Department of Computer Science, Stanford University, Stanford, CA, USA.
School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Nat Methods. 2022 Nov;19(11):1411-1418. doi: 10.1038/s41592-022-01651-8. Epub 2022 Oct 24.
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 2.6 million spatially resolved single cells with dramatic time savings.
准确的细胞类型注释来自于空间分辨的单细胞，这对于理解组织功能的空间生物学至关重要。然而，目前用于注释空间分辨单细胞数据的计算方法通常基于为解离单细胞技术建立的技术，因此没有考虑空间组织。在这里，我们提出了 STELLAR，这是一种用于空间分辨单细胞数据集的细胞类型发现和识别的几何深度学习方法。 STELLAR 自动将细胞分配给注释参考数据集中存在的细胞类型，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，并学习能够捕获更高阶组织结构的细胞表示。我们成功地将 STELLAR 应用于 CODEX 多重荧光显微镜数据和多重 RNA 成像数据集。在人类生物分子图谱计划中，STELLAR 以显著的时间节省对 260 万个空间分辨的单细胞进行了注释。
