We thank the Hotchkiss Brain Institute Advanced Microscopy Platform for imaging infrastructure and expertise. RWJ was supported by postdoctoral fellowship funding from the University of Calgary Eyes High program and by a Multiple Sclerosis Society of Canada and Roche Canada unrestricted educational fellowship. VWY received salary support from the Canada Research Chair Tier 1 program. This work was supported by operating funds from the Canadian Institutes of Health Research Grant 1049959, the Multiple Sclerosis Society of Canada Grant 3236, and the US Department of Defense of the Congressionally Directed Multiple Sclerosis Research Program. Figure 1 is created with BioRender.com. The figures adapted in this publication were originally published in The Journal of Immunology. Rajiv W. Jain, David A. Elliott, and V. Wee Yong. 2023. Single Cell Analysis of High-Parameter Histology Images Using Histoflow Cytometry. J. Immunol. 210: 2038-2049. Copyright &#169; [2023]. The American Association of Immunologists, Inc.

Immunofluorescent Microscopy and Analysis for High-Parameter Tissue Imaging

<ol>
	<li>Bar-Or, A., Li, 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+immunology+of+relapsing+multiple+sclerosis:+interactions,+checks,+and+balances.">Cellular immunology of relapsing multiple sclerosis: interactions, checks, and balances.</a> Lancet Neurol. 20 (6), 470-483 (2021).</li><li>Borst, K., Dumas, A. A., Prinz, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microglia:+Immune+and+non-immune+functions.">Microglia: Immune and non-immune functions.</a> Immunity. 54 (10), 2194-2208 (2021).</li><li>Han, R. T., Kim, R. D., Molofsky, A. V., Liddelow, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Astrocyte-immune+cell+interactions+in+physiology+and+pathology.">Astrocyte-immune cell interactions in physiology and pathology.</a> Immunity. 54 (2), 211-224 (2021).</li><li>Absinta, 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=A+lymphocyte-microglia-astrocyte+axis+in+chronic+active+multiple+sclerosis.">A lymphocyte-microglia-astrocyte axis in chronic active multiple sclerosis.</a> Nature. 597 (7878), 709-714 (2021).</li><li>Piwecka, M., Rajewsky, N., Rybak-Wolf, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+and+spatial+transcriptomics:+Deciphering+brain+complexity+in+health+and+disease.">Single-cell and spatial transcriptomics: Deciphering brain complexity in health and disease.</a> Nat Rev Neurol. 19 (6), 346-362 (2023).</li><li>Schirmer, 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=Neuronal+vulnerability+and+multilineage+diversity+in+multiple+sclerosis.">Neuronal vulnerability and multilineage diversity in multiple sclerosis.</a> Nature. 573 (7772), 75-82 (2019).</li><li>Jain, R. W., Yong, V. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=B+cells+in+central+nervous+system+disease:+Diversity,+locations+and+pathophysiology.">B cells in central nervous system disease: Diversity, locations and pathophysiology.</a> Nat Rev Immunol. 22 (8), 513-524 (2022).</li><li>Lassmann, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiple+sclerosis+pathology.">Multiple sclerosis pathology.</a> Cold Spring Harb Perspect Med. 8 (3), (2018).</li><li>Yong, V. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microglia+in+multiple+sclerosis:+Protectors+turn+destroyers.">Microglia in multiple sclerosis: Protectors turn destroyers.</a> Neuron. 110 (21), 3534-3548 (2022).</li><li>Sharma, S., Boyer, J., Teyton, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+practitioner's+view+of+spectral+flow+cytometry.">A practitioner's view of spectral flow cytometry.</a> Nat Methods. 21 (5), 740-743 (2024).</li><li>Longo, S. K., Guo, M. G., Ji, A. L., Khavari, 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=Integrating+single-cell+and+spatial+transcriptomics+to+elucidate+intercellular+tissue+dynamics.">Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics.</a> Nat Rev Genet. 22 (10), 627-644 (2021).</li><li>Ramaglia, 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=Multiplexed+imaging+of+immune+cells+in+staged+multiple+sclerosis+lesions+by+mass+cytometry.">Multiplexed imaging of immune cells in staged multiple sclerosis lesions by mass cytometry.</a> Elife. 8, e48051 (2019).</li><li>Bolognesi, M. 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=Multiplex+staining+by+sequential+immunostaining+and+antibody+removal+on+routine+tissue+sections.">Multiplex staining by sequential immunostaining and antibody removal on routine tissue sections.</a> J Histochem Cytochem. 65 (8), 431-444 (2017).</li><li>Radtke, A. 