Name: excyt: a graphical user interface for streamlining analysis of high-dimensional cytometry data
Uploaded: 2019-01-16T12:30:00
Description: Watch this Scientific Journal Video about ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data at JoVE.com

The authors have no acknowledgements.

1. Collecting and Preparing Cytometry Data
<ol>
	<li>Place all single stains in a folder by themselves and label by the channel name (by fluorophore, not marker).</li>
</ol>
2. Data Importation &#38; Pre-Processing
<ol>
	<li>To pause or save throughout this analysis pipeline, use the Save Workspace button at the bottom left of the program to save the workspace as a &#8216;.MAT&#8217; file that can later be loaded via the Load Workspace button. Do not run more ...

<ol>
	<li>Benoist, C., Hacohen, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometry,+amped+up.">Flow cytometry, amped up.</a> Science. 332 (6030), 677-678 (2011).</li><li>Ornatsky, O., 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=Highly+multiparametric+analysis+by+mass+cytometry.">Highly multiparametric analysis by mass cytometry.</a> Journal of immunological methods. 361 (1), 1-20 (2010).</li><li>Tanner, S. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometer+with+mass+spectrometer+detection+for+massively+multiplexed+single-cell+biomarker+assay.">Flow cytometer with mass spectrometer detection for massively multiplexed single-cell biomarker assay.</a> Pure and Applied Chemistry. 80 (12), 2627-2641 (2008).</li><li>Maecker, H. T., 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=Standardization+of+cytokine+flow+cytometry+assays.">Standardization of cytokine flow cytometry assays.</a> BMC immunology. 6 (1), 13 (2005).</li><li>Brazma, A., Vilo, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+expression+data+analysis.">Gene expression data analysis.</a> FEBS letters. 480 (1), 17-24 (2000).</li><li>Pyne, 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=Automated+high-dimensional+flow+cytometric+data+analysis.">Automated high-dimensional flow cytometric data analysis.</a> Proceedings of the National Academy of Sciences. 106 (21), 8519-8524 (2009).</li><li>Ge, Y., Sealfon, S. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=flowPeaks:+a+fast+unsupervised+clustering+for+flow+cytometry+data+via+K-means+and+density+peak+finding.">flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding.</a> Bioinformatics. 28 (15), 2052-2058 (2012).</li><li>Venkatesh, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determinants+of+perceived+ease+of+use:+Integrating+control,+intrinsic+motivation,+and+emotion+into+the+technology+acceptance+model.">Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model.</a> Information systems research. 11 (4), 342-365 (2000).</li><li>Bagwell, C. B., Adams, E. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescence+spectral+overlap+compensation+for+any+number+of+flow+cytometry+parameters.">Fluorescence spectral overlap compensation for any number of flow cytometry parameters.</a> Annals of the New York Academy of Sciences. 677 (1), 167-184 (1993).</li><li>Lavin, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Innate+immune+landscape+in+early+lung+adenocarcinoma+by+paired+single-cell+analyses.">Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses.</a> Cell. 169 (4), 750-765 (2017).</li><li>Chevrier, 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=An+immune+atlas+of+clear+cell+renal+cell+carcinoma.">An immune atlas of clear cell renal cell carcinoma.</a> Cell. 169 (4), 736-749 (2017).</li><li>Hartigan, J. A., Wong, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Algorithm+AS+136:+A+k-means+clustering+algorithm.">Algorithm AS 136: A k-means clustering algorithm.</a> Journal of the Royal Statistical Society. Series C (Applied Statistics). 28 (1), 100-108 (1979).</li><li>Ester, M., Kriegel, H. P., Sander, J., Xu, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Density-based+spatial+clustering+of+applications+with+noise.">Density-based spatial clustering of applications with noise.</a> International Conference Knowledge Discovery and Data Mining. 240, (1996).</li><li>Levine, J. H., 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=Data-driven+phenotypic+dissection+of+AML+reveals+progenitor-like+cells+that+correlate+with+prognosis.">Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis.</a> Cell. 162 (1), 184-197 (2015).</li><li>Blondel, V. D., Guillaume, J. L., Lambiotte, R., Lefebvre, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+unfolding+of+communities+in+large+networks.">Fast unfolding of communities in large networks.</a> Journal of statistical mechanics: theory and experiment. 2008 (10), P10008 (2008).</li><li>Le Martelot, E., Hankin, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+multi-scale+detection+of+relevant+communities+in+large-scale+networks.">Fast multi-scale detection of relevant communities in large-scale networks.</a> The Computer Journal. 56 (9), 1136-1150 (2013).</li><li>Newman, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+algorithm+for+detecting+community+structure+in+networks.">Fast algorithm for detecting community structure in networks.</a> Physical review E. 69 (6), 066133 (2004).</li><li>Hespanha, J. 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> An efficient matlab algorithm for graph partitioning. , 1-8 (2004).</li><li>Moon, T. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+expectation-maximization+algorithm.">The expectation-maximization algorithm.</a> IEEE Signal processing. 13 (6), 47-60 (1996).</li><li>Bishop, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Pattern recognition and machine learning. , (2006).</li></ol>

