Introduction
The tumor microenvironment (TME) is a complex and dynamic ecosystem composed of malignant cells, immune infiltrates, stromal elements, and vascular components that collectively influence tumor initiation, progression, immune escape, and therapeutic response. Traditional bulk transcriptomic approaches obscure this complexity by averaging gene expression across heterogeneous cell populations. Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology that overcomes this limitation by profiling gene expression at single-cell resolution, enabling detailed mapping of cellular heterogeneity and functional states within tumors. Over the past decade, scRNA-seq has been widely applied across cancer types to dissect immune phenotypes, stromal interactions, and tumor cell diversity. Landmark studies in breast cancer (Azizi et al., 2018), bladder cancer (Chen et al., 2023), and pancreatic ductal adenocarcinoma (Chen et al., 2021) demonstrate how single-cell approaches provide mechanistic insight into immune evasion and disease progression. More recent integrative analyses further highlight the expanding applications of single-cell sequencing in tumor immunology and precision oncology (Chen et al., 2023).
Classification of the Tumor Microenvironment Using scRNA-seq
Single-cell RNA sequencing enables classification of the tumor microenvironment (TME) based on functional and transcriptional states rather than static lineage markers, capturing the dynamic and context-dependent organisation of tumor ecosystems. This state-based framework allows TME components to be grouped into four interrelated functional compartments.
1. Tumor-Intrinsic Cellular States
Malignant cells within a single tumor exhibit pronounced transcriptional heterogeneity. scRNA-seq identifies tumor cell subpopulations associated with proliferative capacity, metabolic reprogramming, cellular stress responses, epithelial–mesenchymal transition, and immune modulation. In bladder cancer, distinct malignant cell clusters identified through scRNA-seq were linked to prognostic gene signatures, demonstrating that tumor-intrinsic transcriptional states have direct clinical relevance (Chen et al., 2023).
2. Immune-Active and Effector States
Immune effector populations constitute a central functional compartment of the TME. Single-cell profiling reveals that tumor-infiltrating lymphocytes exist along a continuum of activation, ranging from highly cytotoxic effector cells to intermediate and dysfunctional states. In breast cancer, Azizi et al. (2018) demonstrated that immune cells such as T lymphocytes and macrophages do not segregate into discrete functional classes but instead display overlapping transcriptional programs, reflecting dynamic immune activation and regulation within tumors.
3. Immunosuppressive and Regulatory States
A distinct functional compartment within the TME is composed of immunosuppressive and regulatory cell states. These are predominantly driven by myeloid populations, including tumor-associated macrophages and myeloid-derived suppressor cells, as well as regulatory lymphocytes. scRNA-seq studies show that these cells adopt transcriptional programs associated with immune inhibition, angiogenesis, extracellular matrix remodeling, and tissue repair. Such suppressive states play a critical role in immune exclusion, therapeutic resistance, and disease progression across multiple cancer types (Azizi et al., 2018; Chen et al., 2021).
4. Structural and Vascular Support States
Non-immune stromal components form the architectural framework of the tumor microenvironment. Fibroblasts and endothelial cells identified by scRNA-seq exhibit functional heterogeneity related to extracellular matrix organization, vascular remodeling, and immune cell trafficking. In pancreatic ductal adenocarcinoma, these structural and vascular states were shown to evolve during malignant progression, contributing to restricted immune infiltration and enhanced tumor invasiveness (Chen et al., 2021).
Key Insights from Single-Cell Tumor Profiling
A central insight from scRNA-seq studies is that immune evasion arises from coordinated interactions between multiple cell states, rather than from a single suppressive population. Azizi et al. (2018) demonstrated that immune cells within tumors frequently co-express both pro-inflammatory and immunosuppressive genes, highlighting the plasticity of immune phenotypes in the TME.
In pancreatic ductal adenocarcinoma, scRNA-seq revealed that the TME undergoes dynamicremodeling during malignant progression. Early-stage tumors showed relatively balanced immune landscapes, whereas advanced disease was characterized by expansion of immunosuppressive myeloid cells, stromal activation, and enhanced ligand–receptor signaling associated with immune suppression and invasiveness (Chen et al., 2021).
Beyond cellular composition, scRNA-seq enables inference of cellular trajectories and intercellular communication networks. Ligand–receptor analyses uncover signaling pathways through which tumor cells manipulate immune responses, recruit suppressive populations, and evade immune destruction. Comprehensive reviews highlight that such analyses are increasingly combined with spatial transcriptomics and multi-omics approaches to provide contextual understanding of tumor–immune interactions (Chen et al., 2023).
Clinical and Translational Applications
Insights derived from scRNA-seq have significant implications for clinical oncology. By identifying transcriptional signatures associated with immune exhaustion, immunosuppression, and tumor aggressiveness, scRNA-seq supports the development of predictive biomarkers for immunotherapy response and resistance. In bladder cancer, prognostic genes identified from single-cell data were shown to correlate with patient outcomes, demonstrating the translational potential of this approach (Chen et al., 2023).
In pancreatic cancer, understanding stage-specific changes in the TME provides opportunities to identify early intervention targets and design combination therapies aimed at disrupting immune suppression before advanced disease develops (Chen et al., 2021). More broadly, scRNA-seq data are increasingly used to stratify patients, guide personalized treatment strategies, and inform rational combinations of immune checkpoint inhibitors with therapies targeting stromal or myeloid compartments.
Recent advances also emphasize integration of scRNA-seq with epigenomic, proteomic, and spatial data to refine tumor classification and therapeutic targeting. Such integrative approaches are expected to accelerate precision oncology and improve clinical decision-making (Chen et al., 2023).
Conclusion
Single-cell RNA sequencing has fundamentally reshaped the understanding of the tumor microenvironment by revealing cellular diversity, functional states, and immune evasion mechanisms with unprecedented resolution. Studies across multiple cancer types demonstrate that tumor progression and therapy resistance are driven by dynamic and interconnected cellular programs within the TME. By enabling state-based classification, trajectory inference, and communication network analysis, scRNA-seq bridges basic tumor biology and clinical application. As single-cell technologies continue to evolve and integrate with multi-omics platforms, they are poised to play a central role in advancing precision oncology and improving patient outcomes.
