Conversion of linear histogram flow cytometry data to a logarithmic display

A routine is described that readily allows the rescaling of linear histographic data to a corresponding logarithmic histogram. This procedure significantly improves data display, particularly where a wide range in the measured parameter is encountered. The logarithmic scale displays peaks with band widths more proportional to their respective coefficients of variation than is the case in a linear display. Rescaling several linear histograms to a common logarithmic scale allows the combination of these linear data even though the linear ranges are different. This routine is presented as a program written in BASIC for execution on a microcomputer.     

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.     

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.     

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.     

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.     

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.     

A single laser method for subtraction of cell autofluorescence in flow cytometry

In flow cytometry cell autofluorescence often interferes with efforts to measure low levels of bound fluorescent antibody. We have developed a way to correct for autofluorescence on a cell-by-cell basis. This results in improved estimates of real staining and better separation of the fluorescence histograms of stained and non-stained cells. Using a single laser, two-color fluorescence measurement system and two-color compensation electronics, autofluorescence and one fluorescent reagent are measured (rather than two fluorescent reagents). With fluorescein-conjugated antibodies the signal in the 515 to 555 nm range (green fluorescence) includes both fluorescein emission and part of the cellular autofluorescence. In the cases we have investigated, autofluorescence collected at wavelengths above 580 nm ("red") is well correlated with the green autofluorescence of the cells. A fraction of this red fluorescence is subtracted from the green fluorescence to produce an adjusted fluorescein output on which unstained cells have zero average signal. Use of this method facilitates the selection of rare cells transfected with surface antigen genes. Culture conditions affect the level of autofluorescence and the balance between red and green autofluorescence. When applied with fluorescein-conjugated reagents, the technique is compatible with the use of propidium iodide for live/dead cell discrimination.     

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.     

A flow cytometry technique for measuring chromosome-mediated gene transfer

BACKGROUND: Using artificial chromosome expression systems (ACes), we have developed a unique and rapid screening technique to quantify delivery of foreign DNA into cells in vitro. Delivery was measured within 24 h after transfection, using flow cytometry to detect the transfer of ACes labeled with thymidine analogue. This technique can be used to optimize delivery parameters of ACes and heterologous DNA into cells and eventually tissue. METHOD: Chinese hamster ovary (CHO) cells carrying artificial chromosomes were grown in media supplemented with iododeoxyuridine (IdUrd). The 60-mb artificial chromosome was purified by flow cytometry sorting and transfected into Chinese hamster lung fibroblast cells (V79-4) or mouse connective tissue cells [LM(tk-)] using LipofectAMINE 2000trade mark, a cationic lipid, and Superfecttrade mark, a cationic dendrimer. The cells were incubated with an FITC-conjugated anti-bromodeoxyuridine (BrdUrd) antibody and analyzed by flow cytometry. IdUrd-incorporated artificial chromosome expressing green fluorescent protein (GFP) was transfected into V79-4 cells. Delivery was measured at 24 h and GFP expression was detected at 48 h. RESULTS: The delivery of intact artificial chromosomes into V79-4 and LMtk- cells was detected within 2 h and up to 48 h post-transfection. Maximum delivery rates of 20% and 14% were observed using LipofectAMINE 2000 and Superfect, respectively. Flow cytometry data correlated with microscopic observations. IdUrd incorporation resulted in less quenching after staining with Hoechst 33258 and chromomycin A3 than BrdUrd incorporation. The fluorescence intensity of the FITC-conjugated anti-BrdUrd antibody was greater with IdUrd-incorporated chromosomes than with BrdUrd-incorporated chromosomes. CONCLUSION: The results indicate that IdUrd-labeled artificial chromosomes can be detected 24 h after transfection. This efficient, sensitive, high-throughput detection technique is being used to evaluate and optimize other transfer technologies (e.g., electroporation and sonoporation), different delivery reagents, and protocols in a variety of cells in vitro. This work represents the first step in utilizing artificial chromosomes as nonviral vectors for gene therapy.     

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.     

Nonparametric discriminant analysis of phytoplankton species using data from analytical flow cytometry

BACKGROUND: Analytical flow cytometry (AFC) provides rapid and accurate measurement of particles from heterogeneous populations. AFC has been used to classify and identify phytoplankton species, but most methods of discriminant analysis of resulting data have depended on normality assumptions and outcomes have been disappointing. METHODS AND RESULTS: In this study, we consider nonparametric methods based on density estimation. In addition to the familiar kernel method, methods based on wavelets are also implemented. Full five-dimensional wavelet estimation proves to be computationally prohibitive with current workstation power, so we employ projection pursuit for reduction of dimensionality. AFC typically produces very large samples, so we also investigate data simplification through binning. Further modifications to the discrimination strategy are suggested by specific features of phytoplankton data, namely, a hierarchical group structure, the possible presence of many groups, and the likelihood of encountering an aberrant group in a test sample. CONCLUSIONS: We apply all the resultant procedures to appropriate subsets of a very large data set, demonstrate their efficacy, and compare their error rates with those of more conventional methods. We further show that incorporation of the specific features of phytoplankton data into the analysis leads to improved results and provides a general framework for analysis of such data.     

