Spatial and Single-Cell Multi-Omics Analyses of T-Cell Subsets and Interactions in Melanoma Lymph Node Metastasis.

Authors

Ras Azira Ramli, Samuel Tan, Yibi Chen, Glen Boyle, Quan Nguyen

Published in

Experimental dermatology. Volume 35. Issue 10. Pages e70376.

Abstract

Melanoma is a highly immunogenic malignancy, yet responses to systemic therapy remain variable. The tumour-draining lymph node (TDLN), including the sentinel lymph node (SLN) during early regional spread, is not only a site of metastatic dissemination but also an important setting for T-cell priming and immune regulation. Conventional classification of nodes as tumour-positive or tumour-negative provides essential staging information but reveals little about their immune state. Single-cell immune transcriptomics, T-cell receptor sequencing and single-cell epigenomics now allow T-cell states, clonal dynamics and regulatory programmes to be examined in greater detail. Spatial transcriptomics, multiplex imaging, spatial proteomics and emerging spatial metabolomics add the anatomical context needed to determine where these cells and molecular programmes occur within the intact tissue architecture. In this review, we examine how these approaches are being utilised to study T-cell heterogeneity, spatial organisation and immune interactions within melanoma TDLNs, together with the emerging contribution of artificial intelligence (AI)-assisted pathology. We also critically evaluate how these technologies are applied, with attention to tissue requirements, spatial resolution, molecular coverage, computational demands, scalability to archival haematoxylin and eosin (H&E) imaging cohorts and careful interpretation of predicted or inferred spatial features. By combining biological profiling with methodological appraisal, we highlight how multi-omics immune signatures may refine nodal disease assessment and guide future biomarker development, while noting that clinical translation will require standardised analytical pipelines and disease-specific validation across independent melanoma cohorts.

PMID:
42876116
Bibliographic data and abstract were imported from PubMed on 15 Oct 2026.

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