Browsing by Subject "Flow Cytometry"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    A high-affinity inhibitor of human CD59 enhances complement-mediated virolysis of HIV-1: implications for treatment of HIV-1/AIDS
    (The American Association of Immunologists, 2010-01-01) Hu, Weiguo; Yu, Qigui; Hu, Ningjie; Byrd, Daniel; Amet, Tohti; Shikuma, Cecilia; Shiramiz, Bruce; Halperin, Jose A.; Qin, Xuebin; Department of Microbiology and Immunology, IU School of Medicine
    Many pathogenic enveloped viruses, including HIV-1, escape complement-mediated virolysis by incorporating host cell regulators of complement activation into their own viral envelope. The presence of complement regulators including CD59 on the external surface of the viral envelope confers resistance to complement-mediated virolysis, which may explain why human pathogenic viruses such as HIV-1 are not neutralized by complement in human fluids, even in the presence of high Ab titers against the viral surface proteins. In this study, we report the development of a recombinant form of the fourth domain of the bacterial toxin intermedilysin (the recombinant domain 4 of intermedilysin [rILYd4]), a 114 aa protein that inhibits human CD59 function with high affinity and specificity. In the presence of rILYd4, HIV-1 virions derived from either cell lines or peripheral blood mononuclear cells of HIV-1-infected patients became highly sensitive to complement-mediated lysis activated by either anti-HIV-1 gp120 Abs or by viral infection-induced Abs present in the plasma of HIV-1-infected individuals. We also demonstrated that rILYd4 together with serum or plasma from HIV-1-infected patients as a source of anti-HIV-1 Abs and complement did not mediate complement-mediated lysis of either erythrocytes or peripheral blood mononuclear cells. These results indicate that rILYd4 may represent a novel therapeutic agent against HIV-1/AIDS.
  • Thumbnail Image
    Item
    A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects
    (Springer (Biomed Central Ltd.), 2014) Dundar, Murat; Akova, Ferit; Yerebakan, Halid Z.; Rajwa, Bartek; Department of Computer & Information Science, School of Science
    BACKGROUND: Flow cytometry (FC)-based computer-aided diagnostics is an emerging technique utilizing modern multiparametric cytometry systems.The major difficulty in using machine-learning approaches for classification of FC data arises from limited access to a wide variety of anomalous samples for training. In consequence, any learning with an abundance of normal cases and a limited set of specific anomalous cases is biased towards the types of anomalies represented in the training set. Such models do not accurately identify anomalies, whether previously known or unknown, that may exist in future samples tested. Although one-class classifiers trained using only normal cases would avoid such a bias, robust sample characterization is critical for a generalizable model. Owing to sample heterogeneity and instrumental variability, arbitrary characterization of samples usually introduces feature noise that may lead to poor predictive performance. Herein, we present a non-parametric Bayesian algorithm called ASPIRE (anomalous sample phenotype identification with random effects) that identifies phenotypic differences across a batch of samples in the presence of random effects. Our approach involves simultaneous clustering of cellular measurements in individual samples and matching of discovered clusters across all samples in order to recover global clusters using probabilistic sampling techniques in a systematic way. RESULTS: We demonstrate the performance of the proposed method in identifying anomalous samples in two different FC data sets, one of which represents a set of samples including acute myeloid leukemia (AML) cases, and the other a generic 5-parameter peripheral-blood immunophenotyping. Results are evaluated in terms of the area under the receiver operating characteristics curve (AUC). ASPIRE achieved AUCs of 0.99 and 1.0 on the AML and generic blood immunophenotyping data sets, respectively. CONCLUSIONS: These results demonstrate that anomalous samples can be identified by ASPIRE with almost perfect accuracy without a priori access to samples of anomalous subtypes in the training set. The ASPIRE approach is unique in its ability to form generalizations regarding normal and anomalous states given only very weak assumptions regarding sample characteristics and origin. Thus, ASPIRE could become highly instrumental in providing unique insights about observed biological phenomena in the absence of full information about the investigated samples.