We acknowledge funding from the European Commission of a H2020 MSCA award under proposal number 675743 (ISPIC). We thank Tetje van der Sluis and Iris Pardieck for testing the protocol.

All animal experiments were approved by the Animal Experiments Committee of LUMC and were executed according to the animal experimentation guidelines of the LUMC in compliance with the guidelines of Dutch and European committees.
NOTE: For experimental set-up, C57BL/6 mice were subcutaneously inoculated on the right flank with the murine colon tumor MC38 at a concentration of 0.3 x 106 cells/200 &#181;L of phosphate-buffered saline (PBS). After 10 days, when tumors were palpable, the mice were treated with PD-L1 blocking antibodies (clone MIH-5, 200 &#181;g/mouse, intra-peritoneal injection) or were mock-treated. The tumors were resected 3 days later after PD-L1 injection, processed ex vivo, and analyzed by CyTOF mass cytometry using 38 markers13.
1. Equipment and Software for Data Analysis
NOTE: Use a computer (Windows 7 or newer) and processor I5 at 2.4 GHz or equivalent, installed memory RAM 6 GB, and 10 GB of free hard drive space. The R package Cytofast uses existing functions: mainly flowCore, pheatmap, and ggplot. The command lines to be executed in R are included in the protocol. The resource for R instructions can be found at <a href="https://education.rstudio.com/" target="_blank">https://education.rstudio.com/</a>.
<ol>
	<li>To install the Cytofast package, start R (version &#34;3.6&#34;) and install Bioconductor version 3.9 by entering the following code: 
	 
	if (!requireNamespace(&#34;BiocManager&#34;, quietly = TRUE)) 
	install.packages(&#34;BiocManager&#34;) 
	BiocManager::install(&#34;cytofast&#34;)</li>
	<li>Ensure that the package is loaded in the desired environment by running the following: 
	 
	library(cytofast)</li>
</ol>
2. Creating Clusters
NOTE: To showcase the two clustering methods Cytosplore and FlowSOM with Cytofast, the NK cells (CD161+) in the tumor micro-environment 3 days after PD-L1 treatment are analyzed.
<ol>
	<li>Clustering performed by Cytosplore
	<ol>
		<li>After downloading and installing Cytosplore, hosted at &#60;www.Cytosplore.org&#62;, upload the .fcs files (Supplementary Files 1.1&#8211;1.8 [Cytosplore input files]) by clicking File | Open FCS File(s) in Cytosplore. Add a unique sample tag as channel by clicking Add unique sample tag as channel when prompted and select a cofactor for hyperbolic arcsinh transformation (default is 5).</li>
		<li>Select Run H-SNE, run a HSNE level of 3, and wait for the map to be generated. 
		NOTE: This step may require some time, depending on the number of cells analyzed and level of HSNE chosen.</li>
		<li>On the first HSNE level, check the cells that are positive for CD161. Select the CD161+ cells and right-click Zoom into selection. At the second level, repeat the procedure to reach the third level with only CD161+ events.</li>
		<li>Once the last tSNE map is generated, save the clusters defined by Cytosplore by right-clicking on the tSNE map and choose Save Clusters. Choose the directory of the output files as prompted by Cytosplore and note this location, because this directory will then be used to load the .fcs files into R. 
		NOTE: The number of subsets can be manually changed by changing the sigma value. The sigma value is set by default at 30; however, the actual number of subsets depends on the input. Here, Cytosplore detected 10 different subsets, with each file representing one subset.</li>
		<li>Use a simple name (characters only) when renaming the output files, which will make identification and further handling easier. Save the output files by selecting Save. 
		NOTE: After saving, a folder is being created by Cytosplore with the .fcs files, with each file corresponding to the identified clusters in Cytosplore. The next step will be loading the files into R with the help of Cytofast. Here, the generated output files are provided in Supplementary Files 2.1&#8211;2.10 (Cytosplore output files).</li>
		<li>Load the output files generated by Cytosplore into R with the designated function: readCytosploreFCS. 
		 
		dirFCS &#60;- &#34;C:\\Users\\username\\Desktop\\tostudy&#34; 
		cfData &#60;- readCytosploreFCS(dir = dirFCS, colNames = &#34;description&#34;)</li>
		<li>Clean the data by removing some parameters such as &#34;Time&#34; and &#34;Background&#34;. Check the position of the column related to its unnecessary parameters and remove it from the matrix. 
		 
		colnames(cfData@expr) 
		cfData@expr &#60;- cfData@expr[,-c(3,4,6,8:10,46:49,51:54)] 
		 
