Name: generating and analyzing high-parameter histology images with histoflow cytometry
Uploaded: 2024-06-21T15:00:00.000+00:00
Duration: 5 min 22 s
Description: The usage of histology to investigate immune cell diversity in tissue sections such as those derived from the central nervous system (CNS) is critically ...

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

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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 analysis pipeline is provided to segment individual cells from these images. By exporting the fluorescent signatures from these individual cells, it is demonstrated that single-cell analyses like those conducted in flow cytometry can similarly be applied to histology images. Whereas flow cytometry does not include spatial analysis, it is demonstrated that histoflow cytometry can map populations identified by gating strategies back onto tissue sections to evaluate their spatial localization. Overall, it is demonstrated that histoflow cytometry combines the strengths of flow cytometry and histology.
While histoflow cytometry has the benefit of retaining spatial information, it also has many of the same limitations that conventional histology has. While flow cytometry is great for quantifying cells across an entire tissue, histology is typically conducted by analyzing a small sample of tissue sections that are taken to be representative of the tissue. For histology to accurately quantify the number of cells in a tissue, one would need to image every tissue slice taken from the tissue and quantify the number of cells in each, which would be impractical. Another issue is that some cells will be split in half during the sectioning of the tissue, and there is no way to reconstruct these cells. Indeed, a limitation of the protocol listed here is that it does not include any methods to exclude cells at the edges of the tissue. Despite these limitations, one advantage histoflow cytometry has over flow cytometry is that there is no need to dissociate tissues into single-cell suspensions, which can introduce biases into the populations that are isolated or are inadequately captured22. Thus, while histoflow cytometry has several advantages over conventional flow cytometry, obtaining measurements across the entire tissue will continue to be a challenge.
Here, it is demonstrated that eight spectrally overlapping fluorescent parameters can be separated; however, others have successfully separated 13 fluorescent dyes17. The number of dyes that can be separated is dependent on the properties of the microscope in which the data is collected. Fluorescent dyes differ regarding their optimal excitation wavelengths and in their most common emission wavelengths. Microscopes that make use of these properties by using a wide range of excitation wavelengths to more precisely match the optimal excitation wavelengths of fluorophores and by tuning the emission detectors to precisely match the most frequently emitted wavelengths will maximize the separation of fluorescent dyes. Minimizing spectral overlap is essential for generating high-quality data as compensating for bleed-through across channels can potentially affect the dynamic range of detection of the fluorophores interacting with one another, and thus, separation based on emission and excitation is a critical first step in separating fluorophores. Depending on the properties of your microscope and the number of fluorescent parameters that need to be imaged, it may be required to put multiple fluorophores that share the same excitation wavelengths but differ in their emission wavelengths on the same laser lines. This is typically achieved by using base fluorescent dyes in combination with conjugated fluorescent dyes, where the conjugated dyes emit at longer wavelengths and thus can be distinguished based on their emission wavelengths. Overall, dyes must be distinguished either by their excitation or emission frequencies for effective separation.
Obtaining perfect compensation is complicated by several factors, including the spatial aspect of compensation. First, whether a fluorophore emits a photon is in part based on chance23, where the collected photons in each image are biased toward locations of dense staining. However, less densely stained areas may, by chance, emit a photon that appears in one of the overlapping channels but not in the main channel. As compensation is applied on a per-voxel basis, the spectral overlap will not be removed during compensation because of a lack of spatial colocalization. This can be seen in Figure 2, where compensation results in the locations of dense staining being reduced (the edges of the beads) but not the center of the beads, where there is minimal staining and poor overlap with the channels with bleed-through. This staining at the center of the beads is likely because of another issue that can affect compensation, which is that photons produced outside the focal plane may appear in voxels in the focal plane that are unlikely to be reproduced in both the main channel and the channel that needs to be compensated24. As a result, this 'noise' will not be effectively removed by compensation without introducing overcompensation. These issues can be partially addressed by using a line average, measuring the fluorescent values of a single voxel multiple times, and using the average to limit the impact of random chance events. If using a confocal microscope, the pinhole size can also be reduced to exclude out-of-focus photons from being incorporated into the images25,26. Using these fixes comes at the cost of additional imaging acquisition times, which can become extensive.
