Hierarchical clustering of flow cytometry data for the study of conventional central chondrosarcoma

We have investigated the use of hierarchical clustering of flow cytometry data to classify samples of conventional central chondrosarcoma, a malignant cartilage forming tumor of uncertain cellular origin, according to similarities with surface marker profiles of several known cell types. Human primary chondrosarcoma cells, articular chondrocytes, mesenchymal stem cells, fibroblasts, and a panel of tumor cell lines from chondrocytic or epithelial origin were clustered based on the expression profile of eleven surface markers. For clustering, eight hierarchical clustering algorithms, three distance metrics, as well as several approaches for data preprocessing, including multivariate outlier detection, logarithmic transformation, and z‐score normalization, were systematically evaluated. By selecting clustering approaches shown to give reproducible results for cluster recovery of known cell types, primary conventional central chondrosacoma cells could be grouped in two main clusters with distinctive marker expression signatures: one group clustering together with mesenchymal stem cells (CD49b‐high/CD10‐low/CD221‐high) and a second group clustering close to fibroblasts (CD49b‐low/CD10‐high/CD221‐low). Hierarchical clustering also revealed substantial differences between primary conventional central chondrosarcoma cells and established chondrosarcoma cell lines, with the latter not only segregating apart from primary tumor cells and normal tissue cells, but clustering together with cell lines from epithelial lineage. Our study provides a foundation for the use of hierarchical clustering applied to flow cytometry data as a powerful tool to classify samples according to marker expression patterns, which could lead to uncover new cancer subtypes. J. Cell. Physiol. 225: 601–611, 2010. © 2010 Wiley‐Liss, Inc.     

Rapid identification and detection of parasitized human red cells by automated flow cytometry

Rapid and reliable identification of various human red cells parasites is important in many chemotherapeutic and immunologic studies. Because manual microscopic counting is tedious and imprecise, we have developed a simple diagnostic procedure for the automated flow cytometric detection of in vitro infected red cells, using a nucleic acid-binding fluorescent dye, acridine orange. Human malaria (Plasmodium falciparum)-infected red cells from continuous human erythrocyte culture were incubated at room temperature in acridine orange stain for 5 min after which the samples were analyzed by flow cytometry. Since mature red cells contain no DNA, infected red cells were identified with a distinct fluorescent signal. A total of 200,000 cells per sample were counted and analyzed in less than 2 min. Rings, trophozoites, and schizonts were assessed and identified in synchronized infected red cell cultures by flow cytometry. In addition, various stages of infected red cells were isolated with a cell sorter. This rapid method permits accurate and reliable assessment of data with the exclusion of anomalous data such as damaged cells, extraneous material, and contaminating particles.     

Detection and monitoring of normal and leukemic cell populations with hierarchical clustering of flow cytometry data

Flow cytometry is a valuable tool in research and diagnostics including minimal residual disease (MRD) monitoring of hematologic malignancies. However, its gradual advancement toward increasing numbers of fluorescent parameters leads to information rich datasets, which are challenging to analyze by standard gating and do not reflect the multidimensionality of the data. We have developed a novel method to analyze complex flow cytometry data, based on hierarchical clustering analysis (HCA) but with a new underlying algorithm, using Mahalanobis distance measure. HCA is scalable to analyze complex multiparameter datasets (here demonstrated on up to 12 color flow cytometry and on a 20-parameter synthetic dataset). We have validated this method by comparison with standard gating approaches when performed independently by expert cytometrists. Acute lymphoblastic leukemia blast populations were analyzed in diagnostic and follow-up datasets (n = 123) from three centers. HCA results correlated very well (Passing-Bablok correlation coefficient = 0.992, slope = 1, intercept = -0.01) with standard gating data obtained by the I-BFM FLOW-MRD study group. To further improve the performance in follow-up samples with low MRD levels and to automate MRD detection, we combined HCA with support vector machine (SVM) learning. HCA in combination with SVM provides a novel diagnostic tool that not only allows analysis of increasingly complex flow cytometry data but also is less observer-dependent compared with classical gating and has potential for automation.     

Laser flow cytometry and cancer chemotherapy: detection of intracellular anthracyclines by flow cytometry.

