英国牛津大学Katherine R. Bull等研究人员合作发现，多尺度拓扑对亚细胞空间转录组学中的细胞进行分类。该研究于2024年6月19日在线发表于国际一流学术期刊《自然》。

研究人员提出了一种多尺度方法，利用转录组信息和空间上下文在亚细胞水平自动对细胞类型进行分类。研究人员在靶向和全转录组空间平台上展示了这一方法，改进了人类肾脏组织的细胞分类和形态，并在不依赖图像数据的情况下精确定位了单个稀疏分布的肾脏小鼠免疫细胞。通过将这些预测整合到基于多参数持久同源性的拓扑管线中，研究人员确定了狼疮肾炎小鼠模型特有的细胞空间关系，并通过免疫荧光进行了实验验证。研究人员所提出的框架可以很容易地推广到新的平台上，提供了一个全面的管线，连接从基因到组织的不同层次的生物组织。

研究人员表示，空间转录组学测量组织内数百万个位置的原位基因表达，迄今为止，转录组深度、空间分辨率和样本量之间存在一定的权衡。虽然基于图像的整合分割技术在这方面开展了有影响力的工作，但它受到成像质量和组织异质性的限制。相比之下，最近基于阵列的技术能够以亚细胞分辨率测量大量样本的整个转录组。目前，还没有一种细胞类型鉴定方法能直接利用这些信息来注释单个细胞。

附：英文原文

Title: Multiscale topology classifies cells in subcellular spatial transcriptomics

Author: Benjamin, Katherine, Bhandari, Aneesha, Kepple, Jessica D., Qi, Rui, Shang, Zhouchun, Xing, Yanan, An, Yanru, Zhang, Nannan, Hou, Yong, Crockford, Tanya L., McCallion, Oliver, Issa, Fadi, Hester, Joanna, Tillmann, Ulrike, Harrington, Heather A., Bull, Katherine R.

Issue&Volume: 2024-06-19

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 samples3,4,5,6. 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 homology7,8,9, 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.

DOI: 10.1038/s41586-024-07563-1

Source: https://www.nature.com/articles/s41586-024-07563-1