Name: 3d scanning technology bridging microcircuits and macroscale brain images in 3d novel embedding overlapping protocol
Uploaded: 2019-05-12T13:00:00
Description: Watch this Scientific Journal Video about 3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol at JoVE.com

M.S. is grateful for the support from all of the staff in the medical information engineering course in Graduate School of Medicine and Faculty of Medicine, and wants to thank Prof. Tetsuya Takakuwa, Prof. Nobukatsu Sawamoto, and Doris Zakian for their helpful comments. This study was supported by a Grant-in-Aid for Challenging Exploratory Research and by the Leading Initiative for Excellent Young Researchers (LEADER) program to M.S. from MEXT (The Ministry of Education, Culture, Sports, Science, and Technology). The MRI experiments in this work were performed in the Division for Small Animal MRI, Medical Research Support Center, Graduate School of Medicine, Kyoto University, Japan.

All experimental procedures described here have been approved by the Kyoto University Animal Care Committee.
1. Animals (Day 1)
<ol>
	<li>Prepare female C57BL/6J mice (n = 6, aged 3&#8722;5 weeks). 
	NOTE: This protocol is applicable to all rodent species.</li>
</ol>
2. MRI Settings (Day 1)
<ol>
	<li>Put the mouse in an acrylic anesthesia box placed on a heater mat adjusted to 37 &#176;C using a heater mat controller.</li>
	<li>Anesthe...

<ol>
	<li>Vogelsberger, 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=Properties+of+galaxies+reproduced+by+a+hydrodynamic+simulation.">Properties of galaxies reproduced by a hydrodynamic simulation.</a> Nature. 509 (7499), 177-182 (2014).</li><li>Zhang, B., 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=Integrated+systems+approach+identifies+genetic+nodes+and+networks+in+late-onset+Alzheimer&#8217;s+disease.">Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer&#8217;s disease.</a> Cell. 153 (3), 707-720 (2013).</li><li>Sporns, O., Chialvo, D. R., Kaiser, M., Hilgetag, C. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Organization,+development+and+function+of+complex+brain+networks.">Organization, development and function of complex brain networks.</a> Trends in Cognitive Sciences. 8 (9), 418-425 (2004).</li><li>Shimono, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Non-uniformity+of+cell+density+and+networks+in+the+monkey+brain.">Non-uniformity of cell density and networks in the monkey brain.</a> Scientific Reports. 3, 2541 (2013).</li><li>DeFelipe, J., Jones, E. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Cajal on the Cerebral Cortex: An Annotated Translation of the Complete Wrings. , (1988).</li><li>Kandel, E. R., Schwartz, J. H., Jessell, T. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Principles of Neural Science. , (2013).</li><li>Pine, J., Taketani, M., Baudry, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+history+of+MEA+development.">A history of MEA development.</a> Advances in Network Electrophysiology. , 3-23 (2006).</li><li>Grienberger, C., Konnerth, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+calcium+in+neurons.">Imaging calcium in neurons.</a> Neuron. 73 (5), 862-885 (2012).</li><li>Shimono, M., Beggs, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+clusters,+hubs,+and+communities+in+the+cortical+microconnectome.">Functional clusters, hubs, and communities in the cortical microconnectome.</a> Cerebral Cortex. 25 (10), 3743-3757 (2014).</li><li>Amunts, 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=BigBrain:+An+Ultrahigh-Resolution+3D+Human+Brain+Model.">BigBrain: An Ultrahigh-Resolution 3D Human Brain Model.</a> Science. 340, 1472-1475 (2013).</li><li>Ali, 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=Rigid+and+non-rigid+registration+of+polarized+light+imaging+data+for+3D+reconstruction+of+the+temporal+lobe+of+the+human+brain+at+micrometer+resolution.">Rigid and non-rigid registration of polarized light imaging data for 3D reconstruction of the temporal lobe of the human brain at micrometer resolution.</a> NeuroImage. 181, 235-251 (2018).</li><li>Kozai, T. D. Y., 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=Ultrasmall+implantable+composite+microelectrodes+with+bioactive+surfaces+for+chronic+neural+interfaces.">Ultrasmall implantable composite microelectrodes with bioactive surfaces for chronic neural interfaces.</a> Nature Materials. 11, 1065-1073 (2012).</li><li>Kampasi, 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=Dual+color+optogenetic+control+of+neural+populations+using+low-noise,+multishank+optoelectrodes.">Dual color optogenetic control of neural populations using low-noise, multishank optoelectrodes.</a> Microsystems &amp; Nanoengineering. 4 (1), 10 (2018).</li><li>Hellwig, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+quantitative+analysis+of+the+local+connectivity+between+pyramidal+neurons+in+layers+2/3+of+the+rat+visual+cortex.">A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex.</a> Biological Cybernetics. 82 (2), 111-121 (2000).</li><li>Bezgin, G., Reid, A. T., Schubert, D., K&#246;tter, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Matching+spatial+with+ontological+brain+regions+using+Java+tools+for+visualization,+database+access,+and+integrated+data+analysis.">Matching spatial with ontological brain regions using Java tools for visualization, database access, and integrated data analysis.</a> Neuroinformatics. 7 (1), 7-22 (2009).</li><li>Holmgren, C., Harkany, T., Svennenfors, B., Zilberter, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pyramidal+cell+communication+within+local+networks+in+layer+2/3+of+rat+neocortex.">Pyramidal cell communication within local networks in layer 2/3 of rat neocortex.</a> The Journal of Physiology. 551 (1), 139-153 (2003).</li><li>Boucsein, C., Nawrot, M., Schnepel, P., Aertsen, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Beyond+the+cortical+column:+abundance+and+physiology+of+horizontal+connections+imply+a+strong+role+for+inputs+from+the+surround.">Beyond the cortical column: abundance and physiology of horizontal connections imply a strong role for inputs from the surround.</a> Frontiers in Neuroscience. 5 (32), 1-13 (2011).</li><li>Edelsbrunner, H., Aronov, J. P. B., Basu, S., Sharir, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Surface+reconstruction+by+wrapping+finite+point+sets+in+space.">Surface reconstruction by wrapping finite point sets in space.</a> Discrete and Computational Geometry - The Goodman-Pollack Festschrift. , 379-404 (2003).</li><li>Roland, P. E., 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=Human+brain+atlas:+For+high&#8208;resolution+functional+and+anatomical+mapping.">Human brain atlas: For high&#8208;resolution functional and anatomical mapping.</a> Human Brain Mapping. 1 (3), 173-184 (1994).</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=Waxholm+space:+an+image-based+reference+for+coordinating+mouse+brain+research.">Waxholm space: an image-based reference for coordinating mouse brain research.</a> NeuroImage. 53 (2), 365-372 (2010).</li><li>Evans, A. C., Janke, A. L., Collins, D. L., Baillet, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain+templates+and+atlases.">Brain templates and atlases.</a> NeuroImage. 62 (2), 911-922 (2012).</li><li>Okabe, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain/MINDS&#8211;a+new+program+for+comprehensive+analyses+of+the+brain.">Brain/MINDS&#8211;a new program for comprehensive analyses of the brain.</a> Microscopy. 64 (1), 3-4 (2015).</li><li>Shimono, M., Hatano, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Efficient+communication+dynamics+on+macro-connectome,+and+the+propagation+speed.">Efficient communication dynamics on macro-connectome, and the propagation speed.</a> Scientific Reports. 8 (1), 2510 (2018).</li></ol>

