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 than one instance of the program at a time. Therefore, when loading a new workspace, make sure to check there is no other instance of ExCYT running.</li>
	<li>To begin analysis pipeline, first select type of cytometry (Flow Cytometry or Mass Cytometry &#8211; CYTOF), under the File Selection Parameters select number of events to sample from the file (for this example use 2,000). Once data has been successfully imported, a dialogue box will pop up informing the user that the data has been successfully imported.</li>
	<li>Press the Auto-Compensation button to conduct an optional auto-compensation step, as done by Bagwell &#38; Adams9. Select the directory containing single stains. Select the unstained sample within the user interface dialogue.
	<ol>
		<li>Place a forward/side-scatter gate on any of the samples in this directory that will be used to select events to calculate the compensation matrix. It is recommended to use the unstained sample for this purpose. At this point, an algorithm has been implemented to set consistent thresholds at the 99th percentile of the unstained sample to define positive events in each of the single stains to calculate the compensation matrix. When this is finished, a dialogue box will inform the user that the compensation has been performed.</li>
	</ol>
	</li>
	<li>Next, press Gate Population and select the populations of cells of interest, as is the convention in flow cytometry analyses. When population of cells is selected, enter number of percentage of events downstream analysis (in this 10,000 events).</li>
	<li>Next, select the number channels to be used for analysis in the listbox in the far right of the Pre-Processing box (use the specific channels shown in the example).</li>
</ol>
3. t-SNE Analysis
<ol>
	<li>Press the t-SNE button to have the program begin start to compute the reduced dimensionality data set for visualization in the window below the t-SNE button. To save image of t-SNE, press Save TSNE Image. On a machine with 8 CPU @ 3.4 GHz each and 8 GM RAM this step should take about 2 minutes for 10,000 events, 10 minutes for 50,000 events, and 20 minutes for 100,000 events.</li>
	<li>To create a &#8216;t-SNE heatmap&#8217;, as seen in several CYTOF publications10,11, select an option from the Marker-Specific t-SNE pop-up menu (use the specific markers CD64 or CD3 as shown in the example). A figure will pop up showing a heatmap representation of the t-SNE plot that can be saved for figure generation.</li>
	<li>Select areas of interest in the t-SNE plots by the user for further downstream analyses using the Gate t-SNE button.</li>
</ol>
4. Cluster Analysis
<ol>
	<li>To begin clustering analysis, select an option in Clustering Method listbox (in this example us DBSCAN with a distance factor of 5 in dialogue box to the right of the listbox). Press the Cluster button.</li>
	<li>Use one of the following options for automated clustering algorithms found in the &#8216;Automated Clustering Parameters&#8217; panel:
	<ol>
		<li>Hard KMEANS (on t-SNE): Apply k-means clustering to the reduced 2-dimensional t-SNE data and requires the number of clusters to be provided to the algorithm12.</li>
		<li>Hard KMEANS (on HD Data): Apply k-means clustering to the original high-dimensional data that was given to the t-SNE algorithm. Once again, the number of clusters needs to be provided to the algorithm.</li>
		<li>DBSCAN: Apply the clustering method of clustering, called Density-Based Spatial Clustering of Applications with Noise13 that clusters the reduced 2-dimensional t-SNE data and requires a non-dimensional distance factor that determines the general size of the clusters. This type of clustering algorithm is well suited to cluster the t-SNE reduction as it is able to cluster non-spheroidal cluster that are often present in the reduced t-SNE representation. Additionally, due to the fact that it operates on the 2-dimensional data, it is one of the faster clustering algorithms.</li>
		<li>Hierarchical Clustering: Apply the conventional hierarchical clustering method to the high-dimensional data where the entire Euclidean distance matrix is calculated between all events before providing the algorithm a distance factor that sets the size of the cluster.</li>