		NOTE: The unnecessary columns can be seen by reading the column names of the generated matrix. By running colnames(cfData@expr) once more, ensure that only the desired parameters are obtained.</li>
		<li>Re-order the markers so that lineage markers are displayed first, followed by functional markers. 
		 
		cfData@expr &#60;- cfData@expr[,c(1,2,3,35,36,31,9,10,18,8,37,20, 
		29,40,5,30,33,11,34,14,19, 
		32,28,6,7,4,12,13,17,16,15, 
		21,22,24,25,26,27,38,39)] 
		 
		NOTE: Step 2.1.8 is optional.</li>
		<li>Link the meta datafile to the generated data from Cytosplore by uploading the spreadsheet metafile containing clinical information (Supplementary File 3). 
		 
		library(readxl) 
		meta &#60;- read_excel(&#34;C:\\Users\\username\\Desktop\\sample_id.xlsx&#34;) 
		cfData@samples &#60;- data.frame(meta) 
		 
		NOTE: The clustering being performed by Cytosplore is now finished. An alternative clustering option to Cytosplore is FlowSOM and is described in section 2.2. Once one of the two clustering steps is performed, proceed with the visualization step (section 3).</li>
	</ol>
	</li>
	<li>Clustering performed by FlowSOM
	<ol>
		<li>First install FlowSOM in R by running the following command: 
		 
		if (!requireNamespace(&#34;BiocManager&#34;, quietly = TRUE)) 
		install.packages(&#34;BiocManager&#34;) 
		BiocManager::install(&#34;FlowSOM&#34;) 
		library(FlowSOM)</li>
		<li>Install the flowCore package using a similar method and load it in the environment by running the following: 
		 
		if (!requireNamespace(&#34;BiocManager&#34;, quietly = TRUE)) 
		install.packages(&#34;BiocManager&#34;) 
		BiocManager::install(&#34;flowCore&#34;) 
		library(FlowSOM)</li>
		<li>Load the raw data provided in Supplementary Files 4.1&#8211;4.8 (FCS FlowSOM input), which were previously gated on CD161+ events, in R with the read.flowSet function. 
		 
		fcs_raw &#60;- read.flowSet(path=&#34;C:\\Users\\username\\Desktop\\tocluster&#34;, pattern = &#34;.fcs&#34;, transformation = FALSE, truncate_max_range = FALSE, seed=123)</li>
		<li>Select the relevant biological markers (remove &#34;Background&#34; or &#34;Time&#34;) by selecting the proper columns and transform the data in an arcsinh5 manner as shown in the code below (here, remove columns 1, 2, 4, 5, 6, 17, 21, 24, 25, 34, 35, 37, 38, 51, which do not correspond to any biological marker). Apply a cofactor of 5 as previously applied with Cytosplore, by choosing cofactor=5 in the function below. 
		 
		fcs_raw &#60;- fsApply(fcs_raw, function(x, cofactor=5){ 
		colnames(x) &#60;- fcs_raw[[1]]@parameters@data$desc 
		expr &#60;- exprs(x) 
		expr &#60;- asinh(expr[,-c(1,2,4,5,6,17,21,24,25,34,35,37,38,51)]/ cofactor) 
		exprs(x) &#60;- expr 
		return(x)})</li>
		<li>Cluster the data using the FlowSOM function. To compare FlowSOM and Cytosplore, choose to cluster the data in ten subsets as the output previously yielded by Cytosplore. 
		 
		fsom &#60;- FlowSOM(fcs_raw, transformFunction = FALSE, scale = FALSE, 
		scaled.center = FALSE, scaled.scale = FALSE, silent = FALSE, colsToUse = c(1:37), 
		nClus = 10, maxMeta = 10, importance = NULL, seed = 123) 
		 
		NOTE: This can be changed manually by the user.</li>
		<li>Assign each cell to its identified subset and sample ID. 
		 
		subset_id &#60;- as.factor(fsom$FlowSOM$map$mapping[,1]) 
		levels(subset_id) &#60;- fsom$metaclustering 
		head(subset_id)</li>
		<li>Load the metadata file in R (available in Supplementary File 5) containing the group assignment and link it to the .fcs files. 
		 
