The dynamic lives of T cells: New approaches and themes
Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA. Trends in Immunology
(Impact Factor: 10.4).
11/2012; 34(2). DOI: 10.1016/j.it.2012.10.006
Activated T cells have classically been thought to progress unidirectionally through discrete phenotypic states and differentiate into static lineages. It is increasingly evident, however, that T cells exhibit much more complex and flexible dynamic behaviors than initially appreciated, and that these behaviors influence the efficacy of T cell responses to immunological challenges. In this review, we discuss how new technologies for monitoring the dynamics of T cells are enhancing the resolution of the fine phenotypic and functional heterogeneity within populations of T cells and revealing how individual T cells transition among a continuum of states. Such insights into the dynamic properties of T cells should improve immune monitoring and inform strategies for therapeutic interventions.
Available from: PubMed Central
- "These cells can then be stimulated and many potential cytokines analyzed using antibody capture. This system revealed something unique, which is that the individual T cells typically make multiple cytokines, but not necessarily at the same time and over a several day timespan14. This temporal complexity is not captured in other systems and takes advantage of the unique ability of this system to analyze the output of the same cells over time, and in a very high-throughput manner. "
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ABSTRACT: Adaptive immune responses often begin with the formation of a molecular complex between a T-cell receptor (TCR) and a peptide antigen bound to a major histocompatibility complex (MHC) molecule. These complexes are highly variable, however, due to the polymorphism of MHC genes, the random, inexact recombination of TCR gene segments, and the vast array of possible self and pathogen peptide antigens. As a result, it has been very difficult to comprehensively study the TCR repertoire or identify and track more than a few antigen-specific T cells in mice or humans. For mouse studies, this had led to a reliance on model antigens and TCR transgenes. The study of limited human clinical samples, in contrast, requires techniques that can simultaneously survey TCR phenotype and function, and TCR reactivity to many T-cell epitopes. Thanks to recent advances in single-cell and cytometry methodologies, as well as high-throughput sequencing of the TCR repertoire, we now have or will soon have the tools needed to comprehensively analyze T-cell responses in health and disease.
Available from: Véronique Thomas-Vaslin
- "Up to now, simulations and validation of some of these models reveal interesting T cell dynamics properties: how the system grows, self-maintains as well as the effects of perturbations, i.e. how the system reacts to antigens, collapses and reorganizes. However, integrating the heterogeneity of cell populations, phenotypes, lineages, cell location and interactions, cell differentiation across generations (16) in the different biological, and time scales, is problematic in such a mathematical form, which make these models particularly difficult to handle. "
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ABSTRACT: Dynamic modeling of lymphocyte behavior has primarily been based on populations based differential equations or on cellular agents moving in space and interacting each other. The final steps of this modeling effort are expressed in a code written in a programing language. On account of the complete lack of standardization of the different steps to proceed, we have to deplore poor communication and sharing between experimentalists, theoreticians and programmers. The adoption of diagrammatic visual computer language should however greatly help the immunologists to better communicate, to more easily identify the models similarities and facilitate the reuse and extension of existing software models. Since immunologists often conceptualize the dynamical evolution of immune systems in terms of "state-transitions" of biological objects, we promote the use of unified modeling language (UML) state-transition diagram. To demonstrate the feasibility of this approach, we present a UML refactoring of two published models on thymocyte differentiation. Originally built with different modeling strategies, a mathematical ordinary differential equation-based model and a cellular automata model, the two models are now in the same visual formalism and can be compared.
Available from: Chao ma
- "Another version of the microchips employs the micro-engraving technique to fabricate micro-chambers (Varadarajan et al., 2011; Yamanaka et al., 2013). In this technology, hundreds of thousands nano-liter sized micro-chambers can be integrated into one chip, wherein up to three types of cytokines can be measured by antibody on the substrate (Table 1). "
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ABSTRACT: In the past decade, significant progresses have taken place in the field of cancer immunotherapeutics, which are being developed for most human cancers. New immunotherapeutics, such as Ipilimumab (anti-CTLA-4), have been approved for clinical treatment; cell-based immunotherapies such as adoptive cell transfer (ACT) have either passed the final stage of human studies (e.g., Sipuleucel-T) for the treatment of selected neoplastic malignancies or reached the stage of phase II/III clinical trials. Immunotherapetics has become a sophisticated field. Multimodal therapeutic regimens comprising several functional modules (up to five in the case of ACT) have been developed to provide focused therapeutic responses with improved efficacy and reduced side-effects. However, a major challenge remains: the lack of effective and clinically applicable immune assessment methods. Due to the complexity of antitumor immune responses within patients, it is difficult to provide comprehensive assessment of therapeutic efficacy and mechanism. To address this challenge, new technologies have been developed to directly profile the cellular immune functions and the functional heterogeneity. With the goal to measure the functional proteomics of single immune cells, these technologies are informative, sensitive, high-throughput, and highly multiplex. They have been used to uncover new knowledge of cellular immune functions and have been utilized for rapid, informative, and longitudinal monitoring of immune response in clinical anti-cancer treatment. In addition, new computational tools are required to integrate high-dimensional data sets generated from the comprehensive, single cell level measurements of patient's immune responses to guide accurate and definitive diagnostic decision. These single cell immune function assessment tools will likely contribute to new understanding of therapy mechanism, pre-treatment stratification of patients, and ongoing therapeutic monitoring and assessment.
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