While this technique was developed using images taken from a confocal microscope, it will work with other types of microscopes. Others using similar methods have applied these methodologies to images taken from two-photon microscopes17 with great success. While this technique could be applied to images taken from widefield microscopes, there are several advantages associated with confocal microscopes that improve resolving whether a photon originated within the focal plane. As an accurate resolution of the location of photons (i.e., optical sectioning) improves the quality of compensation, it is expected that the quality of the compensation would be relatively poor. This could potentially be addressed by using deconvolution to remove out-of-focus photons to improve the resolution of the widefield images27. An important caveat that will determine whether deconvolution is compatible with this protocol is whether the fluorescence values in each channel remain proportional to one another across all the analyzed images. If the fluorescence values after deconvolution become arbitrary, then the same compensation matrix generated on one image is unlikely to be successfully applied to other images. Overall, the most important factor that determines whether a microscope can use this workflow is whether that microscope has detectors whose detection range can be flexibly tuned. If this is available, then multiple fluorophores stimulated through the same excitation can be resolved through the detection of their emission spectra within different detection windows.
The number of fluorescent parameters can be increased even further if combined with techniques to serially stain the same tissue multiple times13, such as IBEX14,15. Using serial staining, effectively unlimited numbers of parameters can be measured; however, this can be very time-consuming. Using a combination of the compensation methods discussed here and serial staining can considerably reduce the time required to take these images, as more channels can be imaged simultaneously for each round of staining. The analysis pipeline described here will scale for as many markers as desired and be compatible with serially stained tissue sections.
A critical step in this analysis pipeline is the segmentation of single cells from histology images. Finding effective strategies to accurately segment cells from histology images is a common issue28, and newer methods are continually being developed. While this method can effectively separate single cells from closely packed cells, the exact boundaries of the cells are often imperfect. It is therefore recommended that users limit the analysis to just the populations of cells that they are interested in, as the fewer the cells that need to be separated, the more accurate the cell boundaries will be. Users can also achieve better separation of cells by defining multiple populations in ilastik, allowing the program to segment out different cell populations. One issue with this approach, however, is that cells defined in this manner cannot be combined into the same 'cell' object in Software 3 and could possibly lead to double counting of cells. Nonetheless, if more accurate cell boundaries are critical to an experiment, this approach can be used. It is encouraged that users should experiment with newer segmentation algorithms as they are developed and incorporate them into this pipeline for even better results.
Several issues could arise when developing a new histoflow cytometry panel. First, it is critical that different fluorescent markers produce roughly similar amounts of fluorescence. If one fluorophore is much brighter than another, this can be problematic if this fluorophore bleeds into another fluorophore, as the bleed-through may overtake the real staining in that channel. This is seen in Figure 3, where Alexa Fluor 546 bleeds into PE-eFluor 610 severely. While compensation can still remove this bleed-through, there will often still be some lingering fluorescence due to out-of-focus light not being removed, which can make the compensation incomplete. Furthermore, most detectors used on microscopes do not have extensive dynamic ranges like those on flow cytometers. As a result, as the bleed-through in a channel increases, more of the dynamic range in that detector will be saturated with signals from other fluorophores and less from the fluorophore of interest. When compensation is removed from such a channel with extensive bleed-through, the true signal can be resolved; however, the difference between cells positive for that marker and those negative for it will become smaller and may result in an inability to resolve intermediate states or potentially even resolve positives from negatives. Thus, overlap between channels should be avoided. Second, the brightness of markers can change between different tissues. A panel of markers that works on lymph nodes, for example, may no longer work when used on the spleen or central nervous system because a marker may become too bright or dim, resulting in that channel bleeding too much into other channels or becoming overwhelmed by the bleed-through from other channels. In such a case, it may be necessary to switch the fluorophores on markers or switch the usage of primary conjugated antibodies to the usage of secondary antibodies. Lastly, it can be valuable to take advantage of differences in the localization of markers when there is excessive overlap between channels. If two channels overlap heavily with one another, but one is a nuclear marker, and the other is a cell surface marker, then the spectral overlap will minimally affect one another because compensation will not affect the same voxels. If possible, panels should be designed to ensure that overlapping channels differ in their localization or that the markers are expressed on separate cells.
Overall, it is demonstrated that histoflow cytometry is a versatile technique to analyze histology images at a single-cell level. Whereas older techniques can be limited to the analysis of cells not in direct cell-cell contact, this analysis pipeline can separate cells in contact, making it appropriate for the study of cells in tissues in homeostasis or during inflammation when immune cells cluster at sites of damage.

