New method for the analysis of flow cytometric data

A numerical method for deriving the fractions of cells in different phases of the cell cycle from a single observed DNA histogram is presented. The observed histogram is regarded as a polluted version (containing allocation errors) of the true histogram. A mathematical model is used to describe the pollution process. A theoretical histogram, representing the true histogram, is constructed so that G1 cells are put into one channel and G2M cells into another; the distribution of S cells in between is approximated with a set of harmonic functions. This theoretical histogram is subsequently disturbed with Gaussian dispersion functions to stimulate the pollution, yielding a predicted histogram. Using a maximum likelihood estimation technique, the model parameters are adjusted iteratively, matching the predicted histogram to the actually observed one. With the final parameter values substituted, the corresponding final theoretical histogram is regarded as a reliable reconstruction of the true histogram. From the latter, the required percentages can be read directly. The advantage of this approach over other mathematical analysis methods is that it allows a wide range of different, continuous distributions for relatively few model parameters (thus featuring flexibility and realism and a diminished risk of encountering computational problems). In addition, estimation errors providing a measure of accuracy can be obtained. To test the method, it was used to analyze various observed histograms from the literature that have been obtained by either simulation or actual flow cytometric measurements. The method appeared to perform well, as compared to the reported results of several other methods of analysis applied to the same data.     

Automatic identification of algae: neural network analysis of flow cytometric data

The performance of an artificial neural network for automaticidentification of phytoplankton was investigated with data fromalgal laboratory cultures, analysed on the Optical PlanktonAnalyser (OPA), a flow cytometer especially developed for theanalysis of phytoplankton. Data from monocultures of eight algalspecies were used to train a neural network. The performanceof the trained network was tested with OPA data from mixturesof laboratory cultures. The network could distinguish Cyanobacteriafrom other algae with 99% accuracy. The identification of specieswas performed with less accuracy, but was generally >90%.This indicates that a neural network under supervised learningcan be used for automatic identification of species in relativelycomplex mixtures. Incorporation of such a system may also increasethe operational size range of a flow cytometer. The combinationof the OPA and neural network data analysis offers the elementsto build an operational automatic algal identification system.     

A count-dependent filter for smoothing flow cytometric histograms

An adaptive count-dependent algorithm for smoothing statistically limited histograms has been developed. It considers both the spatial frequency limitations of the measurement system (described by the measurement system point spread function) and the reliability of the measured data (indicated by the effective number of counts influencing each channel of the histogram. Windows for smoothing flow cytometric histograms are derived from an assumed Gaussian-shaped point spread function (PSF) with a constant coefficient of variation. The windows are developed by scaling the variances of the Gaussian functions inversely with the statistical reliability of the data contained in each channel of the measured histogram. The reliability of this data is determined by taking the square root of the number of counts influencing the value tabulated for each channel. Using the algorithm, a smoothed version of the measured histogram may be developed from a linear sum of the products of the individual scaled Gaussian functions and the original measured histogram. Data are presented demonstrating the advantages of count-dependent smoothing over non-count-dependent smoothing using synthesized DNA histograms as a function of sample size.     

A proposal for a flow cytometric data file standard

The increasing complexity of multiparameter data collection and analysis in flow cytometry and the development of relatively inexpensive arc-lamp-based flow cytometers, which increases the probability that laboratories or institutions may have more than one type of instrument, creates a need for shareable analysis programs and for the transport of flow cytometric data files within an installation or from one institution to another. To address this need, we propose a standard file format to be used for all flow cytometric data. The general principles of this proposal are: (1) The data file will contain a minimum of three segments, TEXT, DATA, and ANALYSIS; (2) The TEXT and ANALYSIS segments consist of KEYWORDS, which are the names of data fields, and their values; (3) All TEXT is encoded in ASCII; (4) KEYWORDS and their values may be of any length; (5) Certain KEYWORDS will be standard, i.e., having specified formats to be recognized by all programs. The structure of the DATA segment will be uniquely defined by the values of KEYWORDS in the TEXT area. It may be in any bit resolution, facilitating compatibility between machines with different word length and/or allowing bit compression of the data. The structured nature of the TEXT area should facilitate management of flow cytometric data using existing data base management systems. The proposed file format has been implemented on VAX, PDP-11, and HP9920 based flow cytometry data acquisition systems.     

