Advanced Cellular Mapping Preserves Spatial Architecture During Holistic Herb Interventions

Traditional Chinese medicine is widely recognized for its holistic and systematic mode of action, often described as "multi-component, multi-target, and multi-pathway" regulation. In recent years, network pharmacology and systems biology have helped researchers map relationships among herbs, compounds, targets, pathways, and diseases.

However, these methods usually focus on molecular networks and cannot fully capture cellular heterogeneity, spatial organization, or dynamic remodeling of tissue microenvironments after TCM intervention. Conventional cell co-culture and imaging approaches also have limitations, including simplified experimental settings, limited marker coverage, and insufficient spatial or temporal resolution. These gaps have created a need for more precise tools to investigate how TCM regulates multicellular interactions in real tissue contexts.

A study (DOI: 10.48130/targetome-0026-0009) published in Targetome on 05 March 2026 by Xin Shao's & Xiaohui Fan's team, Zhejiang University, presents cell niche analysis as a promising analytical paradigm for revealing the holistic mechanisms of TCM.

In this review, the authors first traced the conceptual development of the cell niche, from its ecological origin to its current use in life sciences as a higher-order functional unit shaped by cell composition, spatial architecture, and cellular states. They then summarized how single-cell omics, spatial omics, and AI-driven computational methods can be used to infer or directly model cell niches. For single-cell data, the reviewed approaches infer niches indirectly by analyzing cellular composition, changes in cell proportions, cell-cell communication, and recurrent multicellular coordination patterns across samples. These methods are useful for large cohorts and cross-condition comparisons, but they lose spatial information during tissue dissociation.

For spatial omics data, the authors reviewed two major strategies. Spatial inference methods, including spatial deconvolution and spatial mapping, integrate spatial data with single-cell references to reconstruct cellular composition or gene expression patterns within tissue regions. These methods make it possible to characterize microenvironments such as lesion-adjacent regions, tumor boundaries, or ischemic border zones with greater biological detail. Direct modeling methods, especially spatial clustering, treat the cell niche itself as the basic unit of analysis and combine molecular profiles with spatial information to identify previously unrecognized tissue domains. The review further compared probabilistic graphical models, feature-augmentation methods, graph neural networks, improved community detection algorithms, and neighborhood phenotype-driven approaches.

Each method has specific strengths: statistical models are often interpretable and stable, while deep learning and graph neural network models are powerful for capturing complex spatial relationships. However, the authors also noted limitations, including dependence on data quality, sensitivity to parameters, limited interpretability, reproducibility challenges, and high computational requirements.

The authors conclude that AI-driven cell niche analysis offers a powerful new route for studying TCM mechanisms at a multicellular and spatially resolved level. This framework can help identify functional niches affected by TCM interventions, reveal how pathological niches are remodeled toward healthier states, and explain multi-component synergy in a more mechanistic way. Future progress will depend on better multimodal omics data, more interpretable and reproducible AI models, standardized benchmarking, and stronger experimental validation. As these advances continue, cell niche analysis may become an important bridge between traditional holistic theory and modern biomedical evidence.