SPaRTAN

SPaRTAN integrates single-cell proteomic (CITE-seq) and transcriptomic data with cis-regulatory and regulatory genomics information to model cell-context-specific signaling receptor–transcription factor interactions and gene regulatory programs.

Key Features:

• Integration of Multi-Omics Data: Combines CITE-seq measurements of cell-surface receptor expression with single-cell transcriptomic profiles to link proteomic and transcriptional states.
• Use of Cis-Regulatory Information: Incorporates cis-regulatory information and regulatory genomics resources to inform predictions of TF activity and target relationships.
• Cell-Context-Specific Mapping: Systematically maps cell-surface receptors to context-specific transcription factors to reflect cell-type-specific regulatory relationships.
• Network Modeling: Produces network models that represent predicted interactions between signaling receptors and transcription factors governing cellular behavior.
• Application to Immune Cells: Applied to blood immune cells to predict coupling between signaling receptors and specific TFs, with validation against existing knowledge and flow cytometry analyses.
• Utility in Cancer Research: Predicts signaling-coupled TF states of tumor-infiltrating CD8+ T cells in malignant peritoneal and pleural mesotheliomas.

Scientific Applications:

• Modeling Cell-Specific Signaling: Linking cell-surface phenotypes to downstream transcriptional programs to study how signaling pathways influence gene regulation at single-cell resolution.
• Enhancing CITE-seq Utility: Enabling discovery of relationships between transcription factors and cell-surface receptors within CITE-seq datasets from healthy and diseased tissues.
• Immunology Research: Investigating receptor–TF coupling in diverse immune cell types from blood.
• Oncology Research: Characterizing TF states and receptor–TF relationships in tumor-infiltrating CD8+ T cells from mesothelioma samples.

Methodology:

Leverages CITE-seq data together with cis-regulatory information and regulatory genomics resources to systematically predict interactions between signaling receptors and context-specific transcription factors and to construct network models.