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Mensching, L.

Publications and source records attributed to Mensching, L..

2 recordsLinked to original sources

HIV-1 promotes cell-to-cell interactions enabling spread from CD4+ T cells to microglia

HIV-1 infection of the brain occurs early in acute infection and results in neuroinflammation and - when untreated - in cognitive impairment, yet the mechanisms by which microglia become infected remain poorly defined. Evidence from simian immunodeficiency virus (SIV) studies supports a model in which infected CD4+ T cells disseminate HIV-1 to tissue macrophages, but this has not yet been confirmed for human microglia. Here, we used human monocyte-derived microglia (MDMi) and autologous HIV-1-infected primary CD4+ T cells to investigate viral transmission and immune cell interactions. Transcriptional profiling of MDMi confirmed microglia signature genes such as CX3CR1, P2RY12 and C1QB, and surface staining showed expression of CD4 and the HIV-1 coreceptor CCR5. Compared to cell-free infection, direct cell-to-cell contact between MDMi and HIV-1-infected CD4+ T cells markedly enhanced productive infection of MDMi. HIV-1 infection downmodulated the "dont-eat-me" signal CD47 and increased phosphatidylserine on the surface of primary CD4+ T cells. Consequently, HIV-1 infection of primary CD4+ T cells increased microglia-CD4+ T cell interactions and resulted in enhanced phagocytosis by MDMi. Together, this supports a mechanism where HIV-1 facilitates cell-to-cell spread from primary CD4+ T cells to microglia, which has important implications for therapeutic targeting of HIV-1 brain reservoir seeding.

immunology↗

GOAT: Deep learning-enhanced Generalized Organoid Annotation Tool

Organoids have emerged as a powerful technology to investigate human development, model diseases and for drug discovery. However, analysis tools to rapidly and reproducibly quantify organoid parameters from microscopy images are lacking. We developed a deep-learning based generalized organoid annotation tool (GOAT) using instance segmentation with pixel-level identification of organoids to quantify advanced organoid features. Using a multicentric dataset, including multiple organoid systems (e.g. liver, intestine, tumor, lung), we demonstrate generalization of the tool to annotate a diverse range of organoids generated in different laboratories and high performance in comparison to previously published methods. In sum, GOAT provides fast and unbiased quantification of organoid experiments to accelerate organoid research and facilitates novel high-throughput applications.

bioinformatics↗