We developed a new protocol called the 3D-NEO protocol to bridge macroscopic and microscopic spatial scales by overlapping two brain surfaces more accurately than before. Originally, there were two challenges in creating this protocol which made possible the accurate overlapping of two brain surface images and recording healthy neuronal activities from living organisms. First, it was necessary to effectively wipe cutting solution surrounding the extracted brain after extracting it from the skull without injuring the brai...

Nonuniform multiscale architectures at various physical and biological organizations are commonly found1,2. The brain is also a very nonuniform and multiscale network organization3,4. Various cognitive functions are coded in such network organizations, holding temporal changes of electrical spike patterns of neuronal populations in submillisecond temporal resolutions. Historically, the complex networks among neurons were structurally observed in detail using the staining techniques by Santiago Ram&#243;n y Cajal from over 150 years ago5. To observe group behaviors of active neurons, researchers have developed various recording technologies6,7,8, and the recent significant developments of such technologies have enabled us to record electrical activities from huge numbers of neurons simultaneously. Furthermore, from such functional activities, scientists have succeeded to reconstruct networks of causal interactions among huge numbers of neurons and have declared the topological architecture of their complex interactions &#8216;microconnectome&#8217;9. Macroscopic observations of the brain also allow for regarding a whole brain as a network organization because many brain regions are connected by multiple fiber-bundles. The embedding of microconnectomes into the global brain map still has clear limitations within current technological advances, which is why this embedding protocol is so important. However, there are many challenges to the development of the embedding protocol. For example, in order to observe activities of living local neuronal circuits in purely isolated brain regions, brain slices need to be produced for in vitro recordings. Additionally, recordings from brain slices for in vitro recordings are still an important choice for at least two reasons. First, it is still not easy to observe activities of many living individual neurons simultaneously from brain regions deeper than ~1.5 mm and in high temporal resolution (&#60;1 ms). Second, when we hope to know the internal architecture of a local neuronal circuit, we need to stop all inputs coming from external brain regions to eliminate confounding factors. In order to identify the directions and positions of produced brain slices, it will be further necessary to integrate the spatial positions of these produced brain slices using coordinates. There are, however, a few systematic and reliable ways to make brain slices in an organized way10,11. Here, a new coregistration protocol is introduced, using 3D scanning technology for neuroscientific research in order to provide an integrative protocol. This protocol acts to coordinate micro- and macroscales and embed multielectrode array (MEA) microdata12,13 and staining data onto a macroscopic MRI space through 3D scan surfaces of extracted brains, as well as of noninvasively recorded brains. Surprisingly, this showed a distance error of only ~50 &#956;m as the mode value of the histogram. As a result, the mode values of minimum distances between two surfaces between the MRI surface and the scanned 3D surface were nearly 50 &#181;m for all six mice, which is a suitable number when checking for commonality among individuals. The typical slice width had a recorded spike activity of around 300 &#181;m.