		sampleid &#60;- read_excel(&#34;C:\\Users\\username\\Desktop\\sample_id.xlsx&#34;) 
		sampleid &#60;- na.omit(sampleid) 
		 
		sampleid$sampleID &#60;- as.factor(sampleid$sampleID) 
		sampleid$group &#60;- as.factor(sampleid$group) 
		sampleid$CSPLR_ST &#60;- as.factor(sampleid$CSPLR_ST) 
		 
		sampleid &#60;- as.data.frame(sampleid) 
		names(sampleid)[3] &#60;- &#34;sampleID&#34; 
		sampleID &#60;- lapply(fsom$FlowSOM$metaData, function(x){rep(x[1], each = length(x[1]:x[2]))}) 
		attr(sampleID, &#39;names&#39;) &#60;- NULL 
		sampleID &#60;- as.factor(unlist(sampleID)) 
		sampleid &#60;- data.frame(sampleid) 
		levels(sampleID) &#60;- paste(&#34;ID&#34;, 1:dim(sampleid)[1], sep=&#34;_&#34;) 
		 
		df &#60;- data.frame(subset_id, sampleID, fsom$FlowSOM$data[, c(1:37)]) 
		rename &#60;- data.frame(colpar=fcs_raw[[1]]@parameters@data$desc) 
		colnames(df) &#60;- c(&#34;clusterID&#34;, &#34;sampleID&#34;, rename$colpar[c(1:37)]) 
		df$ clusterID &#60;- as.factor(df$ clusterID) 
		df$sampleID &#60;- as.factor(df$sampleID)</li>
		<li>Create a cfList based on the dataframe obtained from FlowSOM by running the following script: 
		 
		cfData &#60;- cfList(samples = sampleid, 
		expr = df)</li>
		<li>Re-order the markers to appear similarly to the output from the Cytosplore analysis. 
		 
		cfData@expr &#60;- cfData@expr[,c(1,2,34,36,37,10,23,24,31,22, 
		38,15,8,3,27,9,11,28,35,26, 
		14,33,17,20,21,18,25,29,13, 
		30,12,16,32,4,5,6,7,19,39)] 
		 
		NOTE: The clustering by FlowSOM is now finished. Next, perform visualization of the clustering output.</li>
	</ol>
	</li>
</ol>
3. Visualization: Post-processing Clustering Analysis
NOTE: This step is a method that is common to both clustering methods. Therefore, it can be performed after clustering either with FlowSOM or Cytosplore.
<ol>
	<li>Before creating the heatmaps, generate the count table per sample by using the function cellCounts as shown in the code below. Since some clusters contain fewer cells than others, scale the data per cluster by specifying &#34;scale = TRUE&#34; inside the function cellCounts, so that the dispersion among samples can be easily seen. 
	 
	cfData &#60;- cellCounts(cfData, frequency = TRUE, scale = TRUE) 
	 
	NOTE: The data can now be visualized.</li>
	<li>Visualize with heatmap. 
	NOTE: One of the main functions of this package is cytoHeatmaps, which is used to visualize the phenotype of the created clusters as well as their heterogeneity with respect to the samples. 
	 
	cytoHeatmaps (cfData, group=&#34;group&#34;, legend=TRUE)</li>
	<li>Visualization with boxplots 
	NOTE: Data can be represented in a quantitative way by calling the function cytoBoxplots. The output of this function represents the proportion of each sample for every cluster.
	<ol>
		<li>Generate the cell count as done in step 3.1, but do not scale the data to obtain the frequency of each cluster. 
		 
		cfData &#60;- cellCounts(cfData, frequency = TRUE, scale = FALSE) 
		cytoBoxplots(cfData, group = &#34;group&#34;)</li>
	</ol>
	</li>
	<li>Visualization with median intensity signal histogram 
	NOTE: The data can also be visualized by representing the median intensity signal histogram.
	<ol>
		<li>Visualize the expression intensity of three markers: CD45, CD11c, and CD54.</li>
		<li>Check the names of the desired markers by calling the following line. Note the marker names and include it in the msiPlot function. 
		 
		names(cfData@expr) 
		 
		NOTE: Here, focus on CD45, CD11c, and CD54. Check the exact spelling of the markers and adjust if needed: 
		 
		msiPlot(cfData, markers = c(&#34;89Y_CD45&#34;, &#34;167Er_CD11c&#34;,&#34;164Dy_CD54&#34;), byGroup=&#39;group&#39;)</li>
	</ol>
	</li>
</ol>

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P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+continuous+molecular+roadmap+to+iPSC+reprogramming+through+progression+analysis+of+single-cell+mass+cytometry.">A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry.</a> Cell Stem Cell. 16 (3), 323-337 (2015).</li><li>Van Gassen, 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=FlowSOM:+Using+self-organizing+maps+for+visualization+and+interpretation+of+cytometry+data.">FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.</a> Cytometry A. 87 (7), 636-645 (2015).</li><li>Levine, J. 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The authors have nothing to disclose.

