The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA, 92627, USA.
Department of Mathematics, University of California Irvine, Irvine, CA, 92627, USA.
Nat Commun. 2023 Oct 17;14(1):6554. doi: 10.1038/s41467-023-42343-x.
Spatial gene expression in tissue is characterized by regions in which particular genes are enriched or depleted. Frequently, these regions contain nested inside them subregions with distinct expression patterns. Segmentation methods in spatial transcriptomic (ST) data extract disjoint regions maximizing similarity over the greatest number of genes, typically on a particular spatial scale, thus lacking the ability to find region-within-region structure. We present NeST, which extracts spatial structure through coexpression hotspots-regions exhibiting localized spatial coexpression of some set of genes. Coexpression hotspots identify structure on any spatial scale, over any possible subset of genes, and are highly explainable. NeST also performs spatial analysis of cell-cell interactions via ligand-receptor, identifying active areas de novo without restriction of cell type or other groupings, in both two and three dimensions. Through application on ST datasets of varying type and resolution, we demonstrate the ability of NeST to reveal a new level of biological structure.
组织中的空间基因表达的特点是特定基因丰富或缺失的区域。这些区域通常包含嵌套的具有不同表达模式的子区域。空间转录组学 (ST) 数据中的分割方法提取不相交的区域，在最大数量的基因上最大化相似性，通常在特定的空间尺度上，因此缺乏发现区域内结构的能力。我们提出了 NeST，它通过共表达热点——表现出某些基因集局部空间共表达的区域来提取空间结构。共表达热点可以在任何空间尺度上、任何可能的基因子集上识别结构，并且具有高度的可解释性。NeST 还通过配体-受体进行细胞间相互作用的空间分析，在二维和三维空间中无需限制细胞类型或其他分组，从头识别活跃区域。通过对不同类型和分辨率的 ST 数据集的应用，我们证明了 NeST 揭示新的生物学结构的能力。
