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<p>Elucidating the spatial organization and functional specialization of immune cells within complex tissues remains challenging. We present UCASpatial, an ultra-precision spatial transcriptomics deconvolution algorithm utilizing entropy-based weighting to accurately map cell subpopulations. Benchmarking confirms its superiority in identifying low-abundant cell subpopulations and distinguishing transcriptionally heterogeneous cell subpopulations. Applying UCASpatial to human colorectal cancer, we reveal that chromosome 20q gain in individual cancer clones orchestrates a T cell-excluded microenvironment, associated with <i>HERV-H</i> silencing and impaired type I interferon responses. In murine wound healing models, we reveal spatiotemporal dynamics distinguishing scarring from regenerative phenotypes. Specifically, we identify a pro-fibrotic community comprising <i>Igfbp5</i><sup>+</sup> chondrocytes, <i>Cd36</i><sup><i>+</i></sup> <i>Gpnmb</i><sup><i>+</i></sup> <i>Il1b</i><sup><i>-</i></sup> macrophages, and <i>Fmod</i><sup>+</sup> fibroblasts in scarring-healing mice (C57BL/6). We further demonstrate that IL11-IL11RA signaling within this triad drives the pro-fibrotic community formation and limits regeneration. Together, UCASpatial serves as a versatile tool for deciphering fine-grained cellular landscapes and exploring intercellular mechanisms in complex and dynamic microenvironments.</p>

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Ultra-precision deconvolution of spatial transcriptomics decodes immune heterogeneity and fate-defining programs in tissues

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月20日

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  • Yin Xu,
  • Zurui Huang,
  • Yawei Zhang,
  • Minghui Gong,
  • Zhenghang Wang,
  • Peijin Guo,
  • Feifan Zhang,
  • Jing Yang,
  • Guanghao Liang,
  • Lihui Dong,
  • Renbao Chang,
  • Yu Xia,
  • Haochen Ni,
  • Wenxuan Gong,
  • Boyuan Mei,
  • Yuan Gao,
  • Zhaoqi Liu,
  • Lin Shen,
  • Jian Li,
  • Meng Michelle Xu,
  • Dali Han

摘要

Elucidating the spatial organization and functional specialization of immune cells within complex tissues remains challenging. We present UCASpatial, an ultra-precision spatial transcriptomics deconvolution algorithm utilizing entropy-based weighting to accurately map cell subpopulations. Benchmarking confirms its superiority in identifying low-abundant cell subpopulations and distinguishing transcriptionally heterogeneous cell subpopulations. Applying UCASpatial to human colorectal cancer, we reveal that chromosome 20q gain in individual cancer clones orchestrates a T cell-excluded microenvironment, associated with HERV-H silencing and impaired type I interferon responses. In murine wound healing models, we reveal spatiotemporal dynamics distinguishing scarring from regenerative phenotypes. Specifically, we identify a pro-fibrotic community comprising Igfbp5+ chondrocytes, Cd36+ Gpnmb+ Il1b- macrophages, and Fmod+ fibroblasts in scarring-healing mice (C57BL/6). We further demonstrate that IL11-IL11RA signaling within this triad drives the pro-fibrotic community formation and limits regeneration. Together, UCASpatial serves as a versatile tool for deciphering fine-grained cellular landscapes and exploring intercellular mechanisms in complex and dynamic microenvironments.