Here we present ExCYT, a novel graphical user interface running MATLAB-based algorithms to streamline analysis of high-dimensional cytometry data, allowing individuals with no background in programming to implement the latest in high-dimensional data analysis algorithms. The availability of this software to the broader scientific community will allow scientists to explore their flow cytometry data in an intuitive and straightforward workflow. Through conducting t-SNE dimensionality reduction, applying a clustering method...

Advances in flow cytometry as well as the advent of mass cytometry has allowed clinicians and scientists to rapidly identify and phenotypically characterize biologically and clinically interesting samples with new levels of resolution, creating large high-dimensional data sets that are information rich1,2,3. While conventional methods for analyzing flow cytometry data such as manual gating have been more straightforward for experiments where there are few markers and those markers have visually discernable populations, this approach can fail to generate reproducible results when analyzing higher-dimensional data sets or those with markers staining on a spectrum. For example, in a multi-institutional study, where intra-cellular staining (ICS) assays were being performed to assess the reproducibility of quantitating antigen-specific T cell responses, despite good inter-laboratory precision, analysis, particularly gating, introduced a significant source of variability4. Furthermore, the process of manually gating population of interests, besides being highly subjective is highly time consuming and labor intensive. However, the problem of analyzing high-dimensional data sets in a robust, efficient, and timely manner is not one new to the research sciences. Gene expression studies often generate extremely high-dimensional data sets (often on the order of hundreds of genes) where manual forms of analysis would be simply infeasible. In order to tackle the analysis of these data sets, there has been much work in developing bioinformatic tools to parse gene expression data5. These algorithmic approaches have just been recently adopted in the analysis of cytometry data as the number of parameters has increased and have proven to be invaluable in the analysis of these high dimensional data sets6,7.
Despite the generation and application of a variety of algorithms and software packages that allow scientists to apply these high-dimensional bioinformatic approaches to their flow cytometry data, these analytical techniques still remain largely unused. While there may be a variety of factors that have limited the widespread adoption of these approaches to cytometry data8, the major hindrance we suspect in use of these approaches by scientists, is a lack of computational knowledge. In fact, many of these software packages (i.e., flowCore, flowMeans, and OpenCyto) are written to be implemented in programming languages such as R that still require substantive programming knowledge. Software packages such as FlowJo have found favor among scientists due to simplicity of use and &#39;plug-n-play&#39; nature, as well as compatibility with the PC operating system. In order to provide the variety of accepted and valuable analytical techniques to the scientist unfamiliar programming, we have developed ExCYT, a graphical-user interface (GUI) that can be easily installed on a PC/Mac that pulls many of the latest techniques including dimensionality reduction for intuitive visualization, a variety of clustering methods cited in the literature, along with novel features to explore the output of these clustering algorithms with heatmaps and novel high-dimensional flow/box plots.
ExCYT is a graphical user interface built in MATLAB and therefore can either be run within MATLAB directly or an installer is provided that can be used to install the software on any PC/Mac. The software is available at https://github.com/sidhomj/ExCYT. We present a detailed protocol for how to import data, pre-process it, conduct t-SNE dimensionality reduction, cluster data, sort &#38; filter clusters based on user preferences, and display information about the clusters of interest via heatmaps and novel high-dimensional flow/box plots (Figure 1). Axes in t-SNE plots are arbitrary and in arbitrary units and as such as not always shown in the figures for simplicity of the user interface. The coloring of data points in the &#34;t-SNE Heatmaps&#34; is from blue to yellow based on the signal of the indicated marker. In clustering solutions, the color of the data point is based arbitrary on cluster number. All parts of the workflow can be carried out in the single panel GUI (Figure 2 &#38; Table 1). Finally, we will demonstrate the use of ExCYT on previously published data exploring the immune landscape of renal cell carcinoma in the literature, also analyzed with similar methods. The sample dataset we used to create the figures in this manuscript along with the protocol below can be found at https://premium.cytobank.org/cytobank/projects/875, upon registering an account.