Human thymocyte maturation in vitro: a flow cytometric analysis

Using an in vitro culture system, light scatter analyses, and two-color flow cytometry, we provide evidence that the interleukin-2 (IL-2) and transferrin receptors can be induced within 48 hr on nonproliferating immature thymocytes. The thymocytes (greater than 35%) that expressed the transferrin and IL-2 receptors demonstrated nuclear activation as measured by log 90 degrees light scatter analysis. Increases in antigen-receptor-associated T3-antigen expression followed transferrin and IL-2-receptor induction and occurred on maximally activated T4+T8+ thymocytes on Day 3 of culture. Maximal T3 expression did not occur until Days 5-7 and paralleled loss of T4, T8 coexpression, suggesting an association between a mature T3-Ti antigen receptor complex and a mature T4, T8 phenotype.     

A new spreadsheet method for the analysis of bivariate flow cytometric data

Background  

A useful application of flow cytometry is the investigation of cell receptor-ligand interactions. However such analyses are often compromised due to problems interpreting changes in ligand binding where the receptor expression is not constant. Commonly, problems are encountered due to cell treatments resulting in altered receptor expression levels, or when cell lines expressing a transfected receptor with variable expression are being compared. To overcome this limitation we have developed a Microsoft Excel spreadsheet that aims to automatically and effectively simplify flow cytometric data and perform statistical tests in order to provide a clearer graphical representation of results.     

Eight-parameter PC-AT based flow cytometric data system

An 8-parameter flow cytometric data system is described using an IBM-AT compatible personal computer (PC) and a commercial analog to digital conversion (ADC) board. A dedicated pulse processing interface adapts the flow cytometric pulses to the ADC board and controls the number of parameters to be taken up and the trigger conditions. The trigger thresholds are automatically held at a level immediately above the noise level. For the timing of kinetic measurements a linear voltage ramp of adjustable rise time is available. A built-in precision voltage source can be used for an overall calibration. The data system is operated by software written in assembly language. Data may be collected and processed in 1-8-parameter listmode or 1-3-parameter histogram mode. Functions are available for graphical color displays, numerical integration, multiparameter gating, and printing.     

Use of a neural net computer system for analysis of flow cytometric data of phytoplankton populations

Flow cytometry has been used over the past 5 years to begin detailed exploration of the distribution and abundance of picoplankton in the oceans. Light scattering and fluorescence measurements on individual plankton cells in seawater samples allow construction of population signatures from size and pigment characteristics. The use of "list mode" data has made these studies possible, but on-shore analysis of copious data does not permit on-site reexamination of important or unexpected observations, and overall effort is greatly handicapped by data analysis time. Here we describe the application of neural net computer technology to the analysis of flow cytometry data. Although the data used in this study are from oceanographic research, the results are general and should be directly applicable to flow cytometry data of any sort. Neural net computers are ideally suited to perform the pattern recognition required for the quantitative analysis of flow cytometry data. Rather than being programmed to perform analysis, the neural net computer is "taught" how to analyze the cell populations by presenting examples of inputs and correct results. Once the system is "trained," similar data sets can be analyzed rapidly and objectively, minimizing the need for laborious user interaction. The neural network described here offers the advantages of 1) adaptability to changing conditions and 2) potential real-time analysis. High accuracy and processing speed near that required for real-time classification have been achieved in a software simulation of the neural network on a Macintosh SE personal computer.     

Validation of a mathematical procedure for computer analysis of flow cytometric DNA data in human tumors

The determination of tritiated thymidine labeling index and the percentage of cells with S phase DNA content was performed on cell suspensions obtained from 69 patients with non-Hodgkin's lymphoma. The distributions of cells in the cell cycle by computer analysis of flow cytometric data were obtained by two mathematical procedures: the widely adopted Fried model and a new one proposed by Bruni et al. A significant agreement was observed by checking the Spearman index (rs) between the percentages of cells in the different cell cycle phases (G0/1, rs = 0.76; S, rs = 0.60; and G2 + M, rs = 0.43; p less than 0.001) determined by the two procedures. Similarly, a good correlation was observed between the labeling index (LI) and the S phase values obtained by the Fried (rs = 0.45, p less than 0.001) and Bruni (rs = 0.69, p less than 0.001) models, but with a higher agreement for the latter one. The S phase by the Bruni model was also superior in predicting LI: in fact, by employing the S cutoff value of 12%, a better agreement between low LI and low S phase or high LI and high S phase was observed with the Bruni procedure (90%) than with the Fried model (72%). Finally, the analysis of the prognostic significance of the different kinetic variables confirmed the prognostic relevance of LI at any time; the S phase percentage as determined by Bruni et al. was discriminant of survival only at shorter times, and no prognostic significance could be ascribed to S phase according to the Fried procedure.     