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=IBEX:+An+iterative+immunolabeling+and+chemical+bleaching+method+for+high-content+imaging+of+diverse+tissues.">IBEX: An iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues.</a> Nat Protoc. 17 (2), 378-401 (2022).</li><li>Radtke, A. 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=IBEX:+A+versatile+multiplex+optical+imaging+approach+for+deep+phenotyping+and+spatial+analysis+of+cells+in+complex+tissues.">IBEX: A versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues.</a> Proc Natl Acad Sci U S A. 117 (52), 33455-33465 (2020).</li><li>Gerner, M. Y., Kastenmuller, W., Ifrim, I., Kabat, J., Germain, R. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Histo-cytometry:+A+method+for+highly+multiplex+quantitative+tissue+imaging+analysis+applied+to+dendritic+cell+subset+microanatomy+in+lymph+nodes.">Histo-cytometry: A method for highly multiplex quantitative tissue imaging analysis applied to dendritic cell subset microanatomy in lymph nodes.</a> Immunity. 37 (2), 364-376 (2012).</li><li>Kotov, D. I., Pengo, T., Mitchell, J. S., Gastinger, M. J., Jenkins, M. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chrysalis:+A+new+method+for+high-throughput+histo-cytometry+analysis+of+images+and+movies.">Chrysalis: A new method for high-throughput histo-cytometry analysis of images and movies.</a> J Immunol. 202 (1), 300-308 (2019).</li><li>Jain, R. W., Elliott, D. A., Yong, V. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+analysis+of+high-parameter+histology+images+using+histoflow+cytometry.">Single-cell analysis of high-parameter histology images using histoflow cytometry.</a> J Immunol. 210 (12), 2038-2049 (2023).</li><li>JoVE Science Education Database. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=General+Laboratory+Techniques.+Histological+Sample+Preparation+for+Light+Microscopy.">General Laboratory Techniques. Histological Sample Preparation for Light Microscopy.</a> JoVE. , (2023).</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> Nat Methods. 9 (7), 676-682 (2012).</li><li>Berg, 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=ilastik:+Interactive+machine+learning+for+(bio)image+analysis.">ilastik: Interactive machine learning for (bio)image analysis.</a> Nat Methods. 16 (12), 1226-1232 (2019).</li><li>Reichard, A., Asosingh, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Best+practices+for+preparing+a+single+cell+suspension+from+solid+tissues+for+flow+cytometry.">Best practices for preparing a single cell suspension from solid tissues for flow cytometry.</a> Cytometry A. 95 (2), 219-226 (2019).</li><li>Ruhlandt, 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=Absolute+quantum+yield+measurements+of+fluorescent+proteins+using+a+plasmonic+nanocavity.">Absolute quantum yield measurements of fluorescent proteins using a plasmonic nanocavity.</a> Commun Biol. 3 (1), 627 (2020).</li><li>Combs, C. A., Shroff, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescence+microscopy:+A+concise+guide+to+current+imaging+methods.">Fluorescence microscopy: A concise guide to current imaging methods.</a> Curr Protoc Neurosci. 79, 1-25 (2017).</li><li>Elliott, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Confocal+microscopy:+Principles+and+modern+practices.">Confocal microscopy: Principles and modern practices.</a> Curr Protoc Cytom. 92 (1), e68 (2020).</li><li>Jonkman, J., Brown, C. M., Wright, G. D., Anderson, K. I., North, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tutorial:+Guidance+for+quantitative+confocal+microscopy.">Tutorial: Guidance for quantitative confocal microscopy.</a> Nat Protoc. 15 (5), 1585-1611 (2020).</li><li>McNally, J. G., Karpova, T., Cooper, J., Conchello, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three-dimensional+imaging+by+deconvolution+microscopy.">Three-dimensional imaging by deconvolution microscopy.</a> Methods. 19 (3), 373-385 (1999).</li><li>Stringer, C., Wang, T., Michaelos, M., Pachitariu, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cellpose:+A+generalist+algorithm+for+cellular+segmentation.">Cellpose: A generalist algorithm for cellular segmentation.</a> Nat Methods. 18 (1), 100-106 (2021).</li></ol>

The authors have no financial conflicts of interest.

Here, the use of histoflow cytometry is described, a technique that has been validated previously18. It is demonstrated that when staining tissue sections with spectrally overlapping dyes, that bleed-through across channels can be removed using spectral compensation, resulting in a greater number of fluorescent parameters being clearly resolved than would normally be possible through conventional methods. As high-parameter histology images are difficult to analyze using conventional methods, an an...