		<li>Network Graph-Based: Apply a clustering method that has been most recently introduced into analyzing flow cytometry data when there are rare subpopulations that the user wants to detect11,14. This method relies on first creating a graph that determines the connections between all events in the data. This step consists of providing an initial parameter to create the graph, which is the number of k-nearest neighbors. This parameter generally governs the size of the clusters. At this point, another dialogue box pops up asking the user to employ one of 5 clustering algorithms that is applied to the graph. These include 3 options to maximize the modularity of the graph, the Danon Method, and a spectral clustering algorithm14,15,16,17,18. If one wants a generally faster clustering solution, we recommend Spectral Clustering or the Fast Greedy Modularity Maximization. While the Modularity Maximization methods along with the Danon method determine the optimal number of clusters, Spectral Clustering requires the number of clusters to be given to the program.</li>
		<li>Self-Organized Map: Employ an artificial neural network to cluster the high-dimensional data.</li>
		<li>GMM &#8211; Expectation Maximization: Create a Gaussian Mixture Model using Expectation Maximization (EM) technique to cluster the high-dimensional data.19 This type of clustering method also requires the user to input the number of clusters.</li>
		<li>Variational Bayesian Inference for GMM: Create a Gaussian Mixture Model but unlike EM, it can automatically determine the number of the mixture components k.20 While the program does require a number of clusters to be given (larger than the expected number of clusters), the algorithm will determine the optimal number on its own.</li>
	</ol>
	</li>
	<li>To study a particular area of the t-SNE plot, press the Select Cluster Manually button to draw a set of user-defined clusters. Of note, clusters cannot share members (i.e., each event can only belong to 1 cluster).</li>
</ol>
5. Cluster Filtration
<ol>
	<li>Set(s) of clusters identified either manually or via one of the automatic methods described above can be filter via as follows.
	<ol>
		<li>To sort clusters (in the Cluster Filter panel) by any of the markers measured in the experiment, select an option from the Sort pop-up menu. To set whether the order is ascending or descending, press the Ascending/Descending button to the right of the Sort pop-up menu. This will update the list of Clusters in the &#8216;Clusters (Filtration)&#8217; listbox and re-order them in descending order of median cluster expression of that marker. The percentage denoted in the &#8216;Clusters (Filtration)&#8217; listbox denotes the percent of the population that this cluster represents.</li>
		<li>To set a minimum threshold value for a given cluster across a certain channel, select an option from the Threshold pop-up menu (in this example us the marker CD65 and set a threshold at 0.75). Either type a value in the numerical box below the graph or use the slide-bar to set a threshold. Once threshold is set, press Add Above Threshold or Add Below Threshold to specify the direction of threshold. Once this threshold has been set, it will be listed in the Thresholds box next to the &#8216;Cluster Filter&#8217; panel where the marker, the threshold value, and the direction will be listed so the user is aware of which thresholds are currently being applied. Finally, the t-SNE plot will update by blurring out clusters that do not meet the requirements of the filtration and the &#8216;Clusters (Filtration)&#8217; listbox will update to show clusters that meet the filtration requirements.</li>
		<li>To set a minimum threshold for frequency of a cluster, enter a numerical cut-off in the Cluster Frequency Threshold (%) box in the Cluster Filter panel (in this example use 1%).</li>
	</ol>
	</li>
</ol>
6. Cluster Analysis &#38; Visualization
<ol>
	<li>To select clusters for further analysis and visualization, select clusters In Clusters (Filtration) listbox and press the Select &#224; button to move them to the Cluster Analyze listbox.</li>
	<li>To create heatmaps of clusters, select the clusters of interest in the Cluster Analyze listbox and press the HeatMap of Clusters button. When this button is pressed, a figure will pop up containing a heat map along with dendrograms on the cluster and parameter axes. The dendrogram on the vertical axis will group clusters by those that are closely related while the dendrogram on the horizontal axis will group markers that are co-associated. To save heatmap, press File | Export Setup | Export.</li>
	<li>To create a &#8216;High Dimensional Box Plot&#8217; or &#8216;High Dimensional Flow Plot,&#8217; select the clusters of interest in the Cluster Analyze listbox and press either the High Dimensional Box Plot button or the High Dimensional Flow Plot button. These plots can be used to visually assess the distribution of given channels of various clusters across all dimensions.</li>
	<li>To show clusters in traditional 2D flow plots, select the transformation (linear, log10, arcsinh) and channel in the Conventional Flow Plot panel and press Conventional Flow Plot.</li>
</ol>

