The human oral mucosa contains one of the most complex cellular systems that are essential for normal physiology and defense against a wide variety of local pathogens. Evolving techniques and experimental systems have helped refine our understanding of this complex cellular network. Current single-cell RNA sequencing methods can resolve subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the oral mucosa in health and disease. However, it requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss the contribution of spatial technologies in shaping our understanding of this complex system. We consider the impact on identifying disease cellular neighborhoods and how space defines cell state. We also discuss the limitations and future directions of spatial sequencing technologies with recent advances in machine learning. Finally, we offer a perspective on open questions about mucosal homeostasis that these technologies are well placed to address.

中文翻译：

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用空间基因组学重新定义粘膜炎症

人类口腔粘膜含有最复杂的细胞系统之一，对于正常生理和防御多种局部病原体至关重要。不断发展的技术和实验系统帮助我们加深了对这个复杂的细胞网络的理解。目前的单细胞RNA测序方法可以解决细胞类型和状态之间的细微差异，从而为研究口腔粘膜在健康和疾病中的分子和细胞库提供了一个很好的工具。然而，它需要组织样本的解离，这意味着细胞之间的相互关系丢失。空间转录组学方法绕过组织解离并保留该空间信息，从而允许在组织结构组织的背景下评估数千个细胞的基因表达。在这里，我们讨论空间技术在塑造我们对这个复杂系统的理解方面的贡献。我们考虑对识别疾病细胞邻域的影响以及空间如何定义细胞状态。我们还结合机器学习的最新进展讨论了空间测序技术的局限性和未来方向。最后，我们对有关粘膜稳态的开放性问题提供了一个观点，这些技术可以很好地解决这些问题。