Inflammation driven by cells of the immune system and glial cells can contribute to chronic disorders of the CNS where each population can promote the activity of the other1,2,3. Understanding how the immune system interacts with these elements of the CNS to promote CNS inflammation is currently a major topic of interest and has been greatly facilitated by high-parameter techniques such as single-cell RNA sequencing. Through single-cell RNA sequencing, we have discovered that there is extensive communication occurring between glial cells and the immune system in several CNS disorders4,5,6. Understanding how these interactions are affecting these disorders will be crucial to elucidating the biology of these diseases.
One issue with single-cell sequencing analyses is that these techniques require that the tissue be disrupted to obtain single cells or nuclei, resulting in a complete loss of spatial information. Knowing where a cell exists in a tissue is critical for understanding the cell&#39;s role in driving inflammation. For example, immune cells such as B cells can concentrate in the CNS during neuroinflammation; however, they rarely enter the CNS parenchyma and instead concentrate in CNS barriers7. Given their localization, it is unlikely that these cells contribute to CNS inflammation by physically interacting with glial cells in the CNS parenchyma, suggesting any interactions they may be having with glial cells would occur through secreted factors. Additionally, the pathology occurring in CNS disorders often has structure8,9 such that a cell&#39;s localization in the tissue could critically determine whether it is actively contributing to the disorder or is a bystander. Thus, the usage of spatial orientation to evaluate a cell&#39;s role in pathology is essential.
Studying cells in tissue has typically been accomplished by using immunohistochemistry or immune fluorescence microscopy. An issue with these techniques is that they typically can only image up to four parameters simultaneously. This is a major limitation to these techniques, as we know from flow cytometry and single-cell RNA sequencing analyses that many cell populations require two or more parameters for their identification; also, the number of parameters required typically increases when looking for specific subsets of a cell type10. Thus, it is impractical to use standard imaging techniques for studying how subsets of cells may be interacting within a tissue.
This issue has been partially overcome through newer high-parameter methods that can retain spatial information, such as spatial RNA sequencing11 and imaging mass cytometry12. While these techniques are valuable, they do have several issues, such as not being widely available, reducing three-dimensional data into two dimensions, and requiring considerable expertise to execute. Another technique known as sequential staining, wherein tissues are stained with one set of antibodies followed by inactivation of the previous set of antibodies before staining with another set of antibodies, can achieve high-parameter histology without the need for specialized equipment or expertise13,14,15. However, sequential staining can be extremely labor intensive and requires a large amount of microscopy time, which may be impractical for laboratories that do not own a personal microscope. Thus, there is a need for techniques that can expand the number of fluorescent parameters that can be imaged at one time on microscopes that are widely available and in a timely fashion.
Once the high-parameter data has been acquired, another issue arises: conventional image analysis methods are unlikely to successfully analyze the data. Techniques such as manual counting or thresholding are only viable if the analysis consists of a single parameter or if multiple markers have the same localization where only overlapping signals are counted. This limitation makes traditional analysis inadequate to work with high-parameter datasets. Successful analysis of these datasets has been achieved by segmenting single cells from histology images and then conducting flow cytometry-like gating strategies to identify cell types16,17. However, another issue that affects these analyses is that they only work for datasets wherein all the cells of interest are physically separated from one another, as these techniques do not employ methods that can accurately separate cells that are in physical contact. Thus, a newer method is required that can conduct single-cell analyses on histology sections even if the cells are in physical contact.
