Jiang Will, Jones Jennifer C, Shankavaram Uma, Sproull Mary, Camphausen Kevin, Krauze Andra V
Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Translational Nanobiology Section, Laboratory of Pathology, NIH/NCI/CCR, Bethesda, MD 20892, USA.
Cancers (Basel). 2022 Apr 29;14(9):2227. doi: 10.3390/cancers14092227.
The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.
适体技术的发展与进步为揭示蛋白质组学中存在的生物复杂性开辟了一个全新的可能性领域。凭借超高通量和多重分析能力,以及卓越的特异性和灵敏度,适体可能成为疾病特异性研究中的强大工具,比如支持临床相关生物标志物的发现与验证。过去和当前蛋白质组学技术面临的一个根本挑战一直是将蛋白质组学数据集转化为实践标准的困难。适体能够从单个样本生成涵盖7000多种不同蛋白质的单一组。然而,作为一项新兴技术,它们也带来了独特的挑战,因为基于适体的转化蛋白质组学领域仍缺乏用于分析这些大型数据集的标准化方法,以及针对当前蛋白质组学平台和适体之间差异必须做出的新考量。我们针对调查初始数据、运用适当的统计方法识别差异蛋白质表达以及应用数据集发现多标志物和通路水平的结果等方面,探讨了这些分析考量因素。此外,我们通过探索基于适体的蛋白质组学在基因组学、转录组学和代谢组学以及现有蛋白质组学平台之间的交叉性,在多组学格局中展示了适体数据集。了解适体数据集的更广泛应用将极大地加强当前为临床生成可转化研究结果所做的努力。