CCAST: A Model-Based Gating Strategy to Isolate Homogeneous Subpopulations in a Heterogeneous Population of Single Cells

A model-based gating strategy is developed for sorting cells and analyzing populations of single cells. The strategy, named CCAST, for Clustering, Classification and Sorting Tree, identifies a gating strategy for isolating homogeneous subpopulations from a heterogeneous population of single cells using a data-derived decision tree representation that can be applied to cell sorting. Because CCAST does not rely on expert knowledge, it removes human bias and variability when determining the gating strategy. It combines any clustering algorithm with silhouette measures to identify underlying homogeneous subpopulations, then applies recursive partitioning techniques to generate a decision tree that defines the gating strategy. CCAST produces an optimal strategy for cell sorting by automating the selection of gating markers, the corresponding gating thresholds and gating sequence; all of these parameters are typically manually defined. Even though CCAST is optimized for cell sorting, it can be applied for the identification and analysis of homogeneous subpopulations among heterogeneous single cell data. We apply CCAST on single cell data from both breast cancer cell lines and normal human bone marrow. On the SUM159 breast cancer cell line data, CCAST indicates at least five distinct cell states based on two surface markers (CD24 and EPCAM) and provides a gating sorting strategy that produces more homogeneous subpopulations than previously reported. When applied to normal bone marrow data, CCAST reveals an efficient strategy for gating T-cells without prior knowledge of the major T-cell subtypes and the markers that best define them. On the normal bone marrow data, CCAST also reveals two major mature B-cell subtypes, namely CD123+ and CD123- cells, which were not revealed by manual gating but show distinct intracellular signaling responses. More generally, the CCAST framework could be used on other biological and non-biological high dimensional data types that are mixtures of unknown homogeneous subpopulations.     

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.     

Multiparameter flow cytometry for the characterization of human embryonic stem cells

Using multiparameter staining methods and flow cytometry to investigate the pluripotency of HUES7 human embryonic stem cell cultures, it was found that the multidimensional approach of marker co-expression allowed the different cell populations to be easily identified and demonstrated cross reactivity between the SSEA 4 and SSEA 1 antibodies, resulting in a substantial false positive SSEA 1 population. It is the accepted norm to apply control gates at a 95 % confidence level of the isotype control; however, this study found that adjusting the control gate to a 99 % confidence level significantly reduced the effect of this cross reactivity. Though conversely, this gating shift also decreased the positive marker expression of SSEA 4 and Tra-1-60, indicating that there is a need for strongly expressing markers coupled with increased optimization of fluorophore/antibody combinations before a gating strategy of 99 % can be implemented on a more routine basis.     

Harmonization of the intracellular cytokine staining assay

Active immunotherapy for cancer is an accepted treatment modality aiming to reinforce the T-cell response to cancer. T-cell reactivity is measured by various assays and used to guide the clinical development of immunotherapeutics. However, data obtained across different institutions may vary substantially making comparative conclusions difficult. The Cancer Immunotherapy Immunoguiding Program organizes proficiency panels to identify key parameters influencing the outcome of commonly used T-cell assays followed by harmonization. Our successes with IFNγ-ELISPOT and peptide HLA multimer analysis have led to the current study on intracellular cytokine staining (ICS). We report the results of three successive panels evaluating this assay. At the beginning, 3 out of 9 participants (33 %) were able to detect >6 out of 8 known virus-specific T-cell responses in peripheral blood of healthy individuals. This increased to 50 % of the laboratories in the second phase. The reported percentages of cytokine-producing T cells by the different laboratories were highly variable with coefficients of variation well over 60 %. Variability could partially be explained by protocol-related differences in background cytokine production leading to sub-optimal signal-to-noise ratios. The large number of protocol variables prohibited identification of prime guidelines to harmonize the assays. In addition, the gating strategy used to identify reactive T cells had a major impact on assay outcome. Subsequent harmonization of the gating strategy considerably reduced the variability within the group of participants. In conclusion, we propose that first basic guidelines should be applied for gating in ICS experiments before harmonizing assay protocol variables.     

flowClust: a Bioconductor package for automated gating of flow cytometry data

Background  

As a high-throughput technology that offers rapid quantification of multidimensional characteristics for millions of cells, flow cytometry (FCM) is widely used in health research, medical diagnosis and treatment, and vaccine development. Nevertheless, there is an increasing concern about the lack of appropriate software tools to provide an automated analysis platform to parallelize the high-throughput data-generation platform. Currently, to a large extent, FCM data analysis relies on the manual selection of sequential regions in 2-D graphical projections to extract the cell populations of interest. This is a time-consuming task that ignores the high-dimensionality of FCM data.     

