Single-cell multi-omics technologies have recently advanced to enable the profiling of epigenomic, transcriptomic, and proteomic layers within individual cells, offering new opportunities to characterize cellular states as integrated biological systems. However, developing a unified framework that can seamlessly integrate diverse omics modalities and remain robust to heterogeneous modality missingness remains challenging. Existing methods are often designed for specific modalities or modality pairs, relying on dataset-specific training or paired measurements. Here we present HoloCell, to our knowledge the first generative foundation model for joint representation learning and generative modeling across all three major single-cell omics modalities, i.e., epigenomics, transcriptomics, and proteomics. HoloCell contains over 860 million parameters and is pretrained on the Human-Multi-Omics-Corpus, which comprises approximately 468 million single-cell profiles across these three omics layers, corresponding to over 425 billion tokens. HoloCell introduces a a simple yet biologically motivated hierarchical tokenization strategy that encodes cis-regulatory elements, genes, and proteins as structured tokens within a shared modeling framework. We evaluated HoloCell across single-omics representation learning, paired multi-omics integration, unpaired multi-omics alignment, and cross-modal generation via iterative diffusion and remasking, demonstrating its superior performance and flexibility across diverse omics tasks. From a representation perspective, HoloCell provides a unified digital mapping of cellular states across multiple omics layers, capturing cell heterogeneity as an integrated system. From a generation perspective, its iterative diffusion and remasking frame-work permits flexible generation orders beyond fixed left-to-right causality, enabling in silico simulation of multi-omics information flow. Together, these capabilities position HoloCell as a versatile foundation model toward the emerging concept of a virtual cell, offering both systematic characterization and generative simulation of cellular systems within a unified framework.
单细胞多组学技术近来取得进展，能够对单个细胞内的表观基因组、转录组和蛋白质组层面进行分析，为将细胞状态表征为整合生物系统提供了新机遇。然而，开发一个能够无缝整合不同组学模式并对异质性模式缺失保持稳健性的统一框架仍具挑战。现有方法通常针对特定模式或模式对设计，依赖于数据集特定的训练或配对测量。在此，我们提出HoloCell，据我们所知，这是首个用于跨所有三大主要单细胞组学模式（即表观基因组、转录组和蛋白质组）进行联合表示学习和生成建模的生成式基础模型。HoloCell包含超过8.6亿个参数，并在人类多组学语料库上进行了预训练，该语料库包含约4.68亿个跨这三种组学层面的单细胞图谱，对应超过4250亿个标记。HoloCell引入了一种简单但基于生物学的分层标记化策略，将顺式调控元件、基因和蛋白质编码为共享建模框架内的结构化标记。我们在单组学表示学习、配对多组学整合、非配对多组学对齐以及通过迭代扩散和重新掩码进行的跨模态生成等方面评估了HoloCell，展示了其在不同组学任务中的卓越性能和灵活性。从表示的角度看，HoloCell提供了跨多个组学层面的细胞状态统一数字映射，将细胞异质性作为一个整合系统来捕捉。从生成的角度看，其迭代扩散和重新掩码框架允许超越固定从左到右因果性的灵活生成顺序，实现了多组学信息流的计算机模拟。这些能力共同将HoloCell定位为一个多功能基础模型，朝着新兴的虚拟细胞概念发展，在统一框架内提供细胞系统的系统性表征和生成式模拟。
Jiang, Q. et al. · CC-BY 4.0