Center for Spatial Omics, St Jude Children's Research Hospital, Memphis, TN, USA.
Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, TN, USA.
BMC Genomics. 2023 Nov 28;24(1):717. doi: 10.1186/s12864-023-09722-6.
Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis using existing methods. For the first time, we demonstrate the application of a ML tool across five emerging spatial transcriptomic and proteomic technologies to identify oncogenic stem-like cell types in the tumor microenvironment.
细胞注释是解释单细胞和空间组学数据的关键方法学组成部分。这些方法是为单细胞分析开发的，但在空间组学中往往存在偏差、需要手动整理，并且尚未得到验证。在这里，我们将评估致癌状态的干性模型应用于单细胞和空间组学癌症数据集。这种一类逻辑回归机器学习算法用于从非转化的干细胞中提取转录组特征，以鉴定肿瘤中的去分化细胞状态。我们发现，这种方法可以在不需要细胞注释的情况下识别转移性肿瘤细胞群体中的单细胞状态。该机器学习模型鉴定了使用现有方法在单细胞或空间转录组分析中未鉴定出的类干细胞群体。我们首次展示了一种机器学习工具在五种新兴的空间转录组学和蛋白质组学技术中的应用，以鉴定肿瘤微环境中的致癌类干细胞类型。
