Hyperspectral cytometry at the single-cell level using a 32-channel photodetector

Despite recent progress in cell-analysis technology, rapid classification of cells remains a very difficult task. Among the techniques available, flow cytometry (FCM) is considered especially powerful, because it is able to perform multiparametric analyses of single biological particles at a high flow rate-up to several thousand particles per second. Moreover, FCM is nondestructive, and flow cytometric analysis can be performed on live cells. The current limit for simultaneously detectable fluorescence signals in FCM is around 8-15 depending upon the instrument. Obtaining multiparametric measurements is a very complex task, and the necessity for fluorescence spectral overlap compensation creates a number of additional difficulties to solve. Further, to obtain well-separated single spectral bands a very complex set of optical filters is required. This study describes the key components and principles involved in building a next-generation flow cytometer based on a 32-channel PMT array detector, a phase-volume holographic grating, and a fast electronic board. The system is capable of full-spectral data collection and spectral analysis at the single-cell level. As demonstrated using fluorescent microspheres and lymphocytes labeled with a cocktail of antibodies (CD45/FITC, CD4/PE, CD8/ECD, and CD3/Cy5), the presented technology is able to simultaneously collect 32 narrow bands of fluorescence from single particles flowing across the laser beam in <5 μs. These 32 discrete values provide a proxy of the full fluorescence emission spectrum for each single particle (cell). Advanced statistical analysis has then been performed to separate the various clusters of lymphocytes. The average spectrum computed for each cluster has been used to characterize the corresponding combination of antibodies, and thus identify the various lymphocytes subsets. The powerful data-collection capabilities of this flow cytometer open up significant opportunities for advanced analytical approaches, including spectral unmixing and unsupervised or supervised classification.     

A comparison of methods for classifying clinical samples based on proteomics data: a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.     

Comparison of techniques for the assessment of polymorphonuclear leukocyte polarisation in suspension

Summry— Polarisation of polymorphonuclear leukocytes (PMN) is suspension was assessed using three techniques: 1) visual classification; 2) computerized morphometry; and 3) flow cytometry. While visual classification detected the formation, polarisation and type of cytoplasmic extensions produced by PMN, morphometry and flow cytometry detected only the formation of extensions. The area, perimeter and ellipticity were, in general, statistically different for each subtype of PMN-shape identified by visual classification. Furthermore, the magnitude and direction of changes detected by flow cytometry were affected by the use of erythrocyte lysis (during isolation of the cells) and the fixative used prior to analysis. The findings of this study demonstrate that visual classification is a more sensitive, reliable and appropriate assay of PMN polarisation than current morphometric and flow cytometric methods.     

Apoptosis can be detected in attached colonic adenocarcinoma HT29 cells using annexin V binding, but not by TUNEL assay or sub-G0 DNA content

BACKGROUND: Induction of apoptosis in adherent cell lines is associated with cell loss from the substratum. In this study the adenocarcinoma cell line, HT29, treated with indomethacin (400microM) has been employed as a model system to demonstrate how flow cytometric analysis can be used to quantify the changes that occur during this process. METHODS: Adherent and floating cell populations have been analyzed independently for effects on cell number, cell cycle characteristics and apoptosis using TUNEL assay and Annexin V binding. In addition apoptosis has been assessed using DNA laddering and morphology. RESULTS: Apoptosis was detected in adherent cells treated with indomethacin using Annexin V binding but not by other techniques employed in this study. In contrast, analysis of "floating" cells revealed the presence of apoptotic cells both in control and indomethacin treated cells using all the techniques employed. However quantification by flow cytometry showed that a significantly higher proportion of control "floaters" were late apoptotic/necrotic rather than apoptotic. DISCUSSION: The data here illustrate the need to interpret measures of apoptosis in adherent cell lines with care and the value of using flow cytometric techniques in the quantitative evaluation of the process.     