Emerging concepts in designing next-generation multifunctional nanomedicine for cancer treatment

Nanotherapy has emerged as an improved anticancer therapeutic strategy to circumvent the harmful side effects of chemotherapy. It has been proven to be beneficial to offer multiple advantages, including their capacity to carry different therapeutic agents, longer circulation time and increased therapeutic index with reduced toxicity. Over time, nanotherapy evolved in terms of their designing strategies like geometry, size, composition or chemistry to circumvent the biological barriers. Multifunctional nanoscale materials are widely used as molecular transporter for delivering therapeutics and imaging agents. Nanomedicine involving multi-component chemotherapeutic drug-based combination therapy has been found to be an improved promising approach to increase the efficacy of cancer treatment. Next-generation nanomedicine has also utilized and combined immunotherapy to increase its therapeutic efficacy. It helps in targeting tumor immune response sparing the healthy systemic immune function. In this review, we have summarized the progress of nanotechnology in terms of nanoparticle designing and targeting cancer. We have also discussed its further applications in combination therapy and cancer immunotherapy. Integrating patient-specific proteomics and biomarker based information and harnessing clinically safe nanotechnology, the development of precision nanomedicine could revolutionize the effective cancer therapy.     

Rapid and Robust Analysis of Cellular and Molecular Polarization Induced by Chemokine Signaling

Cells respond to chemokine stimulation by losing their round shape in a process called polarization, and by altering the subcellular localization of many proteins. Classic imaging techniques have been used to study these phenomena. However, they required the manual acquisition of many cells followed by time consuming quantification of the morphology and the co-localization of the staining of tens of cells. Here, a rapid and powerful method is described to study these phenomena on samples consisting of several thousands of cells using an imaging flow cytometry technology that combines the advantages of a microscope with those of a cytometer. Using T lymphocytes stimulated with CCL19 and staining for MHC Class I molecules and filamentous actin, a gating strategy is presented to measure simultaneously the degree of shape alterations and the extent of co-localization of markers that are affected by CCL19 signaling. Moreover, this gating strategy allowed us to observe the segregation of filamentous actin (at the front) and phosphorylated Ezrin-Radixin-Moesin (phospho-ERM) proteins (at the rear) in polarized T cells after CXCL12 stimulation. This technique was also useful to observe the blocking effect on polarization of two different elements: inhibition of actin polymerization by a pharmacological inhibitor and expression of mutants of the Par6/atypical PKC signaling pathway. Thus, evidence is shown that this technique is useful to analyze both morphological alterations and protein redistributions.     

Inversion of Markov processes to determine rate constants from single-channel data.

The determination of rate constants from single-channel data can be very difficult, in part because the single-channel lifetime distributions commonly analyzed by experimenters often have a complicated mathematical relation to the channel gating mechanism. The standard treatment of channel gating as a Markov process leads to the prediction that lifetime distributions are exponential functions. As the number of states of a channel gating scheme increases, the number of exponential terms in the lifetime distribution increases, and the weights and decay constants of the lifetime distributions become progressively more complicated functions of the underlying rate constants. In the present study a mathematical strategy for inverting these functions is introduced in order to determine rate constants from single-channel lifetime distributions. This inversion is easy for channel gating schemes with two or fewer states of a given conductance, so the present study focuses on schemes with more states. The procedure is to derive explicit equations relating the parameters of the lifetime distribution to the rate constants of the scheme. Such equations can be derived using the equality between symmetric functions of eigenvalues of a matrix and sums over principle minors, as well as expressions for the moments, derivatives, and weights of a lifetime distribution. The rate constants are then obtained as roots to this system of equations. For a gating scheme with three sequential closed states and a single gateway state, exact analytical expressions were found for each rate constant in terms of the parameters of the three-exponential closed-time distribution. For several other gating schemes, systems of equations were found that could be solved numerically to obtain the rate constants. Lifetime distributions were shown to specify a unique set of real rate constants in sequential gating schemes with up to five closed or five open states. For kinetic schemes with multiple gating pathways, the analysis of simulated data revealed multiple solutions. These multiple solutions could be distinguished by examining two-dimensional probability density functions. The utility of the methods introduced here are demonstrated by analyzing published data on nicotinic acetylcholine receptors, GABA(A) receptors, and NMDA receptors.     