<table border="0" cellpadding="0" cellspacing="0" width="597">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Desktop</td>
			<td style="width:95px;">SuperMicro</td>
			<td style="width:119px;">Custom Build</td>
			<td style="width:167px;">Computer used to run analysis</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">MATLAB</td>
			<td style="width:95px;">Mathworks</td>
			<td style="width:119px;">N/A</td>
			<td>Software used to develop ExCYT</td>
		</tr>
	</tbody>
</table>

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.

In order to test the usability of ExCYT, we analyzed a curated data set published by Chevrier et al. titled &#39;An Immune Atlas of Clear Cell Renal Carcinoma&#39; where the group conducted CyTOF analysis with an extensive immune panel on tumor samples taken from 73 patients11. Two separate panels, a myeloid and lymphoid panel, were used to phenotypically characterize the tumor microenvironment. The objective of our study was to recapitulate the results of...

Watch this Scientific Journal Video about ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data at JoVE.com

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

This method can help answer key questions in the biomedical field, such as understanding the phenotype of biologically relevant sub-populations. The main advantage of this technique is that it allows an individual with no programming experience to analyze their cytometry data with the latest in high-dimensional techniques. To begin an analysis pipeline, first select the type of cytometry, and the number of events to sample from the file.Next, click Gate Population, select the cell populations of interest, and enter the percentage of events for downstream analysis. Then select the number of channels to be used for the analysis in the list box. For T-distributed Stochastic Neighbor Embedding, or t-SNE analysis, click t-SNE to begin computing the reduced dimensionality dataset.When the set has been computed, click Save TSNE Image, and in the Marker-Specific t-SNE popup menu, select a specific marker of interest. A figure will appear showing a heat map representation of the t-SNE plot that can be saved for figure generation. To begin clustering analysis, select an option in the Clustering Method list box, and click Cluster.To sort the clusters by a marker of interest, select the appropriate corresponding option from the Sort popup menu, and click Ascending, Descending, to update the list of clusters in the Clusters list box. To set a minimal threshold value for a given cluster across a certain channel, select an option from Threshold popup menu, and set an appropriate threshold. Once threshold has been set, click Add Above Threshold, or Add Below Threshold, to specify the direction of the threshold, and enter a numerical cut-off in the Cluster Frequency Threshold box, in the Cluster Filter panel, to set a minimal threshold for the frequency of a cluster.To select clusters for further analysis individualization, select the clusters of interest, in the Clusters list box. And use the Select button, to move the options to the Cluster Analyze list box. To create heat maps of the clusters, select the clusters of interest in the Cluster Analyze list box, and click the HeatMap of Clusters button.To create a high-dimensional box plot, or a high-dimensional flow plot, select the clusters of interest in the Cluster Analyze list box, and click either High-Dimensional Box Plot, or High-Dimensional Flow Plot, to visually assess the distribution of given channels of various clusters across all dimensions. To show clusters in traditional 2D flow plots, select the appropriate transformation and channel in the Conventional Flow Plot panel, and click Conventional Flow Plot. Here, a representative t-SNE analysis of heat maps for various markers within a myeloid panel analysis pipeline, are shown.Using the fast greedy implementation within ExCYT to cluster the data with 100, 000 of the nearest neighbors, 19 sub-populations of cells were revealed. Comparison of the original heat maps to the clusters created by ExCYT allowed similar clusters of the myeloid cells to be identified between the two data groups. Analysis of the lymphoid panel with a more conventional and faster hierarchical clustering approach, yielded similar marker distributions via t-SNE heat maps.Further, clustering of the data via hierarchical clustering demonstrated similar clusters of lymphoid cells. Notably, a unique regulatory T cell population was also identified via a high-dimensional flow plot. To quickly and quantitatively assess co-associations among markers, first a hard K-means clustering algorithm was used to lay down 5000 clusters on the two dimensional t-SNE data.The median expression of all the markers from all the clusters was then used, to create a heat map from these clusters, allowing co-associations to be easily identified, such as the co-association of Tim-3, PD-1, CD38 and 4-1BB. While attempting this procedure, it's important to remember to explore the different parameters, such as different clustering methods, to fully explore the data you are studying.