A comparative study of cell classifiers for image-based high-throughput screening

Background

Millions of cells are present in thousands of images created in high-throughput screening (HTS). Biologists could classify each of these cells into a phenotype by visual inspection. But in the presence of millions of cells this visual classification task becomes infeasible. Biologists train classification models on a few thousand visually classified example cells and iteratively improve the training data by visual inspection of the important misclassified phenotypes. Classification methods differ in performance and performance evaluation time. We present a comparative study of computational performance of gentle boosting, joint boosting CellProfiler Analyst (CPA), support vector machines (linear and radial basis function) and linear discriminant analysis (LDA) on two data sets of HT29 and HeLa cancer cells.

Results

For the HT29 data set we find that gentle boosting, SVM (linear) and SVM (RBF) are close in performance but SVM (linear) is faster than gentle boosting and SVM (RBF). For the HT29 data set the average performance difference between SVM (RBF) and SVM (linear) is 0.42 %. For the HeLa data set we find that SVM (RBF) outperforms other classification methods and is on average 1.41 % better in performance than SVM (linear).

Conclusions

Our study proposes SVM (linear) for iterative improvement of the training data and SVM (RBF) for the final classifier to classify all unlabeled cells in the whole data set.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-342) contains supplementary material, which is available to authorized users.     

Flow cytometric DNA content analysis of neoplastic cells in haemolymph of the cockle Cerastoderma edule

Epizootiological outbreaks of disseminated neoplasia (DN) have been reported in association with mass mortalities in various bivalve species including the cockle Cerastoderma edule. A flow cytometric (FCM) procedure to study DNA content was successfully adapted and tested in haemolymph cells (haemocytes and neoplastic cells) of the cockle. The FCM results were similar to those obtained by histological analysis (DN diagnosis and haemolymph cell features). FCM analysis revealed differences in DNA content among normal haemocytes (diploid) and neoplastic cells. Four types of cells with abnormal DNA content were found in the haemolymph of affected animals: hypodiploid, hyperdiploid, triploid-sesploid and pentaploid. Our results suggest that the flow cytometric DNA content analysis can be applied to identify neoplastic cell types and to study the association between different cell types and the DN progression or remission in this edible and commercially important bivalve species.     

Comparison of discriminant function and classification tree analyses for age classification of marmots

We evaluated the predictive power of two classification techniques, one parametric – discriminant function analysis (DFA) and the other non-parametric – classification and regression tree analysis (CART), in order to provide a non-subjective quantitative method of determining age class in Vancouver Island marmots ( Marmota vancouverensis ) and hoary marmots ( Marmota caligata ). For both techniques we used morphological measurements of known-age male and female marmots from two independent population studies to build and test predictive models of age class. Both techniques had high predictive power (69–86%) for both sexes and both species. Overall, the two methods performed identically with 81% correct classification. DFA was marginally better at discriminating among older more challenging age classes compared to CART. However, in our test samples, cases with missing values in any of the discriminant variables were deleted and hence unclassified by DFA, whereas CART used values from closely correlated variables to substitute for the missing values. Therefore, overall, CART performed better (CART 81% vs DFA 76%) because of its ability to classify incomplete cases. Correct classification rates were approximately 10% higher for hoary marmots than for Vancouver Island marmots, a result that could be attributed to different sets of morphological measurements. Zygomatic arch breadth measured in hoary marmots was the most important predictor of age class in both sexes using both classification techniques. We recommend that CART analysis be performed on data-sets with incomplete records and used as a variable screening tool prior to DFA on more complete data-sets.     

Multiparameter kinetic analysis of killer cell initiation by using immune RNA.

By treating nonsensitized C57BL/6J spleen derived lymphocytes with EL-4 tumor cell directed xenogeneic extracted RNA we were able to monitor early changes in cellular DNA content by flow cytometric (FCM) analysis and 3H-thymidine uptake. These kinetic parameters were correlated with cell mediated cytotoxicity which appeared as early as 8 hr after activation as measured by release of chromium-51 from labeled EL-4 target cells. Flow cytometric analysis and 3H-thymidine uptake data shown peak S phase activity at 72 hr. Maximum cytotoxicity was observed at 48 hr. Cell cycle kinetic parameters were correlated with the appearance of cell mediated cytotoxicity.     

