Data reduction for spectral clustering to analyze high throughput flow cytometry data

Background  

Recent biological discoveries have shown that clustering large datasets is essential for better understanding biology in many areas. Spectral clustering in particular has proven to be a powerful tool amenable for many applications. However, it cannot be directly applied to large datasets due to time and memory limitations. To address this issue, we have modified spectral clustering by adding an information preserving sampling procedure and applying a post-processing stage. We call this entire algorithm SamSPECTRAL.     

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.     

Comparison of five clustering algorithms to classify phytoplankton from flow cytometry data.

BACKGROUND: Artificial neural networks (ANNs) have been shown to be valuable in the analysis of analytical flow cytometric (AFC) data in aquatic ecology. Automated extraction of clusters is an important first stage in deriving ANN training data from field samples, but AFC data pose a number of challenges for many types of clustering algorithm. The fuzzy k-means algorithm recently has been extended to address nonspherical clusters with the use of scatter matrices. Four variants were proposed, each optimizing a different measure of clustering "goodness." METHODS: With AFC data obtained from marine phytoplankton species in culture, the four fuzzy k-means algorithm variants were compared with each other and with another multivariate clustering algorithm based on critical distances currently used in flow cytometry. RESULTS: One of the algorithm variants (adaptive distances, also known as the Gustafson--Kessel algorithm) was found to be robust and reliable, whereas the others showed various problems. CONCLUSIONS: The adaptive distances algorithm was superior in use to the clustering algorithms against which it was tested, but the problem of automatic determination of the number of clusters remains to be addressed.     

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.     

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.     

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.     

Multi-user system for analysis of data from flow cytometry.

A new program is described for the analysis of DNA histograms from flow cytometry. The fundamental model representing the cell population is similar to one described previously. It assumes the population is grouped into compartments, each consisting of cells having approximately the same DNA content. After staining the cells with an appropriate fluorochrome, the fluorescence distribution of cells within each compartment is assumed to be Gaussian. In the present algorithm, the parameters of the model can either be computed directly by the program from the data, or can be specified as input by the user. When synchronous cell populations lacking distinct G1 and G2/M phases are analyzed, the parameter values must first be obtained using an appropriate control. Percentages of cells in the various compartments are computed using a gradient search method described by Bevington.     

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.     

Introduction to flow cytometry data file standard

The Data File Standards Committee of the Society for Analytical Cytology presents a Standard to be used for the storage of data associated with flow cytometric measurements. The Standard specifies a format that provides for the inclusion of all information necessary to fully describe: 1) the instrument used for the measurement; 2) the sample measured; 3) the data obtained; and 4) the results of analysis of the data. The Committee and the Society for Analytical Cytology point out that the use of this Standard by all those individuals and companies that generate or use data taken with flow cytometers or generate methods of analysis for the data will encourage the sharing of such data and methods of analysis.     

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.     

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     

Particle dispersion for further Cryptosporidium and Giardia detection by flow cytometry

AIMS: The aim of this study was to overcome the analytical problems encountered during the detection of protozoans by flow cytometry resulting from particle compaction. METHODS AND RESULTS: Malvern Mastersizer (Malvern Instruments, Malvern, UK) was used to characterize the particle distribution of four different water samples and/or particle concentrates incubated with (i) low ionic strength solution or sequestring agent, (ii) anionic or non-ionic surfactants (iii) industry detergent formulations and (iv) physical treatment. The recovery of oocysts and cysts in seeded and treated particle concentrates was estimated by cytometry and microscopy. The decrease in ionic strength of the aqueous solution was most efficient in particle dispersion for different types of water. Moreover, samples treated with deionized water or tetrasodium pyrophosphate showed the highest recovery with more than 80% of the oocysts and cysts recovered. CONCLUSIONS: Chemical treatments that act by altering the ionic strength of the medium are the most efficient for all water types tested here but the overall detergency performance cannot be predicted for all water types. SIGNIFICANCE AND IMPACT OF THE STUDY: Flow cytometric detection has been replaced largely by immunomagnetic separation but the data recorded still have relevance in this technique as well as in molecular techniques requiring DNA or RNA extraction.     

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.     

一种基于流式细胞术的牛多细胞因子检测方法

本研究旨在建立一种基于流式细胞术的牛多细胞因子检测方法。对前期制备并筛选出的针对牛细胞因子IFN-γ、IL-2、TNF-α、IP-10和MCP-1的单克隆抗体进行荧光标记,与细胞表面分子抗体组合搭配,进行牛多细胞因子流式检测方法的建立和优化。随后利用建立的方法进行BCG体外感染牛外周血单个核细胞的细胞因子表达规律测定,并结合CFP10-ESAT6蛋白刺激剂评价上述细胞因子作为牛结核诊断标识的潜力。建立的牛多细胞因子流式检测方法可以有效测定BCG感染牛外周血T淋巴细胞的细胞因子表达,其中IFN-γ、IL-2、TNF-α在感染40h后持续上升,而IP-10和MCP-1表达水平呈现下降趋势;对于牛外周血CD4⁺T淋巴细胞IFN-γ、IL-2、TNF-α的联合检测能有效区分牛结核阳性和阴性样品。这一方法为牛病原菌感染、疫苗注射后细胞免疫应答水平评价以及疫病诊断提供了重要技术手段。     

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.