scResolve: Recovering single cell expression profiles from multi-cellular spatial transcriptomics.
scResolve: Recovering single cell expression profiles from multi-cellular spatial transcriptomics.
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scResolve：从多细胞空间转录组学中恢复单细胞表达谱。
DOI：
10.1101/2023.12.18.572269
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Lugo-Martinez,Jose
中科院分区：
文献类型：
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作者：
Chen,Hao;Lee,YoungJe;Ovando,JoseA;Rosas,Lorena;Rojas,Mauricio;Mora,AnaL;Bar-Joseph,Ziv;Lugo-Martinez,Jose
许多流行的空间转录组学技术缺乏单细胞分辨率。相反，这些方法测量来自可能含有多种细胞类型的细胞混合物的每个位置的集体基因表达。在这里，我们开发了scResolve，这是一种从多细胞分辨率的空间转录组学测量中恢复单细胞表达谱的方法。scResolve准确地恢复了单个细胞在其位置的表达谱，这是细胞类型去卷积无法实现的。scResolve在人类乳腺癌数据和人类肺部疾病数据上的应用表明，scResolve能够在不同组织背景之间进行细胞类型特异性差异基因表达分析，并准确识别稀有细胞群。通过scResolve获得的空间分辨细胞水平表达谱有助于更灵活和精确的空间分析，补充了原始的多细胞水平分析。
Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable from cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.