流式细胞术分析和分拣植物染色体

流式细胞术是当染色体、细胞核和细胞等颗粒随着流动的液体（水或缓冲液）通过一个测量点时,被探测器探测到,这样根据颗粒的物理和化学特征而将不同的颗粒分开并计数分拣的技术。流式细胞分析在人类基因组计划中发挥了重要作用,流式细胞技术的应用也适用于植物,目前这个技术应用范围包括流式核型分析,分拣纯化染色体,定位基因,构建文库等。文章综述了流式细胞术在植物基因组分析方面的研究进展。     

Flow cytometric applications in cytopathology

Flow cytometric (FCM) methodology represents a powerful analytical tool for screening and detecting abnormal and malignant cells, for subclassifying malignancies beyond conventional morphologic type and grade and for transcending light microscopic features by providing more biologically meaningful information. This paper reviews the historical background leading to the development of the FCM methodology and instrumentation that is presently being used for cytopathologic (and histopathologic) diagnosis, tumor subclassification and identification of aggressive cancers. Emphasis is placed on human carcinomas, with reference to hematologic malignancies when appropriate. Examples of FCM DNA content and cytokinetic techniques are described for the various cytologic sampling methods. The advantages, limitations and future prospects for the application of flow cytometric techniques in cytopathology are discussed.     

Reliability analysis of microarray data using fuzzy c-means and normal mixture modeling based classification methods

MOTIVATION: A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, the elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from microarray data. In this study, we applied fuzzy c-means (FCM) and normal mixture modeling (NMM) based classification methods to separate microarray data into reliable and unreliable signal intensity populations. RESULTS: We compared the results of FCM classification with those of classification based on NMM. Both approaches were validated against reference sets of biological data consisting of only true positives and true negatives. We observed that both methods performed equally well in terms of sensitivity and specificity. Although a comparison of the computation times indicated that the fuzzy approach is computationally more efficient, other considerations support the use of NMM for the reliability analysis of microarray data. AVAILABILITY: The classification approaches described in this paper and sample microarray data are available as Matlab( TM ) (The MathWorks Inc., Natick, MA) programs (mfiles) and text files, respectively, at http://rc.kfshrc.edu.sa/bssc/staff/MusaAsyali/Downloads.asp. The programs can be run/tested on many different computer platforms where Matlab is available. CONTACT: asyali@kfshrc.edu.sa.     

A botanical ramble among the blue-green algae

The freshwater microalga Haematococcus pluvialis exhibits a unique morphological response to environmental stress, accumulating carotenoid pigment during encystment. The complexity of characterizing the different cell stages and monitoring the pigment cell content during the life cycle of this microalga is one of the main problems reported when assessing astaxanthin accumulation and degradation. Therefore, with the aim of studying the potential encystment response in this microalga by means of flow cytometry (FCM), we induced oxidative stress in cultures of vegetative growing cells by treating them with paraquat, a known generator of superoxide anion radicals. Two flow cytometric approaches were successfully used to monitor the effect of oxidative stress on morphological changes and genesis of carotenoids in H. pluvialis: (1) a cytometric characterization of different cell types based on analysis of the fluorescence of chlorophyll a vs the fluorescence of astaxanthin, and (2) staining with the fluorochromes hydroethidium (HE) and dihydrorhodamine 123 (DHR), in order to measure the in vivo intracellular levels of reactive oxygen species (ROS). FCM data showed that astaxanthin accumulation during encystment hampers the production of ROS. Furthermore, the cell content of astaxanthin seems to be a good indicator of the extent to which H. pluvialis cells undergo oxidative stress, and also of how the cells defend themselves under stress conditions.     