PACK: Profile Analysis using Clustering and Kurtosis to find molecular classifiers in cancer

MOTIVATION: Elucidating the molecular taxonomy of cancers and finding biological and clinical markers from microarray experiments is problematic due to the large number of variables being measured. Feature selection methods that can identify relevant classifiers or that can remove likely false positives prior to supervised analysis are therefore desirable. RESULTS: We present a novel feature selection procedure based on a mixture model and a non-gaussianity measure of a gene's expression profile. The method can be used to find genes that define either small outlier subgroups or major subdivisions, depending on the sign of kurtosis. The method can also be used as a filtering step, prior to supervised analysis, in order to reduce the false discovery rate. We validate our methodology using six independent datasets by rediscovering major classifiers in ER negative and ER positive breast cancer and in prostate cancer. Furthermore, our method finds two novel subtypes within the basal subgroup of ER negative breast tumours, associated with apoptotic and immune response functions respectively, and with statistically different clinical outcome. AVAILABILITY: An R-function pack that implements the methods used here has been added to vabayelMix, available from (www.cran.r-project.org). CONTACT: aet21@cam.ac.uk SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.     

Multi-class clustering of cancer subtypes through SVM based ensemble of pareto-optimal solutions for gene marker identification

With the advancement of microarray technology, it is now possible to study the expression profiles of thousands of genes across different experimental conditions or tissue samples simultaneously. Microarray cancer datasets, organized as samples versus genes fashion, are being used for classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer types. In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic clustering of the tissue samples. In this regard, a real-coded encoding of the cluster centers is used and cluster compactness and separation are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of non-dominated solutions. A novel approach to combine the clustering information possessed by the non-dominated solutions through Support Vector Machine (SVM) classifier has been proposed. Final clustering is obtained by consensus among the clusterings yielded by different kernel functions. The performance of the proposed multiobjective clustering method has been compared with that of several other microarray clustering algorithms for three publicly available benchmark cancer datasets. Moreover, statistical significance tests have been conducted to establish the statistical superiority of the proposed clustering method. Furthermore, relevant gene markers have been identified using the clustering result produced by the proposed clustering method and demonstrated visually. Biological relationships among the gene markers are also studied based on gene ontology. The results obtained are found to be promising and can possibly have important impact in the area of unsupervised cancer classification as well as gene marker identification for multiple cancer subtypes.     

Investigation of altering single-nucleotide polymorphism density on the power to detect trait loci and frequency of false positive in nonparametric linkage analyses of qualitative traits

Genome-wide linkage analysis using microsatellite markers has been successful in the identification of numerous Mendelian and complex disease loci. The recent availability of high-density single-nucleotide polymorphism (SNP) maps provides a potentially more powerful option. Using the simulated and Collaborative Study on the Genetics of Alcoholism (COGA) datasets from the Genetics Analysis Workshop 14 (GAW14), we examined how altering the density of SNP marker sets impacted the overall information content, the power to detect trait loci, and the number of false positive results. For the simulated data we used SNP maps with density of 0.3 cM, 1 cM, 2 cM, and 3 cM. For the COGA data we combined the marker sets from Illumina and Affymetrix to create a map with average density of 0.25 cM and then, using a sub-sample of these markers, created maps with density of 0.3 cM, 0.6 cM, 1 cM, 2 cM, and 3 cM. For each marker set, multipoint linkage analysis using MERLIN was performed for both dominant and recessive traits derived from marker loci. Our results showed that information content increased with increased map density. For the homogeneous, completely penetrant traits we created, there was only a modest difference in ability to detect trait loci. Additionally, as map density increased there was only a slight increase in the number of false positive results when there was linkage disequilibrium (LD) between markers. The presence of LD between markers may have led to an increased number of false positive regions but no clear relationship between regions of high LD and locations of false positive linkage signals was observed.     

