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Biomedical subjects

Qunlun Shen

Publications and source records attributed to Qunlun Shen.

2 recordsLinked to original sources

Spatially defined microenvironmental niches are associated with clinical outcome and tumor ecosystem diversity in head and neck cancer.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) exhibits substantial biological heterogeneity that is not fully explained by human papillomavirus (HPV) status. The spatial organization of tumor, immune, and stromal cell populations and its relationship to clinical outcome remain incompletely understood. METHODS: We performed single-cell spatial transcriptomic and proteomic profiling of 44 primary HNSCC tumors, generating a spatial atlas of 19,471,501 cells across whole-slide tissue sections. Spatial niches and ecosystem states were identified through integrated computational analyses and evaluated for associations with tumor programs, clinicopathologic features, and patient outcomes. FINDINGS: HPV-negative tumors were enriched for fibroblast-rich, immune-poor niches associated with epithelial-mesenchymal transition and hypometabolic tumor programs, whereas HPV-positive tumors displayed more diverse immune, stromal, and vascular niche combinations and were enriched for immunogenic ecosystem states. Approximately 20% of HPV-positive tumors exhibited fibroblast-rich ecosystem architectures resembling HPV-negative disease and were associated with less favorable outcomes than other HPV-positive tumors of similar stage. In patient-derived co-culture models, extracellular matrix-associated fibroblasts were associated with epithelial-mesenchymal transition (EMT)-like tumor states, CD8+ T cell dysfunction, and chemotherapy resistance-associated phenotypes. CONCLUSIONS: Spatial ecosystem architecture is associated with clinically relevant heterogeneity beyond conventional HPV-based classification. Fibroblast-rich, immune-poor ecosystem states characterize a high-risk subset of HPV-positive tumors and may provide a framework for improved biological classification and risk stratification in HNSCC. FUNDING: This work was supported by the National Institutes of Health (R01CA291607 and R21CA267527-01) and the Feldstein Medical Foundation.

Humans

Spatial transcriptomics-aided localization for single-cell transcriptomics with STALocator.

Single-cell RNA-sequencing (scRNA-seq) techniques can measure gene expression at single-cell resolution but lack spatial information. Spatial transcriptomics (ST) techniques simultaneously provide gene expression data and spatial information. However, the data quality of the spatial resolution or gene coverage is still much lower than the quality of the single-cell transcriptomics data. To this end, we develop a ST-Aided Locator for single-cell transcriptomics (STALocator) to localize single cells to corresponding ST data. Applications on simulated data showed that STALocator performed better than other localization methods. When applied to the human brain and squamous cell carcinoma data, STALocator could robustly reconstruct the relative spatial organization of critical cell populations. Moreover, STALocator could enhance gene expression patterns for Slide-seqV2 data and predict genome-wide gene expression data for fluorescence in situ hybridization (FISH) and Xenium data, leading to the identification of more spatially variable genes and more biologically relevant Gene Ontology (GO) terms compared with the raw data. A record of this paper's transparent peer review process is included in the supplemental information.

Single-Cell Analysis