120 research outputs found
A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes
No full textThe Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP-IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen-stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14-color staining panel. Two approaches (FlowReMi.1 and flowDensity-flowType-RchyOptimyx) provided statistically significant predictive value in the blinded test set. Manual validation of submitted results indicated that unbiased analysis of single cell phenotypes could reveal unexpected cell types that correlated with outcomes of interest in high dimensional flow cytometry datasets
Interleukin (IL) 4, in the absence of antigen stimulation, induces an anergy-like state in differentiated CD8+ TC1 cells : Loss of IL-2 synthesis and autonomous proliferation but retention of cytotoxicity and synthesis of other cytokines
No full textNRC publication: Ye
Kinetics of CD4 T Cell Cytokine Production, Chemokine Production and Activation after Influenza Vaccination
No full textThesis (Ph.D.)--University of Rochester. School of Medicine & Dentistry. Dept. of Microbiology and Immunology, 2012.The amount and timing of effector molecule secretion are tightly regulated in CD4 T
cells during the immune response. T cell cytokine profiles have been studied
extensively, but how chemokines are expressed during activation is less clear. This
study showed that human CD4 T cells, activated with either influenza antigen or
polyclonal stimulation, produce chemokines and cytokines with different kinetics.
IL-2, IFNγ and TNFα were quickly induced, while chemokines CCL1, CCL3 and
CCL4 were secreted later. Further analysis of sorted early cytokine positive cells
showed that even though the IFNγ and IL-2 secreting cells have a preference to
subsequently produce chemokines, the majority of chemokine producing cells did not
secrete cytokines at early times. In addition to analyzing expression kinetics in
individual cells, the kinetics of expansion of cytokine/chemokine-secreting cells
during the human immune response to influenza vaccination were measured. The
numbers of influenza-responsive CD4 T cells able to secrete chemokines increased
transiently, 7 days after influenza vaccination, while the cytokine response did not
change significantly. The response was then tracked more precisely by daily
sampling, and monitoring of the proliferation marker Ki-67. These two improvements
revealed that a substantial fraction of influenza-specific CD4 T cells responded to
vaccination. After 4-6 days, there was a sharp rise in the numbers of Ki-67-
expressing cells that produced cytokines or chemokines in vitro in response to
influenza vaccine or peptide. Ki-67+ cell numbers then declined rapidly, and ten
days after vaccination, both Ki-67+ and overall influenza-specific cell numbers were
similar to pre-vaccination levels. The response to Live Attenuated Influenza Vaccine
was similar, but had slightly slower kinetics and higher peak responding cell
numbers. Overall, these results demonstrate that CD4 T cells secrete cytokines and
chemokines with different kinetics. Ki-67 and chemokine expression are sensitive
tools for assessing the quality and quantity of responses to different influenza
vaccines, and reveal a response to inactivated influenza vaccine that was difficult to
detect by previous methods. These results also raise the possibility that vaccination
may substantially reshape the anti-influenza T cell memory response, even without
significant changes in the overall memory cell numbers
FloReMi : flow density survival regression using minimal feature redundancy
No full textAdvances in flow cytometry bioinformatics have resulted in a wide variety of clustering, classification and visualization techniques. To objectively evaluate the performance of such methods, common benchmarks such as the FlowCAP initiative have proven to be of great value. In this work, we report on a novel method, FloReMi, which was developed to tackle the most recent FlowCAP IV challenge. This challenge was formulated as a survival modeling problem, where participants were expected to design a model to predict the time until progression to AIDS for HIV patients. It is known that variability in progression rate cannot be fully predicted by simple CD4(+) T cell counts. However, it is hypothesized that the immunopathogenesis established early in HIV already indicates the course of future disease. Adequately estimating the progression rate of HIV patients is crucial in their treatment. Using an automated pipeline to preprocess the data, and subsequently identify and select informative cell subsets, a survival regression method based on random survival forests was built, which obtained the best results of all submitted approaches to the FlowCAP IV challenge.sponsorship: Grant sponsor: Ph.D. Grant of the Agency for Innovation by Science and Technology in Flanders (IWT); Fund for Scientific Research Flanders (FWO-Vlaanderen); Ghent University Multidisciplinary Research Partnership Bioinformatics: from Nucleotides to Networks. (Agency for Innovation by Science and Technology in Flanders (IWT), Fund for Scientific Research Flanders (FWO-Vlaanderen), Ghent University Multidisciplinary Research Partnership Bioinformatics: from Nucleotides to Networks)status: Publishe
Genotoxic mixtures and dissimilar action: Concepts for prediction and assessment
No full textThis article has been made available through the Brunel Open Access Publishing Fund. This article is distributed under the terms of the
creative commons Attribution license which permits any use, distribution, and reproduction in any medium, provided the original author(s)and the source are credited.Combinations of genotoxic agents have frequently been assessed without clear assumptions regarding their expected (additive) mixture effects, often leading to claims of synergisms that might in fact be compatible with additivity. We have shown earlier that the combined effects of chemicals, which induce micronuclei (MN) in the cytokinesis-block micronucleus assay in Chinese hamster ovary-K1 cells by a similar mechanism, were additive according to the concept of concentration addition (CA). Here, we extended these studies and investigated for the first time whether valid additivity expectations can be formulated for MN-inducing chemicals that operate through a variety of mechanisms, including aneugens and clastogens (DNA cross-linkers, topoisomerase II inhibitors, minor groove binders). We expected that their effects should follow the additivity principles of independent action (IA). With two mixtures, one composed of various aneugens (colchicine, flubendazole, vinblastine sulphate, griseofulvin, paclitaxel), and another composed of aneugens and clastogens (flubendazole, doxorubicin, etoposide, melphalan and mitomycin C), we observed mixture effects that fell between the additivity predictions derived from CA and IA. We achieved better agreement between observation and prediction by grouping the chemicals into common assessment groups and using hybrid CA/IA prediction models. The combined effects of four dissimilarly acting compounds (flubendazole, paclitaxel, doxorubicin and melphalan) also fell within CA and IA. Two binary mixtures (flubendazole/paclitaxel and flubendazole/doxorubicin) showed effects in reasonable agreement with IA additivity. Our studies provide a systematic basis for the investigation of mixtures that affect endpoints of relevance to genotoxicity and show that their effects are largely additive.UK Food Standards Agenc
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