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

Sequential Application of Ligand and Structure Based Modeling Approaches to Index Chemicals for Their hH₄R Antagonism

<div><p>The human histamine H₄ receptor (hH₄R), a member of the G-protein coupled receptors (GPCR) family, is an increasingly attractive drug target. It plays a key role in many cell pathways and many hH₄R ligands are studied for the treatment of several inflammatory, allergic and autoimmune disorders, as well as for analgesic activity. Due to the challenging difficulties in the experimental elucidation of hH₄R structure, virtual screening campaigns are normally run on homology based models. However, a wealth of information about the chemical properties of GPCR ligands has also accumulated over the last few years and an appropriate combination of these ligand-based knowledge with structure-based molecular modeling studies emerges as a promising strategy for computer-assisted drug design. Here, two chemoinformatics techniques, the Intelligent Learning Engine (ILE) and Iterative Stochastic Elimination (ISE) approach, were used to index chemicals for their hH₄R bioactivity. An application of the prediction model on external test set composed of more than 160 hH₄R antagonists picked from the chEMBL database gave enrichment factor of 16.4. A virtual high throughput screening on ZINC database was carried out, picking ∼4000 chemicals highly indexed as H₄R antagonists' candidates. Next, a series of 3D models of hH₄R were generated by molecular modeling and molecular dynamics simulations performed in fully atomistic lipid membranes. The efficacy of the hH₄R 3D models in discrimination between actives and non-actives were checked and the 3D model with the best performance was chosen for further docking studies performed on the focused library. The output of these docking studies was a consensus library of 11 highly active scored drug candidates. Our findings suggest that a sequential combination of ligand-based chemoinformatics approaches with structure-based ones has the potential to improve the success rate in discovering new biologically active GPCR drugs and increase the enrichment factors in a synergistic manner.</p></div

Docking results and enrichment factors for the chEMBL hH₄R antagonists (external data set) and the focused library data sets.

<p>Docking results and enrichment factors for the chEMBL hH₄R antagonists (external data set) and the focused library data sets.</p

One of the binding poses of one of the top scored candidates.

<p>It interacts with both residues, D^3.32 and E^5.46 of the H₄ receptor binding pocket, via salt bridge interactions.</p

Computed electrostatic energy (EE) of the test set compounds docking simulations across three model versions (namely: the homology model, min-opt and MD-opt models). For the sake of clarity, only the best docked pose was kept for each compound, to maintain clarity.

<p>Open circles denote compounds with known binding activity (i.e. our positive control group). Closed circles denote random compounds (i.e. non-binders, negative control group). Colors denote group membership by K-means clustering. The plots show a distinct separation between two compound groups around −1.1 Kcal/mol and yet another one around −4 Kcal/mol. This pattern is observed across the model versions, with slightly lower EE values visible in the optimized models.</p

Cartoon representations of the four hH4R models here studied: i) homology model (yellow), equilibrated model (blue), with restrained ligand (green), with unrestrained ligand (red). Residues Asp94 (D3.32) and Glu82 (E5.46) are evidenced as sticks.

<p>Cartoon representations of the four hH4R models here studied: i) homology model (yellow), equilibrated model (blue), with restrained ligand (green), with unrestrained ligand (red). Residues Asp94 (D3.32) and Glu82 (E5.46) are evidenced as sticks.</p

Enrichment plot (a) and Enrichment plot of the highest indexed 1% chemicals (b).

<p>Enrichment plot (a) and Enrichment plot of the highest indexed 1% chemicals (b).</p

Time evolution of hH₄R secondary structure content during equilibration in the absence (panel A) and in the presence (panel B) of the ligand JNJ7777120.

<p>Panel C reports the time evolution of the secondary structure of hH₄R in the presence of the ligand restrained as described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109340#s2" target="_blank">methods</a> section. Color codes are: red/helix, green/coils and blue/turns.</p

RMSD for backbone atoms of hH4R in membrane environment (panel A); with restrained ligand (panel B) and with unrestrained ligand (panel C).

<p>RMSD has been calculated considering starting frame as reference.</p