Lammi Mikko J, Qu Chengjuan
Institute of Medical and Translational Biology, Umeå University, 90187 Umeå, Sweden.
Department of Odontology, Umeå University, 90187 Umeå, Sweden.
Bioengineering (Basel). 2024 Dec 6;11(12):1235. doi: 10.3390/bioengineering11121235.
Spatial transcriptomics, proteomics, and epigenomics are innovative technologies which offer an unparalleled resolution and wealth of data in understanding and the interpretation of cellular functions and interactions. These techniques allow researchers to investigate gene and protein expressions at an individual cell level, revealing cellular heterogeneity within, for instance, bioengineered tissues and classifying novel and rare cell populations that could be essential for the function of the tissues and in disease processes. It is possible to analyze thousands of cells simultaneously, which gives thorough insights into the transcriptomic view of complex tissues. Spatial transcriptomics combines gene expressions with spatial information, conserving tissue architecture and making the mapping of gene activity across different tissue regions possible. Despite recent advancements in these technologies, they face certain limitations. Single-cell transcriptomics can suffer from technical noise and dropout events, leading to incomplete data. Its applicability has been limited by the complexity of data integration and interpretation, although better resolution and tools for the interpretation of data are developing fast. Spatial proteomics and spatial epigenomics provide data on the distribution of proteins and the gene regulatory aspects in tissues, respectively. The disadvantages of these approaches include rather costly and time-consuming analyses. Nevertheless, combining these techniques promises a more comprehensive understanding of cell function and tissue organization, which can be predicted to be useful in achieving better knowledge of cell guidance in tissue-engineered constructs and a higher quality of tissue technology products.
空间转录组学、蛋白质组学和表观基因组学是创新技术,在理解和解释细胞功能及相互作用方面提供了无与伦比的分辨率和丰富的数据。这些技术使研究人员能够在单个细胞水平上研究基因和蛋白质表达,揭示例如生物工程组织内的细胞异质性,并对可能对组织功能和疾病过程至关重要的新型和稀有细胞群体进行分类。能够同时分析数千个细胞,从而深入了解复杂组织的转录组情况。空间转录组学将基因表达与空间信息相结合,保留组织结构并使跨不同组织区域的基因活性图谱成为可能。尽管这些技术最近取得了进展,但它们仍面临某些局限性。单细胞转录组学可能会受到技术噪声和数据丢失事件的影响,导致数据不完整。尽管用于数据解释的分辨率和工具正在迅速发展,但其适用性一直受到数据整合和解释复杂性的限制。空间蛋白质组学和空间表观基因组学分别提供有关组织中蛋白质分布和基因调控方面的数据。这些方法的缺点包括分析成本相当高且耗时。然而,将这些技术结合起来有望更全面地了解细胞功能和组织组织,预计这将有助于更好地了解组织工程构建体中的细胞引导以及提高组织技术产品的质量。