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><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&amp;nbsp;</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&#039;)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&amp;deg;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 compensation matrix generated using single color-stained tissue controls (step 3). Nuclei and cells in images are then identified using ilastik (step 4) and split into single cells in Software 3 (step 5). Additional parameters are incorporated before exporting all data from Software 3 into Software 4 to conduct flow cytometry analysis. Cell identities are then exported from Software 4 and mapped back to the cell IDs in the original tissue to resolve the spatial localization of cells in tissue sections (step 6). Money signs represent commercial software that requires purchase, and computer signs represent free software. The approximate time for each step is indicated beside the hourglass. Commercial software is listed in the Table of Materials. This figure is adapted from Jain et al.18. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66889/66889fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In Figure 3, lymph node tissues were stained with a rat anti-mouse CD138 antibody paired with a donkey anti-rat Dylight 405 secondary antibody, Nuclear Yellow, a rat anti-mouse CD4 antibody conjugated to Alexa Fluor 488, a rat anti-mouse IgD antibody conjugated to PerCP-eF710, a rabbit anti-mouse Iba1 antibody paired with a Donkey anti-rabbit Alexa Fluor 546 secondary antibody, a rat anti-mouse CD3&#949; antibody conjugated to PE-eF610, a rat anti-mouse B220 antibody conjugated with Alexa Fluor 647, and a rat anti-mouse CD45 antibody conjugated to APC-R700. A violet 405 nm laser was used to stimulate the Dylight 405 and Nuclear yellow dyes, and their emissions were detected in the ranges of 408-438 nm and 460-530 nm, respectively. A green 488 nm laser was used to stimulate the Alexa Fluor 488 and PerCP-eF710 dyes, and their emissions were detected in the ranges of 495-545 nm and 682-750 nm, respectively. A yellow-green 552 nm laser was used to stimulate the Alexa Fluor 546 and PE-eF610 dyes, and their emissions were detected in the ranges of 557-600 nm and 615-652 nm, respectively. A red 640 nm laser was used to stimulate the Alexa Fluor 647 and APC-R700 dyes, and their emissions were detected in the 647-682 nm and 700-750 nm ranges, respectively.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66889/66889fig02.jpg" /> 
Figure 2: Examples of poor and quality compensation. Beads were stained with an antibody conjugated to PE-eFluor 610 and imaged in the channels corresponding to the PE-eFluor 610 (PE-eF610) and Alexa Fluor 546 (A546) channels. Different degrees of compensation were applied to the same image in the A546 channel (10% reduction for undercompensation, 28% reduction for correct compensation, and 60% reduction for overcompensation) to remove the bleed-through from the PE-eF610 fluorophore, resulting in different degrees of separation. To better visualize the degrees of compensation, a brightened version of the bleed-through images is provided. The yellow arrow points to the same area in each image corresponding to an area where there is real staining in the PE-eF610 channel. Scale bars: 4 &#181;m. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66889/66889fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66889/66889fig03.jpg" /> 
Figure 3: Fluorescence compensation using stained tissue sections. (A-C) Lymph node tissue sections were stained with single Abs conjugated to the listed fluorophores. Uncompensated single-stained samples were then imaged across all channels (A, top), and compensation was applied to the single-color controls (A, bottom). (B) Bleed-through between channels in (A) was quantified (mean fluorescence intensity of the current image divided by the mean fluorescence intensity of the channel of the current fluorophore) and presented in heatmaps. White squares represent values exceeding the 100% bleed-through. (C) Spectral overlap is assessed in the uncompensated and cell-based compensation conditions by comparing the spectral separation of Iba1-A546 and CD3-PEeF610 in fully stained samples. Scale bars: 30 &#181;m. This figure is adapted from Jain et al.18. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66889/66889fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
By following this protocol (summarized in Figure 1), spectrally overlapping fluorescent dyes should be effectively separated, as shown in Figure 3A,B. While complete separation of the dyes may not be possible, the separation should be sufficient to clearly differentiate different cell types (Figure 3C). If there are still shadows of spectrally overlapping cells in another channel, this may indicate that there is still bleed-through occurring, and the images may need reprocessing (Figure 2). Additionally, overcompensation will be apparent if the compensated area falls below the levels of fluorescence associated with background staining and will appear as black areas where the bleed-through once was (Figure 2). Overcompensation can lead to the loss of real signal in the channel to which compensation has been applied and must be avoided. Nonetheless, if this protocol has been followed correctly, there should be no issues with compensation, and thus, the researcher should consider whether any apparent bleed-through is artificial or a part of the biology.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/66889/66889fig04.jpg" /> 
Figure 4: Example of an ilastik-based nucleus and cell detection. The nuclear yellow staining (top-left) was used to identify nuclei (top-right in yellow) and what is not a nucleus (in blue). Representative CD45 staining (shown in bottom-left) is used to identify cells (bottom-right in yellow) and what is not a cell (in blue). This figure is adapted from Jain et al.18. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66889/66889fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Once dyes are accurately separated, splitting the tissue into individual cells is essential for conducting single-cell analyses. The protocol described here uses user-defined machine learning (Figure 4) to separate cells even when they are closely compacted together, as shown in the lymph nodes shown in Figure 6.
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/66889/66889fig05.jpg" /> 