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	<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. 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The authors have nothing to disclose.

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><tbody><tr><td>Desktop</td><td>SuperMicro</td><td>Custom Build</td><td>Computer used to run analysis</td></tr><tr><td>MATLAB</td><td>Mathworks</td><td>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 ...

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

Research

JoVE Journal

Immunology and Infection

11.4K Views.  Johns Hopkins University School of Medicine. Deze methode kan helpen bij het beantwoorden van belangrijke vragen op biomedisch gebied, zoals het begrijpen van het fenotype van biologisch relevante subpopulaties. Het belangrijkste voordeel van deze techniek is dat het een individu zonder programmeerervaring in staat stelt om hun cytometriegegevens te analyseren met de nieuwste hoogdimensionale technieken. Als u een analysepijplijn wilt starten, selecteert u eerst het type cytometrie en het aantal gebeurtenissen dat u uit het bestand moet...

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

Physiologic Patient Derived 3D Spheroids for Anti-neoplastic Drug Screening to Target Cancer Stem Cells

In this protocol, we outline the procedure for generation of tumor spheroids within 384-well hanging droplets to allow for high-throughput screening of anti-cancer therapeutics in a physiologically representative microenvironment. We outline the formation of patient derived cancer stem cell spheroids, as well as, the manipulation of these spheroids for thorough analysis following drug treatment. Specifically, we describe collection of spheroid morphology, proliferation, viability, drug toxicity, cell phenotype and cell localization data. This protocol focuses heavily on analysis techniques that are easily implemented using the 384-well hanging drop platform, making it ideal for high throughput drug screening. While we emphasize the importance of this model in ovarian cancer studies and cancer stem cell research, the 384-well platform is amenable to research of other cancer types and disease models, extending the utility of the platform to many fields. By improving the speed of personalized drug screening and the quality of screening results through easily implemented physiologically representative 3D cultures, this platform is predicted to aid in the development of new therapeutics and patient-specific treatment strategies, and thus have wide-reaching clinical impact.

This protocol describes generation of patient-derived spheroids, and downstream analysis including quantification of proliferation, cytotoxicity testing, flow cytometry, immunofluorescence staining and confocal imaging, in order to assess drug candidates&#8217; potential as anti-neoplastic therapeutics. This protocol supports precision medicine with identification of specific drugs for each patient and stage of disease.

In this protocol, we outline the procedure for generation of tumor spheroids within 384-well hanging droplets to allow for high-throughput screening of ...

Cancer Research

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

Analysis of Side Population in Solid Tumor Cell Lines

Cancer stem cells (CSCs) are an important cause of tumor growth, metastasis, and recurrence. Isolation and identification of CSCs are of great significance for tumor research. Currently, several techniques are used for the identification and purification of CSCs from tumor tissues and tumor cell lines. Separation and analysis of side population (SP) cells are two of the commonly used methods. The methods rely on the ability of CSCs to rapidly expel fluorescent dyes, such as Hoechst 33342. The efflux of the dye is associated with the ATP-binding cassette (ABC) transporters and can be inhibited by ABC transporter inhibitors. Methods for staining cultured tumor cells with Hoechst 33342 and analyzing the proportion of their SP cells by flow cytometry are described. This assay is convenient, fast, and cost-effective. Data generated in this assay can contribute to a better understanding of the effect of genes or other extracellular and intracellular signals on the stemness properties of tumor cells.

A convenient, fast, and cost-effective method to measure the proportion of side population cells in solid tumor cell lines is presented.

Cancer stem cells (CSCs) are an important cause of tumor growth, metastasis, and recurrence. Isolation and identification of CSCs are of great ...

Kinetx: A Combined Flow Cytometry Assay and Analysis Software Framework to Quantitatively Measure and Categorize Platelet Activation in Real-time.

Platelets react rapidly to vascular injury and undergo activation in response to a range of stimuli to limit blood loss. Many platelet function tests measure endpoint responses after a defined time period and not the rate of platelet activation. However, the rate at which platelets convert extracellular stimuli into a functional response is an essential factor in determining how efficiently they can respond to injury, bind to a forming thrombus, and signal to recruit other platelets. This paper describes a flow cytometry-based platelet function assay that enables simultaneous data acquisition and sample stimulation and utilizes newly developed bespoke open-source software (Kinetx) to enable quantitative kinetic measurements of platelet granule release, fibrinogen binding, and intracellular calcium flux. Kinetix was developed in R so that users can alter parameters such as degree of smoothing, identification of outlying data points, or time scales. To aid users unfamiliar with the R environment, Kinetix analysis of data can be performed by a single command. Together, this allows real-time platelet activation metrics, such as rate, acceleration, time to peak-rate, time to peak-calcium, and qualitative shape changes, to be accurately and reproducibly measured and categorized. Kinetic measurements of platelet activation give a unique insight into platelets' behavior during the first stages of activation and may provide a method of predicting the recruitment of platelets into a forming thrombus.

Platelets react rapidly to a range of stimuli. This paper describes a real-time flow cytometry-based platelet function assay and a newly developed bespoke open-source software (Kinetx) to enable quantitative kinetic measurements of platelet granule release, fibrinogen binding, and intracellular calcium flux.

Platelets react rapidly to vascular injury and undergo activation in response to a range of stimuli to limit blood loss. Many platelet function tests ...

Biochemistry

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 ...

