Querying functional and structural niches on spatial transcriptomics data
This work addresses the need for a query-based analytical paradigm to dissect spatial tissue architecture in health and disease, providing a tool for researchers in spatial transcriptomics, though it is incremental as it builds on existing niche concepts with a specialized method.
The authors tackled the problem of identifying similar spatial niches across spatial transcriptomics samples by defining the Niche Query Task and developing QueST, a method that outperformed existing repurposed methods in simulations and benchmarks, accurately capturing niche structures and demonstrating strong generalizability across platforms.
Cells in multicellular organisms coordinate to form functional and structural niches. With spatial transcriptomics enabling gene expression profiling in spatial contexts, it has been revealed that spatial niches serve as cohesive and recurrent units in physiological and pathological processes. These observations suggest universal tissue organization principles encoded by conserved niche patterns, and call for a query-based niche analytical paradigm beyond current computational tools. In this work, we defined the Niche Query Task, which is to identify similar niches across ST samples given a niche of interest (NOI). We further developed QueST, a specialized method for solving this task. QueST models each niche as a subgraph, uses contrastive learning to learn discriminative niche embeddings, and incorporates adversarial training to mitigate batch effects. In simulations and benchmark datasets, QueST outperformed existing methods repurposed for niche querying, accurately capturing niche structures in heterogeneous environments and demonstrating strong generalizability across diverse sequencing platforms. Applied to tertiary lymphoid structures in renal and lung cancers, QueST revealed functionally distinct niches associated with patient prognosis and uncovered conserved and divergent spatial architectures across cancer types. These results demonstrate that QueST enables systematic, quantitative profiling of spatial niches across samples, providing a powerful tool to dissect spatial tissue architecture in health and disease.