<table>
	<tbody>
		<tr>
			<td>Air compressor</td>
			<td>Kimura Medical</td>
			<td>KA-100</td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>All-in-one fluorescence microscope</td>
			<td>KEYENCE</td>
			<td>BZ-X710</td>
			<td></td>
		</tr>
		<tr>
			<td>Anesthesia box</td>
			<td>Bio Research Center</td>
			<td>RIC-01</td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>Anesthesia system</td>
			<td>ACOMA Medical Industry</td>
			<td>NS-5000A</td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>Anti-GAD67, clone 1G10.2</td>
			<td>Merk Millipore</td>
			<td>MAB5406</td>
			<td>For immunostaining</td>
		</tr>
		<tr>
			<td>Calcium Chrolide</td>
			<td>nacalai tesque</td>
			<td>06729-55</td>
			<td>aCSF</td>
		</tr>
		<tr>
			<td>Choline Chloride</td>
			<td>nacalai tesque</td>
			<td>08809-45</td>
			<td>aCSF</td>
		</tr>
		<tr>
			<td>curved blunt forceps</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Disposal scalpel</td>
			<td>Kai</td>
			<td>10</td>
			<td></td>
		</tr>
		<tr>
			<td>D-PBS(-) without Ca and Mg, liquid(10x)</td>
			<td>nacalai tesque</td>
			<td></td>
			<td>For immunostaining</td>
		</tr>
		<tr>
			<td>D(+)-Glucose</td>
			<td>Wako</td>
			<td>049-31165</td>
			<td>aCSF</td>
		</tr>
		<tr>
			<td>Gelatin</td>
			<td>nacalai tesque</td>
			<td>16605-42</td>
			<td>re-secctioning</td>
		</tr>
		<tr>
			<td>Goat anti-Mouse IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor Plus 488</td>
			<td>Invitrogen</td>
			<td>A32723</td>
			<td>For immunostaining</td>
		</tr>
		<tr>
			<td>Goat anti-Rabbit IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor Plus 555</td>
			<td>Invitrogen</td>
			<td>A32732</td>
			<td>For immunostaining</td>
		</tr>
		<tr>
			<td>Heater mat</td>
			<td>Bio Research Center</td>
			<td>HM-10</td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>Heater mat controller</td>
			<td>Bio Research Center</td>
			<td>BWT-100A</td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>Heater system</td>
			<td>SA Instruments</td>
			<td>MR-compatible Small Animal Heating System</td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>Isoflurane</td>
			<td>AbbVie</td>
			<td></td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>Isoflurane vaporizer</td>
			<td>ACOMA Medical Industry</td>
			<td>MKIIIai</td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>Linear Slicer</td>
			<td>DOSAKA</td>
			<td>Neo Linear Slicer MT</td>
			<td></td>
		</tr>
		<tr>
			<td>L(+)-Ascorbic Acid Sodium Salt</td>
			<td>Wako</td>
			<td>196-01252</td>
			<td>aCSF</td>
		</tr>
		<tr>
			<td>Magnesium Chrolide Hexahydrate</td>
			<td>Wako</td>
			<td>135-00165</td>
			<td>aCSF</td>
		</tr>
		<tr>
			<td>MaxOne Single-Well MEA</td>
			<td>MaxWell Biosystems</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Metal Spatula</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Monitoring system</td>
			<td>SA Instruments</td>
			<td>Model 1025</td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>Monitoring software</td>
			<td>SA Instruments</td>
			<td>PC-SAM V.5.12</td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>MRI compatible cradle</td>
			<td>Bruker BioSpin</td>
			<td>T12812</td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>MRI coil</td>
			<td>Bruker BioSpin</td>
			<td>T9988</td>
			<td>For MRI</td>
		</tr>
		<tr>
			<td>MRI operation software</td>
			<td>Bruker BioSpin</td>
			<td>ParaVision 5.1</td>
			<td>For MRI</td>
		</tr>
		<tr>
			<td>Neo LinearSlicer MT</td>
			<td>D.S.K.</td>
			<td>NLS-MT</td>
			<td></td>
		</tr>
		<tr>
			<td>NeuN (D4G40) XP Rabbit mAb</td>
			<td>Cell Signaling</td>
			<td>24307</td>
			<td>For immunostaining</td>
		</tr>
		<tr>
			<td>Normal Goat Serum</td>
			<td>Wako</td>
			<td>143-06561</td>
			<td>For immunostaining</td>
		</tr>
		<tr>
			<td>Potassium Chloride</td>
			<td>Wako</td>
			<td>163-03545</td>
			<td>aCSF</td>
		</tr>
		<tr>
			<td>Polyethylene Glycol Mono-p-isooctylphenyl Ether</td>
			<td>nacalai tesque</td>
			<td>12967-45</td>
			<td>For immunostaining</td>
		</tr>
		<tr>
			<td>Pressure-sensitive respiration sensor</td>
			<td>SA Instruments</td>
			<td>RS-301</td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>Preclinical MRI scanner</td>
			<td>Bruker BioSpin</td>
			<td>BioSpec 70/20 USR</td>
			<td>For MRI</td>
		</tr>
		<tr>
			<td>Pyruvic Acid Sodium Salt</td>
			<td>nacalai tesque</td>
			<td>29806-54</td>
			<td>aCSF</td>
		</tr>
		<tr>
			<td>SCAN in a BOX</td>
			<td>Open Technologies srl</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>scissors</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Sieve bottle</td>
			<td>TIGERCROWN</td>
			<td>81</td>
			<td>For 3D scan</td>
		</tr>
		<tr>
			<td>SlowFade Gold Antifade Mountant</td>
			<td>Invitrogen</td>
			<td>S36937</td>
			<td>For immunostaining</td>
		</tr>
		<tr>
			<td>Sodium Chloride</td>
			<td>Wako</td>
			<td>191-01665</td>
			<td>aCSF</td>
		</tr>
		<tr>
			<td>Sodium Dihydrogenphosphate</td>
			<td>Wako</td>
			<td>197-09705</td>
			<td>aCSF</td>
		</tr>
		<tr>
			<td>Sodium Hydrogen Carbonate</td>
			<td>Wako</td>
			<td>191-01305</td>
			<td>aCSF</td>
		</tr>
		<tr>
			<td>Sodium Hydrogensulfite</td>
			<td>nacalai tesque</td>
			<td>31220-15</td>
			<td>For immunostaining</td>
		</tr>
		<tr>
			<td>Thermistor temperature probe</td>
			<td>SA Instruments</td>
			<td>RTP-101-B, PLTPC-300</td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>Tooth bar</td>
			<td>Bruker BioSpin</td>
			<td>T10146</td>
			<td>Animal preparation for MRI</td>
		</tr>
		<tr>
			<td>Winged intravenous needle</td>
			<td>TERUMO</td>
			<td>SV-23CLK</td>
			<td>For perfusion</td>
		</tr>
		<tr>
			<td>1 mol/l-Tris-HCl Buffer Solution</td>
			<td>nacalai tesque</td>
			<td>35436-01</td>
			<td>For immunostaining</td>
		</tr>
		<tr>
			<td>1 mol/l-Hydrochloric Acid</td>
			<td>nacalai tesque</td>
			<td>37314-15</td>
			<td>For pH adjustment of solution</td>
		</tr>
		<tr>
			<td>16%-Paraformaldehyde Aqueous Solution</td>
			<td>Electron Microscopy Sciences</td>
			<td>15710</td>
			<td>For immunostaining</td>
		</tr>
	</tbody>
</table>

