Rodov Avital, Baniadam Hosna, Zeiser Robert, Amit Ido, Yosef Nir, Wertheimer Tobias, Ingelfinger Florian
Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel.
European Molecular Biology Laboratory, Heidelberg, Germany.
Eur J Immunol. 2025 Feb;55(2):e202451234. doi: 10.1002/eji.202451234.
Recent advances in multi-omics and spatially resolved single-cell technologies have revolutionised our ability to profile millions of cellular states, offering unprecedented opportunities to understand the complex molecular landscapes of human tissues in both health and disease. These developments hold immense potential for precision medicine, particularly in the rational design of novel therapeutics for treating inflammatory and autoimmune diseases. However, the vast, high-dimensional data generated by these technologies present significant analytical challenges, such as distinguishing technical variation from biological variation or defining relevant questions that leverage the added spatial dimension to improve our understanding of tissue organisation. Generative artificial intelligence (AI), specifically variational autoencoder- or transformer-based latent variable models, provides a powerful and flexible approach to addressing these challenges. These models make inferences about a cell's intrinsic state by effectively identifying complex patterns, reducing data dimensionality and modelling the biological variability in single-cell datasets. This review explores the current landscape of single-cell and spatial multi-omics technologies, the application of generative AI in data analysis and modelling and their transformative impact on our understanding of autoimmune diseases. By combining spatial and single-cell data with advanced AI methodologies, we highlight novel insights into the pathogenesis of autoimmune disorders and outline future directions for leveraging these technologies to achieve the goal of AI-powered personalised medicine.
多组学和空间分辨单细胞技术的最新进展彻底改变了我们描绘数百万种细胞状态的能力，为理解健康和疾病状态下人类组织复杂的分子景观提供了前所未有的机会。这些进展在精准医学方面具有巨大潜力，尤其是在合理设计治疗炎症和自身免疫性疾病的新型疗法方面。然而，这些技术产生的海量高维数据带来了重大分析挑战，例如区分技术变异和生物变异，或者确定利用新增空间维度来增进我们对组织结构理解的相关问题。生成式人工智能（AI），特别是基于变分自编码器或变压器的潜在变量模型，为应对这些挑战提供了一种强大且灵活的方法。这些模型通过有效识别复杂模式、降低数据维度以及对单细胞数据集中的生物变异性进行建模，来推断细胞的内在状态。本综述探讨了单细胞和空间多组学技术的当前状况、生成式AI在数据分析和建模中的应用及其对我们理解自身免疫性疾病的变革性影响。通过将空间和单细胞数据与先进的AI方法相结合，我们突出了对自身免疫性疾病发病机制的新见解，并概述了利用这些技术实现人工智能驱动的个性化医学目标的未来方向。
