The low resolution of spatial transcriptomics data necessitates additional information for optimal use. We developed scDOT, which combines spatial transcriptomics and single cell RNA sequencing to improve the ability to reconstruct single cell resolved spatial maps and identify senescent cells. scDOT integrates optimal transport and expression deconvolution to learn non-linear couplings between cells and spots and to infer cell placements. Application of scDOT to lung spatial transcriptomics data improves on prior methods and allows the identification of the spatial organization of senescent cells, their neighboring cells and novel genes involved in cell-cell interactions that may be driving senescence.

中文翻译：

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scDOT：空间转录组学中绘制衰老细胞的最佳运输


空间转录组学数据的低分辨率需要额外的信息才能实现最佳使用。我们开发了 scDOT，它结合了空间转录组学和单细胞 RNA 测序，以提高重建单细胞解析空间图和识别衰老细胞的能力。scDOT 集成了最佳转运和表达反卷积，以学习细胞和斑点之间的非线性耦合并推断细胞位置。scDOT 在肺空间转录组学数据中的应用比以前的方法有所改进，并允许识别衰老细胞的空间组织、它们的相邻细胞和参与细胞间相互作用的新基因，这些基因可能正在驱动衰老。