Caetano A J, Sharpe P T
Centre for Oral Immunobiology and Regenerative Medicine, Barts Centre for Squamous Cancer, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, UK.
Centre for Craniofacial and Regenerative Biology, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK.
J Dent Res. 2024 Feb;103(2):129-137. doi: 10.1177/00220345231216114. Epub 2024 Jan 3.
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.
人类口腔黏膜包含最复杂的细胞系统之一，该系统对于正常生理功能以及抵御多种局部病原体至关重要。不断发展的技术和实验系统有助于深化我们对这个复杂细胞网络的理解。当前的单细胞RNA测序方法能够分辨细胞类型和状态之间的细微差异，从而为研究健康和疾病状态下口腔黏膜的分子和细胞组成提供了一个强大工具。然而，这需要对组织样本进行解离，这意味着细胞之间的相互关系会丧失。空间转录组学方法绕过了组织解离过程并保留了这种空间信息，从而能够在组织结构背景下评估数千个细胞的基因表达。在此，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所做的贡献。我们考虑其对识别疾病细胞邻域的影响以及空间如何定义细胞状态。我们还将结合机器学习的最新进展讨论空间测序技术的局限性和未来方向。最后，我们针对这些技术有望解决的关于黏膜稳态的开放性问题提出了一个观点。