The relevance of flow cytometry for biochemical analysis

Flow cytometry (FCM) allows the simultaneous measurement of multiple fluorescences and light scatter induced by illumination of single cells or microscopic particles in suspension, as they flow rapidly through a sensing area. In some systems, individual cells or particles may be sorted according to the properties exhibited. By using appropriate fluorescent markers, FCM is unique in that multiple structural and functional parameters can be quantified simultaneously on a single-particle basis, whereas up to thousands of biological particles per second may be examined. FCM is increasingly used for basic, clinical, biotechnological, and environmental studies of biochemical relevance. In this critical review, we summarize the main advantages and limitations of FCM for biochemical studies and discuss briefly the most relevant parameters and analytical strategies. Graphical examples of the biological information provided by multiparametric FCM are presented. Also, this review contains specific sections on flow cytoenzymology, FCM analysis of isolated subcellular organelles, and cell-free FCM.     

Application of bead-based assays for flow cytometry analysis

To date microsphere-based assays in flow cytometry have focused on the detection of antibody or antigen. Most studies have been research based to evaluate the performance of the technique relative to conventional techniques. However, there have not been any carefully controlled studies of the sensitivity and specificity, as well as analytic sensitivity of the FMIA technique. As such, it is difficult to document advantages of this tecnique clearly. The data suggest that FMIA is considerably more sensitive than conventional techniques, and the ability to analyze for multiple analytes in one sample dilution is attractive. This ability to simultaneously analyze for multiple samples is primarily dependent upon the size difference as sensed by FALS of the microspheres. However, it is also possible to use microspheres of the same size but that differ in either fluorescence or RALS signal. If microspheres of the same size are used but one fluoresces red and the signal in the assay uses a green fluorochrome, then the two microspheres can be separated by their red fluorescence. Using this technique, one can increase the number of microspheres that can be used in an assay. It is also possible to use microspheres of the same size but with different abilities to scatter the incident light at right angles. The use of these microspheres is then similar to the nonfluorescent versus red microspheres. By the judicious combination of microsphere size, it is possible to easily differentiate eight different microspheres. With the addition of a fluorescebt dye and/or differences in right-angle light-scatter capabilities, the number of different microspheres that can be used simultaneously becomes quite large. In practice, the number of microspheres that can be differentiated is no doubt greater than the number of analytes that need to be assayed in one assay.Although the apparent increase in sensitivity and the ability to simultaneously detect and quantitate numerous analytes are important attributes of FMIA, there are drawbacks to this method. Although the FMIA lends itself well to one-step no-wash procedures, when wash steps are necessary they are time-consuming and ineffecient. Most wash steps in FMIA use centrifugation of the microspheres to remove them from the reagent. There is a significant loss of microspheres in these wash steps, which are time-consuming. There are studies ussing vacuum filtration of the suspension to separate the microspheres from the reagents. A number of different groups are pursuing an automated or semiautomated method for the efficient washing and reagent delivery system for FMIA. Commercial systems are being developed that may allow for the easier handling of these reagents.Numerous groups are investigating the use of microspheres and flow cytometry primarily in immunoassay development. The procedure has the advantages of the simultaneous yet discrete analysis of multiple analytes and the inherent increase in sensitivity using fluorescence over other signals. There will no doubt be wider applications     

DNA analysis by flow cytometry

Accurate quantification of DNA from cells of several species is possible with flow cytometry. When one species is used as a reference, cytometric readings from two or more different species can be compared to obtain relative percent DNA or DNA indices. Differences in DNA from the male and female of the same species also can be measured. The method allows rapid screening of chromosomal abnormalities among large clinical populations, and evaluation of errors of sex determination such as XY sex reversal.     

Nonparametric flow cytometry analysis.

A nonparametric statistical test for the analysis of flow cytometry derived histograms is presented. The method involves smoothing and translocation of data, area normalization, channel by channel determination of the mean and S.D., and use of Bayes' theorem for unknown histogram classification. With this statistical method, different sets of histograms from numerous biological systems can be compared.     

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.     

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     

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.     

Correlation of grade of urothelial cell carcinomas and DNA histogram features assessed by flow cytometry and automated image cytometry.

OBJECTIVE: To analyse how DNA ploidy and S-phase fraction (SPF) by flow cytometry (FCM) and an optimised fully automatic DNA image cytometer (ICM) correlate with grade in TaT1 urothelial cell carcinomas (UC) of the urinary bladder. MATERIALS AND METHODS: Two-hundred-and twenty-eight consensus cases were analysed. Single cell suspensions were stained (DAPI for FCM, Feulgen for ICM). There was enough material for both FCM and ICM in 202 of these cases. FCM and optimised ICM measurements were performed on the 202 UCs. To discriminate between different grades, single- and multivariate analyses was performed on DNA histogram features obtained with the MultiCycle program (using DNA index (DI) and SPF). RESULTS: Overall measurement time of the adapted ICM method was 10.7 minutes per case (range 5.9-29.8 min.) and required little additional interactive object rejection (average 152 objects (84-298) on 3000 objects per case measured, which took 9.9 minutes on average, range 8.3-15.5 minutes). The ICM histograms looked much "cleaner" with less noise than the FCM graphs. The coefficient of variation (CV) of the diploid peak for ICM (5.4%) was significantly lower than for FCM (5.9%) (p<0.0001). ICM features were more strongly correlated to grade than FCM features. In multivariate analysis, the best discriminating set of features was DNA ploidy and SPF (both by ICM). CONCLUSIONS: The adapted fully automated DNA ICM works very well for UCs. Low CV DNA ICM histograms are obtained in a time comparable to FCM. The DNA ICM results have stronger discriminative power than DNA FCM for grade in TaT1 UCs.