美国普林斯顿大学Benjamin J. Raphael团队近期取得重要工作进展，他们研究提出了一种用于空间转录组数据的排列和整合的新方法。这一研究成果2022年5月16日在线发表于《自然—方法学》杂志上。
研究人员介绍了一种PASTE的方法，用来排列和整合多个相邻组织切片的空间转录组学(ST)数据。 PASTE使用最佳传输公式来计算切片的成对排列，该公式模拟转录相似性和点之间的物理距离。 PASTE进一步组合，成对对齐以构建组织的堆叠3D对齐。另外，PASTE可以将多个ST切片集成到一个一致性切片中。研究人员表明，在模拟和真实的ST数据中，PASTE都能准确地将相邻切片上的点对齐，展示了同时使用转录相似性和空间信息的优势。他们进一步表明，与分析单个ST切片或忽略空间信息的现有方法相比，PASTE集成切片提高了细胞类型和差异表达基因的识别。
据了解，空间转录组学(ST)能够用来测量组织切片中数千个点的mRNA表达，同时也记录每个点的二维(2D)坐标。
附：英文原文
Title: Alignment and integration of spatial transcriptomics data
Author: Zeira, Ron, Land, Max, Strzalkowski, Alexander, Raphael, Benjamin J.
Issue&Volume: 2022-05-16
Abstract: Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a tissue slice while recording the two-dimensional (2D) coordinates of each spot. We introduce probabilistic alignment of ST experiments (PASTE), a method to align and integrate ST data from multiple adjacent tissue slices. PASTE computes pairwise alignments of slices using an optimal transport formulation that models both transcriptional similarity and physical distances between spots. PASTE further combines pairwise alignments to construct a stacked 3D alignment of a tissue. Alternatively, PASTE can integrate multiple ST slices into a single consensus slice. We show that PASTE accurately aligns spots across adjacent slices in both simulated and real ST data, demonstrating the advantages of using both transcriptional similarity and spatial information. We further show that the PASTE integrated slice improves the identification of cell types and differentially expressed genes compared with existing approaches that either analyze single ST slices or ignore spatial information. PASTE aligns and integrates spatial transcriptomics data generated from adjacent tissue slices by leveraging their transcriptomic similarity and spatial coordinates, which ultimately increases the power for downstream analysis.
DOI: 10.1038/s41592-022-01459-6
Source: https://www.nature.com/articles/s41592-022-01459-6
Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex
本期文章：《自然—方法学》：Online/在线发表
