3D) information on mouse neonate brains and skulls using micro-computed tomography (micro-CT). The protocol includes those steps needed to dissect the samples, stain and scan the brain, and obtain morphometric measurements of the whole organ and regions of interest (ROIs). Image analysis includes the segmentation of structures and the digitization of point coordinates. In sum, this work shows that the combination of micro-CT and Lugol's solution as a contrast agent is a suitable alternative for imaging the perinatal brains of small animals. This imaging workflow has applications in developmental biology, biomedicine, and other sciences interested in assessing the effect of diverse genetic and environmental factors on brain development.

Author Spotlight: High-Resolution Imaging of Mouse Neonate Brains &amp;#8211; A Micro-CT Protocol with Lugol&amp;#39;s Solution Contrast Agent

This study describes the steps for obtaining high-resolution images of neonatal mouse brains by combining micro-computed tomography (micro-CT) and a contrast agent in ex vivo samples. We describe basic morphometric analyses to quantify brain size and shape in these images.

Neuroimages are a valuable tool for studying brain morphology in experiments using animal models. Magnetic resonance imaging (MRI) has become the standard ...