2 research outputs found
PICA: Pixel Intensity Correlation Analysis for Deconvolution and Metabolite Identification in Mass Spectrometry Imaging
In-source fragmentation (ISF) is a naturally occurring
phenomenon
in various ion sources including soft ionization techniques such as
matrix-assisted laser desorption/ionization (MALDI). It has traditionally
been minimized as it makes the dataset more complex and often leads
to mis-annotation of metabolites. Here, we introduce an approach termed
PICA (for pixel intensity correlation analysis) that takes advantage
of ISF in MALDI imaging to increase confidence in metabolite identification.
In PICA, the extraction and association of in-source fragments to
their precursor ion results in “pseudo-MS/MS spectra”
that can be used for identification. We examined PICA using three
different datasets, two of which were published previously and included
validated metabolites annotation. We show that highly colocalized
ions possessing Pearson correlation coefficient (PCC) ≥ 0.9
for a given precursor ion are mainly its in-source fragments, natural
isotopes, adduct ions, or multimers. These ions provide rich information
for their precursor ion identification. In addition, our results show
that moderately colocalized ions (PCC < 0.9) may be structurally
related to the precursor ion, which allows for the identification
of unknown metabolites through known ones. Finally, we propose three
strategies to reduce the total computation time for PICA in MALDI
imaging. To conclude, PICA provides an efficient approach to extract
and group ions stemming from the same metabolites in MALDI imaging
and thus allows for high-confidence metabolite identification
PICA: Pixel Intensity Correlation Analysis for Deconvolution and Metabolite Identification in Mass Spectrometry Imaging
In-source fragmentation (ISF) is a naturally occurring
phenomenon
in various ion sources including soft ionization techniques such as
matrix-assisted laser desorption/ionization (MALDI). It has traditionally
been minimized as it makes the dataset more complex and often leads
to mis-annotation of metabolites. Here, we introduce an approach termed
PICA (for pixel intensity correlation analysis) that takes advantage
of ISF in MALDI imaging to increase confidence in metabolite identification.
In PICA, the extraction and association of in-source fragments to
their precursor ion results in “pseudo-MS/MS spectra”
that can be used for identification. We examined PICA using three
different datasets, two of which were published previously and included
validated metabolites annotation. We show that highly colocalized
ions possessing Pearson correlation coefficient (PCC) ≥ 0.9
for a given precursor ion are mainly its in-source fragments, natural
isotopes, adduct ions, or multimers. These ions provide rich information
for their precursor ion identification. In addition, our results show
that moderately colocalized ions (PCC < 0.9) may be structurally
related to the precursor ion, which allows for the identification
of unknown metabolites through known ones. Finally, we propose three
strategies to reduce the total computation time for PICA in MALDI
imaging. To conclude, PICA provides an efficient approach to extract
and group ions stemming from the same metabolites in MALDI imaging
and thus allows for high-confidence metabolite identification