Gao Dalong, Ning Jin, Liu Gang, Sun Shiquan, Dang Xiaoqian
First Department of Orthopedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Sports Medicine and Joint Surgery, Xianyang Central Hospital, Xianyang, China.
Front Genet. 2022 May 26;13:893522. doi: 10.3389/fgene.2022.893522. eCollection 2022.
Recent advances in various single-cell RNA sequencing (scRNA-seq) technologies have enabled profiling the gene expression level with the whole transcriptome at a single-cell resolution. However, it lacks the spatial context of tissues. The image-based transcriptomics studies (e.g., MERFISH and seqFISH) maintain the cell spatial context at individual cell levels but can only measure a limited number of genes or transcripts (up to roughly 1,000 genes). Therefore, integrating scRNA-seq data and image-based transcriptomics data can potentially gain the complementary benefits of both. Here, we develop a computational method, SpatialMap, to bridge the gap, which primarily facilitates spatial mapping of unmeasured gene profiles in spatial transcriptomic data integrating with scRNA-seq data from the same tissue. SpatialMap directly models the count nature of spatial gene expression data through generalized linear spatial models, which accounts for the spatial correlation among spatial locations using conditional autoregressive (CAR) prior. With a newly developed computationally efficient penalized quasi-likelihood (PQL)-based algorithm, SpatialMap can scale up to performing large-scale spatial mapping analysis. Finally, we applied the SpatialMap to four publicly available tissue-paired studies (i.e., scRNA-seq studies and image-based transcriptomics studies). The results demonstrate that the proposed method can accurately predict unmeasured gene expression profiles across various spatial and scRNA-seq dataset pairs of different species and technologies.
各种单细胞RNA测序（scRNA-seq）技术的最新进展使得能够在单细胞分辨率下对整个转录组的基因表达水平进行分析。然而，它缺乏组织的空间背景信息。基于图像的转录组学研究（如MERFISH和seqFISH）在单个细胞水平上保留了细胞空间背景信息，但只能测量有限数量的基因或转录本（最多约1000个基因）。因此，整合scRNA-seq数据和基于图像的转录组学数据可能会获得两者的互补优势。在此，我们开发了一种计算方法SpatialMap来弥合这一差距，该方法主要促进在与来自同一组织的scRNA-seq数据整合的空间转录组数据中对未测量基因谱进行空间映射。SpatialMap通过广义线性空间模型直接对空间基因表达数据的计数性质进行建模，该模型使用条件自回归（CAR）先验来考虑空间位置之间的空间相关性。借助新开发的基于惩罚拟似然（PQL）的高效计算算法，SpatialMap可以扩展到执行大规模空间映射分析。最后，我们将SpatialMap应用于四项公开可用的组织配对研究（即scRNA-seq研究和基于图像的转录组学研究）。结果表明，所提出的方法能够准确预测不同物种和技术的各种空间和scRNA-seq数据集对中未测量的基因表达谱。