In this article, a simple protocol called histoflow cytometry is described that has previously been introduced18 that expands the number of fluorescent parameters that can be imaged simultaneously using widely available microscopes. This protocol works by staining tissues with spectrally overlapping dyes and then using spectral compensation to remove bleed-through from overlapping channels to obtain clear single stains. To facilitate the analysis of high-parameter histology images, a detailed analysis pipeline is described that extracts single cells from tissue sections for the purpose of sorting cells into distinct populations using flow cytometry-like gating strategies. This protocol works in tissues where cells are diffusely present and in tissues where cells are closely compacted together, making this technique versatile for the study of tissues like the CNS in both homeostasis and neuroinflammation. Histoflow cytometry is, therefore, a useful technique for studying interactions between complex cell types that require multiple cell markers to define cells while maintaining spatial information.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>100% Ethanol</td>
			<td>Sigma</td>
			<td>676829-1L</td>
			<td></td>
		</tr>
		<tr>
			<td>4% PFA</td>
			<td>Electron Microscopy Sciences</td>
			<td>157-4</td>
			<td></td>
		</tr>
		<tr>
			<td>Anaconda</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>https://www.anaconda.com/download</td>
		</tr>
		<tr>
			<td>Bovine Serum Albumin</td>
			<td>Sigma</td>
			<td>A4503-50G</td>
			<td></td>
		</tr>
		<tr>
			<td>Cold fish stain gelatin&#160;</td>
			<td>Sigma</td>
			<td>G7765</td>
			<td></td>
		</tr>
		<tr>
			<td>Collating multichannel data from Imaris.ipynb script</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>https://github.com/elliottcalgary/Histoflow-Cytometry-Analysis-</td>
		</tr>
		<tr>
			<td>Convert FlowJo output to txt file for Cell selection in Imaris.ipynb script</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>https://github.com/elliottcalgary/Histoflow-Cytometry-Analysis-</td>
		</tr>
		<tr>
			<td>Donkey anti-rat Alexa Fluor 647</td>
			<td>JacksonImmunoResearch</td>
			<td>712-605-153</td>
			<td>1:300 concentration</td>
		</tr>
		<tr>
			<td>Donkey anti-rat DyLight 405</td>
			<td>Jackson ImmunoResearch</td>
			<td>712-475-153</td>
			<td>1:200 concentration</td>
		</tr>
		<tr>
			<td>Donkey Serum</td>
			<td>JacksonImmunoResearch</td>
			<td>017-000-001</td>
			<td></td>
		</tr>
		<tr>
			<td>F(ab&#39;)2-Goat anti-Mouse IgG PerCP-eFluor 710</td>
			<td>Thermofisher</td>
			<td>46-4010-82</td>
			<td>1:25 concentration</td>
		</tr>
		<tr>
			<td>FIJI</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>https://imagej.net/software/fiji/</td>
		</tr>
		<tr>
			<td>FlowJo</td>
			<td>FlowJo LLC</td>
			<td></td>
			<td>Software 4</td>
		</tr>
		<tr>
			<td>Fluorescence spectraviewer</td>
			<td></td>
			<td></td>
			<td>https://www.thermofisher.com/order/fluorescence-spectraviewer/#!/</td>
		</tr>
		<tr>
			<td>Fluoromount-G</td>
			<td>Southern Biotech</td>
			<td>0100-01</td>
			<td></td>
		</tr>
		<tr>
			<td>Fresh frozen human tonsil sections</td>
			<td>amsbio</td>
			<td>HF-707</td>
			<td></td>
		</tr>
		<tr>
			<td>Glass coverslip</td>
			<td>VWR</td>
			<td>48393 106</td>
			<td></td>
		</tr>
		<tr>
			<td>Goat anti-human IgA Alexa Fluor 488</td>
			<td>JacksonImmunoResearch</td>
			<td>109-546-011</td>
			<td>1:400 concentration</td>
		</tr>
		<tr>
			<td>Goat anti-human IgG Cy3</td>
			<td>JacksonImmunoResearch</td>
