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>
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The authors have no conflicts of interest to disclose.

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...

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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

3.3K Görüntüleme.  University of North Carolina, Chapel Hill. Fare beynindeki yaklaşık 100 milyon hücre ve terabayt ölçeğine yaklaşan tüm beyin hücresel çözünürlüklü görüntülerin boyutlarıyla, hücreleri doğru bir şekilde ölçmek için gelişmiş görüntü analiz araçlarına ihtiyaç vardır. Hesaplamalı işlem hattımız, hücre algılama doğruluğu, görüntüleme süresi ve hesaplama kaynakları arasında makul bir uzlaşmayı korurken görüntüleri önceden işleyebilir ve fare korteksi içindeki çekirdekleri ölçebilir....