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

DataSheet1_Metabolic diversity in a collection of wild and cultivated Brassica rapa subspecies.zip

Brassica rapa (B. rapa) and its subspecies contain many bioactive metabolites that are important for plant defense and human health. This study aimed at investigating the metabolite composition and variation among a large collection of B. rapa genotypes, including subspecies and their accessions. Metabolite profiling of leaves of 102 B. rapa genotypes was performed using ultra-performance liquid chromatography coupled with a photodiode array detector and quadrupole time-of-flight mass spectrometry (UPLC-PDA-QTOF-MS/MS). In total, 346 metabolites belonging to different chemical classes were tentatively identified; 36 out of them were assigned with high confidence using authentic standards and 184 were those reported in B. rapa leaves for the first time. The accumulation and variation of metabolites among genotypes were characterized and compared to their phylogenetic distance. We found 47 metabolites, mostly representing anthocyanins, flavonols, and hydroxycinnamic acid derivatives that displayed a significant correlation to the phylogenetic relatedness and determined four major phylometabolic branches; 1) Chinese cabbage, 2) yellow sarson and rapid cycling, 3) the mizuna-komatsuna-turnip-caitai; and 4) a mixed cluster. These metabolites denote the selective pressure on the metabolic network during B. rapa breeding. We present a unique study that combines metabolite profiling data with phylogenetic analysis in a large collection of B. rapa subspecies. We showed how selective breeding utilizes the biochemical potential of wild B. rapa leading to highly diverse metabolic phenotypes. Our work provides the basis for further studies on B. rapa metabolism and nutritional traits improvement.</p

Principal component analysis, component 1 vs. component 2.

<p>The different sample types are depicted as follows: E, extractable fraction; EL, extractable fraction in low concentration; UE, unextractable fraction; FB, fecal background.</p

Occurrence of the benzoxazinoid HBOA-hexose and its deglucosylated form.

<p>Three replicates from each sample type are shown; the symbols are: squares, extractable fraction (E); circles, extractable fraction in lower concentration (EL); triangles, unextractable fraction (UE); and crosses, faecal background (FB) samples. The different time points are represented with different colors: red, 0 h; green, 4 h; turquoise, 12 h; and blue, 48 h.</p

K-means cluster analysis of the cleaned data set (2147 markers).

<p>The different sample types are depicted as follows: E, extractable fraction; EL, extractable fraction in low concentration; UE, unextractable fraction; FB, fecal background, as three replicates in each sampling time (0, 4, 12, 48 h).</p

Identified and some of the most significantly changing unidentified metabolite markers sorted on heat map.

<p>The hierarchical clustering is performed with Pearson correlation. The different sample types are depicted as follows: E, extractable fraction; EL, extractable fraction in low concentration; UE, unextractable fraction; FB, fecal background followed by the sampling time (0, 4, 12, 48 h) and replicate number (1, 2, 3).</p

Occurrence of the identified lignan metabolites in the analysis.

<p>Three replicates from each sample type are shown; the symbols are: squares, extractable fraction (E); circles, extractable fraction in lower concentration (EL); triangles, unextractable fraction (UE); and crosses, faecal background (FB) samples. The different time points are represented with different colors: red, 0 h; green, 4 h; turquoise, 12 h; and blue, 48 h.</p

<i>STK</i> has a pivotal role in the control of PA production.

<p>Schematic representation of the pathways for PA production. The genes involved in these pathways are shown in boxes. <i>BAN</i>, that was found to be up-regulated in RNA-Seq, has been analysed by <i>in situ</i> hybridization, qRT-PCR and ChIP assay. The ChIP assay demonstrated that STK directly regulates <i>BAN</i>, <i>ABS</i> and <i>EGL3</i> (solid line). The ChIP assay revealed that STK function negatively correlates with the level of the H3K9ac mark on the <i>BAN</i> promoter (solid line with nucleosome).</p

<i>stk</i> mutant seeds present defects in seed coat PA accumulation.

<p>(<i>A</i>) Sections of wild-type seeds stained with the toluidine blue O revealed the presence of phenolic compounds in the endothelium (ii1). (<i>B</i>) Scheme of Arabidopsis seed coat anatomy. (<i>C</i>) In the <i>stk</i> mutant phenolic compounds are accumulated in the endothelium (ii1) and also in the second layer of the inner integument (ii2, asterisk). (<i>D</i>) Whole-mount vanillin staining confirmed the presence of PAs in the wild-type and (<i>E</i>) in the <i>stk</i> mutant endothelium. In the <i>stk</i> mutant PAs are also accumulated outside the endothelium in the second layer of the inner integument (asterisk). mi, micropyle; en, endothelium. Scale bars = 30 µm (<i>A–E</i>).</p