Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
New York Genome Center, New York, NY, USA.
Nat Rev Mol Cell Biol. 2023 Oct;24(10):695-713. doi: 10.1038/s41580-023-00615-w. Epub 2023 Jun 6.
Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.
单细胞多组学技术和方法通过同时整合各种单模态组学方法来描述转录组、基因组、表观基因组、表转录组、蛋白质组、代谢组和其他（新兴）组学，从而对细胞状态和活性进行特征化。这些方法共同推动了分子细胞生物学研究的发展。在这篇全面的综述中，我们讨论了已建立的多组学技术以及该领域的前沿和最先进的方法。我们讨论了多组学技术在过去十年中如何通过优化通量和分辨率、模式集成、独特性和准确性的框架进行了适应和改进，我们还讨论了多组学的局限性。我们强调了单细胞多组学技术在细胞谱系追踪、组织特异性和细胞特异性图谱生成、肿瘤免疫学和癌症遗传学，以及在基础和转化研究中对细胞空间信息进行映射方面所产生的影响。最后，我们讨论了已经开发的生物信息学工具，这些工具通过使用更好的数学建模和计算方法来链接不同的组学模式并阐明其功能。