Cytofast is a rapid computational tool that provides a quick and global exploration of cytometric data by highlighting and quantifying treatment-specific cellular subsets. The protocol described aims to further process clustering analyses with Cytosplore or FlowSOM. Other clustering analysis tools are suitable for Cytofast, but this requires the use of Cytofast to assign each cell to a subset. Cytofast, however, is not a clustering method, and therefore requires clustering procedures before use.
The analysis performed here showed that certain CD161+ NK cell subsets in the tumor microenvironment were sensitive to a PD-L1 blockade. This was evidenced by changes in their phenotype and abundance, which were observed using both Cytosplore and FlowSOM as clustering methods. Both methods distinguished the main NK cell cluster (CD11b+ NKG2A+) with slightly different frequencies (15%&#8211;20% for Cytosplore, 30%&#8211;40% for FlowSOM). The differences in abundance and this approximation did not affect the global pattern, because both dendrograms displayed in the right panels of Figure 2 and Figure 3 showed similar results. By using Cytofast, it is thus possible (independent from the clustering method chosen) to segregate PD-L1-treated and untreated mice based on analyses of NK cell cluster phenotype and abundance.
Depending on the recorded parameters, modifications to the protocol are needed. Specifically, certain parameters such as time and background must be removed while performing the clustering analysis. In addition, it is important that each cell is assigned to a subset. The cfData function will simply add the raw cell counts per cluster per sample into the cfList. From this step, the cytoheatmap can be built as explained in section 3.
Cytofast has been successfully used as a visualization and quantification tool to compare different clustering methods13. This R package is also compatible with advanced features, such as the globaltest14, which can test associations between groups of clusters using clinical variables. In the future, the globaltest tool and other algorithms can be integrated with Cytofast for more in-depth visualization and quantification.

Mass cytometry (cytometry by time-of-flight, or CyTOF) allows detection of a wide range of intracellular or extracellular biomarkers in millions of single cells. The high-dimensional nature of mass cytometry data necessitates certain analysis tools, such as cell clustering techniques like SPADE1, FlowMaps2, FlowSOM3, Phenograph4, VorteX5, and scaffold maps6. In addition, various dimensionality reduction-based techniques have been developed (i.e., principal component analysis [PCA]7, t-distributed stochastic neighbor embedding [t-SNE]8, hierarchical stochastic neighbor embedding [HSNE]9, uniform manifold approximation and projection [UMAP]10, and diffusion maps11) to improve the speed, interpretation and visualization of high-dimensional datasets.
Downstream analysis of high-dimensional flow and mass cytometric data often lacks automatic processes to perform statistical tests on cluster frequency and links with clinical outcomes. Previously, we developed an R-based workflow known as Cytofast12, which allows for visual and quantitative downstream analyses of clustering techniques by Cytosplore or FlowSOM.
The protocol described here clarifies the use of Cytofast in R and shows how to generate quantitative and qualitative heatmaps and graphs. Furthermore, it facilitates the determination of connections between observed immune phenotypes and clinical outcomes. This report also describes the analysis of a specific mass cytometry dataset using two different clustering procedures: FlowSOM and Cytosplore. By using Cytofast with both clustering methods, it is correspondingly shown that the activation phenotype of NK cells is influenced by PD-L1 immune checkpoint blockade.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Computer</td>
			<td>Dell</td>
			<td>NA</td>
			<td>NA</td>
		</tr>
	</tbody>
</table>

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).

