Rozenman, B. | Search bioRxiv

bioRxiv2026

Immune Checkpoint Therapy Drives Maturation of a Cellular Neighborhood Nucleated by T Cell-APC Triads Enabling Spatially Compartmentalized Tumor Immunity

Spatially organized immune hubs of T cells and antigen-presenting cells (APCs) have been linked to immune checkpoint therapy (ICT) efficacy, yet the mechanisms underlying their function remain unclear. Using CODEX multiplex imaging, we longitudinally characterized the dynamic evolution of intratumoral cellular neighborhoods (CN) defined by "triad" interactions of CD4 and CD8 T cells with two distinct myeloid APC populations: cDC1s and IFN{gamma}-activated macrophages. We termed this CN the immunity-promoting CN (IP-CN) and tracked its progressive development during tumor rejection induced by -CTLA-4/-PD-1 therapy. A coordinated IFN{gamma} and TNF signaling signature accompanied the IP-CN assembly. Over time, the IP-CN underwent functional maturation, forming specialized sub-neighborhoods that compartmentalized proliferating T cells at the tumor periphery versus cytotoxic T effector cells interacting with tumor cell targets. Our findings reveal a spatiotemporal mechanism by which the IP-CN sustains and amplifies cytotoxic T cell responses, demonstrating how T cell-APC neighborhoods orchestrate tumor immunity.

immunology↗

bioRxiv2025

ARCADIA Reveals Spatially Dependent Transcriptional Programs through Integration of scRNA-seq and Spatial Proteomics

MotivationCellular states are strongly influenced by spatial context, but single-cell RNA sequencing (scRNA-seq) loses information about local tissue organization, while spatial proteomic assays capture limited marker panels that constrain transcriptomic inference. Integrating these modalities can elucidate how spatial niches shape transcriptional programs, yet existing approaches depend on either feature-level correspondence such as gene-protein linkage or cell-level barcode pairing, which is often unavailable. ResultsWe present ARCADIA (ARchetype-based Clustering and Alignment with Dual Integrative Autoencoders), a generative framework for cross-modal integration that operates without cell barcode pairing and does not assume direct feature-to-feature correspondence. ARCADIA identifies modality-specific archetypes, i.e., convex combinations of cells representing extreme phenotypic states, and aligns these anchors across modalities by minimizing the discrepancy between their cell-type composition profiles. The aligned archetypes define a shared coordinate system that anchors dual variational autoencoders (VAEs) trained with cross-modal geometric regularization, preserving archetype structure and spatial neighborhood information while enabling bidirectional translation between modalities. On semi-synthetic CITE-seq data, ARCADIA outperforms existing weak-linkage methods. Applied to independent human tonsil scRNA-seq and CODEX data, ARCADIA reconstructs known tissue architecture and reveals spatially dependent transcriptional programs linking B-cell maturation and T-cell activation or exhaustion to microenvironmental niches. Availability and ImplementationSource code is accessible at https://github.com/azizilab/ARCADIA_public. Reproducibility scripts and data are available at https://github.com/azizilab/arcadia_reproducibility.

bioinformatics↗