top of page
Search

Metabolomics-based In Vitro Cancer Screening by Ambient Ionization Mass Spectrometry


Author: Xiaowei Song, Ph.D., Stanford University, Department of Chemistry


Editor: Dan Wang, PhD (CC, TC), Medical Director of Clinical Chemistry, Akron Children’s; Assistant Professor, Northeast Ohio Medical University  


Electrospray Ionization is a soft ionization technique widely used in direct infusion mass spectrometry and liquid chromatography-mass spectrometry (LC-MS), revolutionizing the application of MS in metabolomics, drug metabolism, and proteomics. Unfortunately, this technique is not compatible with raw biospecimen such as biofluids, tissues, or cells. Laborious pretreatment steps—such as extraction, purification, and enrichment of target molecules—are required before final injection into an LC-MS system. These time-consuming processes, along with long chromatographic analysis cycles, are major issues of inter-batch variation effects when conducting large-scale metabolomics assay in clinical settings. It also represents a key technical bottleneck limiting the broader application of LC-MS-based metabolomics technologies in high-throughput clinical diagnostics.


The concept of in situ sampling/ambient ionization mass spectrometry (AIMS) aims to simply the entire workflow—biospecimen collection, pretreatment, data recording, and result feedback—so that the detection of metabolic biomarkers in biological samples can be performed instantly in clinics (1). Driven by this vision, the conductive polymer spray ionization mass spectrometry (CPSI-MS) method was established, which took carbon nanotube-doped polymethyl methacrylate (PMMA) composite as the direct ionization supporting material.


This in-situ ionization approach enables direct mass spectrometric profiling of trace biological fluid samples. Applicable biological samples include serum, plasma, saliva, sweat, tears, biopsy tissue smears, urine, and cell culture suspensions. The detectable metabolites cover a wide range, including amino acids, organic acids, carbohydrates, fatty acids, polyamines, nucleotides, glycerides, cholesterol esters, phospholipids, and sphingolipids. The entire sample data acquisition process is fast and simple, requiring only a 10-second acquisition time and as little as 5 μL of spray solvent. Over 800 endogenous small-molecule metabolites, spanning more than 40 metabolic pathways, can be directly desorbed and ionized from as little as 1 μL of dried biological fluid spots. This eliminates the need for complex and time-consuming pretreatment procedures, such as extraction, purification, and enrichment prior to LC-MS data acquisition. This method greatly simplifies the metabolomic data acquisition and analysis process for intact biological samples and enables rapid profiling or biomarker detection of at least 1,000 samples per day. This presents significant application potential for scenarios requiring fast analytical feedback, such as clinical in vitro diagnostics, pathological tissue biopsy, therapeutic drug monitoring, and forensic testing (2).


The researcher then applied CPSI-MS to conduct rapid screening of oral squamous cell carcinoma (OSCC). Salivary metabolomics were collected from 373 volunteers consisting of healthy control, premalignant lesion, and OSCC patients. By deploying a machine learning model on the MATLAB platform, the team achieved automated extraction of biomarker information and real-time prediction feedback from saliva samples, offering a cost-effective approach for the non-invasive OSCC screening (3). The accuracy and reliability of the CPSI-MS method in clinical cancer metabolomic screening and diagnostics were further validated through a large cohort of 819 serum samples (control: n=241; oral squamous cell carcinoma: n=578), providing effective cross-validation of the technique (4).


Figure. Diagram of CPSI-MS method workflow and in vitro OSCC screening performance. HC: healthy control, PML: premalignant lesion; OSCC: oral squamous cell carcinoma; CPSI-MS: conductive polymer spray ionization mass spectrometry.


