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Sinn, R.

Publications and source records attributed to Sinn, R..

2 recordsLinked to original sources

Inflammatory endothelial cells promote infiltration of antigen-licensed cytotoxic T cells in malignant gliomas after irradiation

Insufficient T cell infiltration into malignant gliomas fundamentally limits the efficacy of adoptive T cell therapy. Here we show that fractionated irradiation overcomes this barrier by reprogramming the tumor endothelium towards an immune-recruiting interface. Using complementary murine glioma models combined with adoptive T cell transfer, antigen-specific vaccination, and single-cell transcriptomic and T cell receptor profiling, we demonstrate that irradiation enhances the accumulation, clonal expansion, and effector differentiation of tumor-specific CD8+ T cells. Irradiated tumors showed increased T cell receptor clonality and local enrichment of proliferative effector CD8 T cells with enhanced cytotoxic, interferon-responsive, and oxidative metabolic programs. Mechanistically, irradiation triggers a conserved interferon-driven endothelial program marked by antigen presentation and upregulation of adhesion molecules, including ICAM-1 and VCAM-1. This radiation-induced endothelial activation program preferentially seen in inflammatory endothelial subsets was conserved in human glioblastoma and linked to T cell recruitment and maintenance of activated CD8 T cell states. Functionally, irradiation synergized with adoptive T cell transfer and antigen-specific vaccination to promote glioma-specific T cell accumulation and effector differentiation, improving tumor control and survival. Together, these findings identify radiation-induced endothelial activation as a key regulator of T cell trafficking across the brain tumor vasculature highlighting the vascular niche as a critical determinant of immunotherapy efficacy and a rational target for combination strategies in glioblastoma.

immunology↗

Beyond Letters: Optimal Transport as a Model for Sub-Letter Orthographic Processing

Letter processing plays a key role in visual word recognition. However, word recognition models typically overlook or greatly simplify early perceptual processes of letter recognition. We suggest that optimal transport theory may provide a computational framework for describing letter shape processing. We use representational similarity analysis to show that optimal transport cost (Wasserstein distance) between pairs of letters aligns with neural activity elicited by visually presented letters <225 ms after stimulus onset, outperforming an existing approach based on shape overlap. We additionally show that optimal transport can capture the emergence of geometric invariances (e.g., to position or size) observed in letter perception. Finally, we demonstrate that Wasserstein distance predicts neural activity similarly well to features from artificial networks trained to classify images and letters. However, whereas representations in artificial neural networks emerge in a computationally unconstrained manner, our proposal provides a computationally explicit route to modeling the earliest orthographic processes.

neuroscience↗