Figure 5: Evaluating whether a gate has been adjusted correctly. Representative CD138 staining (colored in cyan) in a lymph node is shown, and three separate gates (the dot plot to the left) were made to identify CD138+ cells in the tissue. An overly conservative estimate of CD138+ cells is shown as the blue gate and mapped to the tissue in blue. A balanced estimate of CD138+ cells is shown as the green gate and mapped to the tissue in green. An overly permissive estimate of CD138+ cells is shown as the red gate and mapped to the tissue in red. The yellow circle identifies an area where CD138+ cells are not being identified by the overly conservative gate, and the magenta circle identifies an area with no CD138+ cells, which is claimed to contain CD138+ cells by the overly permissive gate. Scale bars: 30 &#181;m. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66889/66889fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Care should be taken when determining the precise positions of gates in the flow cytometry analysis (shown in Figure 5) to ensure that these gates are not overestimating or underestimating the abundance of cells. This should be determined by mapping the cells back onto the tissue, as in Figure 5, to see if the cells that do express your markers of interest are being captured in the analysis. If too many cells are being selected (for example, cells that do not express CD138 are being selected as being CD138+), then the gates should be shifted to be less permissive, and if too few cells are being selected, then the gates should be made more permissive. Gates should be optimized for selectivity such that the upstream gates are optimized first, then each subsequent gate in the chain is optimized next. In the end, effective optimization of a gating strategy should be capable of resolving cell subsets of interest (Figure 6 and Figure 7).
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/66889/66889fig06.jpg" /> 
Figure 6: Flow cytometry-based identification of cells in tissue sections. (A) Fluorescent values exported from single cells from Software 3 in lymph node sections were analyzed in Software 4 to identify the stated cell populations based on the shown gating strategy. (B) Populations identified in Software 4 were mapped back onto the same tissue section, and surfaces were generated for each population. The CD45 stain is not shown. Scale bar: 30 &#181;m. This figure is adapted from Jain et al.18. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66889/66889fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/66889/66889fig07.jpg" /> 
Figure 7: Breakdown of individual stains and populations in mouse lymph nodes. Individual stains for immune cell markers are shown in the top panels, and their associated gated population identified in Figure 6A is shown below each single stain. Scale bar: 30 &#181;m, shown in the top left panel. This figure is adapted from Jain et al.18. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66889/66889fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
This technique has been previously18 used to characterize immune cell infiltrates into spinal cords during neuroinflammation and validated that equivalent proportions of infiltrating immune cells are found using histoflow cytometry and flow cytometry. The technique has also been validated, demonstrating that it works in mouse spleens and brains and can identify differences between treatment conditions, which were reproduced in identical flow cytometry experiments (unpublished). This technique has also been used to analyze the spinal cords of mice receiving stereotactic injections of the CNS models.
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/66889/66889fig08.jpg" /> 
Figure 8: Separation of 8 dyes in a human tonsil section. Human tonsil sections were stained with CD20 to identify B cells and IgM, IgA, and IgG to identify plasma cells. Blimp1 and CD38 stains were incorporated to further validate plasma cell identities, and Ki67 to identify proliferating cells. The Nuclear yellow stain is not shown in the combined image. Scale bar: 22 &#181;m. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66889/66889fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
This technique can also be applied to analyze human tissue sections. As an example of staining within human tissues, human tonsil sections were stained with a selection of 8 markers: a goat anti-human IgM antibody conjugated to Dylight 405, Nuclear Yellow, a goat anti-human IgA antibody conjugated to Alexa Fluor 488, a mouse anti-human CD20 antibody paired with a goat anti-mouse F(ab&#39;)2 fragment conjugated to PerCP-eFluor 710, a goat anti-human IgG conjugated to Cy3, a rabbit anti-human Ki67 antibody paired with a goat anti-rabbit antibody conjugated to PE-Alexa Fluor 610, a rat anti-human Blimp1 antibody paired with an donkey anti-rat antibody conjugated to Alexa Fluor 647, and a mouse anti-human CD38 antibody conjugated to APC-R700. After compensation, the separation of these markers in the tonsil section can be seen (Figure 8). This demonstrates the utility of this technique in different tissues and species.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Solution name</td>
			<td colspan="2">Composition</td>
			<td colspan="4">How to make</td>
		</tr>
		<tr>
			<td>Blocking buffer</td>
			<td colspan="2">10% Horse Serum, 1% BSA, and 0.1% cold fish stain gelatin in PBS</td>
			<td colspan="4">Add 5 mL horse serum, 0.5 g BSA, 50 &#956;L cold fish stain gelatin to 45 mL of PBS. Aliquot these solution into 15 mL tubes and store them at -20 &#176;C until needed.</td>
		</tr>
		<tr>
			<td>Perm and blocking buffer</td>
			<td colspan="2">10% Horse Serum, 1% BSA, 0.1% cold fish stain gelatin, 0.1% Triton X-100, 0.05% Tween-20 in PBS</td>
			<td colspan="4">Add 5 mL horse serum, 0.5 g BSA, 50 &#956;L cold fish stain gelatin, 50 &#956;L Triton X-100, and 25 &#956;L Tween-20 to 45 mL of PBS. Aliquot these solution into 15 mL tubes and store them at -20 &#176;C until needed.</td>
		</tr>
		<tr>
			<td>Perm and staining buffer</td>
			<td colspan="2">1% BSA, 0.1% cold fish stain gelatin, 0.5% Triton X-100 in PBS</td>
			<td colspan="4">Add 0.5 g BSA, 50 &#956;L cold fish stain gelatin, and 250 &#956;L Triton X-100 to 50 mL of PBS. Aliquot these solution into 15 mL tubes and store them at -20 &#176;C until needed.</td>
		</tr>
		<tr>
			<td>Quenching buffer</td>
			<td colspan="2">5% Trueblack, 66.5% ethanol, 28.5% water</td>
			<td colspan="4">Preheat Trueblack solution at 70 &#176;C for 5 min. Then add 50 &#956;L of Trueblack to 950 &#956;L of 70% ethanol to make the Trueblack buffer. Make this fresh each time.You will need ~500 &#956;L per slide although the specific amounts will depend on the nature of your tissues.</td>
		</tr>
		<tr>
			<td>Staining buffer</td>
			<td colspan="2">1% BSA and 0.1% cold fish stain gelatin in PBS</td>
			<td colspan="4">Add 0.5 g BSA and 50 &#956;L cold fish stain gelatin to 50 mL of PBS. Aliquot these solution into 15 mL tubes and store them at -20 &#176;C until needed.</td>
		</tr>
	</tbody>
</table>
Table 1: A list of the solutions and their compositions needed for staining tissue sections.
Supplementary Coding File 1: A Python script that collects the fluorescence values from each channel and creates one summary file. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66889/Supplementary Coding File 1.zip" target="_blank">Please click here to download this File.</a>
Supplementary Coding File 2: A Python script that creates a list of all the ID numbers of gated cells into a text file. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66889/Supplementary Coding File 2v2.zip" target="_blank">Please click here to download this File.</a>