Proteome profile of human breast cancer tissue generated by LC-ESI-MS/MS combined with sequential protein precipitation and solubilization

Comprehensive proteome profiling of breast cancer tissue samples is challenging, as the tissue samples contain many proteins with varying concentrations and modifications. We report an effective sample preparation strategy combined with liquid chromatography (LC) electrospray ionization (ESI) quadrupole time-of-flight (QTOF) MS/MS for proteome analysis of human breast cancer tissue. The complexity of the breast cancer tissue proteome was reduced by using protein precipitation from a tissue extract, followed by sequential protein solubilization in solvents of different solubilizing strength. The individual fractions of protein mixtures or subproteomes were subjected to trypsin digestion and the resultant peptides were separated by strong cation exchange (SCX) chromatography, followed by reversed-phase capillary LC combined with high resolution and high accuracy ESI-QTOF MS/MS. This approach identified 14407 unique peptides from 3749 different proteins based on peptide matches with scores above the threshold scores at the 95% confidence level in MASCOT database search of the acquired MS/MS spectra. The false positive rate of peptide matches was determined to be 0.95% by using the target-decoy sequence search strategy. On the basis of gene ontology categorization, the identified proteins represented a wide variety of biological functions, cellular processes, and cellular locations.     

Quantum dot-based immunochromatography test strip for rapid, quantitative and sensitive detection of alpha fetoprotein

Rapid, quantitative detection of tumor markers with high sensitivity and specificity is critical to clinical diagnosis and treatment of cancer. We describe here a novel portable fluorescent biosensor that integrates quantum dot (QD) with an immunochromatography test strip (ICTS) and a home-made test strip reader for detection of tumor markers in human serum. Alpha fetoprotein (AFP), which is valuable for diagnosis of primary hepatic carcinoma, is used as a model tumor marker to demonstrate the performance of the proposed immunosensor. The principle of this sensor is on the basis of a sandwich immunoreaction that was performed on an ICTS. The fluorescence intensity of captured QD labels on the test line and control line served as signals was determined by the home-made test strip reader. The strong luminescence and robust photostability of QDs combined with the promising advantages of an ICTS and sensitive detection with the test strip reader result in good performance. Under optimal conditions, this biosensor is capable of detecting as low as 1 ng/mL AFP standard analyte in 10 min with only 50 μL sample volume. Furthermore, 1000 clinical human serum samples were tested by both the QD-based ICTS and a commercial electrochemiluminescence immunoassay AFP kit simultaneously to estimate the sensitivity, specificity and concordance of the assays. Results showed high consistency except for 24 false positive cases (false positive rate 3.92%) and 17 false negative cases (false negative rate 4.38%); the error rate was 4.10% in all. This demonstrates that the QD-based ICTS is capable of rapid, sensitive, and quantitative detection of AFP and shows a great promise for point-of-care testing of other tumor markers.     

Ion-channel gating mechanisms: model identification and parameter estimation from single channel recordings

Patch-clamp data may be analysed in terms of Markov process models of channel gating mechanisms. We present a maximum likelihood algorithm for estimation of gating parameters from records where only a single channel is present. Computer simulated data for three different models of agonist receptor gated channels are used to demonstrate the performance of the procedure. Full details of the implementation of the algorithm are given for an example gating mechanism. The effects of omission of brief openings and closings from the single-channel data on parameter estimation are explored. A strategy for discriminating between alternative possible gating models, based upon use of the Schwarz criterion, is described. Omission of brief events is shown not to lead to incorrect model identification, except in extreme circumstances. Finally, the algorithm is extended to include channel gating models exhibiting multiple conductance levels.     