图注:导电聚合物电离质谱流程及口腔鳞癌体外筛查。HC:健康对照;PML:癌前病变;OSCC:口腔鳞细胞癌;CPSI-MS: 导电聚合物电离质谱

 



基于原位电离质谱代谢组的体外癌症筛查

作者: 宋肖炜,博士,研究科学家,斯坦福大学化学系

电喷雾电离(Electrospray Ionization, ESI)作为一种广泛应用于直接注入质谱或液质联用分析(LC-MS)的软电离技术,革命性地推动了质谱在代谢组学、药物代谢、蛋白质组学等生物医药领域的广泛应用。然而,该方法对生物样品的纯净度要求较高,无论是体液、组织还是细胞样品,均需经过复杂且耗时的前处理步骤,包括目标代谢物的提取、纯化和富集,才能进入LC-MS系统进行分析。这一繁琐的前处理过程以及较长的色谱分析周期,是导致临床代谢组学大规模队列数据采集中出现显著批次效应的重要原因,同时也成为限制基于LC-MS的代谢组学技术在临床诊断高通量检测中广泛应用的主要技术瓶颈。


基于常压原位电离的直接质谱检测理念,旨在将完整的生物样本采集、前处理、数据记录和结果反馈等环节集成化、自动化,即时完成临床生物样本中的代谢标志物检测[1]。以此愿景为驱动力,制备了碳纳米管掺杂的聚甲基丙烯酸甲酯复合物作为原位电离基底材料,建立了导电聚合物喷雾电离质谱分析方法,该原位电离方法适用于对微量生物体液样品进行代谢轮廓的直接质谱表征。适用的生物样本包括血清、血浆、唾液、汗液、泪液、活检组织涂片、尿液、细胞培养悬液等。可检出代谢物包括氨基酸、有机酸、碳水化合物、脂肪酸、多胺、核苷酸、甘油脂、胆固醇酯、磷脂和鞘脂等。整个样品数据采集流程快速简便,仅需10秒的采集周期和低至5 μL的喷雾溶剂消耗,即可将涵盖40多条代谢通路的800种以上内源性小分子代谢物组分,从低至1μL的干体液斑点基质中直接解吸与电离,免除了对生物体液样本提取、纯化、富集等液质联用数据采集前端复杂、耗时的前处置环节,最大程度地简化了完整生物样本的代谢组数据采集与分析过程,可以实现单日不少于1000例样品的代谢轮廓或代谢标志物群的快速检测,这对一些需要快速反馈分析检测结果的场景,如临床体外诊断、病理组织活检、治疗药物监测、法医学检测等具有重要的应用前景[2]。


作者与南京大学口腔医院合作展开了基于代谢轮廓特征的口腔鳞状细胞癌代谢分子诊断研究,利用CPSI-MS技术对健康志愿者、癌前病变与口腔癌患者中收集373例唾液样本进行了非靶向代谢组学分析,发现了一批以多胺和碱性氨基酸为代表的差异代谢物群,建立了口腔鳞状细胞癌早期诊断的回归预测模型,通过部署在MATLAB 平台上的机器学习模型,实现了唾液样本中代谢标志物信息的自动化提取与即时预测结果反馈,为口腔鳞状细胞癌的无损筛查提供了一种经济有效的方法[3]。作者建立的CPSI-MS方法,其在临床癌症代谢筛查与诊断中的准确性与可靠性,还通过 819 例血清组成的大型队列样本(对照:n=241,口腔鳞癌:n=578)进行了有效交叉验证 [4]。

 

References

1.       Ferreira, C.R., Yannell, K.E., Jarmusch, A.K., Pirro, V., Ouyang, Z., Cooks, R.G., Ambient ionization mass spectrometry for point-of-care diagnostics and other clinical measurements. Clin. Chem., 2016; 62: 99-110.

2.      Song, X., Chen, H., Zare, R.N., Conductive polymer spray ionization mass spectrometry for biofluid analysis. Anal. Chem., 2018; 90: 12878-12885.

3.      Song, X., Yang, X., Narayanan, R., Shankar, V., Ethiraj, S., Wang, X., Duan, N., Ni, Y.H., Hu, Q., Zare, R.N., Oral squamous cell carcinoma diagnosed from saliva metabolic profiling. Proc. Nat. Acad. Sci., 2020; 117: 16167-16173.

4.      Yang, X., Song, X., Yang, X., Han, W., Fu, Y., Wang, S., Zhang, X., Sun, G., Lu, Y., Wang, Z., Ni, Y. Zare, R.N., Hu, Q., 2022. Big cohort metabolomic profiling of serum for oral squamous cell carcinoma screening and diagnosis. Nat. Sci., 2022; 2: p.e20210071. https://onlinelibrary.wiley.com/doi/full/10.1002/ntls.20210071

 
 
 

Comments


bottom of page