Center for Cancer Genomics and Precision Oncology, Wake Forest Baptist Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston Salem, NC 27157, USA.
Department of Cancer Biology, Wake Forest School of Medicine, Winston Salem, NC 27157, USA.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbaa414.
Recent development of spatial transcriptomics (ST) is capable of associating spatial information at different spots in the tissue section with RNA abundance of cells within each spot, which is particularly important to understand tissue cytoarchitectures and functions. However, for such ST data, since a spot is usually larger than an individual cell, gene expressions measured at each spot are from a mixture of cells with heterogenous cell types. Therefore, ST data at each spot needs to be disentangled so as to reveal the cell compositions at that spatial spot. In this study, we propose a novel method, named deconvoluting spatial transcriptomics data through graph-based convolutional networks (DSTG), to accurately deconvolute the observed gene expressions at each spot and recover its cell constitutions, thus achieving high-level segmentation and revealing spatial architecture of cellular heterogeneity within tissues. DSTG not only demonstrates superior performance on synthetic spatial data generated from different protocols, but also effectively identifies spatial compositions of cells in mouse cortex layer, hippocampus slice and pancreatic tumor tissues. In conclusion, DSTG accurately uncovers the cell states and subpopulations based on spatial localization. DSTG is available as a ready-to-use open source software (https://github.com/Su-informatics-lab/DSTG) for precise interrogation of spatial organizations and functions in tissues.
最近发展起来的空间转录组学（ST）能够将组织切片中不同位置的空间信息与每个位置内细胞的 RNA 丰度相关联，这对于理解组织细胞结构和功能尤为重要。然而，对于这样的 ST 数据，由于一个点通常比单个细胞大，因此在每个点测量的基因表达来自具有异质细胞类型的混合细胞。因此，需要对每个点的 ST 数据进行解卷积，以揭示该空间点的细胞组成。在本研究中，我们提出了一种新的方法，称为通过基于图的卷积网络对空间转录组学数据进行去卷积（DSTG），以准确地去卷积每个点观察到的基因表达，并恢复其细胞组成，从而实现高级分割并揭示组织内细胞异质性的空间结构。DSTG 不仅在不同方案生成的合成空间数据上表现出优异的性能，而且还能够有效地识别小鼠皮层、海马切片和胰腺肿瘤组织中细胞的空间组成。总之，DSTG 能够基于空间定位准确地揭示细胞状态和亚群。DSTG 作为一个现成的开源软件（https://github.com/Su-informatics-lab/DSTG），可用于精确研究组织中的空间结构和功能。
