AI Article Synopsis
- Mass spectrometry (MS) is a popular analytical tool used for studying biomolecules because of its sensitivity and quantitative capabilities, with single cell mass spectrometry (SCMS) allowing for detailed analysis of individual cells.
- Traditional software for SCMS data processing has limitations, such as inadequate peak alignment and background removal, making it less effective for current MS applications.
- To address these issues, a new Python platform called MassLite has been developed, featuring user-friendly tools for data pretreatment, including an innovative peak alignment method and additional functions like void scan filtering and advanced background removal for better analysis efficiency.
Article Abstract
Mass spectrometry (MS) has been one of the most widely used tools for bioanalytical analysis due to its high sensitivity, capability of quantitative analysis, and compatibility with biomolecules. Among various MS techniques, single cell mass spectrometry (SCMS) is an advanced approach to molecular analysis of cellular contents in individual cells. In tandem with the creation of novel experimental techniques, the development of new SCMS data analysis tools is equally important. As most published software packages are not specifically designed for pretreatment of SCMS data, including peak alignment and background removal, their applicability on processing SCMS data is generally limited. Hereby we introduce a Python platform, MassLite, specifically designed for rapid SCMS metabolomics data pretreatment. This platform is made user-friendly with graphical user interface (GUI) and exports data in the forms of each individual cell for further analysis. A core function of this tool is to use a novel peak alignment method that avoids the intrinsic drawbacks of traditional binning method, allowing for more effective handling of MS data obtained from high resolution mass spectrometers. Other functions, such as void scan filtering, dynamic grouping, and advanced background removal, are also implemented in this tool to improve pretreatment efficiency.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11462640 | PMC |
| http://dx.doi.org/10.1016/j.aca.2024.343124 | DOI Listing |
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