细胞表面蛋白充当主要药物靶点和细胞身份标记。 CITE-seq（通过测序对转录组和表位进行细胞索引）等技术可以同时定量单个细胞内的表面蛋白丰度和转录本表达。已发表的数据已用于训练机器学习模型，仅根据转录本表达来预测表面蛋白丰度。然而，预测的蛋白质规模较小，并且这些计算方法在不同环境（例如不同组织/疾病状态）下的泛化能力较差，阻碍了它们的广泛采用。在这里，我们提出了 SPIDER（使用来自单细胞 RNA 测序的深度集成的表面蛋白预测），这是一种上下文无关的零样本深度集成模型，它能够实现大规模蛋白质丰度预测并更好地推广到各种上下文。综合基准​​测试表明 SPIDER 优于其他最先进的方法。利用单细胞转录组中超过 2,500 个蛋白质的预测表面丰度，我们证明了 SPIDER 的广泛应用，包括肝细胞癌和结直肠癌中的细胞类型注释、生物标志物/靶标识别以及细胞间相互作用分析。补充信息中包含了本文透明同行评审过程的记录。版权所有 © 2024 Elsevier Inc. 保留所有权利。

Cell surface proteins serve as primary drug targets and cell identity markers. Techniques such as CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) have enabled the simultaneous quantification of surface protein abundance and transcript expression within individual cells. The published data have been utilized to train machine learning models for predicting surface protein abundance solely from transcript expression. However, the small scale of proteins predicted and the poor generalization ability of these computational approaches across diverse contexts (e.g., different tissues/disease states) impede their widespread adoption. Here, we propose SPIDER (surface protein prediction using deep ensembles from single-cell RNA sequencing), a context-agnostic zero-shot deep ensemble model, which enables large-scale protein abundance prediction and generalizes better to various contexts. Comprehensive benchmarking shows that SPIDER outperforms other state-of-the-art methods. Using the predicted surface abundance of >2,500 proteins from single-cell transcriptomes, we demonstrate the broad applications of SPIDER, including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in hepatocellular carcinoma and colorectal cancer. A record of this paper's transparent peer review process is included in the supplemental information.Copyright © 2024 Elsevier Inc. All rights reserved.