excyt-graphical-user-interface-for-streamlining-analysis-high

Research

JoVE Journal

Immunology and Infection

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Visual analytics (VA) has emerged as a new way to analyze large dataset through interactive visual display. We demonstrated the utility and the flexibility of a VA approach in the analysis of biological datasets. Examples of these datasets in immunology include flow cytometry, Luminex data, and genotyping (e.g., single nucleotide polymorphism) data. Contrary to the traditional information visualization approach, VA restores the analysis power in the hands of analyst by allowing the analyst to engage in real-time data exploration process. We selected the VA software called Tableau after evaluating several VA tools. Two types of analysis tasks analysis within and between datasets were demonstrated in the video presentation using an approach called paired analysis. Paired analysis, as defined in VA, is an analysis approach in which a VA tool expert works side-by-side with a domain expert during the analysis. The domain expert is the one who understands the significance of the data, and asks the questions that the collected data might address. The tool expert then creates visualizations to help find patterns in the data that might answer these questions. The short lag-time between the hypothesis generation and the rapid visual display of the data is the main advantage of a VA approach.

Visual analytics (VA) is a new approach of analyzing data interactively. In this video, we discuss the data overload problem brought on by high-throughput biological experiments, and propose VA as a solution to such problem. The video demonstrates analysis within and between immunological datasets using a VA tool called Tableau.

Visual analytics (VA) has emerged as a new way to analyze large dataset through interactive visual display. We demonstrated the utility and the ...

Flow Cytometry Analysis of Immune Cells Within Murine Aortas

Atherosclerosis is a chronic inflammatory process of medium and large size vessels that is characterized by the formation of plaques consisting of foam cells, immune cells, vascular endothelial and smooth muscle cells, platelets, extracellular matrix, and a lipid-rich core with extensive necrosis and fibrosis of surrounding tissues.1 The innate and adaptive arms of the immune response are involved in the initiation, development and persistence of atherosclerosis.2, 3 There is a significant body of evidence that different subsets of the immune cells, such as macrophages, dendritic cells, T and B lymphocytes, are present within the aortas of healthy and atherosclerosis-prone mice4. Additionally, immune cells are found in the surrounding aortic adventitia which suggests an important role of this tissue in atherogenesis.2
For some time, the quantitative detection of different types of immune cells, their activation status, and the cellular composition within the aortic wall was limited by RT-PCR and immunohistochemical methods for the study of atherosclerosis. Few attempts were made to perform flow cytometry using human aortas, and a number of problems, such as a high autofluorescence, have been reported5,6. Human atherosclerotic plaques were digested with collagenase 1, and free cells were collected and stained for CD14+/CD11c+ to highlight macrophage-derived foam cells. In this study, a "mock" channel was used to avoid false-positive staining.6 Necrotic materials accumulating during the digestion process give rise in a large amount of debris that generates a high autofluorescence in aortic samples. To resolve this problem, a panel of negative and positive controls has been proposed, but only double staining could be applied in these samples. We have developed a new flow cytometry-based method7 to analyze the immune cell composition and characterize the activation, proliferation, differentiation of immune cells in healthy and atherosclerosis-prone aorta. This method allows the investigation of the immune cell composition of the aortic wall and opens possibilities to use a broad spectrum of immunological methods for investigations of immune aspects of this disease.