3d scanning technology bridging microcircuits and macroscale brain images in 3d novel embedding overlapping protocol

The human brain, being a multiscale system, has both macroscopic electrical signals, globally flowing along thick white-matter fiber bundles, and microscopic neuronal spikes, propagating along axons and dendrites. Both scales complement different aspects of human cognitive and behavioral functions. At the macroscopic level, MRI has been the current standard imaging technology, in which the smallest spatial resolution, voxel size, is 0.1&#8211;1 mm3. Also, at the microscopic level, previous physiological studies were aware of nonuniform neuronal architectures within such voxels. This study develops a powerful way to accurately embed microscopic data into a macroscopic map by interfacing biological scientific research with technological advancements in 3D scanning technology. Since 3D scanning technology has mostly been used for engineering and industrial design until now, it is repurposed for the first time to embed microconnectomes into the whole brain while preserving natural spiking in living brain cells. In order to achieve this purpose, first, we constructed a scanning protocol to obtain accurate 3D images from living bio-organisms inherently challenging to image due to moist and reflective surfaces. Second, we trained to keep speed to prevent the degradation of living brain tissue, which is a key factor in retaining better conditions and recording more natural neuronal spikes from active neurons in the brain tissue. Two cortical surface images, independently extracted from two different imaging modules, namely MRI and 3D scanner surface images, surprisingly show a distance error of only 50 &#956;m as mode value of the histogram. This accuracy is comparable in scale to the microscopic resolution of intercellular distances; also, it is stable among different individual mice. This new protocol, the 3D novel embedding overlapping (3D-NEO) protocol, bridges macroscopic and microscopic levels derived by this integrative protocol and accelerates new scientific findings to study comprehensive connectivity architectures (i.e., microconnectome).

We evaluated distances between cortical surfaces, produced by stripping MRI volume, and surfaces obtained from 3D scans of extracted brains. The mode values of the histogram of the distances are only 55 &#956;m (Figure 3a). Additionally, when accumulating the histogram from the point where the distance equals zero, the accumulated value reaches 90% of total sample numbers at ~300 &#956;m (Figure 3b). The final histogram of the di...

Watch this Scientific Journal Video about 3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol at JoVE.com

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

This article introduces an experimental protocol using 3D scanning technology bridging two spatial scales: the macroscopic spatial scale of whole-brain anatomy imaged by MRI at &#62;100 &#956;m and the microscopic spatial scale of neuronal distributions using immunohistochemistry staining and a multielectrode array system and other methods (~10 &#956;m).

The human brain, being a multiscale system, has both macroscopic electrical signals, globally flowing along thick white-matter fiber bundles, and ...