Rapid detection of efflux pumps and their relation with drug resistance in yeast cells

BACKGROUND: Cell drug resistance can be due to the presence of active efflux pumps (AEP). Identification of yeast cells with a resistance phenotype is important either from a clinical, agricultural or biotechnological point of view. Rapid and reliable methods to detect AEP can be therefore very useful. METHODS: Some yeast cells change their staining by calcein-AM, BCECF-AM, rhodamine 123 and DiOC(5), when pretreated with verapamil, CCCP or ATP depletion, or when pretreated with specific antimicrobial agents. This fact may be interpreted as an indication of the presence/absence of AEP. Six yeast species were tested with a flow cytometric method (FCM) and an epifluorescence microscopic method (EFM), and ten other species were evaluated only by EFM. The minimum inhibitory concentration (MIC) of penconazol, benomyl and cycloheximide for Saccharomyces cerevisiae and Kluyveromyces marxianus, were determined by growth inhibition on solid medium and were compared to the staining changes detected by FCM. RESULTS: The FCM and the EFM allowed the detection of AEP in all the yeast species tested. High MIC values for a drug were related with the presence of at least one AEP indicated by the cytometric data. CONCLUSIONS: The FCM revealed to be a robust assay whereas the EFM can be used as a preliminary test. It is possible to identify resistance/sensitivity patterns in yeast cells through cytometric detection methods of different efflux pumping systems.     

Flow cytometry in oceanography 1989--1999: environmental challenges and research trends.

BACKGROUND: The present review is based on the identification of four major environmental crises that have been approached from a biological oceanographic viewpoint. These crises are the release of contaminants in near shore marine waters, the collapse of marine resources that were renewable until recently, the loss of biodiversity, and global climate change METHODS: The review examines the contribution of cytometry-based biological oceanography to the resolution of the four environmental crises. Using a database of 302 papers, flow cytometric (FCM) studies in biological oceanography over the 1989--1999 decade are examined. Future biological oceanographic applications of FCM are discussed. RESULTS: Most of the published FCM oceanographic studies focus on phytoplankton and bacterioplankton. Analysis of our 1989-1999 database shows the predominance of studies dedicated to phytoplankton (77%), followed by heterotrophic bacteria (21%). The latter progressively increased over the last decade, together with the improved understanding of the biogeochemical and trophic roles of marine bacteria. Most studies on these two microorganisms were conducted in vitro until 1996, after which the trend reversed in favor of in situ research. The most investigated areas were those with major international sampling efforts, related to the changing climate. Concerning environmental topics, 62% of papers on phytoplankton and bacterioplankton focused on the structure of microbial communities and fluxes (e.g., production, grazing); this provides the basis for biological oceanographic studies on resources and climate change. CONCLUSIONS: Future progress in the biological oceanographic use of FCM will likely fall into two categories, i.e., applications where FCM will be combined with the development of other methods and those where FCM will be the main analytical tool. It is expected that FCM and other cytometric approaches will improve the ability of biological oceanography to address the major environmental challenges that are confronting human societies.     

ACOUSTIC IDENTIFICATION OF NINE DELPHINID SPECIES IN THE EASTERN TROPICAL PACIFIC OCEAN

Acoustic methods may improve the ability to identify cetacean species during shipboard surveys. Whistles were recorded from nine odontocete species in the eastern tropical Pacific to determine how reliably these vocalizations can be classified to species based on simple spectrographic measurements. Twelve variables were measured from each whistle ( n = 908). Parametric multivariate discriminant function analysis (DFA) correctly classified 41.1% of whistles to species. Non-parametric classification and regression tree (CART) analysis resulted in 51.4% correct classification. Striped dolphin whistles were most difficult to classify. Whistles of bottlenose dolphins, false killer whales, and pilot whales were most distinctive. Correct classification scores may be improved by adding prior probabilities that reflect species distribution to classification models, by measuring alternative whistle variables, using alternative classification techniques, and by localizing vocalizing dolphins when collecting data for classification models.     

Flow cytometric DNA index and karyotype in childhood lymphoblastic leukemia.

Flow cytometric DNA-index (DI(FCM)) and karyotype were analysed in 82 consecutive children with acute lymphoblastic leukemia (ALL) during a 10 year period. A statistically significant correlation existed between modal chromosome number and DI(FCM) (p = 0.009). DI(FCM) could reliably identify leukemias with >51 chromosomes, whereas only three out of 12 cases with modal chromosome numbers between 47-51 were classified as aneuploid by DI(FCM). In the pseudodiploid group only one out of 20 leukemias had a DI(FCM) > 1.0. Five leukemias with a diploid karyotype showed an aneuploid DI(FCM) and in three patients the flow cytometric measurement revealed biclonality undetected by karyotyping. During treatment aneuploid clones could be detected by DI(FCM) in a substantial number of cases where the cytogenetic analysis was normal, and the opposite was also demonstrated in one case. DI(FCM) gave prognostic information, showing that cases with a DI > 1.12 (corresponding to 51 chromosomes) had a superior outcome with treatment protocols today in use.     

