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Biology subjects

Siam, M. H. B.

Publications and source records attributed to Siam, M. H. B..

2 recordsLinked to original sources

High-Dimensional Single-Cell Analysis Reveals Coordinated Age-Dependent Neuroinflammatory Microglia-T cell Circuits in the Brain

Aging and cerebrovascular pathology drive neuroinflammation in vascular dementia (VaD) but immune mechanisms underlying this interplay remain unresolved. Leveraging multi-modal high-dimensional imaging, flow cytometry, and split pool ligation transcriptomic sequencing in a mouse model of VaD, we constructed a brain immune cell atlas spanning young and aged mice in health and disease. We profiled microglia, T cells, macrophages, neutrophils, and B cells and integrated transcriptomics, cell-cell communication, multiplex imaging, and comparative analysis with human microglia. We found striking depletion of Ccr7+ naive T cells and expansion of Gzmk+ cytotoxic Cd8+ effector memory T cells in the aging brain. At the same time, microglia shifted toward a pro-inflammatory state with enhanced activity of major histocompatibility class complex I (MHC-I) to T cell receptor and co-stimulation from CD86 to CD28. These shifts suggest enhanced neuroinflammatory polarization within the aged brain and in VaD. These signals were strongest from activated microglia to Gzmk+ Cd8+ TEM cells, indicating that age-related microglial polarization may sustain cytotoxic T cell activation in the aged brain. Our findings suggest pro-inflammatory microglia and Gzmk+ CD8+ TEM cells are central drivers of immune brain aging and highlights a therapeutic potential to disrupt age-related neuroinflammatory cascades in VaD.

immunology↗

CAFE: An Integrated Web App for High-Dimensional Analysis and Visualization in Spectral Flow Cytometry

Spectral flow cytometry provides greater insights into cellular heterogeneity by simultaneous measurement of up to 50 markers. However, analyzing such high-dimensional (HD) data is complex through traditional manual gating strategy. To address this gap, we developed CAFE as an open-source Python-based web application with a graphical user interface. Built with Streamlit, CAFE incorporates libraries such as Scanpy for single-cell analysis, Pandas and PyArrow for efficient data handling, and Matplotlib, Seaborn, Plotly for creating customizable figures. Its robust toolset includes density-based down-sampling, dimensionality reduction, batch correction, Leiden-based clustering, cluster merging and annotation. Using CAFE, we demonstrated analysis of a human PBMC dataset of 350,000 cells identifying 16 distinct cell clusters. CAFE can generate publication-ready figures in real time via interactive slider controls and dropdown menus, eliminating the need for coding expertise and making HD data analysis accessible to all. CAFE is licensed under MIT and is freely available at https://github.com/mhbsiam/cafe.

systems biology↗