This protocol is the first to introduce 3D scanning technology in the anatomical, specifically neuroanatomical research field, and has achieved highly accurate overlapping performance. The 3D neuro embedding overlap, or 3D neuro protocol, embeds micro-scale sterile sockets into macro-scale brain images, and bridges these different spatial scales seamlessly. We also have to apply the 3D neuro protocol to human MRIs and post-mortem brain.The overlapping enables us to identify known MRI contrast patterns relating to disease. 3D scan systems are sensitive to water surrounding the extracted bio organism, so it is critical to wipe the water very carefully. Furthermore, the procedure must be performed as quickly as possible.Begin this procedure with preparation of a mouse, as well as MRI acquisitions and equipment preparations as described in the text protocol. On day two, cut the scalp of a previously euthanized mouse along the sagittal suture using surgical scissors. Use surgical scissors to cut the skull in diagonal directions from the lambda point to a point a little in front of the bregma.Then, gently peel off the skull from the brain using curved tweezers. Lift the brain with a spatula and transfer it to a Petri dish with ice-cold cutting solution. Using an external gas bomb with a controlled rotating pump speed, flow air containing 95%oxygen and 5%carbon dioxide to generate small bubbles in the ice-cold cutting solution.After one to two minutes, put the brain on a microfiber cloth whose surface is slightly covered by a flour sieve. Gently wipe the fluid from the brain surface using the microfiber cloth. Put the brain, with its dorsal aspect up, onto the sample stand.Then, place the sample stand at the center of the automatic turntable. Darken the room during the 3D scan and perform a 3D scan on the turntable. To test whether the 3D scan works well, click 3D Scan by selecting the angle between two shots as 22.5, the starting angle as zero, and the final angle as 360.Move the brain to a Petri dish and bubble the brain in the cutting solution for approximately 10 seconds. Cut the brain into two blocks at the middle of the coronal plane, using a scalpel. Then, move the two brain blocks gently to the flat surface using a surgical spatula.Attach the brain blocks of the brain block base using instant glue. Softly and carefully wipe the fluid from the brain surface within one to two minutes, using the microfiber cloth. Then, perform a 3D scan again.Attach a blade to the blade holder of the vibratome. Attach the black tape covering the center of the brain block base to the center of the cutting stage for a vibratome. Pour cutting solution into the cutting stage and set the cutting stage on the vibratome.Adjust the cutting speed and amplitude. Make two to five coronal slices of 300 microns thick from the two brain blocks. Keep the solution in the cutting stage bubbling if possible.Optimize the cutting speed, frequency, and vibration amplitude of the system by setting them as 12.7 millimeters per minute for the speed, 87 to 88 hertz for the frequency, and 0.8 to 1.0 millimeters for the swing width. While cutting the brain slices, record the sliced coordinate in the format, including the anterior-posterior coordinate, hemisphere, and other conditions. Gently transfer the brain slices to a beaker filled with pre-warmed ACSF using a thick plastic pipette.Incubate the brain slices in the beaker at approximately 34 degrees Celsius for one hour. During this time, perform a 3D scan of the remaining brain blocks on the cutting stage. Now, set a multielectrode array, or MEA chip, on a recording unit.Connect the chip to a peristaltic pump using two tubes. Use one tube to guide the same ACSF into the MEA chip and the other tube to guide the ACSF out of the MEA chip. Attach two needles, connected at the tips of the two tubes, to the top of the wall of the MEA chip.Fix their positions with their tips following the inner wall of the MEA chip. Set the flow rate of the ACSF to 4.1 RPM. Perform MRI data processing to extract cortical volumes as described in the text protocol.To perform MRI image processing, download the 3D Slicer free software. Open the MR images of the extracted brains that were produced using the volume rendering and editor modules in the 3D Slicer. Change the mode from editor to volume rendering, and click a target button to make the image of the brain come to the center of the screen.Select the MRT-2 brain mode and tune the thresholds by moving the shift bar. Then, move back from volume rendering to editor and click the threshold effect button. Apply the label 41, cerebral cortex.Then, save the brain surface data as an STL file by changing the file format from VTK to STL on the form checklist. Perform an automatic coregistration among eight or 16 images taken from eight or 16 different angles within a sequence of a scan to correct small mismatches. To do so, click Global Registration included in the alignment option and integrate the images.Repeat scans from different angles to obtain the whole brain surface. If the integration among images scanned from different angles was not successful, click Manual Alignment and select a pair of fixed image and a moving image. Start the manual alignment of the images by selecting three or four common points in different images.Then, click OK.The optimization algorithm is the iterative closest-point algorithm and does not include a non-linear deformation. Make a mesh of all the aligned images by selecting all images and clicking the mesh generation button. Then, select the option, small artistic object, to get the mesh at the highest resolution.Save the image as STL Binary or an ASCII format. Now, open the MRI surface and merge it with the 3D scan surface using the surface processing software. Perform a manual alignment process as before.Then, save these coregistered surface images again. If necessary, clean the individual surface data by erasing small noises surrounding the brain region, especially in the case of the MRI data, and by filling any holes, especially in the case of the 3D scan data. Finally, open the surface data with data analysis software such as MATLAB.Generate and evaluate the histograms of the minimum distances between the two surfaces. Distances were evaluated between cortical surfaces produced by stripping MRI volume and surfaces obtained from 3D scans of extracted brains. The mode values of the histogram of the distances are only 55 microns.Additionally, when accumulating the histogram from the point where the distance equals zero, the accumulated value reaches 90%of total sample numbers at approximately 300 microns. The final histogram of the distances between two surfaces showed a typical peak around 50 microns. From a macroscopic viewpoint, this mode value corresponds to the geometrical limitation, which was 100 microns from the voxel size of MRIs.This point indirectly suggests that the overlapping algorithm between the MRI and 3D scan worked superbly well, and that the noise levels of both the MRI and the 3D scan were suppressed as a low value. This protocol is the first to uprise through the scanning technology to bio-organism. The technology was originally utilized for purely engineering demands.Applying this technology to medicine can answer new questions. Following 3D scanning, we can use calcium-imaging wall patch gram recordings to get complementary knowledge in terms of temporal and the spacial resolution and recordable numbers of cells. The highly accurate overlapping performance this protocol provides will make a seamless association between the anatomical spatial scale and the zero-spatial scale more realistic than before.