The workflow Cytofast (Figure 1) is meant to provide a quantitative and qualitative overview of the data originally clustered by analysis software (i.e., FlowSOM or Cytosplore). Cytofast runs several possible outputs, including the heatmap of all clusters identified in the analysis and based on marker expression (Figure 2 and Figure 3). The dendrogram on the top represents the hierarchical similarity between the identified clusters. The upper panel displays another heatmap showing the relative quantity of corresponding subsets in each sample. The dendrogram on the right shows the similarity between samples and is based on hierarchical clustering performed on the Euclidean distances between samples. The combined heatmaps are shown for FlowSOM followed by Cytofast in Figure 2 and for Cytosplore followed by Cytofast in Figure 3. Cytofast can also be used to present the data quantitatively and display the results in boxplots (by using cytoBoxplots function), as shown in Figure 4 and Figure 5.
Similar clusters were found between the two different methods (e.g., cluster 8 from Cytosplore corresponds to cluster 10 from FlowSOM), and co-expression of some inhibitory markers like PD-1 and LAG-3 were still visible in both methods). Both clustering methods allowed discrimination between PD-L1 vs. PBS treated mice. In contrast, some differences between both methods can be highlighted. FlowSOM identifies 2 clusters (MHC-II+), whereas Cytosplore shows only one cluster (MHC-II+dim). This is due to the initial gating strategy in which NK cells were manually gated on CD161+ cells, then further processed by FlowSOM. However, Cytosplore automatically gated cells from the CD45+ population on the first HSNE level, which were then clustered in a higher hierarchical level. Thus, Cytosplore defined the NK cell subsets more precisely than how manual gating focused on CD161. Nevertheless, hierarchical clustering of the samples was preserved, as shown in the dendrogram on the right, indicating that segregation between the two groups (PD-L1 and PBS) was not dependent on the chosen clustering method.
The number of clusters can be manually defined using both methods. Cytofast enables the user to assess the heterogeneity of their data and can provide insight into how to choose the number of clusters into which the data should be divided. Other features are included in the Cytofast package, such as the msiPlot function (step 3.4.2), showing the median signal Intensity (MSI) plot of every marker per group (Figure 6 and Figure 7). This function allows detection of global changes, such as increases in the expression of CD54 or CD11c in NK cells of the PD-L1-treated group. Optional features can be incorporated in the Cytofast package, such as displaying data in bar graphs and other methods of data representation. The latter requires the addition of ggplot tools, which can be generated by R.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60525/60525fig01.jpg" /> 
Figure 1: Workflow of Cytofast package. The data were generated by mass cytometry from a tumor 3 days after treatment with immunotherapy or left untreated. Two different clustering techniques were compared: Cytosplore and FlowSOM. Cytofast was used to visualize differences between the two techniques. <a href="https://www.jove.com/files/ftp_upload/60525/60525fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60525/60525fig02.jpg" /> 
Figure 2: Cluster overview and cluster abundance per group as analyzed by Cytofast following Cytosplore. Heatmap of all NK cell clusters (CD161+ cells defined automatically by Cytosplore), which were identified 3 days after immunotherapy (PD-L1). Data shown is based on Cytosplore clustering and pooled from the untreated and PD-L1 treated groups. Levels of ArcSinh5-transformed expression marker are displayed on a rainbow scale. On the lower panel, the relative abundance of each sample is represented by the green-to-purple scale. The dendrogram on the right represents the similarity between samples based on subset frequencies. The frequency scale represents the dispersion of the mean. A low or a high frequency is represented by a green or purple color, respectively. <a href="https://www.jove.com/files/ftp_upload/60525/60525fig02large.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/60525/60525fig03.jpg" /> 
Figure 3: Cluster overview and cluster abundance per group as analyzed by Cytofast following FlowSOM. Heatmap of all NK cell clusters (pre-gated on CD161+ events), which were identified 3 days after immunotherapy (PD-L1). Data shown is based on FlowSOM clustering and pooled from the untreated and PD-L1 treated groups. Levels of ArcSinh5-transformed expression marker are displayed on a rainbow scale. On the lower panel, the relative abundance of each sample is represented by the green-to-purple scale. The dendrogram on the right represents the similarity between samples based on subset frequencies. The frequency scale represents the dispersion of the mean. A low or a high frequency is represented by a green or purple color, respectively. <a href="https://www.jove.com/files/ftp_upload/60525/60525fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60525/60525fig04.jpg" /> 
Figure 4: Cytofast representation with boxplots of the clusters defined by Cytosplore. The frequency of each cluster is represented in a boxplot, separated into the two groups (PBS and PD-L1). One individual dot corresponds to one mouse. <a href="https://www.jove.com/files/ftp_upload/60525/60525fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60525/60525fig05.jpg" /> 
Figure 5: Cytofast representation with boxplots of the clusters defined by FlowSOM. The frequency of each cluster is represented in a boxplot, separated into the two groups (PBS and PD-L1). One individual dot corresponds to one mouse. <a href="https://www.jove.com/files/ftp_upload/60525/60525fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/60525/60525fig06.jpg" /> 
Figure 6: Distribution of signal intensity plots from NK cells automatically gated by Cytosplore. Distribution of signal intensities are shown in a histogram for three specific markers: CD45, CD11c, and CD54. <a href="https://www.jove.com/files/ftp_upload/60525/60525fig6large.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/60525/60525fig07.jpg" /> 
Figure 7: Distribution of signal intensity plots from NK cells automatically gated by FlowSOM. Distribution of signal intensities are shown in a histogram for three specific markers: CD45, CD11c, and CD54, segregated by groups PBS and PD-L1. <a href="https://www.jove.com/files/ftp_upload/60525/60525fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Files 1.1&#8211;1.8. <a href="https://cloudflare.jove.com/files/ftp_upload/60525/Supplementary_Files_1.1-1.8.zip" target="_blank">Please click here to view this file (Right click to download).</a>
Supplementary Files 2.1&#8211;2.10. <a href="https://cloudflare.jove.com/files/ftp_upload/60525/Supplementary_Files_2.1-2.10.zip" target="_blank">Please click here to view this file (Right click to download).</a>
Supplementary File 3. <a href="https://cloudflare.jove.com/files/ftp_upload/60525/Supplementary_File_3_sample_id_Cytosplore.xlsx.zip" target="_blank">Please click here to view this file (Right click to download).</a>
Supplementary Files 4.1&#8211;4.8. <a href="https://cloudflare.jove.com/files/ftp_upload/60525/Supplementary_Files_4.1-4.8.zip" target="_blank">Please click here to view this file (Right click to download).</a>
Supplementary File 5. <a href="https://cloudflare.jove.com/files/ftp_upload/60525/Supplementary_File_5._sample_id_FlowSOM.xlsx.zip" target="_blank">Please click here to view this file (Right click to download).</a>

