PDF(Pubmed)
Abstract：
Identifying cell types and states remains a time-consuming and error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data, using unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integration of TACIT-identified cell with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discover under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.
摘要：
识别细胞类型和状态对于空间生物学来说仍然是耗时且容易出错的挑战。随着深度学习的使用越来越多，由于细胞水平的可变性，很难一概而论，邻里,以及健康和疾病的利基。为了解决这个问题，我们开发了TACIT,一种无监督的细胞注释算法，使用预定义的签名，在没有训练数据的情况下运行，使用无偏阈值将阳性细胞与背景区分开来，专注于相关标记，以在多体分析中识别模糊的细胞。使用来自三个生态位（大脑，肠,压盖),TACIT在准确性和可扩展性方面优于现有的无监督方法。TACIT鉴定的细胞与新型Shiny应用程序的整合揭示了两种炎症性腺体疾病的新表型。最后,结合空间转录组学和蛋白质组学，我们在感兴趣的区域发现了不足和过多的免疫细胞类型和状态，表明多模态对于将空间生物学转化为临床应用至关重要。
