Biology subjects

Vardaman, D.

Publications and source records attributed to Vardaman, D..

3 recordsLinked to original sources

bioRxiv2025

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↗

bioRxiv2024

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↗

bioRxiv2024

Development of a Spectral Flow Cytometry Analysis Pipeline for High-Dimensional Immune Cell Characterization

Flow cytometry is a widely used technique for immune cell analysis, offering insights into cell composition and function. Spectral flow cytometry allows for high-dimensional analysis of immune cells, overcoming limitations of conventional flow cytometry. However, analyzing data from large antibody panels can be challenging using traditional bi-axial gating strategies. Here, we present a novel analysis pipeline designed to improve analysis of spectral flow cytometry. We employ this method to identify rare T cell populations in aging. We isolated splenocytes from young (2-3 months) and aged (18-19 months) female mice then stained these with a panel of 20 fluorescently labeled antibodies. Spectral flow cytometry was performed, followed by data processing and analysis using Python within a Jupyter Notebook environment to perform batch correction, unsupervised clustering, dimensionality reduction, and differential expression analysis. Our analysis of 3,776,804 T cells from 11 spleens revealed 34 distinct T cell clusters identified by surface marker expression. We observed significant differences between young and aged mice, with certain clusters enriched in one age group over the other. Naive, effector memory, and central memory CD8+ and CD4+ T cell subsets exhibited age-associated changes in abundance and marker expression. Additionally, {gamma}{delta} T cell clusters showed differential abundance between age groups. By leveraging high-dimensional analysis methods borrowed from single-cell RNA sequencing analysis, we identified age-related differences in T cell subsets, providing insights into the immune aging process. This approach offers a robust, free, and easily implemented analysis pipeline for spectral flow cytometry data that may facilitate the discovery of novel therapeutic targets for age-related immune dysfunction.

immunology↗