Institute of Biotechnology, Life Sciences Center, Vilnius University, Sauletekio Av. 7, Vilnius LT-10257, Lithuania.
Institute of Biotechnology, Life Sciences Center, Vilnius University, Sauletekio Av. 7, Vilnius LT-10257, Lithuania.
Curr Opin Biotechnol. 2019 Feb;55:159-166. doi: 10.1016/j.copbio.2018.09.012. Epub 2018 Oct 24.
Biological samples are inherently heterogeneous and complex. Tackling this complexity requires innovative technological and analytical solutions. Recent advances in high-throughput single-cell isolation and nucleic acid barcoding methods are rapidly changing the technological landscape of biological sciences and now make it possible to measure the (epi)genomic, transcriptomic, or proteomic state of individual cells. In addition, few experimental approaches enable multi-omics measurements of the same cell. However, merging-omics data collected from different experiments remains a considerable challenge. Although several strategies for merging transcriptomics datasets have recently been introduced, cell-to-cell variability and heterogeneity remains one of the confounding factors limiting data fusion and integration. Here, we focus our discussion on the latest single-cell technological and analytical solutions to achieve high data dimensionality and resolution. Obtaining datasets with a wealth of multi-omics information will undoubtedly provide new avenues for researchers to unravel the complexity of biological samples encountered in modern biological research and molecular diagnostics.
生物样本本质上具有异质性和复杂性。要解决这个复杂性，需要创新的技术和分析解决方案。高通量单细胞分离和核酸条形码方法的最新进展正在迅速改变生物科学的技术格局，现在使得测量单个细胞的（表观基因组、转录组或蛋白质组）状态成为可能。此外，很少有实验方法能够对同一细胞进行多组学测量。然而，合并来自不同实验的组学数据仍然是一个相当大的挑战。尽管最近已经提出了几种合并转录组数据集的策略，但细胞间的可变性和异质性仍然是限制数据融合和整合的混杂因素之一。在这里，我们将重点讨论最新的单细胞技术和分析解决方案，以实现高数据维度和分辨率。获得具有丰富多组学信息的数据集无疑将为研究人员提供新的途径，以揭示现代生物学研究和分子诊断中遇到的生物样本的复杂性。