The intracellular distribution of important chemotherapeutic antibiotics belonging to the anthracycline group (e.g. adriamycin) can be detected by laser flow cytometry. The indirect method is based on the interference of these compounds with the binding of propidium iodide to the nuclear DNA. While in the direct method, the intracellular fluorescence of these antibiotics is excited and detected with a laser beam in a flow system. The present report demonstrates the use of these two methods for intracellular detection and quantitation of a number of important anthracyclines.     

FuGEFlow: data model and markup language for flow cytometry

Background  

Flow cytometry technology is widely used in both health care and research. The rapid expansion of flow cytometry applications has outpaced the development of data storage and analysis tools. Collaborative efforts being taken to eliminate this gap include building common vocabularies and ontologies, designing generic data models, and defining data exchange formats. The Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard was recently adopted by the International Society for Advancement of Cytometry. This standard guides researchers on the information that should be included in peer reviewed publications, but it is insufficient for data exchange and integration between computational systems. The Functional Genomics Experiment (FuGE) formalizes common aspects of comprehensive and high throughput experiments across different biological technologies. We have extended FuGE object model to accommodate flow cytometry data and metadata.     

Development and use of flow cytometry for detection of airborne fungi

Traditional methods for the enumeration of airborne fungi are slow, tedious, and rather imprecise. In this study, the possibility of using flow cytometry (FCM) for the assessment of exposure to the fungus aerosol was evaluated. Epifluorescence microscopy direct counting was adopted as the standard for comparison. Setting up of the method was achieved with pure suspensions of Aspergillus fumigatus and Penicillium brevicompactum conidia at different concentrations, and then analyses were extended to field samples collected by an impinger device. Detection and quantification of airborne fungi by FCM was obtained combining light scatter and propidium iodide red fluorescence parameters. Since inorganic debris are unstainable with propidium iodide, the biotic component could be recognized, whereas the preanalysis of pure conidia suspensions of some species allowed us to select the area corresponding to the expected fungal population. A close agreement between FCM and epifluorescence microscopy counts was found. Moreover, data processing showed that FCM can be considered more precise and reliable at any of the tested concentrations.     

Modeling flow cytometry data for cancer vaccine immune monitoring

Flow cytometry (FCM) is widely used in cancer research for diagnosis, detection of minimal residual disease, as well as immune monitoring and profiling following immunotherapy. In all these applications, the challenge is to detect extremely rare cell subsets while avoiding spurious positive events. To achieve this objective, it helps to be able to analyze FCM data using multiple markers simultaneously, since the additional information provided often helps to minimize the number of false positive and false negative events, hence increasing both sensitivity and specificity. However, with manual gating, at most two markers can be examined in a single dot plot, and a sequential strategy is often used. As the sequential strategy discards events that fall outside preceding gates at each stage, the effectiveness of the strategy is difficult to evaluate without laborious and painstaking back-gating. Model-based analysis is a promising computational technique that works using information from all marker dimensions simultaneously, and offers an alternative approach to flow analysis that can usefully complement manual gating in the design of optimal gating strategies. Results from model-based analysis will be illustrated with examples from FCM assays commonly used in cancer immunotherapy laboratories.     

Modified histogram subtraction technique for analysis of flow cytometry data

Analysis of flow cytometry histogram data by the subjective selection of an integration window can be a tedious and time-consuming task and is often inaccurate. A new method for automated calculation of the percent positive from immunofluorescence histograms is presented. This new method is a modification of the currently used method of channel-by-channel histogram subtraction. Its accuracy is compared to that of the channel-by-channel histogram subtraction method and to another currently used automated method, which selects an integration window by finding the channels that contain the most fluorescent 2% of a control histogram. The new histogram subtraction method is objective, easy to use, and is more accurate than other currently used automated analysis methods. PASCAL source code is given for each method of analysis.     

Using flowViz to visualize flow cytometry data

Summary: Automated analysis of flow cytometry (FCM) data isessential for it to become successful as a high throughput technology.We believe that the principles of Trellis graphics can be adaptedto provide useful visualizations that can aid such automation.In this article, we describe the R/Bioconductor package flowVizthat implements such visualizations. Availability: flowViz is available as an R package from theBioconductor project: http://bioconductor.org Contact: dsarkar{at}fhcrc.org Associate Editor: Olga Troyanskaya     

On multiparameter data analysis in flow cytometry

Increasing numbers of parameters that are accessible to simultaneous measurement in flow cytometric instruments, combined with the extremely large sample sizes common in flow cytometry, make it necessary to examine methods of multivariate statistics for their applicability to problems of visualization and quantitative analysis of flow cytometric data. This article describes some approaches to dimensionality reduction that appear well suited for data sets obtained by flow cytometry.     

