Tumour Single-Cell Bioinformatics: From Immune Profiling to Molecular Dynamics

Abstract

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of tumours by enabling high-resolution profiling of their cellular composition. Traditionally perceived as masses of homogeneous cancer cells, tumours are now recognised as complex ecosystems shaped by the tumour microenvironment (TME), which includes diverse immune cells, cancer-associated fibroblasts and extracellular matrix components. scRNA-seq has revealed remarkable heterogeneity within the TME, identifying novel or rare immune cell subsets and delineating their dynamic functional states. In particular, it has illuminated intercellular signalling networks and temporal cell-state transitions that drive tumour progression and immune evasion. Moreover, the integration of scRNA-seq data with clinical information has highlighted its potential in improving patient stratification, biomarker discovery and therapeutic target identification. Here, we systematically summarise recent advances in applying scRNA-seq to dissect the TME, discuss the implications of these findings for immunotherapy resistance and precision oncology, and outline future opportunities for integrating scRNA-seq with emerging technologies to develop more effective and personalised cancer treatment strategies.

Keywords: immune landscape; single‐cell RNA sequencing; tumour microenvironment.

FIGURE 1

The Applications of scRNA‐seq in dissecting the TME. scRNA‐seq serves as a powerful tool for elucidating the complexity of the TME by enabling: (1) the establishment of comprehensive immune cell atlases, mapping the diverse immune landscape within tumours; (2) the discovery of novel and rare immune cell populations with potential functional significance; (3) the characterisation of temporal dynamics, capturing cellular transitions and lineage differentiation over time; (4) the identification of intercellular interactions, uncovering key signalling networks that shape the immune response; and (5) clinical applications, including biomarker discovery, patient stratification and the development of targeted immunotherapies.

FIGURE 2

The general procedures of scRNA‐seq. The general procedures of scRNA‐seq include (1) single cell isolation and capture, (2) cell lysis and mRNA extraction, (3) reverse transcription, (4) cDNA amplification and (5) cDNA library preparation and sequencing.

FIGURE 3

Identification of rare and novel immune cell subtypes in the TME by scRNA‐seq. Different rare and novel immune cell types in the TME were identified by scRNA‐seq, including TUBB3⁺ MNT, α‐SMA⁺ MMT, SPP1⁺ TAM, CD73^hi TAM, C1QC⁺ TAM, TREM2⁺ TAM, LYVE⁺ TAM, IL4I1⁺ TAM, +FCER1G‐expressing innate‐like T cell, CXCL13⁺BHLHE4⁺ Th1 cell, ZNF683⁺CXCR6⁺ tissue‐resident memory T cell, KIR⁺ NK‐like T cell, GZMK⁺ effector memory T cell, CD20‐CD19‐CD79A⁺CD79B⁺ B cell, BCMA⁺ IgGhi B plasma cell, CLEC9A⁺ DC, and LAMP3⁺ DC.

FIGURE 4

Clinical implications of TME discoveries by scRNA‐seq. This figure summarises key TME‐related findings by scRNA‐seq, highlighting their clinical implications in 1. Predicting therapy response: (a) Enrichment of TCF7⁺ CD8⁺ T cells in melanoma, high LCAM scores in NSCLC, and high TMC in EAD are associated with improved immunotherapy outcomes. (b) Chemotherapy response can be predicted by the presence of SPP1⁺ TAMs (resistance) or GZMA⁺ lymphocytes (sensitive) in ovarian cancer. (c) In breast cancer, the upregulation of immune checkpoints is linked to poor radiotherapy response. 2. TME remodelling after therapies: (a) Immunotherapy induces expansion of cytotoxic and effector cells, while reducing immunosuppressive Tregs in NSCLC. (b) Chemotherapy alters immune composition across cancers, increasing cytotoxic and MDSC‐like cells, ARG1⁺ macrophages and suppressing γδ T cells. (c) Radiotherapy leads to dynamic immune reshaping in ESCC and breast cancer, including accumulation of immunosuppressive TAMs, DCs and activated T cells. 3. Therapeutic implications: (a) Targeting CD73⁺ TAMs in glioblastoma. (b) CRISPR‐based KO of TLE4 or IKZF2 enhances CAR T cell function in glioblastoma. (c) PMEL, TYRP1 and EDNRB are highly expressed in exhausted CD8⁺ T cell subpopulations in melanoma and represent potential therapeutic targets.