Zhao Peiyao A, Li Ruoxin, Adewunmi Temi, Garber Jessica, Gustafson Claire, Kim June, Malone Jocelin, Savage Adam, Skene Peter, Li Xiao-Jun
Allen Institute for Immunology, Seattle, WA 98109, USA.
Allen Institute for Immunology, Seattle, WA 98109, USA.
Cell Syst. 2025 Mar 19;16(3):101235. doi: 10.1016/j.cels.2025.101235.
Spatially resolved transcriptomics technologies have advanced our understanding of cellular characteristics within tissue contexts. However, current analytical tools often treat cell-type inference and cellular neighborhood identification as separate and hard clustering processes, limiting comparability across scales and samples. SPARROW addresses these challenges by jointly learning latent embeddings and soft clusterings of cell types and cellular organization. It outperformed state-of-the-art methods in cell-type inference and microenvironment zone delineation and uncovered zone-specific cell states in human and mouse tissues that competing methods missed. By integrating spatially resolved transcriptomics and single-cell RNA sequencing (scRNA-seq) data in a shared latent space, SPARROW achieves single-cell spatial resolution and whole-transcriptome coverage, enabling the discovery of both established and unknown microenvironment zone-specific ligand-receptor interactions in the human tonsil. Overall, SPARROW is a computational framework that provides a comprehensive characterization of tissue features across scales, samples, and conditions.
空间分辨转录组学技术提升了我们对组织环境中细胞特征的理解。然而，当前的分析工具常常将细胞类型推断和细胞邻域识别视为相互独立的硬聚类过程，限制了跨尺度和样本的可比性。SPARROW通过联合学习细胞类型和细胞组织的潜在嵌入和软聚类来应对这些挑战。在细胞类型推断和微环境区域划分方面，它优于现有方法，并揭示了竞争方法遗漏的人类和小鼠组织中特定区域的细胞状态。通过在共享潜在空间中整合空间分辨转录组学和单细胞RNA测序（scRNA-seq）数据，SPARROW实现了单细胞空间分辨率和全转录组覆盖，从而能够发现人类扁桃体中已有的和未知的特定微环境区域配体-受体相互作用。总体而言，SPARROW是一个计算框架，可跨尺度、样本和条件全面表征组织特征。