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

Nanyang Technological University

Research

JoVE Journal

Neuroscience

296 Views.  University of Calgary. Ons overkoepelende doel is om te bestuderen hoe cellen van het immuunsysteem en gliacellen in het centrale zenuwstelsel bijdragen aan de ontwikkeling en progressie van neurologische aandoeningen zoals multiple sclerose. Immunofluorescerende microscopie is een standaard en veelgebruikte techniek in biologische disciplines en is essentieel voor neurowetenschappelijk en immunologisch onderzoek. Een van de belangrijkste problemen die onderzoekers teisteren, is dat immunofluorescerende microscopie...

Video: Auteur in de schijnwerpers: Verbeterd multiplex immunofluorescerend microscopieprotocol voor neurowetenschappelijk onderzoek

Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. However, the analysis and quantification of cellular localization is difficult, as in many cases it relies on manual counting, thus bearing the risk of introducing a rater-dependent bias and reducing interrater reliability. Moreover, it is often difficult to judge whether the co-localization between two cells results from random positioning, especially when cell types differ strongly in the frequency of their occurrence. Here, a method for unbiased quantification of cellular co-localization in the bone marrow is introduced. The protocol describes the sample preparation used to obtain histological sections of whole murine long bones including the bone marrow, as well as the staining protocol and the acquisition of high-resolution images. An analysis workflow spanning from the recognition of hematopoietic and non-hematopoietic cell types in 2-dimensional (2D) bone marrow images to the quantification of the direct contacts between those cells is presented. This also includes a neighborhood analysis, to obtain information about the cellular microenvironment surrounding a certain cell type. In order to evaluate whether co-localization of two cell types is the mere result of random cell positioning or reflects preferential associations between the cells, a simulation tool which is suitable for testing this hypothesis in the case of hematopoietic as well as stromal cells, is used. This approach is not limited to the bone marrow, and can be extended to other tissues to permit reproducible, quantitative analysis of histological data.

A strategy to quantitatively analyze histological data in the bone marrow is presented. Confocal microscopy of fluorescently labeled cells in tissue sections results in 2-dimensional images, which are automatically analyzed. Co-localization analyses of different cell types are compared to data from simulated images, giving quantitative information about cellular interactions.

