Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics

Spatial transcriptomics has the potential to transform our understanding of RNA expression in tissues. Classical array-based technologies produce multiple-cell-scale measurements requiring deconvolution to recover single cell information. However, rapid advances in subcellular measurement of RNA expression at whole-transcriptome depth necessitate a fundamentally different approach. To integrate single-cell RNA-seq data with nanoscale spatial transcriptomics, we present a topological method for automatic cell type identification (TopACT). Unlike popular decomposition approaches to multicellular resolution data, TopACT is able to pinpoint the spatial locations of individual sparsely dispersed cells without prior knowledge of cell boundaries. In extant mouse brain data, TopACT locates previously undetectable macrophages. Pairing TopACT with multiparameter persistent homology landscapes predicts immune cells forming a peripheral ring structure within kidney glomeruli in a murine model of lupus nephritis, which we experimentally validate with multiplex imaging. The proposed topological data analysis unifies multiple biological scales, from subcellular gene expression to multicellular tissue organization.

空间转录组学（Spatial transcriptomics）有望革新我们对组织内RNA表达的认知。传统基于阵列的转录组检测技术仅能产出多细胞尺度的测量数据，需通过解卷积操作方能还原单细胞信息。然而，全转录组层面下的亚细胞RNA表达检测技术快速发展，亟需一套截然不同的研究思路。为实现单细胞RNA测序（single-cell RNA-seq）数据与纳米尺度空间转录组学数据的整合，我们提出了一种用于自动细胞类型识别的拓扑学方法（TopACT）。与针对多细胞分辨率数据的主流分解类方法不同，TopACT无需预先掌握细胞边界信息，即可精准定位单个稀疏分散细胞的空间位置。在现有小鼠脑数据集样本中，TopACT成功定位到了此前未被检测到的巨噬细胞。将TopACT与多参数持久同调景观（multiparameter persistent homology landscapes）相结合后，该方法可预测狼疮肾炎小鼠模型中肾肾小球内形成外周环结构的免疫细胞，并通过多重成像技术完成了实验验证。本研究提出的拓扑数据分析方法实现了多生物学尺度的统一，覆盖从亚细胞基因表达到多细胞组织架构的全层级。