New LC–MS/MS Data Analysis Framework, MCnebula, Streamlines Untargeted Analysis
Researchers have introduced MCnebula, a new framework for untargeted LC–MS/MS data analysis. MCnebula focuses on critical chemical classes and utilizes visualization techniques to streamline the analysis process, enabling efficient pathway analysis, biomarker discovery, and classification of unknown compounds beyond the limits of spectral libraries.
Researchers from Zhejiang Chinese Medical University in Hangzhou, China, have introduced a new framework, Multi-Chemical nebula (MCnebula), designed to enhance the analysis of untargeted liquid chromatography-tandem mass spectrometry (LC–MS/MS) data. A research article, published in Analytical Chemistry, describes how MCnebula focuses on critical chemical classes and employs visualization techniques to streamline the analysis process, facilitating pathway analysis, biomarker discovery, and classification of unknown compounds (1).
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Untargeted mass spectrometry is a powerful tool for biological research, but its data analysis process can be time-consuming, especially in the context of system biology. To address this challenge, the researchers developed MCnebula, a comprehensive framework aimed at improving the efficiency and effectiveness of LC–MS data analysis.
MCnebula consists of three key steps. First, an abundance-based classes (ABC) selection algorithm identifies critical chemical classes by considering the abundance of compounds. Next, these critical chemical classes are employed to classify "features," which correspond to individual compounds in the dataset. Finally, the framework employs visualization techniques using multiple Child-Nebulae, represented as network graphs, which include annotations, chemical classifications, and structural information.
One notable advantage of MCnebula is its ability to explore the classification and structural characteristics of unknown compounds, going beyond the limitations of spectral libraries. This feature makes it intuitive and convenient for pathway analysis and biomarker discovery. MCnebula was implemented in the R language and offers a range of tools in R packages to facilitate downstream analysis, including feature selection, homology tracing, pathway enrichment analysis, heat map clustering analysis, spectral visualization, chemical information query, and generation of analysis reports.
To demonstrate the broad utility of MCnebula, the researchers applied it to a human-derived serum dataset for metabolomics analysis. The results successfully identified "Acyl carnitines" as biomarkers through the structural classification of compounds, aligning with the reference data. Additionally, the framework was used to rapidly annotate and discover compounds in E. ulmoides, a plant-derived dataset.
With its ability to streamline and enhance the analysis of untargeted LC–MS/MS data, MCnebula represents a valuable tool for researchers in the field of metabolomics. By providing a comprehensive framework that integrates critical chemical classes and visualization, MCnebula simplifies the identification of compounds, facilitates pathway analysis, and accelerates biomarker discovery, contributing to advancements in systems biology and the understanding of complex biological systems.
Reference
(1) Huang, L.; Shan, Q.; Lyu, Q.; Zhang, S.; Wang, L.; Cao, G. MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC–MS/MS Data Analysis. Anal. Chem. 2023. DOI: https://doi.org/10.1021/acs.analchem.3c01072
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