Publications by authors named "S C Herda"

Insight into processes that determine CD8+ T cell memory formation has been obtained from infection models. These models are biased toward an inflammatory milieu and often use high-avidity CD8+ T cells in adoptive-transfer procedures. It is unclear whether these conditions mimic the differentiation processes of an endogenous repertoire that proceed upon noninflammatory conditions prevailing in premalignant tumor lesions.

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Adoptive T cell therapy (ATT) has revolutionized the treatment of cancer patients. A sufficient number of functional T cells are indispensable for ATT efficacy; however, several ATT dropouts have been reported due to T cell expansion failure or lack of T cell persistence in vivo. With the aim of providing ATT also to those patients experiencing insufficient T cell manufacturing via standard protocol, we evaluated if minimally manipulative prolongation of in vitro expansion (long-term [LT] >3 weeks with IL-7 and IL-15 cytokines) could result in enhanced T cell yield with preserved T cell functionality.

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Recent studies have demonstrated that CD4+ T cells can efficiently reject MHC-II-negative tumors. This requires indirect presentation of tumor-associated antigens on surrounding antigen-presenting cells. We hypothesized that intercellular transfer of proteins is not the sole consequence of cell death-mediated protein release, but depends on heat-shock cognate protein 70 (HSC70) and its KFERQ-like binding motif on substrate proteins.

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Adoptive T-cell therapy (ATT) efficacy is limited when targeting large solid tumors. The evaluation of ATT outcomes using accessory treatment would greatly benefit from an monitoring tool, allowing the detection of functional parameters of transferred T cells. Here, we generated transgenic bioluminescence imaging of T cells (BLITC) mice expressing an NFAT-dependent click-beetle luciferase and a constitutive luciferase, which supports concomitant analysis of migration and activation of T cells.

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Background: Combinatorial complexity is a central problem when modeling biochemical reaction networks, since the association of a few components can give rise to a large variation of protein complexes. Available classical modeling approaches are often insufficient for the analysis of very large and complex networks in detail. Recently, we developed a new rule-based modeling approach that facilitates the analysis of spatial and combinatorially complex problems.

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