Phagocytosis-coupled flow cytometry for detection and size discrimination of anionic polystyrene particles

Flow cytometry was evaluated for its capacity to detect and distinguish a wide size range (20–2000 nm) of fluorescent polystyrene particles (PSPs). Side scatter and fluorescence parameters could predict dispersed PSP sizes down to 200 nm, but the forward scatter parameter was not discriminatory. Confocal microscopy of flow-sorted fractions confirmed that dispersed PSPs appeared as a single sharp peak on fluorescence histograms, whereas agglomerated PSPs were detected as smaller adjacent peaks. Particles as small as 200 nm could also be detected by flow cytometry after they were first phagocytized by J774A.1 murine macrophages. Confocal microscopy demonstrated that these PSPs were internalized within the cytoplasm. MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] and calcein–AM (acetoxymethyl ester) assays showed that they were not cytotoxic. Internalized PSP size correlated to both cellular side scatter (R² = 0.9821) and fluorescence intensity (R² = 0.9993). Furthermore, PSPs of various sizes could be distinguished when J774A.1 cells were loaded with a single size of PSP and mixed with cells containing other sizes. However, spectra of cells loaded with a mixture of PSP sizes resembled those containing only the largest PSP. These data demonstrate the capacity and limitations of phagocytosis-coupled flow cytometry to distinguish between dispersed and agglomerated states and detect a wide size range of particles.     

Rapid identification of sex in birds by flow cytometry

A rapid method to identify sex in birds is described. The method requires microliter volumes of blood, and, under appropriate conditions, results can be available within an hour of sample collection. Samples can be stored at 4 degrees C or -20 degrees C without sacrificing the ability to discriminate sex differences in DNA content. The assay will find utility in laboratory, field, and applied studies, in other classes of vertebrates, and in studies on the dynamics of genome size within and among populations.     

Misty Mountain clustering: application to fast unsupervised flow cytometry gating

Background  

There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 10⁶ points that are often generated by high throughput experiments.     

A novel statistical analysis and interpretation of flow cytometry data

A recently developed class of models incorporating the cyton model of population generation structure into a conservation-based model of intracellular label dynamics is reviewed. Statistical aspects of the data collection process are quantified and incorporated into a parameter estimation scheme. This scheme is then applied to experimental data for PHA-stimulated CD4+T and CD8+T cells collected from two healthy donors. This novel mathematical and statistical framework is shown to form the basis for accurate, meaningful analysis of cellular behaviour for a population of cells labelled with the dye carboxyfluorescein succinimidyl ester and stimulated to divide.     

Parallel processing data acquisition system for multilaser flow cytometry and cell sorting

This report describes the data acquisition electronics for a flow cytometer. The design differs from most instruments in that the signals from a large number of detectors are processed in parallel. Each of the input channels is capable of autonomously measuring and digitizing the fluorescence signals. The digitized values that belong to one particle are collected by digital circuitry and are presented as a compact data package on a special bus. In addition to the pulse values, the data package contains a time marker, information needed for sort decisions, and an error detection code. Specially designed electronic modules that read the information from the bus can take complex multiparameter sort decisions at a very high speed. All events can also be recorded as data lists by a computer. The lists can be used to reconstruct a sort or analysis run. The raw data lists can also be reduced to kinetic curves and/or (gated) multivariate histograms. As a result of the applied scheme of parallel pulse processing, the dead time of the system is independent of the number of parameters measured and the number and time separation of the excitation beams. The instrument has a cycle time of 5 microseconds, which corresponds to a throughput rate of 2 x 10(5) events/s. At this rate, the incidence of correlation errors is well below 1 in 10(8) analyzed particles. The system has proved to be reliable and convenient to use in a variety of experiments. Its high speed and low error rate make it well suited for high-resolution measurements, rare-event analysis, kinetic measurements, and high-speed cell sorting.     