Watch this Scientific Journal Video about Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore at JoVE.com

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

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 ...

visualization-quantification-high-dimensional-cytometry-data-using

Research

JoVE Journal

Immunology and Infection

8.3K Views.  Leiden University Medical Center. 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.

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

Use of Image Cytometry for Quantification of Pathogenic Fungi in Association with Host Cells

Studies of the cellular pathogenesis mechanisms of pathogenic yeasts such as Candida albicans, Histoplasma capsulatum, and Cryptococcus neoformans commonly employ infection of mammalian hosts or host cells (i.e. macrophages) followed by yeast quantification using colony forming unit analysis or flow cytometry. While colony forming unit enumeration has been the most commonly used method in the field, this technique has disadvantages and limitations, including slow growth of some fungal species on solid media and low and/or variable plating efficiencies, which is of particular concern when comparing growth of wild-type and mutant strains. Flow cytometry can provide rapid quantitative information regarding yeast viability, however, adoption of flow cytometric detection for pathogenic yeasts has been limited for a number of practical reasons including its high cost and biosafety considerations. Here, we demonstrate an image-based cytometric methodology using the Cellometer Vision (Nexcelom Bioscience, LLC) for the quantification of viable pathogenic yeasts in co-culture with macrophages. Our studies focus on detection of two human fungal pathogens: Histoplasma capsulatum and Candida albicans. H. capsulatum colonizes alveolar macrophages by replicating within the macrophage phagosome, and here, we quantitatively assess the growth of H. capsulatum yeasts in RAW 264.7 macrophages using acridine orange/propidium iodide staining in combination with image cytometry. Our method faithfully recapitulates growth trends as measured by traditional colony forming unit enumeration, but with significantly increased sensitivity. Additionally, we directly assess infection of live macrophages with a GFP-expressing strain of C. albicans. Our methodology offers a rapid, accurate, and economical means for detection and quantification of important human fungal pathogens in association with host cells.

Here, we demonstrate how image cytometry can be used for quantification of pathogenic fungi in association with host cells in culture. This technique can be used as an alternative to CFU enumeration.

Studies of the cellular pathogenesis mechanisms of pathogenic yeasts such as Candida albicans, Histoplasma capsulatum, and Cryptococcus neoformans ...

Assessing Retinal Microglial Phagocytic Function In Vivo Using a Flow Cytometry-based Assay

Microglia are the tissue resident macrophages of the central nervous system (CNS) and they perform a variety of functions that support CNS homeostasis, including phagocytosis of damaged synapses or cells, debris, and/or invading pathogens. Impaired phagocytic function has been implicated in the pathogenesis of diseases such as Alzheimer's and age-related macular degeneration, where amyloid-&#946; plaque and drusen accumulate, respectively. Despite its importance, microglial phagocytosis has been challenging to assess in vivo. Here, we describe a simple, yet robust, technique for precisely monitoring and quantifying the in vivo phagocytic potential of retinal microglia. Previous methods have relied on immunohistochemical staining and imaging techniques. Our method uses flow cytometry to measure microglial uptake of fluorescently labeled particles after intravitreal delivery to the eye in live rodents. This method replaces conventional practices that involve laborious tissue sectioning, immunostaining, and imaging, allowing for more precise quantification of microglia phagocytic function in just under six hours. This procedure can also be adapted to test how various compounds alter microglial phagocytosis in physiological settings. While this technique was developed in the eye, its use is not limited to vision research.

