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  • Single-Cell and Spatial Multi-Omics in Cancer: Deciphering Tumor Heterogeneity and Advancing Precision Oncology

  • Hasti Hosseini,1,* Farzad Soleimani,2
    1. Tehran University of Medical Sciences
    2. Kurdistan University of Medical Sciences, Cellular and Molecular Research Center


  • Introduction: Cancer is a highly heterogeneous disease characterized by genetic, epigenetic, transcriptional, and cellular diversity both between patients and within individual tumors. Conventional bulk sequencing approaches provide valuable molecular information but represent an average signal across large populations of cells, potentially masking rare cellular states, resistant subpopulations, and clinically relevant interactions within the tumor microenvironment (TME). The emergence of single-cell and spatial multi-omics technologies has transformed cancer research by enabling high-resolution characterization of tumor ecosystems at the level of individual cells while preserving, in spatial approaches, their anatomical context. These technologies provide new opportunities to understand tumor heterogeneity, cellular interactions, immune regulation, therapeutic response, and resistance, thereby supporting the development of precision and personalized oncology.
  • Methods: A narrative literature review was conducted using PubMed, Web of Science, and Google Scholar. Relevant publications were identified using combinations of the following keywords: “single-cell omics,” “single-cell RNA sequencing,” “single-cell multi-omics,” “spatial transcriptomics,” “spatial multi-omics,” “cancer,” “tumor heterogeneity,” “tumor microenvironment,” “cell-cell communication,” “biomarker discovery,” “drug resistance,” “immunotherapy,” “precision oncology,” and “personalized medicine.” Publications from 2015 to 2026 were considered, with particular emphasis on recent studies and reviews published between 2024 and 2026. Evidence concerning single-cell genomics, transcriptomics, epigenomics, proteomics, spatial profiling, computational integration, and clinical translation was qualitatively synthesized.
  • Results: The reviewed evidence demonstrates that single-cell and spatial multi-omics can resolve previously unrecognized cellular and molecular heterogeneity within tumors. Single-cell RNA sequencing and related approaches enable identification of distinct malignant cell states, immune-cell populations, cancer-associated fibroblast subtypes, endothelial populations, and rare cellular subsets that may contribute to tumor progression, metastasis, immune evasion, or therapeutic resistance. Integration of transcriptomic, genomic, epigenomic, and proteomic information further enables characterization of regulatory programs and molecular states associated with tumor evolution and treatment response. Spatial omics provides complementary information by preserving the anatomical organization of cells and molecular features within tissues. Spatial transcriptomics and related spatial profiling approaches can identify spatially distinct tumor regions, immune-excluded niches, invasive margins, cellular neighborhoods, and localized cell-cell communication networks. Integration of single-cell and spatial datasets therefore enables the reconstruction of interactions among malignant cells, immune cells, cancer-associated fibroblasts, endothelial cells, and extracellular matrix components within the TME. Importantly, these technologies are increasingly being applied to identify predictive biomarkers and mechanisms of therapeutic response and resistance. Single-cell and spatial analyses can reveal resistant cellular states, tumor subclones, T-cell exhaustion programs, immunosuppressive microenvironments, and signaling pathways associated with treatment failure. Such molecular and spatial profiles may facilitate patient stratification, prediction of immunotherapy or targeted-therapy response, identification of therapeutic vulnerabilities, and development of individualized treatment strategies. Integration with artificial intelligence and machine-learning approaches may further enhance the interpretation of complex multi-omics datasets and their translation into clinically relevant biomarkers.
  • Conclusion: Single-cell and spatial multi-omics provide a comprehensive framework for dissecting cancer heterogeneity beyond the limitations of bulk molecular profiling. By integrating cellular identity, molecular states, spatial organization, and intercellular communication, these approaches can improve understanding of tumor evolution, the TME, therapy response, and drug resistance. Their integration with computational analysis, artificial intelligence, and other multi-omics platforms may accelerate biomarker discovery and patient-specific therapeutic decision-making. Nevertheless, challenges including high costs, complex data analysis, tissue preservation, technical variability, standardization, reproducibility, and clinical validation remain. Continued development of robust and clinically scalable workflows will be essential for translating single-cell and spatial multi-omics from research applications into routine precision oncology.
  • Keywords: Single-cell omics; Spatial multi-omics; Tumor heterogeneity; Tumor microenvironment; Precision oncol

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