This paper presents a flow cytometry-based method to investigate the immune composition of aortas. The paper also illustrates an additional technique that allows examining surrounding adventitia and vessel wall separately. This method opens possibilities to perform phenotypical analyses of aortic leukocytes and apply several immunological assays for atherosclerosis studies.

Atherosclerosis is a chronic inflammatory process of medium and large size vessels that is characterized by the formation of plaques consisting of foam ...

Single-cell Analysis of Bacillus subtilis Biofilms Using Fluorescence Microscopy and Flow Cytometry

Biofilm formation is a general attribute to almost all bacteria 1-6. When bacteria form biofilms, cells are encased in extracellular matrix that is mostly constituted by proteins and exopolysaccharides, among other factors 7-10. The microbial community encased within the biofilm often shows the differentiation of distinct subpopulation of specialized cells 11-17. These subpopulations coexist and often show spatial and temporal organization within the biofilm 18-21.
Biofilm formation in the model organism Bacillus subtilis requires the differentiation of distinct subpopulations of specialized cells. Among them, the subpopulation of matrix producers, responsible to produce and secrete the extracellular matrix of the biofilm is essential for biofilm formation 11,19. Hence, differentiation of matrix producers is a hallmark of biofilm formation in B. subtilis.
We have used fluorescent reporters to visualize and quantify the subpopulation of matrix producers in biofilms of B. subtilis 15,19,22-24. Concretely, we have observed that the subpopulation of matrix producers differentiates in response to the presence of self-produced extracellular signal surfactin 25. Interestingly, surfactin is produced by a subpopulation of specialized cells different from the subpopulation of matrix producers 15.
We have detailed in this report the technical approach necessary to visualize and quantify the subpopulation of matrix producers and surfactin producers within the biofilms of B. subtilis. To do this, fluorescent reporters of genes required for matrix production and surfactin production are inserted into the chromosome of B. subtilis. Reporters are expressed only in a subpopulation of specialized cells. Then, the subpopulations can be monitored using fluorescence microscopy and flow cytometry (See Fig 1).
The fact that different subpopulations of specialized cells coexist within multicellular communities of bacteria gives us a different perspective about the regulation of gene expression in prokaryotes. This protocol addresses this phenomenon experimentally and it can be easily adapted to any other working model, to elucidate the molecular mechanisms underlying phenotypic heterogeneity within a microbial community.

Single-cell Analysis of Bacillus subtilis Biofilms Using Fluorescence Microscopy and Flow Cytometry

Microbial biofilms are generally constituted by distinct subpopulations of specialized cells. Single-cell analysis of these subpopulations requires the use of fluorescent reporters. Here we describe a protocol to visualize and monitor several subpopulationswithin B. subtilis biofilms using fluorescence microscopy and flow cytometry.

Biofilm formation is a general attribute to almost all bacteria 1-6. When bacteria form biofilms, cells are encased in extracellular matrix that is mostly ...

A Simple and Rapid Protocol to Non-enzymatically Dissociate Fresh Human Tissues for the Analysis of Infiltrating Lymphocytes

The ability of malignant cells to evade the immune system, characterized by tumor escape from both innate and adaptive immune responses, is now accepted as an important hallmark of cancer. Our research on breast cancer focuses on the active role that tumor infiltrating lymphocytes play in tumor progression and patient outcome. Toward this goal, we developed a methodology for the rapid isolation of intact lymphoid cells from normal and abnormal tissues in an effort to evaluate them proximate to their native state. Homogenates prepared using a mechanical dissociator show both increased viability and cell recovery while preserving surface receptor expression compared to enzyme-digested tissues. Furthermore, enzymatic digestion of the remaining insoluble material did not recover additional CD45+ cells indicating that quantitative and qualitative measurements in the primary homogenate likely genuinely reflect infiltrating subpopulations in the tissue fragment. The lymphoid cells in these homogenates can be easily characterized using immunological (phenotype, proliferation, etc.) or molecular (DNA, RNA and/or protein) approaches. CD45+ cells can also be used for subpopulation purification, in vitro expansion or cryopreservation. An additional benefit of this approach is that the primary tissue supernatant from the homogenates can be used to characterize and compare cytokines, chemokines, immunoglobulins and antigens present in normal and malignant tissues. This protocol functions extremely well for human breast tissues and should be applicable to a wide variety of normal and abnormal tissues.