流式细胞技术在微生物检测领域的应用研究

研究应用流式细胞技术(flow cytometry method,FCM)进行快速微生物检测的方法。与传统微生物检测方法相比,FCM法更快速和准确。经Pearson相关系数分析表明,在一定浓度范围内,FCM检测法与标准平板检测法(SPC)具有极强线性相关性。经Q检验法分析,FCM检测法具有良好的重复性。由此可见,FCM法可成为一种替代传统微生物检测法的自动化仪器检测新技术。     

Web servlet-assisted, dial-in flow cytometry data analysis

BACKGROUND: The obvious benefits of centralized data storage notwithstanding, the size of modern flow cytometry data files discourages their transmission over commonly used telephone modem connections. The proposed solution is to install at the central location a web servlet that can extract compact data arrays, of a form dependent on the requested display type, from the stored files and transmit them to a remote client computer program for display. METHODS: A client program and a web servlet, both written in the Java programming language, were designed to communicate over standard network connections. The client program creates familiar numerical and graphical display types and allows the creation of gates from combinations of user-defined regions. Data compression techniques further reduce transmission times for data arrays that are already much smaller than the data file itself. RESULTS: For typical data files, network transmission times were reduced more than 700-fold for extraction of one-dimensional (1-D) histograms, between 18 and 120-fold for 2-D histograms, and 6-fold for color-coded dot plots. Numerous display formats are possible without further access to the data file. CONCLUSIONS: This scheme enables telephone modem access to centrally stored data without restricting flexibility of display format or preventing comparisons with locally stored files.     

Variable selection and multivariate methods for the identification of microorganisms by flow cytometry.

BACKGROUND: When exploited fully, flow cytometry can be used to provide multiparametric data for each cell in the sample of interest. While this makes flow cytometry a powerful technique for discriminating between different cell types, the data can be difficult to interpret. Traditionally, dual-parameter plots are used to visualize flow cytometric data, and for a data set consisting of seven parameters, one should examine 21 of these plots. A more efficient method is to reduce the dimensionality of the data (e.g., using unsupervised methods such as principal components analysis) so that fewer graphs need to be examined, or to use supervised multivariate data analysis methods to give a prediction of the identity of the analyzed particles. MATERIALS AND METHODS: We collected multiparametric data sets for microbiological samples stained with six cocktails of fluorescent stains. Multivariate data analysis methods were explored as a means of microbial detection and identification. RESULTS: We show that while all cocktails and all methods gave good accuracy of predictions (>94%), careful selection of both the stains and the analysis method could improve this figure (to > 99% accuracy), even in a data set that was not used in the formation of the supervised multivariate calibration model. CONCLUSIONS: Flow cytometry provides a rapid method of obtaining multiparametric data for distinguishing between microorganisms. Multivariate data analysis methods have an important role to play in extracting the information from the data obtained. Artificial neural networks proved to be the most suitable method of data analysis.     

Modified Box-Cox transform for modulating the dynamic range of flow cytometry data

We describe an algorithm, Vout = Integer ([2(12)-1/2(12 lambda)-1] V lambda in-1) + 1; lambda greater than 0 based upon Box-Cox transformations as an alternative to nonlinear electronic amplifiers to expand or compress high- or low-amplitude flow cytometer-derived signals. If the indexing parameter lambda less than 1, input channels in the high-amplitude input range are compressed in the output range as occurs when an electronic logarithmic amplifier is used. However, if lambda greater than 1, input channels in the low-amplitude input range are compressed in the output range as occurs when an electronic power amplifier is used. Our modified Box-Cox transform can be implemented either during data collection or off-line for the transformation of previously collected raw data. The transform is the equivalent of an infinite class of nonlinear amplifiers. As the transform is implemented in software, it does not suffer from many of the disadvantages of nonlinear electronic amplifiers.     

Optimizing transformations for automated,high throughput analysis of flow cytometry data

Background  

In a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gating have received considerable scrutiny from the community, the influence of data transformation on the output of high throughput analysis has been largely overlooked. Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of automated gating in a high throughput, fully automated setting. We examine the most common transformations used in flow cytometry, including the generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox, all within the BioConductor flowCore framework that is widely used in high throughput, automated flow cytometry data analysis. All of these transformations have adjustable parameters whose effects upon the data are non-intuitive for most users. By making some modelling assumptions about the transformed data, we develop maximum likelihood criteria to optimize parameter choice for these different transformations.