3d-scanning-technology-bridging-microcircuits-macroscale-brain-images

Research

JoVE Journal

Neuroscience

Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

The combination of patch clamp recordings from two (or more) synaptically coupled neurons (paired recordings) in acute brain slice preparations with simultaneous intracellular biocytin filling allows a correlated analysis of their structural and functional properties. With this method it is possible to identify and characterize both pre- and postsynaptic neurons by their morphology and electrophysiological response pattern. Paired recordings allow studying the connectivity patterns between these neurons as well as the properties of both chemical and electrical synaptic transmission. Here, we give a step-by-step description of the procedures required to obtain reliable paired recordings together with an optimal recovery of the neuron morphology. We will describe how pairs of neurons connected via chemical synapses or gap junctions are identified in brain slice preparations. We will outline how neurons are reconstructed to obtain their 3D morphology of the dendritic and axonal domain and how synaptic contacts are identified and localized. We will also discuss the caveats and limitations of the paired recording technique, in particular those associated with dendritic and axonal truncations during the preparation of brain slices because these strongly affect connectivity estimates. However, because of the versatility of the paired recording approach it will remain a valuable tool in characterizing different aspects of synaptic transmission at identified neuronal microcircuits in the brain.

Patch-clamp recordings and simultaneous intracellular biocytin filling of synaptically coupled neurons in acute brain slices allow a correlated analysis of their structural and functional properties. The aim of this protocol is to describe the essential technical steps of electrophysiological recording from neuronal microcircuits and their subsequent morphological analysis.

The combination of patch clamp recordings from two (or more) synaptically coupled neurons (paired recordings) in acute brain slice preparations with ...

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

Currently, large-scale networks derived from dissociated neurons growing and developing in vitro on extracellular micro-transducer devices are the gold-standard experimental model to study basic neurophysiological mechanisms involved in the formation and maintenance of neuronal cell assemblies. However, in vitro studies have been limited to the recording of the electrophysiological activity generated by bi-dimensional (2D) neural networks. Nonetheless, given the intricate relationship between structure and dynamics, a significant improvement is necessary to investigate the formation and the developing dynamics of three-dimensional (3D) networks. In this work, a novel experimental platform in which 3D hippocampal or cortical networks are coupled to planar Micro-Electrode Arrays (MEAs) is presented. 3D networks are realized by seeding neurons in a scaffold constituted of glass microbeads (30-40 &#181;m in diameter) on which neurons are able to grow and form complex interconnected 3D assemblies. In this way, it is possible to design engineered 3D networks made up of 5-8 layers with an expected final cell density. The increasing complexity in the morphological organization of the 3D assembly induces an enhancement of the electrophysiological patterns displayed by this type of networks. Compared with the standard 2D networks, where highly stereotyped bursting activity emerges, the 3D structure alters the bursting activity in terms of duration and frequency, as well as it allows observation of more random spiking activity. In this sense, the developed 3D model more closely resembles in vivo neural networks.

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

In this work, a novel experimental model in which 3D neuronal cultures are coupled to planar Micro-Electrode Arrays (MEAs) is presented. 3D networks are built by seeding neurons in a scaffold made up of glass microbeads on which neurons grow and form interconnected 3D structures.

Currently, large-scale networks derived from dissociated neurons growing and developing in vitro on extracellular micro-transducer devices are the ...

Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning

Magnetic resonance imaging (MRI) allows for the delineation between normal and abnormal tissue on a macroscopic scale, sampling an entire tissue volume three-dimensionally. While MRI is an extremely sensitive tool for detecting tissue abnormalities, association of signal changes with an underlying pathological process is usually not straightforward. In the central nervous system, for example, inflammation, demyelination, axonal damage, gliosis, and neuronal death may all induce similar findings on MRI. As such, interpretation of MRI scans depends on the context, and radiological-histopathological correlation is therefore of the utmost importance. Unfortunately, traditional pathological sectioning of brain tissue is often imprecise and inconsistent, thus complicating the comparison between histology sections and MRI. This article presents novel methodology for accurately sectioning primate brain tissues and thus allowing precise matching between histology and MRI. The detailed protocol described in this article will assist investigators in applying this method, which relies on the creation of 3D printed brain slicers. Slightly modified, it can be easily implemented for brains of other species, including humans.

The overall goal of this protocol is to accurately align magnetic resonance imaging (MRI) image volumes with histology sections via the creation of customized 3D-printed brain holders and slicer boxes.

Magnetic resonance imaging (MRI) allows for the delineation between normal and abnormal tissue on a macroscopic scale, sampling an entire tissue volume ...

High-resolution Confocal Imaging of the Blood-brain Barrier: Imaging, 3D Reconstruction, and Quantification of Transcytosis

The blood-brain barrier (BBB) is a dynamic multicellular interface that regulates the transport of molecules between the circulation and the brain. Transcytosis across the BBB regulates the delivery of hormones, metabolites, and therapeutic antibodies to the brain parenchyma. Here, we present a protocol that combines immunofluorescence of free-floating sections with laser scanning confocal microscopy and image analysis to visualize subcellular organelles within endothelial cells at the BBB. Combining this data-set with 3D image analysis software allows for the semi-automated segmentation and quantification of capillary volume and surface area, as well as the number and intensity of intracellular organelles at the BBB. The detection of mouse endogenous immunoglobulin (IgG) within intracellular vesicles and their quantification at the BBB is used to illustrate the method. This protocol can potentially be applied to the investigation of the mechanisms controlling BBB transcytosis of different molecules in vivo.

