Elham Azizi | PubMed Health

PubMed2026

Spatiotemporal single-cell profiling reveals T cell clonal dynamics and phenotypic plasticity in human graft-versus-host disease.

Allogeneic hematopoietic cell transplantation cures hematologic diseases but is limited by acute graft‑versus‑host disease. How human T cell clones drive epithelial injury remains poorly mapped. We studied 31 transplant recipients, integrating longitudinal T cell antigen receptor (TCR) profiling with single-cell RNA sequencing/TCR sequencing and spatial transcriptomics to track T cell clonal dynamics. We developed DecompTCR to resolve temporal dynamics and adapted computational tools to map clone phenotypes and niches in tissue. Our analyses revealed that cyclophosphamide selectively depletes alloreactive clones, although insufficient early expansion leads to incomplete depletion and severe disease. Severe graft‑versus‑host disease is marked by persistent expansion of alloreactive clones, rewiring of homeostatic cell types and diversification of donor-derived CD8+ clonotypes that acquire Hobit (ZNF683)+ tissue‑resident memory T (TRM) cell programs during migration to epithelium. Spatial deconvolution identified CD8+ effector/Hobit+ TRM hubs near intestinal stem‑cell-rich crypt bases and crypt‑loss regions. This clonotype‑resolved framework links tissue‑instructed TRM cell remodeling to localized epithelial injury, nominating early-repertoire dynamics and spatial hub burden as biomarkers.

Journal Article↗

PubMed2026

ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.

MOTIVATION: Cellular 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. RESULTS: We 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, that is, 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 IMPLEMENTATION: Source code is accessible at https://github.com/azizilab/ARCADIA_public. Reproducibility scripts and data are available at https://github.com/azizilab/arcadia_reproducibility.

Proteomics↗