Advancing precision medicine through single-cell sequencing: Insights and implications

Xu Zhang, Rongrong Gao, Liuke Yang, Youwei Zhu, Tiancheng Zhang, Xiaorong Shen, Wenwen Gu, Long Yang, Shenjie Peng
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Abstract

Background

Single-cell sequencing (SCS) marks the advent of a transformative period in biomedical studies, enabling unprecedented insight into the cellular intricacies of health and disease.

Methods

By dissecting the genetic, epigenetic and proteomic landscapes at the single-cell level, SCS transcends traditional bulk sequencing methodologies, illuminating the heterogeneity and dynamics of individual cells.

Results

This analytical leap facilitates a deeper understanding of disease mechanisms, offers novel diagnostic and therapeutic targets and underpins the development of precision medicine across diverse fields such as neurology, oncology and immunology.

Conclusions

Despite its profound potential, SCS encounters challenges, including complex sample preparation, sophisticated data analysis and cost considerations. Nevertheless, ongoing advancements promise to overcome these barriers, integrating SCS with other omics data and leveraging machine learning to enhance biological understanding and clinical application. With the advancement of SCS technologies, personalised healthcare might be fundamentally altered, facilitating tailored and efficacious treatment strategies.

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通过单细胞测序推进精准医疗:见解和影响
背景 单细胞测序(SCS)标志着生物医学研究进入了一个变革时期,使人们能够以前所未有的方式深入了解健康和疾病的细胞奥秘。 方法 通过在单细胞水平剖析遗传、表观遗传和蛋白质组景观,单细胞测序超越了传统的批量测序方法,揭示了单个细胞的异质性和动态性。 结果 这一分析上的飞跃有助于加深对疾病机制的理解,提供新的诊断和治疗目标,并为神经学、肿瘤学和免疫学等不同领域的精准医学发展提供支持。 结论 尽管 SCS 潜力巨大,但它也面临着各种挑战,包括复杂的样本制备、复杂的数据分析和成本因素。不过,不断取得的进步有望克服这些障碍,将 SCS 与其他全息数据整合起来,并利用机器学习增强对生物的理解和临床应用。随着 SCS 技术的进步,个性化医疗可能会发生根本性的改变,从而促进量身定制的有效治疗策略。
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