Microfluidic Single-Cell Bioanalysis for Decoding Tumor Heterogeneity

Abstract

Tumor heterogeneity drives cancer progression, dissemination, therapeutic adaptation, and relapse, yet many clinically relevant cell states are rare, transient, context-dependent, and obscured by population-averaged analysis. This review examines how microfluidic platforms preserve biologically meaningful linkages among cell identity, molecular state, secreted output, functional phenotype, perturbation history, and microenvironmental context, which are frequently disrupted by conventional workflows. We first define analytical requirements imposed by tumor heterogeneity, then examine microwell- and microchamber-based systems, droplet microfluidic platforms, valve-assisted and other active manipulation or capture systems, and integrated multimodal workflows that preserve single-cell information while introducing distinct engineering trade-offs. We further discuss major readout modalities, including genomic and transcriptomic profiling, extracellular vesicle and secretome analysis, metabolic measurements, and proteomic readouts, and applications in circulating tumor-cell dissemination, tumor–microenvironment interactions, and therapy-response heterogeneity. Finally, we highlight bottlenecks in measurement fidelity, source attribution, reproducibility, benchmarking, biological representation, multimodal integration, and clinical validation. We propose that microfluidic single-cell oncology should advance from descriptive profiling toward decision-oriented systems that preserve cell-resolved states, source-attributed outputs, perturbation histories, and longitudinal responses within reproducible workflows and connect them to clinically actionable information.