Li Zhixin, Ngan Elly Sau-Wai
Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.
Comput Struct Biotechnol J. 2022 May 18;20:2464-2472. doi: 10.1016/j.csbj.2022.05.025. eCollection 2022.
With the rapid development of single-cell sequencing technologies, it has become a powerful strategy for the discovery of rare cells and delineating the molecular basis underlying various biological processes. Use of single-cell multimodal sequencing to explore the chromatin accessibility, gene expression and spatial transcriptome has propelled us to success in untangling the unknowns in the enteric nervous system (ENS) and provided unprecedented resources for building new diagnostic framework for enteric neuropathies. Here, we summarize the recent findings of single-cell multimodal sequencing, especially focusing on the most commonly used single-cell RNA sequencing (scRNA-seq) on ENS cells, ranged from the progenitors, neural crest (NC) cells, to the mature ENS circuit, in both human and mouse. These studies have highlighted the heterogeneity of ENS cells at various developmental stages and discovered numerous novel cell types. We will also discuss various computational methods that were used to reconstruct the differentiation trajectories of the developing ENS and to elucidate the cell fate decisions. Profiling disease mechanisms and cellular drug responses with single-cell multimodal omics techniques likely leads to a paradigm shift in the field of biomedical research. Further improvements in the high-resolution sequencing platforms and integrative computational tools will greatly hasten their applications in both the basic and translational medicine.
随着单细胞测序技术的迅速发展，它已成为发现稀有细胞和阐明各种生物学过程背后分子基础的有力策略。利用单细胞多组学测序来探索染色质可及性、基因表达和空间转录组，推动我们成功解开了肠神经系统（ENS）中的未知之谜，并为构建肠道神经病变的新诊断框架提供了前所未有的资源。在这里，我们总结了单细胞多组学测序的最新发现，特别关注在人类和小鼠中对ENS细胞（从祖细胞、神经嵴（NC）细胞到成熟的ENS回路）最常用的单细胞RNA测序（scRNA-seq）。这些研究突出了ENS细胞在不同发育阶段的异质性，并发现了许多新型细胞类型。我们还将讨论用于重建发育中的ENS的分化轨迹和阐明细胞命运决定的各种计算方法。用单细胞多组学技术分析疾病机制和细胞药物反应可能会导致生物医学研究领域的范式转变。高分辨率测序平台和综合计算工具的进一步改进将极大地加速它们在基础医学和转化医学中的应用。