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. ...

Developmental Biology

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy (ATOM)

Scaling the number of measurable parameters, which allows for multidimensional data analysis and thus higher-confidence statistical results, has been the main trend in the advanced development of flow cytometry. Notably, adding high-resolution imaging capabilities allows for the complex morphological analysis of cellular/sub-cellular structures. This is not possible with standard flow cytometers. However, it is valuable for advancing our knowledge of cellular functions and can benefit life science research, clinical diagnostics, and environmental monitoring. Incorporating imaging capabilities into flow cytometry compromises the assay throughput, primarily due to the limitations on speed and sensitivity in the camera technologies. To overcome this speed or throughput challenge facing imaging flow cytometry while preserving the image quality, asymmetric-detection time-stretch optical microscopy (ATOM) has been demonstrated to enable high-contrast, single-cell imaging with sub-cellular resolution, at an imaging throughput as high as 100,000 cells/s. Based on the imaging concept of conventional time-stretch imaging, which relies on all-optical image encoding and retrieval through the use of ultrafast broadband laser pulses, ATOM further advances imaging performance by enhancing the image contrast of unlabeled/unstained cells. This is achieved by accessing the phase-gradient information of the cells, which is spectrally encoded into single-shot broadband pulses. Hence, ATOM is particularly advantageous in high-throughput measurements of single-cell morphology and texture &#8211; information indicative of cell types, states, and even functions. Ultimately, this could become a powerful imaging flow cytometry platform for the biophysical phenotyping of cells, complementing the current state-of-the-art biochemical-marker-based cellular assay. This work describes a protocol to establish the key modules of an ATOM system (from optical frontend to data processing and visualization backend), as well as the workflow of imaging flow cytometry based on ATOM, using human cells and micro-algae as the examples.

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

This protocol describes the implementation of an asymmetric-detection time-stretch optical microscopy system for single-cell imaging in ultrafast microfluidic flow and its applications in imaging flow cytometry.

Scaling the number of measurable parameters, which allows for multidimensional data analysis and thus higher-confidence statistical results, has been the ...

Engineering

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as hydrodynamics, ecology and environmental engineering. Modeling bacterial transport in porous environments at different spatial scales is critical to better predict the consequences of bacterial transport, yet current models often fail to up-scale from laboratory to field conditions. Here, we introduce experimental tools to study bacterial transport in porous media at two spatial scales. The aim of these tools is to obtain macroscopic observables (such as breakthrough curves or deposition profiles) of bacteria injected into transparent porous matrices. At the small scale (10-1000 &#181;m), microfluidic devices are combined with optical video-microscopy and image processing to obtain breakthrough curves and, at the same time, to track individual bacterial cells at the pore scale. At larger scale, flow cytometry is combined with a self-made robotic dispenser to obtain breakthrough curves. We illustrate the utility of these tools to better understand how bacteria are transported in complex porous media such as the hyporheic zone of streams. As these tools provide simultaneous measurements across scales, they pave the way for mechanism-based models, critically important for upscaling. Application of these tools may not only contribute to the development of novel bioremediation applications but also shed new light on the ecological strategies of microorganisms colonizing porous substrates.

Breakthrough curves (BTCs) are efficient tools to study the transport of bacteria in porous media. Here we introduce tools based on fluidic devices in combination with microscopy and flow cytometric counting to obtain BTCs.

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as ...

Environment

Qualitative and Quantitative Analysis of the Immune Synapse in the Human System Using Imaging Flow Cytometry

The immune synapse is the area of communication between T cells and antigen-presenting cells (APCs). T cells polarize surface receptors and proteins towards the immune synapse to assure a stable binding and signal exchange. Classical confocal, TIRF, or super-resolution microscopy have been used to study the immune synapse. Since these methods require manual image acquisition and time-consuming quantification, the imaging of rare events is challenging. Here, we describe a workflow that enables the morphological analysis of tens of thousands of cells. Immune synapses are induced between primary human T cells in pan-leukocyte preparations and Staphylococcus aureus enterotoxin B (SEB)-loaded Raji cells as APCs. Image acquisition is performed with imaging flow cytometry, also called In-Flow microscopy, which combines features of a flow cytometer and a fluorescence microscope. A complete gating strategy for identifying T cell/APC couples and analyzing the immune synapses is provided. As this workflow allows the analysis of immune synapses in unpurified pan-leukocyte preparations and hence requires only a small volume of blood (i.e., 1 mL), it can be applied to samples from patients. Importantly, several samples can be prepared, measured, and analyzed in parallel.