			<td>709-166-098</td>
			<td>1:400 concentration</td>
		</tr>
		<tr>
			<td>Goat anti-human IgM Dylight 405</td>
			<td>JacksonImmunoResearch</td>
			<td>109-476-129</td>
			<td>1:300 concentration</td>
		</tr>
		<tr>
			<td>Goat anti-rabbit A546</td>
			<td>Thermo Fisher Scientific</td>
			<td>A-11035</td>
			<td>1:250 concentration</td>
		</tr>
		<tr>
			<td>Goat anti-rabbit IgG PE-Alexa Fluor 610</td>
			<td>Thermofisher</td>
			<td>A-20981</td>
			<td>1:250 concentration</td>
		</tr>
		<tr>
			<td>Horse Serum</td>
			<td>Sigma</td>
			<td>H1138</td>
			<td></td>
		</tr>
		<tr>
			<td>Ilastik</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>https://www.ilastik.org/</td>
		</tr>
		<tr>
			<td>Ilastik FIJI plugin</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>https://www.ilastik.org/documentation/fiji_export/plugin</td>
		</tr>
		<tr>
			<td>Imaris File Converter</td>
			<td>Oxford Instruments</td>
			<td></td>
			<td>Software 2</td>
		</tr>
		<tr>
			<td>Imaris with cell module</td>
			<td>Oxford Instruments</td>
			<td></td>
			<td>Software 3</td>
		</tr>
		<tr>
			<td>kimwipe</td>
			<td>Kimtech</td>
			<td>34155</td>
			<td></td>
		</tr>
		<tr>
			<td>LasX Life Science software</td>
			<td>Leica</td>
			<td></td>
			<td>Software 1</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD20</td>
			<td>VWR</td>
			<td>CA95024-322</td>
			<td>1:40 concentration</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD38 APC-R700</td>
			<td>BD Biosciences</td>
			<td>564980</td>
			<td>1:20 concentration</td>
		</tr>
		<tr>
			<td>Normal Goat Serum</td>
			<td>JacksonImmunoResearch</td>
			<td>005-000-001</td>
			<td></td>
		</tr>
		<tr>
			<td>Normal Mouse Serum</td>
			<td>JacksonImmunoResearch</td>
			<td>015-000-001</td>
			<td></td>
		</tr>
		<tr>
			<td>Normal Rabbit Serum</td>
			<td>JacksonImmunoResearch</td>
			<td>011-000-001</td>
			<td></td>
		</tr>
		<tr>
			<td>Normal Rat Serum</td>
			<td>JacksonImmunoResearch</td>
			<td>012-000-120</td>
			<td></td>
		</tr>
		<tr>
			<td>Nuclear Yellow</td>
			<td>Abcam</td>
			<td>ab138903</td>
			<td>Dissolve in DMSO at a concentration of 2 mg/ml and store at 4&#176;C in the dark</td>
		</tr>
		<tr>
			<td>PAP pen</td>
			<td>Cedarlane</td>
			<td>MU22</td>
			<td></td>
		</tr>
		<tr>
			<td>PBS</td>
			<td>Gibco</td>
			<td>10010-023</td>
			<td></td>
		</tr>
		<tr>
			<td>Rabbit anti-human Ki67</td>
			<td>Abcam</td>
			<td>ab15580</td>
			<td>1:500 concentration</td>
		</tr>
		<tr>
			<td>Rabbit anti-mouse Iba1</td>
			<td>Wako</td>
			<td>019-19741</td>
			<td>1:500 concentration</td>
		</tr>
		<tr>
			<td>Rat anti-human Blimp1</td>
			<td>Thermofisher</td>
			<td>14-5963-82</td>
			<td>1:40 concentration</td>
		</tr>
		<tr>
			<td>Rat anti-mouse B220 Alexa Fluor 647</td>
			<td>BioLegend</td>
			<td>103226</td>
			<td>1:250 concentration</td>
		</tr>
		<tr>
			<td>Rat anti-mouse CD138</td>
			<td>Biolegend</td>
			<td>142502</td>
			<td>1:200 concentration</td>
		</tr>
		<tr>
			<td>Rat anti-mouse CD3 PE-eFluor 610</td>
			<td>Thermo Fisher Scientific</td>
			<td>61-0032-82</td>
			<td>1:40 concentration</td>
		</tr>
		<tr>
			<td>Rat anti-mouse CD4 Alexa Fluor 488</td>
			<td>BioLegend</td>
			<td>100529</td>
			<td>1:200 concentration</td>
		</tr>
		<tr>
			<td>Rat anti-mouse CD45 allophycocyanin-R700</td>
			<td>BD Biosciences</td>
			<td>565478</td>
			<td>1:50 concentration</td>
		</tr>
		<tr>
			<td>Rat anti-mouse IgD PerCP-eFluor 710</td>
			<td>Thermo Fisher Scientific</td>
			<td>46-5993-82</td>
			<td>1:50 concentration</td>
		</tr>
		<tr>
			<td>SP8 Confocal microscope</td>
			<td>Leica</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Triton X-100</td>
			<td>Sigma</td>
			<td>X100-500ml</td>
			<td></td>
		</tr>
		<tr>
			<td>Trueblack</td>
			<td>Biotium</td>
			<td>23007</td>
			<td></td>
		</tr>
		<tr>
			<td>Tween-20</td>
			<td>Sigma</td>
			<td>P7949-500ml</td>
			<td></td>
		</tr>
		<tr>
			<td>Ultracomp ebeads</td>
			<td>Thermofisher</td>
			<td>01-2222-42</td>
			<td></td>
		</tr>
	</tbody>
</table>

