由尖端数据处理管道和生物信息学工具实现的人类疾病诊断的 MALDI-ToF 质谱表型分析

Title:MALDI-ToF Mass Spectra Phenomic Analysis for Human Disease Diagnosis Enabled by Cutting-Edge Data Processing Pipelines and Bioinformatics Tools

Volume: 28 Issue: 32

关键词: 质谱、MALDI ToF、表型组学、机器学习、生物信息学、预测建模。

摘要: Current methods for diagnosing human disease are still incapable of rapidly and accurately screening for multiple diseases simultaneously on a large scale, and at an affordable price. MALDI-ToF mass spectrometry is an ultra-sensitive, ultra-fast, lowcost, high-throughput technology that has the potential to achieve this goal, allowing human phenotype characterization and thus phenomic screening for multiple disease states. In this review, we will discuss the main advances achieved so far, putting forward targeted applications of MALDI-ToF mass spectrometry in the service of human disease detection. This review focuses on the methodological workflow as MALDI-ToF data processing for phenomic analysis, using state-of-the-art bioinformatic pipelines and software tools. The role of mathematical modelling, machine learning, and artificial intelligence algorithms for disease screening are considered. Moreover, we present some previously developed tools for disease diagnostics and screening based on MALDI-ToF analysis. We discuss the remaining challenges that are ahead when implementing MALDI-ToF into clinical laboratories. Differentiating a standard profile from a single disease phenotype is challenging, but the potential to simultaneously run multiple algorithm screens for different disease phenotypes may only be limited by computing power once this initial hurdle is overcome. The ability to explore the full potential of human clinical phenomics may be closer than imagined; this review gives an insight into the benefits this technology may reap for the future of clinical diagnostics.

Cite this article as:

MALDI-ToF Mass Spectra Phenomic Analysis for Human Disease Diagnosis Enabled by Cutting-Edge Data Processing Pipelines and Bioinformatics Tools, Current Medicinal Chemistry 2021; 28 (32) . https://dx.doi.org/10.2174/0929867327666201027154257