Deciphering T-Cell Dysregulation in Sjögren’s Syndrome: A Multi-Framework scRNA-seq Analysis

(January 2026 - February 2026)

Project Overview

This project investigates the transcriptional remodeling of peripheral blood T-cells in Sjögren’s Syndrome (SjD). The objective is to identify functional programs (cytotoxicity, IFN response) defining the pathological state compared to Healthy Donors (HD), using a comparative and multi-platform approach.

Key Biological Observations

The analysis highlights a coordinated transition from a surveillance state toward a hyper-activated axis:

• CD8+ Axis: A shift from Early Memory cells (IL7R, TCF7) toward an expanded Cytotoxic Effector pool (GZMB, PRF1, GNLY).
• CD4+ Axis: A reduction in Th1-like cells, replaced by a dominant IFN-high signature (ISG15, MX1).
• Key Insight: Treg proportions remain numerically stable, suggesting that autoimmunity stems from functional dysregulation rather than a loss of regulatory cell numbers.

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Repository Structure & Multi-Framework Approach

To ensure robustness and demonstrate technical versatility, the analysis was implemented using both Scanpy (Python) and Seurat v5 (R).

.
├── Scanpy/               # Exploratory & Differential Analysis (Python)
│   ├── Notebooks/        # NB01 to NB05 (QC to Interpretation)
│   ├── Results/          # UMAPs, Heatmaps, and DE Dot plots
│   └── README.md         # Scanpy-specific documentation
│
├── Seurat/               # High-resolution Integration (R)
│   ├── Scripts/          # Sub-clustering and Harmony integration
│   ├── Results/          # Proportion Bar plots and multimodal VIZ
│   └── README.md         # Seurat-specific documentation
│
└── README.md             # Global project synthesis (this file)

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Standardized Workflow & QC

Both pipelines adhere to identical quality control standards to ensure results comparability:

• Downsampling: Strategic reduction to a representative subset of 40,000 cells to optimize computational resources.
• QC Thresholds: Filtering of debris and doublets (500 < nFeature_RNA < 4000) and dying cells (percent.mt < 15%).
• Visual Validation: Systematic threshold validation using Scatter plots (count/gene correlation) and Box plots per sample.
• T-Cell Focus: Targeted extraction of T-cell compartments based on lineage markers identified during global atlas generation (CD8A/B, IL7R, CCR7, GZMK, FOXP3, etc.).

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Multimodal Visualization & Results

The pathological landscape is characterized through a combination of complementary visualizations:

• UMAPs (Split by condition): To visualize the spatial expansion of pathological clusters.
• Dot Plots (Split by condition): To compare expression intensity and frequency of key genes (e.g., ISG signature).
• Proportion Bar Plots: To quantify the numerical redistribution of cell states (loss of Naive/Memory vs. gain of Effector).

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Concluding Observations: The SjD Immune Axis

Our analysis demonstrates that Sjögren’s Syndrome induces a significant reprogramming of the T-cell landscape, shifting from homeostatic surveillance toward a pathological activation profile:

1. CD8+ Shift: A major transition from Early Memory states toward an expanded Cytotoxic Effector compartment.
2. CD4+ Activation: Significant loss of homeostatic Th1-like cells in favor of a dominant IFN-high signature.
3. Regulatory Stability: Maintained Treg numbers despite chronic inflammation, pointing toward potential functional impairment rather than numerical deficiency.

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Strategic Roadmap (PhD Perspectives)

• Trajectory Inference: Modeling the differentiation continuum using Slingshot.
• Cell-Cell Communication: Mapping ligand-receptor interactions via CellChat.
• TCR Integration: Investigating clonal expansion within the identified cytotoxic clusters.

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Dataset & Reference

• Source: NCBI GEO Accession GSE253568.
• Publication: McDermott et al., 2024. Regulatory T cells and IFN-γ-producing Th1 cells play a critical role in the pathogenesis of Sjögren’s Syndrome.