美国斯坦福大学Jure Leskovec小组利用SATURN整合跨物种单细胞RNA-seq数据集。该项研究成果于2024年2月16日在线发表在《自然—方法学》杂志上。

研究人员介绍一种用于学习通用细胞嵌入的深度学习方法——SATURN，它利用蛋白质语言模型对基因的生物学特性进行编码。通过将语言模型中的蛋白质嵌入与RNA表达相结合，SATURN可以整合不同物种的数据集，而不管它们的基因组是否相似。SATURN可以检测跨物种共表达的功能相关基因，为跨物种分析重新定义差异表达。

研究人员将SATURN应用于三个物种的全机体图谱以及青蛙和斑马鱼胚胎发生数据集，结果表明SATURN可以有效地跨物种转移注释，即使它们在演化上相距甚远。研究人员还证明，SATURN可用于发现人类和其他四个物种的青光眼相关基因之间潜在的不同基因功能。

研究人员表示，对来自不同生物体的单细胞数据集进行分析，为揭示细胞类型保存和多样化的基本进化过程提供了前所未有的机会。然而，物种间的基因组差异限制了对同源基因进行跨物种数据集的联合分析。

附：英文原文

Title: Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN

Author: Rosen, Yanay, Brbi, Maria, Roohani, Yusuf, Swanson, Kyle, Li, Ziang, Leskovec, Jure

Issue&Volume: 2024-02-16

Abstract: Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, interspecies genomic differences limit the joint analysis of cross-species datasets to homologous genes. Here we present SATURN, a deep learning method for learning universal cell embeddings that encodes genes’ biological properties using protein language models. By coupling protein embeddings from language models with RNA expression, SATURN integrates datasets profiled from different species regardless of their genomic similarity. SATURN can detect functionally related genes coexpressed across species, redefining differential expression for cross-species analysis. Applying SATURN to three species whole-organism atlases and frog and zebrafish embryogenesis datasets, we show that SATURN can effectively transfer annotations across species, even when they are evolutionarily remote. We also demonstrate that SATURN can be used to find potentially divergent gene functions between glaucoma-associated genes in humans and four other species.

DOI: 10.1038/s41592-024-02191-z

Source: https://www.nature.com/articles/s41592-024-02191-z