generating and analyzing high-parameter histology images with histoflow cytometry

The usage of histology to investigate immune cell diversity in tissue sections such as those derived from the central nervous system (CNS) is critically limited by the number of fluorescent parameters that can be imaged at a single time. Most immune cell subsets have been defined using flow cytometry by using complex combinations of protein markers, often requiring four or more parameters to conclusively identify, which is beyond the capabilities of most conventional microscopes. As flow cytometry dissociates tissues and loses spatial information, there is a need for techniques that can retain spatial information while interrogating the roles of complex cell types. These issues are addressed here by creating a method for expanding the number of fluorescent parameters that can be imaged by collecting the signals of spectrally overlapping fluorophores and using spectral unmixing to separate the signals of each individual fluorophore. These images are then processed using an analysis pipeline to take high-parameter histology images and extract single cells from these images so that the unique fluorescent properties of each cell can be analyzed at a single-cell level. Using flow cytometry-like gating strategies, cells can then be profiled into subsets and mapped back onto the histology sections to not only quantify their abundance, but also establish how they interact with the tissue environment. Overall, the simplicity and potential of using histoflow cytometry to study complex immune populations in histology sections is demonstrated.

<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66889/66889fig01.jpg" /> 
Figure 1: Histoflow cytometry workflow. Tissue sections are stained with spectrally overlapping dyes (step 1). Images are collected across individual excitation lasers paired with tunable bandpass filters to minimize spectral bleed-through between fluorophores (step 2). Spectral bleed-through between channels is corrected based on a compensatio...

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Described here is a method that can be used to image five or more fluorescent parameters by immunofluorescent microscopy. An analysis pipeline for extracting single cells from these images and conducting single-cell analysis through flow cytometry-like gating strategies is outlined, which can identify cell subsets in tissue sections.

Generating and Analyzing High-Parameter Histology Images with Histoflow Cytometry

The usage of histology to investigate immune cell diversity in tissue sections such as those derived from the central nervous system (CNS) is critically ...

generating-analyzing-high-parameter-histology-images-with-histoflow

Research

JoVE Journal

Neuroscience

278 Views.  University of Calgary. Described here is a method that can be used to image five or more fluorescent parameters by immunofluorescent microscopy. An analysis pipeline for extracting single cells from these images and conducting single-cell analysis through flow cytometry-like gating strategies is outlined, which can identify cell subsets in tissue sections.

Video: Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Membrane Potentials, Synaptic Responses, Neuronal Circuitry, Neuromodulation and Muscle Histology Using the Crayfish: Student Laboratory Exercises

The purpose of this report is to help develop an understanding of the effects caused by ion gradients across a biological membrane. Two aspects that influence a cell's membrane potential and which we address in these experiments are: (1) Ion concentration of K+ on the outside of the membrane, and (2) the permeability of the membrane to specific ions. The crayfish abdominal extensor muscles are in groupings with some being tonic (slow) and others phasic (fast) in their biochemical and physiological phenotypes, as well as in their structure; the motor neurons that innervate these muscles are correspondingly different in functional characteristics. We use these muscles as well as the superficial, tonic abdominal flexor muscle to demonstrate properties in synaptic transmission. In addition, we introduce a sensory-CNS-motor neuron-muscle circuit to demonstrate the effect of cuticular sensory stimulation as well as the influence of neuromodulators on certain aspects of the circuit. With the techniques obtained in this exercise, one can begin to answer many questions remaining in other experimental preparations as well as in physiological applications related to medicine and health. We have demonstrated the usefulness of model invertebrate preparations to address fundamental questions pertinent to all animals.

The experiments demonstrate an easy approach for students to gain experience in examining muscle structure, synaptic responses, the effects of ion gradients and permeability on membrane potentials. Also, a sensory-CNS-motor-muscle circuit is presented to show a means to test effects of compounds on a neuronal circuit.

The purpose of this report is to help develop an understanding of the effects caused by ion gradients across a biological membrane. Two aspects that ...

Combining Lipophilic dye, in situ Hybridization, Immunohistochemistry, and Histology

Going beyond single gene function to cut deeper into gene regulatory networks requires multiple mutations combined in a single animal. Such analysis of two or more genes needs to be complemented with in situ hybridization of other genes, or immunohistochemistry of their proteins, both in whole mounted developing organs or sections for detailed resolution of the cellular and tissue expression alterations. Combining multiple gene alterations requires the use of cre or flipase to conditionally delete genes and avoid embryonic lethality. Required breeding schemes dramatically enhance effort and cost proportional to the number of genes mutated, with an outcome of very few animals with the full repertoire of genetic modifications desired. Amortizing the vast amount of effort and time to obtain these few precious specimens that are carrying multiple mutations necessitates tissue optimization. Moreover, investigating a single animal with multiple techniques makes it easier to correlate gene deletion defects with expression profiles. We have developed a technique to obtain a more thorough analysis of a given animal; with the ability to analyze several different histologically recognizable structures as well as gene and protein expression all from the same specimen in both whole mounted organs and sections. Although mice have been utilized to demonstrate the effectiveness of this technique it can be applied to a wide array of animals. To do this we combine lipophilic dye tracing, whole mount in situ hybridization, immunohistochemistry, and histology to extract the maximal possible amount of data.

Combining Lipophilic dye, in situ Hybridization, Immunohistochemistry, and Histology

A combination of different techniques to maximize data collection from mouse tissue is presented.

