Journal of translational medicine · 2026
SP-printer: reconstruction of tumor stage-specific microenvironments via phenotype-integrated spatial transcriptomics.
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Deng Weihao, Shi Yantao, Tang Hui
Аннотация
BACKGROUND: Spatial transcriptomics (ST) technologies have rapidly advanced the investigation of tumor microenvironment (TME) mechanisms by enabling high-resolution mapping of mRNA molecules within their native spatial context. However, existing methods often overlook phenotypic indices like malignancy status, limiting their interpretability within the tumor microenvironment.
METHODS: This work proposes SP-printer (Spatial-Phenotype printer), a flexible integrating approach that integrates phenotypic and spatial data to quantify stage-specific malignancy levels at each spot. SP-printer first identifies stage-specific metagenes from bulk RNA-seq datasets by using a S-score strategy. These metagenes are then projected onto ST data at pixel level via an information-enhanced mapping strategy, generating spatially resolved stage phenotypes. Finally, the pixel-level mappings are aggregated to spot resolution, enabling compatibility with standard ST workflows and supporting downstream analyses.
RESULTS: SP-printer outperforms state-of-the-art methods in identifying malignant regions across diverse tumor tissues. Specifically, we identified early microenvironment diagnostic markers for primary liver cancer and liver metastasis and evaluated the effects of seven drugs on the tumor microenvironment, providing a comprehensive assessment of their therapeutic potential.
CONCLUSIONS: SP-printer offers a unified framework to dissect the tumor spatial microenvironment and bridge the gap between cellular context and phenotypic outcomes, thereby enhancing the precision of tumor microenvironment analysis and facilitating informed clinical decision-making.
METHODS: This work proposes SP-printer (Spatial-Phenotype printer), a flexible integrating approach that integrates phenotypic and spatial data to quantify stage-specific malignancy levels at each spot. SP-printer first identifies stage-specific metagenes from bulk RNA-seq datasets by using a S-score strategy. These metagenes are then projected onto ST data at pixel level via an information-enhanced mapping strategy, generating spatially resolved stage phenotypes. Finally, the pixel-level mappings are aggregated to spot resolution, enabling compatibility with standard ST workflows and supporting downstream analyses.
RESULTS: SP-printer outperforms state-of-the-art methods in identifying malignant regions across diverse tumor tissues. Specifically, we identified early microenvironment diagnostic markers for primary liver cancer and liver metastasis and evaluated the effects of seven drugs on the tumor microenvironment, providing a comprehensive assessment of their therapeutic potential.
CONCLUSIONS: SP-printer offers a unified framework to dissect the tumor spatial microenvironment and bridge the gap between cellular context and phenotypic outcomes, thereby enhancing the precision of tumor microenvironment analysis and facilitating informed clinical decision-making.