This protocol describes the rapid non-enzymatic dissociation of fresh human tissue fragments for qualitative and quantitative assessment of CD45+ cells (lymphocytes/leukocytes) present in various normal and malignant human tissues. Additionally, the supernatant obtained from the primary tissue homogenate can be collected and stored for further analysis or experimentation.

The ability of malignant cells to evade the immune system, characterized by tumor escape from both innate and adaptive immune responses, is now accepted ...

Isolation of Leukocytes from the Murine Tissues at the Maternal-Fetal Interface

Immune tolerance in pregnancy requires that the immune system of the mother undergoes distinctive changes in order to accept and nurture the developing fetus. This tolerance is initiated during coitus, established during fecundation and implantation, and maintained throughout pregnancy. Active cellular and molecular mediators of maternal-fetal tolerance are enriched at the site of contact between fetal and maternal tissues, known as the maternal-fetal interface, which includes the placenta and the uterine and decidual tissues. This interface is comprised of stromal cells and infiltrating leukocytes, and their abundance and phenotypic characteristics change over the course of pregnancy. Infiltrating leukocytes at the maternal-fetal interface include neutrophils, macrophages, dendritic cells, mast cells, T cells, B cells, NK cells, and NKT cells that together create the local micro-environment that sustains pregnancy. An imbalance among these cells or any inappropriate alteration in their phenotypes is considered a mechanism of disease in pregnancy. Therefore, the study of leukocytes that infiltrate the maternal-fetal interface is essential in order to elucidate the immune mechanisms that lead to pregnancy-related complications. Described herein is a protocol that uses a combination of gentle mechanical dissociation followed by a robust enzymatic disaggregation with a proteolytic and collagenolytic enzymatic cocktail to isolate the infiltrating leukocytes from the murine tissues at the maternal-fetal interface. This protocol allows for the isolation of high numbers of viable leukocytes (&#62;70%) with sufficiently conserved antigenic and functional properties. Isolated leukocytes can then be analyzed by several techniques, including immunophenotyping, cell sorting, imaging, immunoblotting, mRNA expression, cell culture, and in vitro functional assays such as mixed leukocyte reactions, proliferation, or cytotoxicity assays.

Described herein is a protocol to isolate and analyze the infiltrating leukocytes of tissues at the maternal-fetal interface (uterus, decidua, and placenta) of mice. This protocol maintains the integrity of most cell surface markers and yields enough viable cells for downstream applications including flow cytometry analysis.

Immune tolerance in pregnancy requires that the immune system of the mother undergoes distinctive changes in order to accept and nurture the developing ...

Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

Monocytes are key contributors in various inflammatory disorders and alterations to these cells, including their subset proportions and functions, can have pathological significance. An ideal method for examining alterations to monocytes is whole blood flow cytometry as the minimal handling of samples by this method limits artifactual cell activation. However, many different approaches are taken to gate the monocyte subsets leading to inconsistent identification of the subsets between studies. Here we demonstrate a method using whole blood flow cytometry to identify and characterize human monocyte subsets (classical, intermediate, and non-classical). We outline how to prepare the blood samples for flow cytometry, gate the subsets (ensure contaminating cells have been removed), and determine monocyte subset expression of surface markers &#8212; in this example M1 and M2 markers. This protocol can be extended to other studies that require a standard gating method for assessing monocyte subset proportions and monocyte subset expression of other functional markers.

Here we present a protocol for characterizing monocyte subsets by whole blood flow cytometry. This includes outlining how to gate the subsets and assess their expression of surface markers and giving an example of the assessment of the expression of M1 (inflammatory) and M2 markers (anti-inflammatory).

Monocytes are key contributors in various inflammatory disorders and alterations to these cells, including their subset proportions and functions, can ...