Assessing Retinal Microglial Phagocytic Function In Vivo Using a Flow Cytometry-based Assay

Microglial phagocytosis is critical for the maintenance of tissue homeostasis and inadequate phagocytic function has been implicated in pathology. However, assessing microglia function in vivo is technically challenging. We have developed a simple but robust technique for precisely monitoring and quantifying the phagocytic potential of microglia in a physiological setting.

Microglia are the tissue resident macrophages of the central nervous system (CNS) and they perform a variety of functions that support CNS homeostasis, ...

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 ...

Isolation, Characterization, and Purification of Macrophages from Tissues Affected by Obesity-related Inflammation

Obesity promotes a chronic inflammatory state that is largely mediated by tissue-resident macrophages as well as monocyte-derived macrophages. Diet-induced obesity (DIO) is a valuable model in studying the role of macrophage heterogeneity; however, adequate macrophage isolations are difficult to acquire from inflamed tissues. In this protocol, we outline the isolation steps and necessary troubleshooting guidelines derived from our studies for obtaining a suitable population of tissue-resident macrophages from mice following 18 weeks of high-fat (HFD) or high-fat/high-cholesterol (HFHCD) diet intervention. This protocol focuses on three hallmark tissues studied in obesity and atherosclerosis including the liver, white adipose tissues (WAT), and the aorta. We highlight how dualistic usage of flow cytometry can achieve a new dimension of isolation and characterization of tissue-resident macrophages. A fundamental section of this protocol addresses the intricacies underlying tissue-specific enzymatic digestions and macrophage isolation, and subsequent cell-surface antibody staining for flow cytometric analysis. This protocol addresses existing complexities underlying fluorescent-activated cell sorting (FACS) and presents clarifications to these complexities so as to obtain broad range characterization from adequately sorted cell populations. Alternate enrichment methods are included for sorting cells, such as the dense liver, allowing for flexibility and time management when working with FACS. In brief, this protocol aids the researcher to evaluate macrophage heterogeneity from a multitude of inflamed tissues in a given study and provides insightful troubleshooting tips that have been successful for favorable cellular isolation and characterization of immune cells in DIO-mediated inflammation.

This protocol allows a researcher to isolate and characterize tissue-resident macrophages in various hallmark inflamed tissues extracted from diet-induced models of metabolic disorders.

Obesity promotes a chronic inflammatory state that is largely mediated by tissue-resident macrophages as well as monocyte-derived macrophages. ...

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 ...

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density and the location of immune cells in the TME have important diagnostic and prognostic values. Multiomic profiling of the TME has exponentially increased our understanding of the numerous cellular and molecular networks regulating tumor initiation and progression. However, these techniques do not provide information about the spatial organization of cells or cell-cell interactions. Affordable, accessible, and easy to execute multiplexing techniques that allow spatial resolution of immune cells in tissue sections are needed to complement single cell-based high-throughput technologies. Here, we describe a strategy that integrates serial imaging, sequential labeling, and image alignment to generate virtual multiparameter slides of whole tissue sections. Virtual slides are subsequently analyzed in an automated fashion using user-defined protocols that enable identification, quantification, and mapping of cell populations of interest. The image analysis is done, in this case using the analysis modules Tissuealign, Author, and HISTOmap. We present an example where we applied this strategy successfully to one clinical specimen, maximizing the information that can be obtained from limited tissue samples and providing an unbiased view of the TME in the entire tissue section.

Here, we describe a simple and accessible strategy for visualizing, quantifying, and mapping immune cells in formalin-fixed paraffin-embedded tumor tissue sections. This methodology combines existing imaging and digital analysis techniques with the purpose of expanding the multiplexing capability and multiparameter analysis of imaging assays.

The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density ...

Visualization of Bacterial Resistance using Fluorescent Antibiotic Probes

Fluorescent antibiotics are multipurpose research tools that are readily used for the study of antimicrobial resistance, due to their significant advantage over other methods. To prepare these probes, azide derivatives of antibiotics are synthesized, then coupled with alkyne-fluorophores using azide-alkyne dipolar cycloaddition by click chemistry. Following purification, the antibiotic activity of the fluorescent antibiotic is tested by minimum inhibitory concentration assessment. In order to study bacterial accumulation, either spectrophotometry or flow cytometry may be used, allowing for much simpler analysis than methods relying on radioactive antibiotic derivatives. Furthermore, confocal microscopy can be used to examine localization within the bacteria, affording valuable information about mode of action and changes that occur in resistant species. The use of fluorescent antibiotic probes in the study of antimicrobial resistance is a powerful method with much potential for future expansion.