Cell-by-cell flow cytometric analysis of chromosomes

The authors discuss various aspects of a recently developed method permitting a detailed flow cytometric analysis of the individual cell karyotypes such as instrumentation, histochemistry, data proceeding algorithms. Possible drawbacks of the method and the ways of their overcoming are considered. Results of analysis of the Chinese hamster cells are presented that illustrate the possibilities of the method, including the metaphase chromosome distribution according to their fluorescence intensity, the analysed cell distribution according to their chromosomes number, the table in which the individual cell karyotypes are distributed according to their fluorescence. The results obtained show that the developed method may be successfully used for investigating chromosomal iNstability and heterogeneity of the mammalian cells.     

A general solution for the time delay introduced by a low-pass Butterworth digital filter: An application to musculoskeletal modeling

Low-pass Butterworth digital filters are commonly used in biomechanics-related research. In general, the input signal is filtered in the forward and reverse directions so that a temporal shift in the output signal does not occur. There are times, however, when introducing a specific time delay is an important consideration when modeling a physiological event. Filtering the data in the forward direction only can be used as an efficient method to account for a specific time delay. Specific delays are possible by carefully selecting the filter order and cut-off frequency. The purpose of this paper is to present the analytical formulation of a general solution for the time delay introduced by a low-pass Butterworth digital filter.     

Automated identification of subpopulations in flow cytometric list mode data using cluster analysis

The application of K-means (ISODATA) cluster analysis to flow cytometric data is described. The results of analyses of flow cytometric data for mixtures of fluorescent microspheres and samples of peripheral blood mononuclear cells are presented. A method for simultaneously displaying list mode data for any number of parameters, which had previously been applied to a continuous set of parameters such as multi-angle light scattering data, is used to present the results of cluster analysis on physically unrelated parameters; this method allows rapid evaluation of the success of subpopulation identification. The factors that influence automated identification of subpopulations are examined, and methods for determining optimal values for these factors are described.     

Mouse strain differences in resident peritoneal cells: a flow cytometric analysis

A flow-cytometric study of resident peritoneal cells among 8 mouse strains showed a more than twofold variation in the ratio of macrophages to macrophages plus lymphocytes, ranging from 27% in A/J to 62% in C57B/L10, with significant strain differences in a number of other cellular parameters. There was a particular deficiency of lymphocytes in strain CBA/N, which carries the xid mutation. Studies of the phagocytosis of fluorescent beads also revealed large differences in the number of beads taken up, ranging from 0.99 per cell in MFI to 1.64 per cell in BALB/c mice in a 20-min period. The total number of peritoneal cells collected also varied between strains, ranging from 2.75 x 10(6) in CBA/Ca to 5.85 x 10(6) in MF1. The total yield of macrophages per mouse ranged from 0.93 x 10(6) in A/J to 3.16 x 10(6) in C57BL/10. These differences should be taken into account when designing experiments which use resident peritoneal cells.     

Analysis of flow cytometric data of irradiated cell populations

Summary Flow cytometry (FCM) and autoradiography have been applied to determine changes in the cell kinetics of irradiated cells. Synchronized L-929 cells were irradiated with 10 Gy of X-rays when progressing from G₁-to S-phase of the cell cycle. In this study three methods to analyse DNA histograms were tested for applicability on FCM data obtained from cell populations blocked or retarded in the cycle: A) the Gaussian integral method, B) the peak-half-reflection method, and C) the rectangle method. Since histograms from synchronized cells are heavily distorted as compared to those obtained from exponentially growing cells and are quite similar to histograms from irradiated cells, they were used to test the suitability of the evaluation methods. Comparing the evaluated FCM data with the autoradiographic results from the same experimental series, the Gaussian integral method proved to be superior to the two other relatively simple approximation methods. The FCM histograms of irradiated cells were therefore analyzed only by the Gaussian integral method. It was shown that a considerable fraction of cells is still in the S-phase 25 h post irradiation, the DNA synthesis of which has ceased, as shown by autoradiography. This indicated that parallel measurements using FCM and autoradiography yield additional information on cell kinetic changes that cannot be obtained from applying one of the two methods used.     