Going beyond single gene function to cut deeper into gene regulatory networks requires multiple mutations combined in a single animal.  Such analysis of ...

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 ...

Mass Histology to Quantify Neurodegeneration in Drosophila

Progressive neurodegenerative diseases like Alzheimer&#39;s disease (AD) or Parkinson&#39;s disease (PD) are an increasing threat to human health worldwide. Although mammalian models have provided important insights into the underlying mechanisms of pathogenicity, the complexity of mammalian systems together with their high costs are limiting their use. Therefore, the simple but well-established Drosophila model-system&#160;provides an alternative for investigating the molecular pathways that are affected in these diseases. Besides behavioral deficits, neurodegenerative diseases are characterized by histological phenotypes such as neuronal death and axonopathy. To quantify neuronal degeneration and to determine how it is affected by genetic and environmental factors, we use a histological approach that is based on measuring the vacuoles in adult fly brains. To minimize the effects of systematic error and to directly compare sections from control and experimental flies in one preparation, we use the &#39;collar&#39; method for paraffin sections. Neurodegeneration is then assessed by measuring the size and/or number of vacuoles that have developed in the fly brain. This can either be done by focusing on a specific region of interest or by analyzing the entire brain by obtaining serial sections that span the complete head. Therefore, this method allows one to measure not only severe degeneration but also relatively mild phenotypes that are only detectable in a few sections, as occurs during normal aging.

Mass Histology to Quantify Neurodegeneration in Drosophila

Drosophila is widely used as a model system to study neurodegeneration. This protocol describes a method by which degeneration, as determined by vacuole formation in the brain, can be quantified. It also minimizes effects due to the experimental procedure by processing and sectioning control and experimental flies as one sample.

Progressive neurodegenerative diseases like Alzheimer&#39;s disease (AD) or Parkinson&#39;s disease (PD) are an increasing threat to human health ...

Analyzing Dendritic Morphology in Columns and Layers

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and columns to facilitate brain wiring during development and information processing in developed animals. Postsynaptic neurons elaborate dendrites in type-specific patterns in specific layers to synapse with appropriate presynaptic terminals. The fly medulla neuropil is composed of 10 layers and about 750 columns; each column is innervated by dendrites of over 38 types of medulla neurons, which match with the axonal terminals of some 7 types of afferents in a type-specific fashion. This report details the procedures to image and analyze dendrites of medulla neurons. The workflow includes three sections: (i) the dual-view imaging section combines two confocal image stacks collected at orthogonal orientations into a high-resolution 3D image of dendrites; (ii) the dendrite tracing and registration section traces dendritic arbors in 3D and registers dendritic traces to the reference column array; (iii) the dendritic analysis section analyzes dendritic patterns with respect to columns and layers, including layer-specific termination and planar projection direction of dendritic arbors, and derives estimates of dendritic branching and termination frequencies. The protocols utilize custom plugins built on the open-source MIPAV (Medical Imaging Processing, Analysis, and Visualization) platform and custom toolboxes in the matrix laboratory language. Together, these protocols provide a complete workflow to analyze the dendritic routing of Drosophila medulla neurons in layers and columns, to identify cell types, and to determine defects in mutants.

Here, we show how to analyze dendritic routing of Drosophila medulla neurons in columns and layers. The workflow includes a dual-view imaging technique to improve the image quality and computational tools for tracing, registering dendritic arbors to the reference column array and for analyzing the dendritic structures in 3D space.

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and ...

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 ...

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra- and inter-regional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra- and inter-regional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra- and inter-regional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

We present two analytical protocols that can be used to analyze intracranial electroencephalography data using the Statistical Parametric Mapping (SPM) software: time-frequency statistical parametric mapping analysis for neural activity, and dynamic causal modeling of induced responses for intra- and inter-regional connectivity.

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive ...

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Most methods for conducting meta-analysis of voxel-based neuroimaging studies do not assess whether effects are not null, but whether there is a convergence of peaks of statistical significance, and reduce the assessment of the evidence to a binary classification exclusively based on p-values (i.e., voxels can only be &ldquo;statistically significant&rdquo; or &ldquo;non-statistically significant&rdquo;). Here, we detail how to conduct a meta-analysis using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI), a novel method that uses a standard permutation test to assess whether effects are not null. We also show how to grade the strength of the evidence according to a set of criteria that considers a range of statistical significance levels (from more liberal to more conservative), the amount of data or the detection of potential biases (e.g., small-study effect and excess of significance). To exemplify the procedure, we detail the conduction of a meta-analysis of voxel-based morphometry studies in obsessive-compulsive disorder, and we provide all the data already extracted from the manuscripts to allow the reader to replicate the meta-analysis easily. SDM-PSI can also be used for meta-analyses of functional magnetic resonance imaging, diffusion tensor imaging, position emission tomography and surface-based morphometry studies.

