Dataset Information

Stability screening of arrays of major histocompatibility complexes on combinatorially encoded flow cytometry beads.

ABSTRACT: The binding and stabilization capacity of potential T cell epitopes to class I MHC molecules form the basis for their immunogenicity and provide fundamental insight into factors that dictate cellular immune responses. We have developed a versatile high throughput cell-free method to measure MHC stability by capturing a variety of MHC products on the surface of streptavidin-coated particles followed by flow cytometry analysis. Arrays of peptide-MHC combinations, generated by exchanging conditional ligand-loaded MHC, could be probed in a single experiment, thus combining the molecular precision of biochemically purified MHCs with high content multiparametric flow cytometry-based assays. Semiquantitative determination of the peptide affinity for the restriction element could also be accomplished through competition experiments using this bead-based assay. Furthermore, the generated peptide-MHC reagents could directly be applied to antigen-specific CD8(+) T lymphocyte analysis. The combinatorial labeling of beads allowed straightforward identification by their unique fluorescent signatures and provided a convenient means for extended assay multiplexing.

SUBMITTER: Chew SL

PROVIDER: S-EPMC3151089 | biostudies-other | 2011 Aug

REPOSITORIES: biostudies-other

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Stability screening of arrays of major histocompatibility complexes on combinatorially encoded flow cytometry beads.

Chew Shi Ling SL Or Ming Yan MY Chang Cynthia Xin Lei CX Gehring Adam J AJ Bertoletti Antonio A Grotenbreg Gijsbert M GM

The Journal of biological chemistry 20110616 32

The binding and stabilization capacity of potential T cell epitopes to class I MHC molecules form the basis for their immunogenicity and provide fundamental insight into factors that dictate cellular immune responses. We have developed a versatile high throughput cell-free method to measure MHC stability by capturing a variety of MHC products on the surface of streptavidin-coated particles followed by flow cytometry analysis. Arrays of peptide-MHC combinations, generated by exchanging conditiona ...[more]

PMID: 21680737

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Project description:Manual analysis of flow cytometry data and subjective gate-border decisions taken by individuals continue to be a source of variation in the assessment of antigen-specific T cells when comparing data across laboratories, and also over time in individual labs. Therefore, strategies to provide automated analysis of major histocompatibility complex (MHC) multimer-binding T cells represent an attractive solution to decrease subjectivity and technical variation. The challenge of using an automated analysis approach is that MHC multimer-binding T cell populations are often rare and therefore difficult to detect. We used a highly heterogeneous dataset from a recent MHC multimer proficiency panel to assess if MHC multimer-binding CD8+ T cells could be analyzed with computational solutions currently available, and if such analyses would reduce the technical variation across different laboratories. We used three different methods, FLOw Clustering without K (FLOCK), Scalable Weighted Iterative Flow-clustering Technique (SWIFT), and ReFlow to analyze flow cytometry data files from 28 laboratories. Each laboratory screened for antigen-responsive T cell populations with frequency ranging from 0.01 to 1.5% of lymphocytes within samples from two donors. Experience from this analysis shows that all three programs can be used for the identification of high to intermediate frequency of MHC multimer-binding T cell populations, with results very similar to that of manual gating. For the less frequent populations (<0.1% of live, single lymphocytes), SWIFT outperformed the other tools. As used in this study, none of the algorithms offered a completely automated pipeline for identification of MHC multimer populations, as varying degrees of human interventions were needed to complete the analysis. In this study, we demonstrate the feasibility of using automated analysis pipelines for assessing and identifying even rare populations of antigen-responsive T cells and discuss the main properties, differences, and advantages of the different methods tested.

Project description:Natural microbial communities affect human life in countless ways, ranging from global biogeochemical cycles to the treatment of wastewater and health via the human microbiome. In order to probe, monitor, and eventually control these communities, fast detection and evaluation methods are required. In order to facilitate rapid community analysis and monitor a community's dynamic behavior with high resolution, we here apply community flow cytometry, which provides single-cell-based high-dimensional data characterizing communities with high acuity over time. To interpret time series data, we draw inspiration from macroecology, in which a rich set of concepts has been developed for describing population dynamics. We focus on the stability paradigm as a promising candidate to interpret such data in an intuitive and actionable way and present a rapid workflow to monitor stability properties of complex microbial ecosystems. Based on single-cell data, we compute the stability properties resistance, resilience, displacement speed, and elasticity. For resilience, we also introduce a method which can be implemented for continuous online community monitoring. The proposed workflow was tested in a long-term continuous reactor experiment employing both an artificial and a complex microbial community, which were exposed to identical short-term disturbances. The computed stability properties uncovered the superior stability of the complex community and demonstrated the global applicability of the protocol to any microbiome. The workflow is able to support high temporal sample densities below bacterial generation times. This may provide new opportunities to unravel unknown ecological paradigms of natural microbial communities, with applications to environmental, biotechnological, and health-related microbiomes. IMPORTANCE Microbial communities drive many processes which affect human well-being directly, as in the human microbiome, or indirectly, as in natural environments or in biotechnological applications. Due to their complexity, their dynamics over time is difficult to monitor, and current sequence-based approaches are limited with respect to the temporal resolution. However, in order to eventually control microbial community dynamics, monitoring schemes of high temporal resolution are required. Flow cytometry provides single-cell-based data in the required temporal resolution, and we here use such data to compute stability properties as easy to interpret univariate indicators of microbial community dynamics. Such monitoring tools will allow for a fast, continuous, and cost-effective screening of stability states of microbiomes. Applicable to various environments, including bioreactors, surface water, and the human body, it will contribute to the development of control schemes to manipulate microbial community structures and performances.

Dataset Information

Stability screening of arrays of major histocompatibility complexes on combinatorially encoded flow cytometry beads.

Publications

Stability screening of arrays of major histocompatibility complexes on combinatorially encoded flow cytometry beads.

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