Gaspard-Boulinc Lucie C, Gortana Luca, Walter Thomas, Barillot Emmanuel, Cavalli Florence M G
Institut Curie, PSL University, Paris, France.
Institut National de la Santé et de la Recherche Médicale (INSERM), U1331, Paris, France.
Nat Rev Genet. 2025 May 14. doi: 10.1038/s41576-025-00845-y.
Spatial transcriptomics is a powerful method for studying the spatial organization of cells, which is a critical feature in the development, function and evolution of multicellular life. However, sequencing-based spatial transcriptomics has not yet achieved cellular-level resolution, so advanced deconvolution methods are needed to infer cell-type contributions at each location in the data. Recent progress has led to diverse tools for cell-type deconvolution that are helping to describe tissue architectures in health and disease. In this Review, we describe the varied types of cell-type deconvolution methods for spatial transcriptomics, contrast their capabilities and summarize them in a web-based, interactive table to enable more efficient method selection.
空间转录组学是研究细胞空间组织的一种强大方法,而细胞空间组织是多细胞生物发育、功能和进化中的一个关键特征。然而,基于测序的空间转录组学尚未达到细胞水平分辨率,因此需要先进的反卷积方法来推断数据中每个位置的细胞类型贡献。最近的进展催生了多种用于细胞类型反卷积的工具,这些工具有助于描述健康和疾病状态下的组织结构。在本综述中,我们描述了用于空间转录组学的不同类型的细胞类型反卷积方法,对比了它们的能力,并将其总结在一个基于网络的交互式表格中,以实现更高效的方法选择。