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Abstract：
Spatial transcriptomics measures in situ gene expression at millions of locations within a tissue1, hitherto with some trade-off between transcriptome depth, spatial resolution and sample size2. Although integration of image-based segmentation has enabled impactful work in this context, it is limited by imaging quality and tissue heterogeneity. By contrast, recent array-based technologies offer the ability to measure the entire transcriptome at subcellular resolution across large samples3-6. Presently, there exist no approaches for cell type identification that directly leverage this information to annotate individual cells. Here we propose a multiscale approach to automatically classify cell types at this subcellular level, using both transcriptomic information and spatial context. We showcase this on both targeted and whole-transcriptome spatial platforms, improving cell classification and morphology for human kidney tissue and pinpointing individual sparsely distributed renal mouse immune cells without reliance on image data. By integrating these predictions into a topological pipeline based on multiparameter persistent homology7-9, we identify cell spatial relationships characteristic of a mouse model of lupus nephritis, which we validate experimentally by immunofluorescence. The proposed framework readily generalizes to new platforms, providing a comprehensive pipeline bridging different levels of biological organization from genes through to tissues.
摘要：
空间转录组学测量组织1内数百万个位置的原位基因表达，迄今在转录组深度之间进行了一些权衡，空间分辨率和样本大小2。尽管基于图像的分割的集成在这种情况下实现了有影响力的工作，它受到成像质量和组织异质性的限制。相比之下,最近的基于阵列的技术提供了在大样本中以亚细胞分辨率测量整个转录组的能力3-6。目前,没有直接利用这些信息来注释单个细胞的细胞类型鉴定方法。在这里，我们提出了一种多尺度方法来自动分类这个亚细胞水平的细胞类型，使用转录组信息和空间上下文。我们在目标和全转录组空间平台上展示了这一点，改善人肾组织的细胞分类和形态，并精确定位单个稀疏分布的肾小鼠免疫细胞，而不依赖于图像数据。通过将这些预测整合到基于多参数持续同源7-9的拓扑管道中，我们确定了狼疮性肾炎小鼠模型的细胞空间关系特征。我们通过免疫荧光实验验证了这一点。拟议的框架很容易推广到新的平台，提供了一个全面的管道，桥接从基因到组织的不同水平的生物组织。
