深圳腾讯AI实验室Jianhua Yao和南开大学Han Zhang共同合作，近期取得重要工作进展。他们研究提出了scPROTEIN工具，这是一种用于单细胞蛋白质组学嵌入的通用深度图对比学习框架。相关研究成果2024年3月19日在线发表于《自然—方法学》杂志上。

据介绍，单细胞蛋白质组学测序技术揭示了细胞中蛋白质-蛋白质的相互作用、翻译后修饰和蛋白质形态动力学。然而，多肽定量的不确定性估计、数据缺失、批量效应和高噪声阻碍了单细胞蛋白质组数据的分析。共同解决这一系列复杂的问题很重要，但现有的针对单细胞转录组的方法无法完全解决这一任务。

研究人员提出了一种用于单细胞蛋白质组学数据分析的通用框架，称为scPROTEIN，它包括基于多任务异方差回归模型的肽不确定性估计和基于图形对比学习的细胞嵌入生成。scPROTEIN可以估计多肽定量的不确定性，去噪蛋白质组学数据，消除批量效应，并在一个统一的框架中编码单细胞蛋白质组学特异性嵌入。

总之，这一研究证明scPROTEIN在细胞聚类、批量校正、细胞类型注释、临床分析和空间分辨蛋白质组数据探索方面是有效的。

附：英文原文

Title: scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding

Author: Li, Wei, Yang, Fan, Wang, Fang, Rong, Yu, Liu, Linjing, Wu, Bingzhe, Zhang, Han, Yao, Jianhua

Issue&Volume: 2024-03-19

Abstract: Single-cell proteomics sequencing technology sheds light on protein–protein interactions, posttranslational modifications and proteoform dynamics in the cell. However, the uncertainty estimation for peptide quantification, data missingness, batch effects and high noise hinder the analysis of single-cell proteomic data. It is important to solve this set of tangled problems together, but the existing methods tailored for single-cell transcriptomes cannot fully address this task. Here we propose a versatile framework designed for single-cell proteomics data analysis called scPROTEIN, which consists of peptide uncertainty estimation based on a multitask heteroscedastic regression model and cell embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings in a unified framework. We demonstrate that scPROTEIN is efficient for cell clustering, batch correction, cell type annotation, clinical analysis and spatially resolved proteomic data exploration.

DOI: 10.1038/s41592-024-02214-9

Source: https://www.nature.com/articles/s41592-024-02214-9