Fluorescently tagged antibiotics are powerful tools that can be used to study multiple aspects of antimicrobial resistance. This article describes the preparation of fluorescently tagged antibiotics and their application to studying antibiotic resistance in bacteria. Probes can be used to study mechanisms of bacterial resistance (e.g., efflux) by spectrophotometry, flow cytometry, and microscopy.

Fluorescent antibiotics are multipurpose research tools that are readily used for the study of antimicrobial resistance, due to their significant ...

Visualization of SARS-CoV-2 using Immuno RNA-Fluorescence In Situ Hybridization

This manuscript provides a protocol for in situ hybridization chain reaction (HCR) coupled with immunofluorescence to visualize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in cell line&#160;and three-dimensional (3D) cultures of human airway epithelium. The method allows highly specific and sensitive visualization of viral RNA by relying on HCR initiated by probe localization. Split-initiator probes help amplify the signal by fluorescently labeled amplifiers, resulting in negligible background fluorescence in confocal microscopy. Labeling amplifiers with different fluorescent dyes facilitates the simultaneous recognition of various targets. This, in turn, allows the mapping of the infection in tissues to better understand viral pathogenesis and replication at the single-cell level. Coupling this method with immunofluorescence may facilitate better understanding of host-virus interactions, including alternation of the host epigenome and immune response pathways. Owing to sensitive and specific HCR technology, this protocol can also be used as a diagnostic tool. It is also important to remember that the technique may be modified easily to enable detection of any RNA, including non-coding RNAs and RNA viruses that may emerge in the future.

Here, we describe a simple method that combines RNA fluorescence in situ&#160;hybridization (RNA-FISH) with immunofluorescence to visualize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA. This protocol may increase understanding of the molecular characteristics of SARS-CoV-2 RNA-host interactions at a single-cell level.

This manuscript provides a protocol for in situ hybridization chain reaction (HCR) coupled with immunofluorescence to visualize severe acute respiratory ...

Single-Cell Characterization of Calcium Influx and HIV-1 Infection using a Multiparameter Optofluidic Platform

HIV-1 causes a chronic infection that affects more than 37 million people worldwide. People living with human immunodeficiency virus (HIV) experience comorbidity related to chronic inflammation despite antiretroviral therapy. However, these inflammatory signaling has not been fully characterized. The role of early entry events on the activation of cellular signaling events and downstream gene expression has not been captured at the single-cell level. Here the authors describe a method that applies principles of live-cell fluorescence microscopy to an automated single-cell platform that cultures and images cells over user-customized time courses, allowing for high-throughput analysis of dynamic cellular processes. This assay can track single-cell live fluorescence microscopy of early events that immediately follow HIV-1 infection, notably the influx of calcium that accompanies exposure to the virus and the development of productive infection using a fluorescent reporter virus. MT-4 cells are loaded with a calcium-sensitive dye and cultured in isolated pens on a nanofluidic device. The cultured cells are infected with an HIV-1 reporter virus (HIV-1 NLCI). A fluorescence microscope positioned above the nanofluidic device measures calcium influx over an 8-min time course following acute HIV-1 exposure. HIV-1 productive infection is measured in those same cells over a 4-day interval. Imaging data from these time courses are analyzed to define virus-host receptor interactions and signaling pathway dynamics. The authors present an integrated, scalable alternative to traditional imaging methods using a novel optofluidic platform capable of single-cell sorting, culturing, imaging, and software automation. This assay can measure the kinetics of events under various conditions, including cell type, agonist, or antagonist effect, while measuring an array of parameters. This is the first established method for nanofluidic high-throughput longitudinal single-cell culture and imaging: This technique can be broadly adapted to study cellular signaling kinetics and dynamic molecular interactions.

Here, we present a protocol in which single cells are monitored for acute events and productive HIV-1 infection on a nanofluidic device. Imaging data define virus-host receptor interactions and signaling pathway dynamics. This is the first method for nanofluidic high-throughput longitudinal single-cell culture and imaging to study signaling kinetics and molecular interactions.

HIV-1 causes a chronic infection that affects more than 37 million people worldwide. People living with human immunodeficiency virus (HIV) experience ...