Annotation of complex mass spectra by multi-layered analysis.

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Mass Spectrometry

Background:

• Electrospray ionization in mass spectrometry generates complex spectra with multiple ion forms.
• Standard annotation methods struggle with unexpected adducts, in-source fragmentation, and isotopic variations.
• Unidentified ions impact reproducibility, quantification, and data-dependent acquisition (DDA) precursor selection.

Purpose of the Study:

• To develop and present a novel multi-layered approach (MLA) for enhanced small molecule mass spectra annotation.
• To address the challenges of identifying unknown ions and adducts in electrospray ionization mass spectra.
• To improve the accuracy and reproducibility of mass spectral data analysis.

Main Methods:

• A multi-layered approach (MLA) was implemented, involving successive matching of spectra against calculated target ion lists.
• Residual ions were reprocessed after explained ions were removed, allowing focus on unknowns.
• Target ion lists were generated from expected/observed masses, potential adducts, or predefined contaminant lists.

Main Results:

• The MLA successfully identified adducts with Ca, Al, Fe, Ba, and potentially Mg and Sr in spectra of known standards.
• Multiple unknown compounds and adducts were detected in a complex mixture from LC-MS analysis.
• The approach facilitates progressive increase in target list complexity by removing explained ions.

Conclusions:

• The multi-layered approach (MLA) offers a robust method for annotating challenging electrospray mass spectra.
• MLA enhances the identification of unexpected adducts and co-eluting species, improving data interpretation.
• This method increases confidence in spectral annotation and aids in the discovery of novel chemical entities.