近日，美国斯隆凯特琳学院Dana Peer、Anna-Katerina Hadjantonakis研究小组的研究显示，协方差环境定义了空间推理的细胞生态位。相关论文发表在2024年4月2日出版的《自然—生物技术》杂志上。

据了解，从高分辨率空间分析技术分析数据的一个关键挑战，是适当地表示细胞邻域或生态位的特征。

该团队介绍了协方差环境(COVET)，这是一种利用生态位中细胞间基因-基因协变量结构来捕捉其中细胞相互作用的多变量性质的表示方法。研究小组在COVET生态位之间定义了一个原则性的基于运输的最佳距离度量，该度量可扩展到数百万个细胞。

利用协方差环境对空间内容进行编码，课题组开发了环境变分推理(ENVI)，这是一种条件变分自编码器，可将空间和单细胞RNA测序数据共同嵌入到潜在空间中。ENVI包括两个解码器：一个用于跨空间模态推算基因表达，另一个用于将空间信息投射到单细胞数据上。ENVI可以为来自单个分离细胞的基因组学数据赋予空间背景，并且优于在不同空间数据集上输入基因表达的替代方法。

附：英文原文

Title: The covariance environment defines cellular niches for spatial inference

Author: Haviv, Doron, Remk, Jn, Gatie, Mohamed, Snopkowski, Catherine, Takizawa, Meril, Pereira, Nathan, Bashkin, John, Jovanovich, Stevan, Nawy, Tal, Chaligne, Ronan, Boire, Adrienne, Hadjantonakis, Anna-Katerina, Peer, Dana

Issue&Volume: 2024-04-02

Abstract: A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene–gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.

DOI: 10.1038/s41587-024-02193-4

Source: https://www.nature.com/articles/s41587-024-02193-4