Here, we describe a complete workflow for the qualitative and quantitative analysis of immune synapses between primary human T cells and antigen-presenting cells. The method is based on imaging flow cytometry, which allows the acquisition and evaluation of several thousand cell images within a relatively short period of time.

The immune synapse is the area of communication between T cells and antigen-presenting cells (APCs). T cells polarize surface receptors and proteins ...

Immunology and Infection

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

With the advent of flow cytometers capable of measuring an increasing number of parameters, scientists continue to develop larger panels to phenotypically explore characteristics of their cellular samples. However, these technological advancements yield high-dimensional data sets that have become increasingly difficult to analyze objectively within traditional manual-based gating programs. In order to better analyze and present data, scientists partner with bioinformaticians with expertise in analyzing high-dimensional data to parse their flow cytometry data. While these methods have been shown to be highly valuable in studying flow cytometry, they have yet to be incorporated in a straightforward and easy-to-use package for scientists who lack computational or programming expertise. To address this need, we have developed ExCYT, a MATLAB-based Graphical User Interface (GUI) that streamlines the analysis of high-dimensional flow cytometry data by implementing commonly employed analytical techniques for high-dimensional data including dimensionality reduction by t-SNE, a variety of automated and manual clustering methods, heatmaps, and novel high-dimensional flow plots. Additionally, ExCYT provides traditional gating options of select populations of interest for further t-SNE and clustering analysis as well as the ability to apply gates directly on t-SNE plots. The software provides the additional advantage of working with either compensated or uncompensated FCS files. In the event that post-acquisition compensation is required, the user can choose to provide the program a directory of single stains and an unstained sample. The program detects positive events in all channels and uses this select data to more objectively calculate the compensation matrix. In summary, ExCYT provides a comprehensive analysis pipeline to take flow cytometry data in the form of FCS files and allow any individual, regardless of computational training, to use the latest algorithmic approaches in understanding their data.

ExCYT is a MATLAB-based Graphical User Interface (GUI) that allows users to analyze their flow cytometry data via commonly employed analytical techniques for high-dimensional data including dimensionality reduction via t-SNE, a variety of automated and manual clustering methods, heatmaps, and novel high-dimensional flow plots.

With the advent of flow cytometers capable of measuring an increasing number of parameters, scientists continue to develop larger panels to phenotypically ...

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

T cells utilize different metabolic programs to match their functional needs during differentiation and proliferation. Mitochondria are crucial cellular components responsible for supplying cell energy; however, excess mitochondria also produce reactive oxygen species (ROS) that could cause cell death. Therefore, the number of mitochondria must constantly be adjusted to fit the needs of the cells. This dynamic regulation is achieved in part through the function of lysosomes that remove surplus/damaged organelles and macromolecules. Hence, cellular mitochondrial and lysosomal contents are key indicators to evaluate the metabolic adjustment of cells. With the development of probes for organelles, well-characterized lysosome or mitochondria-specific dyes have become available in various formats to label cellular lysosomes and mitochondria. Multicolor flow cytometry is a common tool to profile cell phenotypes, and has the capability to be integrated with other assays. Here, we present a detailed protocol of how to combine organelle-specific dyes with surface markers staining to measure the amount of lysosomes and mitochondria in different T cell populations on a flow cytometer.

This article illustrates a powerful method to quantify mitochondria or lysosomes in living cells. The combination of lysosome- or mitochondria-specific dyes with fluorescently conjugated antibodies against surface markers allows the quantification of these organelles in mixed cell populations, like primary cells harvested from tissue samples, by using multicolor flow cytometry.

T cells utilize different metabolic programs to match their functional needs during differentiation and proliferation. Mitochondria are crucial cellular ...

High-Dimensionality Flow Cytometry for Immune Function Analysis of Dissected Implant Tissues

The success of implanting laboratory-grown tissue or a medical device in an individual is subject to the immune response of the recipient host. Considering an implant as a foreign body, a hostile and dysregulated immune response may result in the rejection of the implant, while a regulated response and regaining of homeostasis can lead to its acceptance. Analyzing the microenvironments of implants dissected out under in vivo or ex vivo&#160;settings can help in understanding the pattern of immune response, which can ultimately help in developing new generations of biomaterials. Flow cytometry is a well-known technique for characterizing immune cells and their subsets based on their cell surface markers. This review describes a protocol based on manual dicing, enzymatic digestion, and filtration through a cell strainer for the isolation of uniform cell suspensions from dissected implant tissue. Further, a multicolor flow cytometry staining protocol has been explained, along with steps for initial cytometer settings to characterize and quantify these isolated cells by flow cytometry.

Isolation of cells from dissected implants and their characterization by flow cytometry can significantly contribute to understanding the pattern of immune response against implants. This paper describes a precise method for the isolation of cells from dissected implants and their staining for flow cytometric analysis.

