哈佛医学院Peter V. Kharchenko小组取得一项新突破。他们的研究开发出了空间转录组学中分割错误的影响和纠正。相关论文于2026年1月20日发表于国际顶尖学术期刊《自然—遗传学》杂志上。

在这里，该研究团队重新分析了来自多个组织和平台的数据，发现分割错误目前混淆了大多数细胞状态的下游分析，包括差异表达、邻居影响和配体-受体相互作用。错误分配的分子对结果的影响程度可能是惊人的，经常主导结果。他们表明，局部分子邻域的矩阵分解可以有效地识别和分离这些分子混合物，从而减少它们对下游分析的影响，其方式类似于单细胞RNA测序中的双重过滤。随着空间转录组学分析的应用越来越广泛，对分割错误的解释对于解决组织生物学的分子机制将是重要的。

据悉，空间转录组学旨在通过将细胞状态与其原生微环境联系起来，阐明细胞如何在组织内协调。基于成像的分析尤其有希望，在亚细胞分辨率下捕获分子和细胞特征。然而，这些数据的解释取决于准确的细胞分割。将单个分子分配给正确的细胞仍然具有挑战性。

附：英文原文

Title: Impact and correction of segmentation errors in spatial transcriptomics

Author: Mitchel, Jonathan, Gao, Teng, Petukhov, Viktor, Cole, Eli, Kharchenko, Peter V.

Issue&Volume: 2026-01-20

Abstract: Spatial transcriptomics aims to elucidate how cells coordinate within tissues by connecting cellular states to their native microenvironments. Imaging-based assays are especially promising, capturing molecular and cellular features at subcellular resolution in three dimensions. Interpretation of such data, however, hinges on accurate cell segmentation. Assigning individual molecules to the correct cells remains challenging. Here we re-analyze data from multiple tissues and platforms to find that segmentation errors currently confound most downstream analysis of cellular state, including differential expression, neighbor influence and ligand–receptor interactions. The extent to which misassigned molecules impact the results can be striking, frequently dominating the results. Thus, we show that matrix factorization of local molecular neighborhoods can effectively identify and isolate such molecular admixtures, thereby reducing their impact on downstream analyses, in a manner analogous to doublet filtering in single-cell RNA sequencing. As the applications of spatial transcriptomics assays become more widespread, accounting for segmentation errors will be important for resolving molecular mechanisms of tissue biology.

DOI: 10.1038/s41588-025-02497-4

Source: https://www.nature.com/articles/s41588-025-02497-4