干性血样蛋白质组及代谢组前处理新方法的研究

A NEW METHOD FOR PROTEOME AND METABOLOME PRETREATMENT OF DRY BLOOD SAMPLES

张巧云

11849258

硕士

化学工程领域工程

田瑞军

2020-06-02

2020-07-20

哈尔滨工业大学

深圳

血液中的蛋白质和代谢物是最为重要的生物标记物，并已被广泛用于当前的临床诊断中。近年来血液中的蛋白质和代谢物是最为重要的生物标记物，并已被广泛用于当前的临床诊断中。近年来，随着生物质谱技术的飞跃式发展，基于生物质谱技术的蛋白质组学和代谢组学分析技术已经被广泛地应用于生物标志物的大规模发现和分析中。由于蛋白质和代谢物物化性质的差异，上述两种组学分析一般要采用截然不同的样品前处理步骤、分离系统和质谱检测方法。因此，针对大量临床样品的组学分析往往具有通量低和分析误差难以精确控制的问题。基于上述限制，目前的临床组学往往基于单一组学结果进行生物学和疾病表型分析，容易造成结论单一、有失准确的问题。结合多种大数据类型的多组学分析是近年来一种新兴的分析策略，该策略有助于更深层次和全面地揭示潜在的生物学关系，但也对相关样品前处理和组学分析技术提出了更高的要求。在本研究中，我们设计了一种集成化且操作简便的样品前处理技术，称为Multi-omic-SISPROT。该技术一站式地处理干燥血浆并同时进行代谢组学和蛋白质组学分析。通过结合指尖采血，该方法可以简单、轻松地将1 μL血液样品富集到离心式器件中，并以干血形式保存和室温形式运输样品。该方法可以在3 h内将富集的蛋白质和代谢物组分进行集成化样品前处理，并在2 h内使用同一液相色谱-质谱联用系统进行蛋白质组和代谢组分析，共鉴定超过150种蛋白质和160种代谢物。使用相同的样品前处理装置和分析系统，能够最大程度地减少由分析流程不同产生的代谢组学和蛋白质组学数据偏差。为了进一步提高对低丰度血浆生物标志物的检测能力，我们进一步采用靶向性质谱检测模式MRM对10种已报道的癌症蛋白标志物进行分析，获得了良好的分析信噪比和最大5 pg/mL的检测下限。更为重要的是，富集在该样品前处理器件中的干燥血浆样品在经过24 h的室温保存后，仍保持足够的稳定性，血浆中的蛋白质和代谢物仍能达到良好的鉴定水平。总之，Multi-omic-SISPROT技术巧妙地将微量血液样品的采集、运输、集成化样品前处理和基于同一色谱-质谱联用系统地多组学分析灵活的结合在一起，同时兼顾了血浆蛋白质组和代谢组分析的广度和深度，大大提高了受试者参与度，进而促进蛋白质组学和代谢组学在精准医学中的应用。随着生物质谱技术的飞跃式发展，基于生物质谱技术的蛋白质组学和代谢组学分析技术已经被广泛地应用于生物标志物的大规模发现和分析中。由于蛋白质和代谢物物化性质的差异，上述两种组学分析一般要采用截然不同的样品前处理步骤、分离系统和质谱检测方法。因此，针对大量临床样品的组学分析往往具有通量低和分析误差难以精确控制的问题。基于上述限制，目前的临床组学往往基于单一组学结果进行生物学和疾病表型分析，容易造成结论单一、有失准确的问题。结合多种大数据类型的多组学分析是近年来一种新兴的分析策略，该策略有助于更深层次和全面地揭示潜在的生物学关系，但也对相关样品前处理和组学分析技术提出了更高的要求。

Proteins and metabolites in blood as the most important biomarkers have been broadly applied for clinical diagnosis. With the development of biomass spectrometry, proteomics and metabolomics analysis technology MS-based has been widely used in the large-scale analysis of biomarkers. Generally, proteomic and metabolomic analyses need to adopt different sample preparation steps, separation systems and mass spectrometry methods due to the differences in physico-chemical properties. Therefore, the omics analysis of a large number of clinical samples often has the problems of low throughput and difficult to control. Due to these limitation, the current clinical omics analysis is often based on the results of a single cohort for biological and disease phenotype analysis, which is prone to single conclusion and inaccurate. In recent years, multi-omics analysis combined with various big data types is a new strategy. This strategy helps to reveal the biological relationship in a deeper way, but also places higher demands on sample preparation and analysis techniques. In this study, we designed an integrated and simple sample preparation technology called Multi-omic-SISPROT. This technique treats dried plasma one-stop and simultaneously performs metabolomic and proteomic analyses. (1) Combining fingertip blood sampling, this method can easily enrich 1 μL blood samples into centrifugal devices of dry blood in room temperature. (2) The method can integrate protein and metabolite for sample pretreatment in 3 h and analyze them using the same LC-MS/MS in 2 h, identifying more than 150 proteins and 160 metabolites. Using the same sample preparation device and analysis system can minimize the data deviation caused by different processes. In order to improve the detection ability of low abundance biomarkers, we further analyzed 10 reported cancer protein biomarkers using the targeted property spectrum detection mode MRM, and obtained a good signal-to-noise ratio and a maximum detection threshold of 5 pg/mL. (3) Moreover, the dried plasma samples enriched in the device could still reach a good identification level after being stored at room temperature for 24 h. Anyhow, Multi-omic-SISPROT technology will trace blood sample collection, transportation and integrated sample preparation and based on the same LC-MS system to multiple omics analysis together. The breadth and depth of plasma proteome and metabolome analysis are considered. This greatly improves subject participation and promotes the application of proteomics and metabolomics in precision medicine.

蛋白质组学 代谢组学 LC-MS/MS 蛋白质组学样品前处理 SISPROT技术

proteomics metabolomics LC-MS/MS proteomics sample preparation SISPROT

中文

联合培养

南方科技大学

张巧云. 干性血样蛋白质组及代谢组前处理新方法的研究[D]. 深圳. 哈尔滨工业大学,2020.