The success of implanting laboratory-grown tissue or a medical device in an individual is subject to the immune response of the recipient host. ...

Bioengineering

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

The complexity of data generated by mass cytometry has necessitated new tools to rapidly visualize analytic outcomes. Clustering methods like Cytosplore or FlowSOM are used for the visualization and identification of cell clusters. For downstream analysis, a newly developed R package, Cytofast, can generate a rapid visualization of results from clustering methods. Cytofast takes into account the phenotypic characterization of cell clusters, calculates the cell cluster abundance, then quantitatively compares groups. This protocol explains the applications of Cytofast to the use of mass cytometry data based on modulation of the immune system in the tumor microenvironment (i.e., the natural killer [NK] cell response) upon tumor challenge followed by immunotherapy (PD-L1 blockade). Demonstration of the usefulness of Cytofast with FlowSOM and Cytosplore is shown. Cytofast rapidly generates visual representations of group-related immune cell clusters and correlations with immune system composition. Differences are observed in the clustering analysis, but separation between groups are visible with both clustering methods. Cytofast visually shows the patterns induced by PD-L1 treatment that include a higher abundance of activated NK cell subsets, expressing a higher intensity of activation markers (i.e., CD54 or CD11c).

Cytofast is a visualization tool used to analyze output from clustering. Cytofast can be used to compare two clustering methods: FlowSOM and Cytosplore. Cytofast can rapidly generate a quantitative and qualitative overview of mass cytometry data and highlight the main differences between different clustering algorithms.

The complexity of data generated by mass cytometry has necessitated new tools to rapidly visualize analytic outcomes. Clustering methods like Cytosplore ...

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Few techniques can assess phenotype and fate for the same cell simultaneously. Most of the current protocols used to characterize phenotype, although able to generate large datasets, necessitate the destruction of the cell of interest, making it impossible to assess its functional fate. Heterogeneous biological differentiating systems like hematopoiesis are therefore difficult to describe. Building on cell division tracking dyes, we further developed a protocol to simultaneously determine kinship, division number, and differentiation status for many single hematopoietic progenitors. This protocol allows the assessment of the ex vivo differentiation potential of murine and human hematopoietic progenitors, isolated from various biological sources. Moreover, as it is based on flow cytometry and a limited number of reagents, it can quickly generate a large amount of data, at the single-cell level, in a relatively inexpensive manner. We also provide the analytical pipeline for single-cell analysis, combined with a robust statistical framework. As this protocol allows the linking of cell division and differentiation at the single-cell level, it can be used to quantitatively assess symmetric and asymmetric fate commitment, the balance between self-renewal and differentiation, and the number of divisions for a given commitment fate. Altogether, this protocol can be used in experimental designs aiming to unravel the biological differences between hematopoietic progenitors, from a single-cell perspective.

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Presented here is a flow cytometry-based technique that allows for simultaneously measuring the number of cell divisions, surface cell phenotype, and cellular kinship. Those properties can be tested statistically using a permutation-based framework.

Few techniques can assess phenotype and fate for the same cell simultaneously. Most of the current protocols used to characterize phenotype, although able ...

High-Throughput, Multi-Image Cryohistology of Mineralized Tissues

There is an increasing need for efficient phenotyping and histopathology of a variety of tissues. This phenotyping need is evident with the ambitious projects to disrupt every gene in the mouse genome. The research community needs rapid and inexpensive means to phenotype tissues via histology. Histological analyses of skeletal tissues are often time consuming and semi-quantitative at best, regularly requiring subjective interpretation of slides from trained individuals. Here, we present a cryohistological paradigm for efficient and inexpensive phenotyping of mineralized tissues. First, we present a novel method of tape-stabilized cryosectioning that preserves the morphology of mineralized tissues. These sections are then adhered rigidly to glass slides and imaged repeatedly over several rounds of staining. The resultant images are then aligned either manually or via computer software to yield composite stacks of several layered images. The protocol allows for co-localization of numerous molecular signals to specific cells within a given section. In addition, these fluorescent signals can be quantified objectively via computer software. This protocol overcomes many of the shortcomings associated with histology of mineralized tissues and can serve as a platform for high-throughput, high-content phenotyping of musculoskeletal tissues moving forward.

In this manuscript, we present a high-throughput, semi-automated cryohistology platform to produce aligned composite images of multiple response measures from several rounds of fluorescent imaging on frozen sections of mineralized tissues.

There is an increasing need for efficient phenotyping and histopathology of a variety of tissues. This phenotyping need is evident with the ambitious ...

Generating and Analyzing High-Parameter Histology Images with Histoflow Cytometry

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