美国德克萨斯大学Tao Wang和Li Wang小组合作取得一项新成果。经过不懈努力，他们的研究开发出了基于位置和图像信息的对空间分辨转录组数据进行去噪的Sprod。该研究于2022年8月4日发表于国际学术期刊《自然-方法学》杂志。

在这项工作中，研究人员基于匹配位置和成像数据的潜在学习方法开发了Sprod，以估算准确的空间分辨转录组学(SRT)基因表达。研究人员对Sprod进行了全面验证，并证明了它在消除单细胞RNA测序数据丢失方面的优势。研究表明，在经过Sprod进行插补后，差异表达分析、通路富集和对细胞间相互作用的推断更加准确。总体而言，该研究表明Sprod的降噪功能会为SRT技术在生物医学发现中的应用起关键作用。

研究人员表示，SRT提供了接近甚至优于单细胞分辨率的基因表达情况数据，同时保留了测序物理位置，并且通常还提供了匹配的病理图像。然而，SRT表达数据受到高噪声的影响，这是由于每个测序单元的覆盖范围较浅并且保留测序位置所需额外的实验步骤。幸运的是，可以利用来自测序物理位置的信息以及相应病理图像中所反映的组织组织来消除此类噪声。

附：英文原文

Title: Sprod for de-noising spatially resolved transcriptomics data based on position and image information

Author: Wang, Yunguan, Song, Bing, Wang, Shidan, Chen, Mingyi, Xie, Yang, Xiao, Guanghua, Wang, Li, Wang, Tao

Issue&Volume: 2022-08-04

Abstract: Spatially resolved transcriptomics (SRT) provide gene expression close to, or even superior to, single-cell resolution while retaining the physical locations of sequencing and often also providing matched pathology images. However, SRT expression data suffer from high noise levels, due to the shallow coverage in each sequencing unit and the extra experimental steps required to preserve the locations of sequencing. Fortunately, such noise can be removed by leveraging information from the physical locations of sequencing, and the tissue organization reflected in corresponding pathology images. In this work, we developed Sprod, based on latent graph learning of matched location and imaging data, to impute accurate SRT gene expression. We validated Sprod comprehensively and demonstrated its advantages over previous methods for removing drop-outs in single-cell RNA-sequencing data. We showed that, after imputation by Sprod, differential expression analyses, pathway enrichment and cell-to-cell interaction inferences are more accurate. Overall, we envision de-noising by Sprod to become a key first step towards empowering SRT technologies for biomedical discoveries. Sprod accurately denoises spatially resolved transcriptomics data and improves downstream analysis results.

DOI: 10.1038/s41592-022-01560-w

Source: https://www.nature.com/articles/s41592-022-01560-w