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

We detail how to conduct a meta-analysis of voxel-based neuroimaging studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI).

Most methods for conducting meta-analysis of voxel-based neuroimaging studies do not assess whether effects are not null, but whether there is a ...

Visualizing and Analyzing Intracellular Transport of Organelles and Other Cargos in Astrocytes

Astrocytes are among the most abundant cell types in the adult brain, where they play key roles in a multiplicity of functions. As a central player in brain homeostasis, astrocytes supply neurons with vital metabolites and buffer extracellular water, ions, and glutamate. An integral component of the &#8220;tri-partite&#8221; synapse, astrocytes are also critical in the formation, pruning, maintenance, and modulation of synapses. To enable these highly interactive functions, astrocytes communicate among themselves and with other glial cells, neurons, the brain vasculature, and the extracellular environment through a multitude of specialized membrane proteins that include cell adhesion molecules, aquaporins, ion channels, neurotransmitter transporters, and gap junction molecules. To support this dynamic flux, astrocytes, like neurons, rely on tightly coordinated and efficient intracellular transport. Unlike neurons,&#160;where intracellular trafficking has been extensively delineated, microtubule-based transport in astrocytes has been less studied. Nonetheless, exo- and endocytic trafficking of cell membrane proteins and intracellular organelle transport orchestrates astrocytes&#8217; normal biology, and these processes are often affected in disease or in response to injury. Here we present a straightforward protocol to culture high quality murine astrocytes, to fluorescently label astrocytic proteins and organelles of interest, and to record their intracellular transport dynamics using time-lapse confocal microscopy. We also demonstrate how to extract and quantify relevant transport parameters from the acquired movies using available image analysis software (i.e., ImageJ/FIJI) plugins.

Here we describe an in vitro live-imaging method to visualize intracellular transport of organelles and trafficking of plasma membrane proteins in murine astrocytes. This protocol also presents an image analysis methodology to determine cargo transport itineraries and kinetics.

Astrocytes are among the most abundant cell types in the adult brain, where they play key roles in a multiplicity of functions. As a central player in ...

Dissection of Single Skeletal Muscle Fibers for Immunofluorescent and Morphometric Analyses of Whole-Mount Neuromuscular Junctions

The neuromuscular junction (NMJ) is a specialized point of contact between the motor nerve and the skeletal muscle. This peripheral synapse exhibits high morphological and functional plasticity. In numerous nervous system disorders, NMJ is an early pathological target resulting in neurotransmission failure, weakness, atrophy, and even in muscle fiber death. Due to its relevance, the possibility to quantitatively assess certain aspects of the relationship between NMJ components can help to understand the processes associated with its assembly/disassembly. The first obstacle when working with muscles is to gain the technical expertise to quickly identify and dissect without damaging their fibers. The second challenge is to utilize high-quality detection methods to obtain NMJ images that can be used to perform quantitative analysis. This article presents a step-by-step protocol for dissecting extensor digitorum longus and soleus muscles from rats. It also explains the use of immunofluorescence to visualize pre and postsynaptic elements of whole-mount NMJs. Results obtained demonstrate that this technique can be used to establish the microscopic anatomy of the synapsis and identify subtle changes in the status of some of its components under physiological or pathological conditions.

The ability to accurately detect neuromuscular junction components is crucial in evaluating modifications in its architecture because of pathological or developmental processes. Here we present a complete description of a straightforward method to obtain high-quality images of whole-mount neuromuscular junctions that can be used to perform quantitative measurements.

The neuromuscular junction (NMJ) is a specialized point of contact between the motor nerve and the skeletal muscle. This peripheral synapse exhibits high ...

Generating and Analyzing High-Parameter Histology Images with Histoflow Cytometry

Podsumowanie

Przeglądaj więcej filmów

Rozdziały w tym wideo

Membrane Potentials, Synaptic Responses, Neuronal Circuitry, Neuromodulation and Muscle Histology Using the Crayfish: Student Laboratory Exercises

Combining Lipophilic dye, in situ Hybridization, Immunohistochemistry, and Histology

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

Mass Histology to Quantify Neurodegeneration in Drosophila

Analyzing Dendritic Morphology in Columns and Layers

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

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Visualizing and Analyzing Intracellular Transport of Organelles and Other Cargos in Astrocytes

Dissection of Single Skeletal Muscle Fibers for Immunofluorescent and Morphometric Analyses of Whole-Mount Neuromuscular Junctions