Interactive multi-window integration of two-parameter flow cytometric data fields

Integration is necessary to determine the particle content of regions of interest of flow cytometric two-parameter fields. The improved program of the Cytomic 12 analyzer (1) offers: window trace integration for relatively simple window structures. The field of interest is surrounded by an integration trace (window). Eight independent windows can be stored and successively evaluated. It also offers painted field integration for complicated window structures. The pointer or a small window is interactively moved over the structures to be integrated like the brush of a painter. The "painted field" defines the window to be integrated. Window sets and painted fields can be stored on a floppy disk. Painted fields can be added and may also serve as look up tables for sorting.     

CUBIC: a three-dimensional colored projection of Consort 30 generated trivariate flow cytometric data.

The CUBIC program displays three-dimensional colored dot plots of flow cytometric trivariate data collected by unmodified commercial instruments (FACScan flow cytometer, FACS 440 cell sorter). Assuming a bimodal distribution of the fluorescence intensity of the cells, the eight theoretical subpopulations involved in a three-color fluorescence histogram are clearly localized in the 3-D space by colored dots that are clustered near each corner of a cubic frame. Rotation, tilting, and zoom functions are available. Table look-up is not needed. CUBIC was illustrated by two experiments: 1) three-color immunofluorescence of antigens on human lymphocytes using monoclonal antibodies conjugated either to fluorescein (FITC), to R-phycoerythrin (PE), or to biotin revealed by a streptavidin coupled to a PE-Texas red tandem conjugate (TC); 2) two-color immunofluorescence of CD4 and CD8 antigens on thymocytes of healthy or preleukemic mice correlated to the DNA content quantified by 7-amino-actinomycin D (7-AAD). The three fluorescences were excited by a single argon-ion laser emitting at 488 nm.     

Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data.

BACKGROUND: Polychromatic flow cytometry (PFC) allows the simultaneous determination of multiple antigens in the same cell, resulting in the generation of a high number of subsets. As a consequence, data analysis is the main difficulty with this technology. Here we show the use of cluster analysis (CA) and principal component analyses (PCA) to simplify multicolor data visualization and to allow subjects' classification. METHODS: By eight-colour cytofluorimetric analysis, we investigated the T cell compartment in donors of different age (young, middle-aged, and centenarians). T cell subsets were identified by combining positive and negative expression of antigens. The resulting data set was organized into a matrix and subjected to CA and PCA. RESULTS: CA clustered people of different ages on the basis of cytofluorimetric profile. PCA of the cellular subsets identified centenarians within a different cluster from young donors, while middle-aged donors were scattered between these groups. These approaches identified T cell phenotypes that changed with increasing age. In young donors, memory T cell subsets tended to be CD127+ and CD95- whereas CD127-, CD95+ phenotypes were found at higher frequencies in people with advanced age. CONCLUSIONS: Our data suggest the use of bioinformatic approaches to analyze large data-sets generated by PFC and to obtain the rapid identification of key populations that best characterize a group of subjects.     

Real-time technique for conversion of skin temperature into skin blood flow: human skin as a low-pass filter for thermal waves

Monitoring of skin blood flow oscillations related with mechanical activity of vessels is a very useful modality during diagnosis of peripheral hemodynamic disorders. In this study, we developed a new model and technique for real-time conversion of skin temperature into skin blood flow oscillations, and vice versa. The technique is based on the analogy between the thermal properties of the human skin and electrical properties of the special low-pass filter. Analytical and approximated impulse response functions for the low- and high-pass filters are presented. The general algorithm for the reversible conversion of temperature into blood flow is described. The proposed technique was verified using simulated or experimental data of cold stress, deep inspiratory gasp, and post-occlusive reactive hyperaemia tests. The implementation of the described technique will enable to turn a temperature sensor into a blood flow sensor.