Here we present a microscopy-based protocol for high-resolution imaging and a three-dimensional reconstruction of the mouse neurovascular unit and blood-brain barrier using brain free-floating sections. This method allows for the visualization, analysis, and quantification of intracellular organelles at the BBB.

The blood-brain barrier (BBB) is a dynamic multicellular interface that regulates the transport of molecules between the circulation and the brain. ...

Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy

Type 2 diabetes mellitus (T2DM) is a global health crisis which is characterized by insulin signaling impairment and chronic inflammation in peripheral tissues. The hypothalamus in the central nervous system (CNS) is the control center for energy and insulin signal response regulation. Chronic inflammation in peripheral tissues and imbalances of certain chemokines (such as CCL5, TNF&#945;, and IL-6) contribute to diabetes and obesity. However, the functional mechanism(s) connecting chemokines and hypothalamic insulin signal regulation still remain unclear.
In vitro primary neuron culture models are convenient and simple models which can be used to investigate insulin signal regulation in hypothalamic neurons. In this study, we introduced exogeneous GLUT4 protein conjugated with GFP (GFP-GLUT4) into primary hypothalamic neurons to track GLUT4 membrane translocation upon insulin stimulation. Time-lapse images of GFP-GLUT4 protein trafficking were recorded by deconvolution microscopy, which allowed users to generate high-speed, high-resolution images without damaging the neurons significantly while conducting the experiment. The contribution of CCR5 in insulin regulated GLUT4 translocation was observed in CCR5 deficient hypothalamic neurons, which were isolated and cultured from CCR5 knockout mice. Our results demonstrated that the GLUT4 membrane translocation efficiency was reduced in CCR5 deficient hypothalamic neurons after insulin stimulation.

This protocol describes a technique for observation of real-time Green Fluorescence Protein (GFP) tagged Glucose Transporter 4 (GLUT4) protein trafficking upon insulin stimulation and characterization of the biological role of CCR5 in the insulin&#8211;GLUT4 signaling pathway with Deconvolution Microscopy.

Type 2 diabetes mellitus (T2DM) is a global health crisis which is characterized by insulin signaling impairment and chronic inflammation in peripheral ...

Assay Development for High Content Quantification of Sod1 Mutant Protein Aggregate Formation in Living Cells

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that can be caused by inherited mutations in the gene encoding copper-zinc superoxide dismutase 1 (SOD1). The structural instability of SOD1 and the detection of SOD1-positive inclusions in familial-ALS patients supports a potential causal role for misfolded and/or aggregated SOD1 in ALS pathology. In this study, we describe the development of a cell-based assay designed to quantify the dynamics of SOD1 aggregation in living cells by high content screening approaches. Using lentiviral vectors, we generated stable cell lines expressing wild-type and mutant A4V SOD1 tagged with yellow fluorescent protein and found that both proteins were expressed in the cytosol without any sign of aggregation. Interestingly, only SOD1 A4V stably expressed in HEK-293, but not in U2OS or SH-SY5Y cell lines, formed aggregates upon proteasome inhibitor treatment. We show that it is possible to quantify aggregation based on dose-response analysis of various proteasome inhibitors, and to track aggregate-formation kinetics by time-lapse microscopy. Our approach introduces the possibility of quantifying the effect of ALS mutations on the role of SOD1 in aggregate formation as well as screening for small molecules that prevent SOD1 A4V aggregation.

We describe a method to quantify the aggregation of misfolded proteins. Our protocol details lentiviral induced stable cell line generation, automated confocal imaging, and image analysis of protein aggregates. As an illustrative application, we studied the effect of small molecules in promoting SOD1 aggregation in a time- and dose-dependent manner.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that can be caused by inherited mutations in the gene encoding copper-zinc ...

High-Resolution 3D Imaging of Rabies Virus Infection in Solvent-Cleared Brain Tissue

The visualization of infection processes in tissues and organs by immunolabeling is a key method in modern infection biology. The ability to observe and study the distribution, tropism, and abundance of pathogens inside of organ tissues provides pivotal data on disease development and progression. Using conventional microscopy methods, immunolabeling is mostly restricted to thin sections obtained from paraffin-embedded or frozen samples. However, the limited 2D image plane of these thin sections may lead to the loss of crucial information on the complex structure of an infected organ and the cellular context of the infection. Modern multicolor, immunostaining-compatible tissue clearing techniques now provide a relatively fast and inexpensive way to study high-volume 3D image stacks of virus-infected organ tissue. By exposing the tissue to organic solvents, it becomes optically transparent. This matches the sample&#8217;s refractive indices and eventually leads to a significant reduction of light scattering. Thus, in combination with long free working distance objectives, large tissue sections up to 1 mm in size can be imaged by conventional confocal laser scanning microscopy (CLSM) at high resolution. Here, we describe a protocol to apply deep-tissue imaging after tissue clearing to visualize rabies virus distribution in infected brains in order to study topics like virus pathogenesis, spread, tropism, and neuroinvasion.