Bacterial fingerprinting by flow cytometry: bacterial species discrimination.

BACKGROUND: A flow cytometric measurement (FCM) technique has been developed to size DNA fragments. Individual fragments of a restriction digest of genomic DNA, stained with an intercalating dye, are passed through an ultrasensitive cytometer. The measured fluorescence intensity from each fragment is proportional to the fragment length. METHODS: The isolation of bacterial genomic DNA and digestion by restriction enzymes were performed inside an agarose plug. Rare cutting enzymes were employed to produce a manageable number of DNA fragments. Electroelution was used to move the DNA fragments from the agarose plug into a solution containing polyamines to protect the DNA from shear-induced breakage. The DNA was stained with the bisintercalating dye thiazole orange homodimer and introduced into our ultrasensitive flow cytometer. A histogram of the fluorescence intensities (fingerprint) was constructed. RESULTS: Gram-positive Bacillus globigii and gram-negative bacteria Escherichia coli and Erwinia herbicola were distinguished by the fingerprint pattern of restriction fragments of their genomic DNA. DNA sizes determined by FCM are in good agreement with pulsed-field gel electrophoresis (PFGE) analysis. Flow cytometry requires only picogram quantities of purified DNA and takes less than 10 min for data collection and analysis. When the total sample preparation time is included, the analysis times for PFGE and FCM are similar ( approximately 3 days). CONCLUSIONS: FCM is an attractive technique for the identification of bacterial species. It is more sensitive and potentially much faster than PFGE.     

Qualitative and quantitative analysis of peritoneal fluids from women with gynecologic diseases. Comparison of cytology and flow cytometry for the detection of malignancy in lavage and ascitic fluids

A prospective study was undertaken to compare flow cytometric (FCM) analysis to conventional cytologic evaluation for the detection of malignant cells in peritoneal fluids (peritoneal lavages and ascitic fluids) from women with gynecologic diseases. The 94 peritoneal fluids analyzed came from 63 cancer patients (with epithelial ovarian carcinomas) and 31 control patients (with benign gynecologic diseases). The FCM DNA histograms were generated using propidium iodide as a DNA fluorochrome. Samples for cytologic analysis were stained with the standard May-Grünwald-Giemsa or Papanicolaou stains. Of the 94 samples, 90 were evaluable cytologically while 70 were suitable for FCM analysis. The sensitivities were 55% for FCM DNA analysis and 80% for cytologic analysis. FCM DNA analysis had a 30% false-positive rate; cytologic analysis produced no false-positive results. These results indicate that there is no advantage in employing FCM analysis instead of conventional cytologic evaluation for the detection of malignant cells in peritoneal fluids from gynecologic cases.     

Textural analysis in the diagnosis of benign and malignant breast cells

OBJECTIVE: To study the discriminatory capacity of textural variables to classify the nuclei of breast tumor cells as benign or malignant, using a statistical approach. STUDY DESIGN: Image analysis techniques were used to automatically segment nuclei of cells obtained by fine needle aspiration and Papanicolaou stained. The sample comprised 95 cases of malignant lesions and 47 cases of benign lesions (approximately 25 nuclei per case), and 27 textural variables were measured. Two methods were used to analyze the data: classification and regression trees (CART) and discriminant analysis. RESULTS: The variance in gray levels was the most decisive variable in the CART analysis, correctly classifying 57% and 97% of benign and malignant cases, respectively. Discriminant analysis yielded the best results, correctly classifying 79% and 85% of benign and malignant cases, respectively. CONCLUSION: The classifier obtained by a statistical approach to the textural analysis of Papanicolaou-stained nuclei did not prove useful for diagnostic discrimination. Staining techniques that are not chromatin specific are highly variable, and other features have proven more effective with this type of staining.