De Marinis, Y. | bioRxiv Science

bioRxiv2025

Shedding of mitochondrial Voltage-Dependent Anion Channel-1 (VDAC1) Reflects COVID-19 Severity and Reveals Macrophage Dysfunction

COVID-19 severity correlates with lymphopenia and increased pro-inflammatory cytokines. However, the dysfunction of tissue macrophages in COVID-19 patients during the inflammatory cytokine storm has not been fully elucidated. Hospitalized COVID-19 patients were divided into three groups based on their symptomatic severity: exhibiting mild, moderate, or severe symptoms. Patients exhibited successively increased serum levels of mitochondrial voltage-dependent anion channel 1 (VDAC1) at days 0, 3, 7, 10, and 14, returning to those of non-infected subjects at day 28. Serum level of VDAC1 was positively correlated with COVID-19 severity and with increased white blood cell (WBC), neutrophil, lymphocyte, procalcitonin (PCT), and gamma-glutamyltransferase (GT) levels. Peripheral blood mononuclear cells (PBMCs) from hospitalized COVID-19 patients showed increased VDAC1 content concomitant with a reduced ATP content. Culture of monocytes, isolated from healthy individuals, and differentiated into polarized M1 macrophages, together with a cytokine mixture (IL-1{beta}, IFN-{gamma}, and TNF-), to mimic the inflammatory cytokine storm, for 24 h markedly increased VDAC1 and Monocyte chemoattractant protein-1 (MCP-1) release in culture medium. The presence of the cytokine mixture reduced the ATP content, cell viability, and the phagocytic capability of macrophages. Co-staining of VDAC1 and the plasma membrane marker Na+/K+-ATPase showed that cytokine-treatment mistargeted VDAC1 to the cell surface of macrophages. All these effects were prevented by VDAC1 inhibition using VBIT-4, VDAC1-specific antibody (VDAC1-ab), or metformin. Our findings indicate that increased VDAC1 expression and cell surface mistargeting in immune cells might be associated with cell dysfunction, potentially contributing to the severity of COVID-19 infection. The data also indicate serum VDAC1 as a biomarker of COVID-19 severity and the use of VDAC1 inhibitors as potential drug candidates restoring macrophages and PBMCs function in individuals severely affected by COVID-19.

pathology↗

bioRxiv2023

stGCL: A versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics

Advances in spatial transcriptomics (ST) technologies have provided unprecedented opportunities to depict transcriptomic and histological landscapes in the spatial context. Multi-modal ST data provide abundant and comprehensive information about cellular status, function, and organization. However, in dealing with the processing and analysis of spatial transcriptomics data, existing algorithms struggle to effectively fuse the multi-modal information contained within ST data. Here, we propose a graph contrastive learning-based cross-modality fusion model named stGCL for accurate and robust integrating gene expression, spatial information as well as histological profiles simultaneously. stGCL adopts a novel histology-based Vision Transformer (H-ViT) method to effectively encode histological features and combines multi-modal graph attention auto-encoder (GATE) with contrastive learning to fuse cross-modality features. In addition, stGCL introduces a pioneering spatial coordinate correcting and registering strategy for tissue slices integration, which can reduce batch effects and identify cross-sectional domains precisely. Compared with state-of-the-art methods on spatial transcriptomics data across platforms and resolutions, stGCL achieves a superior clustering performance and is more robust in unraveling spatial patterns of biological significance. Additionally, stGCL successfully reconstructed three-dimensional (3D) brain tissue structures by integrating vertical and horizontal slices respectively. Application of stGCL in human bronchiolar adenoma (BA) data reveals intratumor spatial heterogeneity and identifies candidate gene biomarkers. In summary, stGCL enables the fusion of various spatial modality data and is a powerful tool for analytical tasks such as spatial domain identification and multi-slice integration.

bioinformatics↗