Novel, immunostaining-compatible tissue clearing techniques like the ultimate 3D imaging of solvent-cleared organs allow the 3D visualization of rabies virus brain infection and its complex cellular environment. Thick, antibody-labeled brain tissue slices are made optically transparent to increase imaging depth and to enable 3D analysis by confocal laser scanning microscopy.

The visualization of infection processes in tissues and organs by immunolabeling is a key method in modern infection biology. The ability to observe and ...

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction of high-resolution imaged stacks to reveal ultrastructural patterns that could not be resolved using 2D images. Indeed, the latter might lead to a misinterpretation of morphologies, like in the case of mitochondria; the use of 3D models is, therefore, more and more common and applied to the formulation of morphology-based functional hypotheses. To date, the use of 3D models generated from light or electron image stacks makes qualitative, visual assessments, as well as quantification, more convenient to be performed directly in 3D. As these models are often extremely complex, a virtual reality environment is also important to be set up to overcome occlusion and to take full advantage of the 3D structure. Here, a step-by-step guide from image segmentation to reconstruction and analysis is described in detail.

The described pipeline is designed for the segmentation of electron microscopy datasets larger than gigabytes, to extract whole-cell morphologies. Once the cells are reconstructed in 3D, customized software designed around individual needs can be used to perform a qualitative and quantitative analysis directly in 3D, also using virtual reality to overcome view occlusion.

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction ...

Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging

Functional ultrasound (fUS) imaging is a novel brain imaging modality that relies on the high-sensitivity measure of the cerebral blood volume achieved by ultrafast doppler angiography. As brain perfusion is strongly linked to local neuronal activity, this technique allows the whole-brain 3D mapping of task-induced regional activation as well as resting-state functional connectivity, non-invasively, with unmatched spatio-temporal resolution and operational simplicity. In comparison with fMRI (functional magnetic resonance imaging), a main advantage of fUS imaging consists in enabling a complete compatibility with awake and behaving animal experiments. Moreover, fMRI brain mapping in mice, the most used preclinical model in Neuroscience, remains technically challenging due to the small size of the brain and the difficulty to maintain stable physiological conditions. Here we present a simple, reliable and robust protocol for whole-brain fUS imaging in anesthetized and awake mice using an off-the-shelf commercial fUS system with a motorized linear transducer, yielding significant cortical activation following sensory stimulation as well as reproducible 3D functional connectivity pattern for network identification.

This protocol describes the quantification of volumetric cerebral hemodynamic variations in the mouse brain using functional ultrasound (fUS). Procedures for 3D functional activation map following sensory stimulation as well as resting-state functional connectivity are provided as illustrative examples, in anesthetized and awake mice.

Functional ultrasound (fUS) imaging is a novel brain imaging modality that relies on the high-sensitivity measure of the cerebral blood volume achieved by ...

A 3D Blood-Brain Barrier Model Derived from Human Pluripotent Stem-Cells

Reconstruction of the Blood-Brain Barrier In Vitro to Model and Therapeutically Target Neurological Disease

The blood-brain barrier (BBB) is a key physiological component of the central nervous system (CNS), maintaining nutrients, clearing waste, and protecting the brain from pathogens. The inherent barrier properties of the BBB pose a challenge for therapeutic drug delivery into the CNS to treat neurological diseases. Impaired BBB function has been related to neurological disease. Cerebral amyloid angiopathy (CAA), the deposition of amyloid in the cerebral vasculature leading to a compromised BBB, is a co-morbidity in most cases of Alzheimer&#39;s disease (AD), suggesting that BBB dysfunction or breakdown may be involved in neurodegeneration. Due to limited access to human BBB tissue, the mechanisms that contribute to proper BBB function and BBB degeneration remain unknown. To address these limitations, we have developed a human pluripotent stem cell-derived BBB (iBBB) by incorporating endothelial cells, pericytes, and astrocytes in a 3D matrix. The iBBB self-assembles to recapitulate the anatomy and cellular interactions present in the BBB. Seeding iBBBs with amyloid captures key aspects of CAA. Additionally, the iBBB offers a flexible platform to modulate genetic and environmental factors implicated in cerebrovascular disease and neurodegeneration, to investigate how genetics and lifestyle affect disease risk. Finally, the iBBB can be used for drug screening and medicinal chemistry studies to optimize therapeutic&#160;delivery to the CNS. In this protocol, we describe the differentiation of the three types of cells (endothelial cells, pericytes, and astrocytes) arising from human pluripotent stem cells, how to assemble the differentiated cells into the iBBB, and how to model CAA in vitro using exogenous amyloid. This model overcomes the challenge of studying live human brain tissue with a system that has both biological fidelity and experimental flexibility, and enables the interrogation of the human BBB and its role in neurodegeneration.

Author Spotlight: A Personalized Approach Towards Investigating Alzheimer&#039;s Disease Using an In Vitro Blood-Brain Barrier Model 

The blood-brain barrier (BBB) has a crucial role in sustaining a stable and healthy brain environment. BBB dysfunction is associated with many neurological diseases. We have developed a 3D, stem-cell-derived model of the BBB to investigate cerebrovascular pathology, BBB integrity, and how the BBB is altered by genetics and disease.

The blood-brain barrier (BBB) is a key physiological component of the central nervous system (CNS), maintaining nutrients, clearing waste, and protecting ...