The use of dwell time cross-correlation functions to study single-ion channel gating kinetics.

The derivation of cross-correlation functions from single-channel dwell (open and closed) times is described. Simulation of single-channel data for simple gating models, alongside theoretical treatment, is used to demonstrate the relationship of cross-correlation functions to underlying gating mechanisms. It is shown that time irreversibility of gating kinetics may be revealed in cross-correlation functions. Application of cross-correlation function analysis to data derived from the locust muscle glutamate receptor-channel provides evidence for multiple gateway states and time reversibility of gating. A model for the gating of this channel is used to show the effect of omission of brief channel events on cross-correlation functions.     

False "false-positive" results in diagnostic cytology

With the introduction of transbronchial brushings and fine needle aspiration biopsy, which enable us to obtain samples directly from lesions, the diagnostic potential of cytology for the detection of malignancy, including early cancer, has been greatly enhanced. From 1976 to 1982, five positive cytology reports were initially considered to be "false positives" on the basis of negative gross findings, benign operative biopsies or negative histologic findings in the resected surgical specimens. However, these proved to be false "false positives," based upon the clinical follow-up or further examination of the surgical specimens. Presentation is made of three of these cases with positive cytologic findings and initially negative histologic diagnoses, with an analysis of the causes of the latter. From our experience, four types of cancerous lesions seem prone to being missed during gross examination, namely: any small cancer with a consistency similar to that of the parenchyma of the organ in which the tumor is located, superficially invasive carcinoma, scar cancer and a radiologically occult lung cancer in the presence of a coexisting radiologically demonstrable lesion. With more clinical application of these cytologic methods, false "false positives" are expected to occur more often.     

Estimating kinetic parameters for single channels with simulation. A general method that resolves the missed event problem and accounts for noise.

Analysis of currents recorded from single channels is complicated by the limited time resolution (filtering) of the data which can prevent the detection of brief intervals. Although a number of approaches have been used to correct for the undetected intervals (missed events) when identifying kinetic models and estimating parameters, none of them provide a general method which takes into account the true effects of noise and limited time resolution. This paper presents such a method. The approach is to use simulated single-channel currents to incorporate the true effects of filtering and noise on missed events and interval durations. The simulated currents are then analyzed in a manner identical to that used to analyze the experimental currents. An iterative search process using likelihood comparison of two-dimensional dwell-time distributions obtained from the simulated and experimental single-channel currents then allows the most likely rate constants to be determined. The large errors and false solutions that can result from the more typically applied assumptions of no noise and an absolute dead time (idealized filtering) are excluded by the iterative simulation method, and the correlation information contained in the two-dimensional distributions should increase the ability to distinguish among different gating mechanisms. The iterative simulation method is generally applicable to channels which typically open to a single conductance level. For these channels the method places no restrictions on the proposed gating mechanism or the form of the predicted dwell-time distributions.     

NOGEA: A Network-oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes in the interactome network, which provides a new way for predicting drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.     

Evaluation of a Semiautomated System for Direct Fluorescent Antibody Detection of Salmonellae

A semi-automatic system under development by Aerojet Medical and Biological Systems for the direct fluorescent antibody detection of salmonellae was evaluated with various food, feed, and environmental samples. All samples were simultaneously examined by Automated Bioassay System (ABS), manual direct fluorescent antibody procedures and cultural procedures. The ABS gave satisfactory results with the processed samples. It detected all of the culturally positive powdered egg and candy samples with no false negative results and gave only 6.6 and 5.3% false positive rates, respectively. With meatmeal samples the ABS failed to detect one culturally positive specimen that was also positive by manual fluorescent antibody and gave one (1.1%) false-positive result. A high rate of false-negative results was obtained by ABS on unprocessed samples of creek water, poultry, and sausage. Adding another enrichment step to the protocol reduced the false-negative rate considerably but severely increased the false-positive rate. The instruments worked reasonably well, but research is needed to improve enrichment procedures for samples to be processed by the system.