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly dependent on modes of single-cell analysis. Methods which deliver composite data reflecting the mean values of biomarkers from cell populations risk losing subpopulation dynamics that reflect the heterogeneity of the studied biological system. In keeping with this, traditional approaches are being replaced by, or supported with, more sophisticated forms of cellular assay developed to allow assessment by high-content microscopy. These assays potentially generate large numbers of images of fluorescent biomarkers, which enabled by accompanying proprietary software packages, allows for multi-parametric measurements per cell. However, the relatively high capital costs and overspecialization of many of these devices have prevented their accessibility to many investigators.
Described here is a universally applicable workflow for the quantification of multiple fluorescent marker intensities from specific subcellular regions of individual cells suitable for use with images from most fluorescent microscopes. Key to this workflow is the implementation of the freely available Cell Profiler software1&#160;to distinguish individual cells in these images, segment them into defined subcellular regions and deliver fluorescence marker intensity values specific to these regions. The extraction of individual cell intensity values from image data is the central purpose of this workflow and will be illustrated with the analysis of control data from a siRNA screen for G1 checkpoint regulators in adherent human cells. However, the workflow presented here can be applied to analysis of data from other means of cell perturbation (e.g., compound screens) and other forms of fluorescence based cellular markers and thus should be useful for a wide range of laboratories.

Presented is a flexible informatics workflow enabling multiplexed image-based analysis of fluorescently labeled cells. The workflow quantifies nuclear and cytoplasmic markers and computes marker translocation between these compartments. Procedures are provided for perturbation of cells using siRNA and reliable methodology for marker detection by indirect immunofluorescence in 96-well formats.

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly ...

Biology

Quantification of Reactive Oxygen Species Using 2&#8242;,7&#8242;-Dichlorofluorescein Diacetate Probe and Flow-Cytometry in M&#252;ller Glial Cells

The redox balance has an important role in maintaining cellular homeostasis. The increased generation of reactive oxygen species (ROS) promotes the modification of proteins, lipids, and DNA, which finally may lead to alteration in cellular function and cell death. Therefore, it is beneficial for cells to increase their antioxidant defense in response to detrimental insults, either by activating an antioxidant pathway like Keap1/Nrf2 or by improving redox scavengers (vitamins A, C, and E, &#946;-carotene, and polyphenols, among others). Inflammation and oxidative stress are involved in the pathogenesis and progression of retinopathies, such as diabetic retinopathy (DR) and retinopathy of prematurity (ROP). Since M&#252;ller glial cells (MGCs) play a key role in the homeostasis of neural retinal tissue, they are considered an ideal model to study these cellular protective mechanisms. In this sense, quantifying ROS levels with a reproducible and simple method is essential to assess the contribution of pathways or molecules that participate in the antioxidant cell defense mechanism. In this article, we provide a complete description of the procedures required for the measurement of ROS with DCFH-DA probe and flow cytometry in MGCs. Key steps for flow cytometry data processing with the software are provided here, so the readers will be able to measure ROS levels (geometric means of FITC) and analyze fluorescence histograms. These tools are highly helpful to evaluate not only the increase in ROS after a cellular insult but also to study the antioxidant effect of certain molecules that can provide a protective effect on the cells.

Here, we propose a systematized, accessible, and reproducible protocol to detect cellular reactive oxygen species (ROS) using 2&#8242;,7&#8242;-dichlorofluorescein diacetate probe (DCFH-DA) in M&#252;ller glial cells (MGCs). This method quantifies total cellular ROS levels with a flow cytometer. This protocol is very easy to use, suitable, and reproducible.

The redox balance has an important role in maintaining cellular homeostasis. The increased generation of reactive oxygen species (ROS) promotes the ...

Neuroscience

Sample Preparation for Mass Cytometry Analysis

Mass cytometry utilizes antibodies conjugated with heavy metal labels, an approach that has greatly increased the number of parameters and opportunities for deep analysis well beyond what is possible with conventional fluorescence-based flow cytometry. As with any new technology, there are critical steps that help ensure the reliable generation of high-quality data. Presented here is an optimized protocol that incorporates multiple techniques for the processing of cell samples for mass cytometry analysis. The methods described here will help the user avoid common pitfalls and achieve consistent results by minimizing variability, which can lead to inaccurate data. To inform experimental design, the rationale behind optional or alternative steps in the protocol and their efficacy in uncovering new findings in the biology of the system being investigated is covered. Lastly, representative data is presented to illustrate expected results from the techniques presented here.

This article describes the collection and processing of samples for mass cytometry analysis.

Mass cytometry utilizes antibodies conjugated with heavy metal labels, an approach that has greatly increased the number of parameters and opportunities ...

Immunofluorescent Microscopy and Analysis for High-Parameter Tissue Imaging

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.

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.

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

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

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