Preparation of Whole Bone Marrow for Mass Cytometry Analysis of Neutrophil-lineage Cells

In this article, we present a protocol that is optimized to preserve neutrophil-lineage cells in fresh BM for whole BM CyTOF analysis. We utilized a myeloid-biased 39-antibody CyTOF panel to evaluate the hematopoietic system with a focus on the neutrophil-lineage cells by using this protocol. The CyTOF result was analyzed with an open-resource dimensional reduction algorithm, viSNE, and the data was presented to demonstrate the outcome of this protocol. We have discovered new neutrophil-lineage cell populations based on this protocol. This protocol of fresh whole BM preparation may be used for 1), CyTOF analysis to discover unidentified cell populations from whole BM, 2), investigating whole BM defects for patients with blood disorders such as leukemia, 3), assisting optimization of fluorescence-activated flow cytometry protocols that utilize fresh whole BM.

Here, we present a protocol to process fresh bone marrow (BM) isolated from mouse or human for high-dimensional mass cytometry (Cytometry by Time-Of-Flight, CyTOF) analysis of neutrophil-lineage cells.

In this article, we present a protocol that is optimized to preserve neutrophil-lineage cells in fresh BM for whole BM CyTOF analysis. We utilized a ...

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 ...

Isolation of Mouse Kidney-Resident CD8+ T cells for Flow Cytometry Analysis

Tissue-resident memory T cell (TRM) is a rapidly expanding field of immunology research. Isolating T cells from various non-lymphoid tissues is one of the key steps to investigate TRMs. There are slight variations in lymphocyte isolation protocols for different organs. Kidney is an essential non-lymphoid organ with numerous immune cell infiltration especially after pathogen exposure or autoimmune activation. In recent years, multiple labs including our own have started characterizing kidney resident CD8+ T cells in various physiological and pathological settings in both mouse and human. Due to the abundance of T lymphocytes, kidney represents an attractive model organ to study TRMs in non-mucosal or non-barrier tissues. Here, we will describe a protocol commonly used in TRM-focused labs to isolate CD8+ T cells from mouse kidneys following systemic viral infection. Briefly, using an acute lymphocytic choriomeningitis virus (LCMV) infection model in C57BL/6 mice, we demonstrate intravascular CD8+ T cell labeling, enzymatic digestion, and density gradient centrifugation to isolate and enrich lymphocytes from mouse kidneys to make samples ready for the subsequent flow cytometry analysis.

Isolation of Mouse Kidney-Resident CD8+ T cells for Flow Cytometry Analysis

Following viral infection, kidney harbors a relatively large number of CD8+ T cells and offers an opportunity to study non-mucosal TRM cells. Here, we describe a protocol to isolate mouse kidney lymphocytes for flow cytometry analysis.

Tissue-resident memory T cell (TRM) is a rapidly expanding field of immunology research. Isolating T cells from various non-lymphoid tissues is one of the ...

Isolation of Uterine Innate Lymphoid Cells for Analysis by Flow Cytometry

Described here is a simple method to isolate and phenotype mouse group 1 uterine innate lymphoid cells (g1 uILCs) from individual pregnant uterus by flow cytometry. The protocol describes how to set up time mating to obtain multiple synchronous dams, the mechanical and enzymatic digestion of the pregnant uterus, the staining of single-cell suspensions, and a FACS strategy to phenotype and discriminate g1 uILCs. Although this method inevitably loses the spatial information of cellular distribution within the tissue, the protocol has been successfully applied to determine uILC heterogeneity, their response to maternal and foetal factors affecting pregnancy, their gene expression profile, and their functions.

This is a method to isolate uterine lymphoid cells from both pregnant and non-pregnant mice. This method can be used for multiple downstream applications such as FACS phenotyping, cell sorting, functional assays, RNA-seq, and proteomics. The protocol here demonstrates how to phenotype group 1 uterine innate lymphoid cells by flow cytometry.

Described here is a simple method to isolate and phenotype mouse group 1 uterine innate lymphoid cells (g1 uILCs) from individual pregnant uterus by flow ...