美国斯坦福大学Jure Leskovec和Garry P. Nolan共同合作近期取得重要工作进展，他们研究利用STELLAR算法对空间分辨率的单细胞数据进行注释。该项研究成果2022年10月24日在线发表于《自然—方法学》杂志上。

研究人员提出了STELLAR，这是一种用于在空间分辨率单细胞数据集中发现和识别细胞类型的几何深度学习方法。STELLAR自动将细胞分配到注释参考数据集中的细胞类型，并发现新的细胞类型和细胞状态。STELLAR在不同的解剖区域、不同的组织和不同的供体之间转移注释，并学习捕捉高阶组织结构的细胞表现。研究人员成功地将STELLAR应用于CODEX复用荧光显微镜数据和复用RNA成像数据集。在人类生物分子图谱计划中，STELLAR已经为260万个空间分辨率单细胞做了注释，大大节省了时间成本。

据介绍，来自空间分辨率的单细胞精准细胞类型注释，对于理解作为组织结构基础的功能空间生物学至关重要。然而，目前对空间分辨率单细胞数据进行注释的计算方法，通常是基于为分离的单细胞技术建立的技术，因此没有考虑到空间组织。

附：英文原文

Title: Annotation of spatially resolved single-cell data with STELLAR

Author: Brbi, Maria, Cao, Kaidi, Hickey, John W., Tan, Yuqi, Snyder, Michael P., Nolan, Garry P., Leskovec, Jure

Issue&Volume: 2022-10-24

Abstract: Accurate cell-type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell-type discovery and identification in spatially resolved single-cell datasets. STELLAR automatically assigns cells to cell types present in the annotated reference dataset and discovers novel cell types and cell states. STELLAR transfers annotations across different dissection regions, different tissues and different donors, and learns cell representations that capture higher-order tissue structures. We successfully applied STELLAR to CODEX multiplexed fluorescent microscopy data and multiplexed RNA imaging datasets. Within the Human BioMolecular Atlas Program, STELLAR has annotated 2.6million spatially resolved single cells with dramatic time savings.

DOI: 10.1038/s41592-022-01651-8

Source: https://www.nature.com/articles/s41592-022-01651-8