Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.
Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Nucleic Acids Res. 2022 Jun 10;50(10):e57. doi: 10.1093/nar/gkac084.
Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, we developed a method, CellDART, which estimates the spatial distribution of cells defined by single-cell level data using domain adaptation of neural networks and applied it to the spatial mapping of human lung tissue. The neural network that predicts the cell proportion in a pseudospot, a virtual mixture of cells from single-cell data, is translated to decompose the cell types in each spatial barcoded region. First, CellDART was applied to a mouse brain and a human dorsolateral prefrontal cortex tissue to identify cell types with a layer-specific spatial distribution. Overall, the proposed approach showed more stable and higher accuracy with short execution time compared to other computational methods to predict the spatial location of excitatory neurons. CellDART was capable of decomposing cellular proportion in mouse hippocampus Slide-seq data. Furthermore, CellDART elucidated the cell type predominance defined by the human lung cell atlas across the lung tissue compartments and it corresponded to the known prevalent cell types. CellDART is expected to help to elucidate the spatial heterogeneity of cells and their close interactions in various tissues.
解析全基因组空间分辨转录组数据中的细胞组成对于阐明组织中细胞的空间背景是一项关键任务。在这项研究中，我们开发了一种方法 CellDART，该方法使用神经网络的域自适应来估计单细胞水平数据定义的细胞的空间分布，并将其应用于人类肺组织的空间映射。预测伪斑点（单细胞数据中细胞的虚拟混合物）中细胞比例的神经网络被转化为分解每个空间条形码区域中的细胞类型。首先，CellDART 被应用于小鼠大脑和人类背外侧前额叶皮层组织，以鉴定具有层特异性空间分布的细胞类型。总体而言，与其他用于预测兴奋性神经元空间位置的计算方法相比，该方法具有更稳定、更高的准确性和更短的执行时间。CellDART 能够分解小鼠海马 Slice-seq 数据中的细胞比例。此外，CellDART 阐明了人类肺细胞图谱定义的细胞类型在整个肺组织隔室中的优势，这与已知的流行细胞类型相对应。CellDART 有望帮助阐明各种组织中细胞的空间异质性及其紧密相互作用。
