DetectTLC: Automated Reaction Mixture Screening Utilizing Quantitative Mass Spectrometry Image Feature

Full characterization of complex reaction mixtures is necessary to understand mechanisms, optimize yields, and elucidate secondary reaction pathways. Molecular-level information for species in such mixtures can be readily obtained by coupling mass spectrometry imaging (MSI) with thin layer chromatog...

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Veröffentlicht in:Journal of the American Society for Mass Spectrometry 2015-10, Vol.27 (2), p.359-365
Hauptverfasser: Kaddi, Chanchala D., Bennett, Rachel V., Paine, Martin R. L., Banks, Mitchel D., Weber, Arthur L., Fernández, Facundo M., Wang, May D.
Format: Artikel
Sprache:eng
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Zusammenfassung:Full characterization of complex reaction mixtures is necessary to understand mechanisms, optimize yields, and elucidate secondary reaction pathways. Molecular-level information for species in such mixtures can be readily obtained by coupling mass spectrometry imaging (MSI) with thin layer chromatography (TLC) separations. User-guided investigation of imaging data for mixture components with known m/z values is generally straightforward; however, spot detection for unknowns is highly tedious, and limits the applicability of MSI in conjunction with TLC. To accelerate imaging data mining, we developed DetectTLC, an approach that automatically identifies m/z values exhibiting TLC spot-like regions in MS molecular images. Furthermore, DetectTLC can also spatially match m/z values for spots acquired during alternating high and low collision-energy scans, pairing product ions with precursors to enhance structural identification. As an example, DetectTLC is applied to the identification and structural confirmation of unknown, yet significant, products of abiotic pyrazinone and aminopyrazine nucleoside analog synthesis.
ISSN:1044-0305
1879-1123
DOI:10.1007/s13361-015-1293-9