Gas-Phase Formation and Fragmentation Reactions of the Organomagnesates [RMgX₂]⁻

A range of mononuclear organomagnesates [RMgX₂]⁻ were generated in the gas phase by decarboxylation of the magnesium carboxylate precursors [RCO₂MgX₂]⁻ (R = Me, Et, Pr, <i>i</i>Pr, <i>t</i>Bu, vinyl, allyl, HCC, Ph, PhCH₂, PhCH₂CH₂; X = Cl, Br, I). The gas-phase formation and unimolecular reactivity of these organomagnesates were examined using a combination of experiments carried out in linear ion trap and triple-quadrupole mass spectrometers and DFT calculations. Halide loss was found to directly compete with decarboxylation in the formation of mononuclear [RMgX₂]⁻. However, sterically unhindered, stable R^– substituents and strong Mg–Cl bonds can be employed to facilitate the decarboxylation reaction at the expense of the halide loss channel. Thus, in the case of R = HCC, PhCH₂, decarboxylation is the main fragmentation pathway. The resultant mononuclear organomagnesates [RMgX₂]⁻ were mass-selected, and their unimolecular chemistry was examined. Four competing fragmentations were observed: bond homolysis, bond heterolysis, halide loss, and β-hydride transfer. Which of these competing reactions dominates depends on the nature of R and X. A conjugatively stabilized R^• allows the observation of [MgX₂]^•–, whereas the presence of a β-hydride generates [HMgX₂]⁻. Weaker Mg–X bonds (e.g., Br and I) promote the formation of X^– upon CID

Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer

Metabolic profiles of biofluids obtained by atmospheric pressure ionization mass spectrometry-based technologies contain hundreds to thousands of features, most of them remaining unknown or at least not characterized in analytical systems. We report here on the annotation of the human adult urinary metabolome and metabolite identification from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics data sets. Features of biological interest were first of all annotated using the ESI-MS database of the laboratory. They were also grouped, thanks to software tools, and annotated using public databases. Metabolite identification was achieved using two complementary approaches: (i) formal identification by matching chromatographic retention times, mass spectra, and also product ion spectra (if required) of metabolites to be characterized in biological data sets to those of reference compounds and (ii) putative identification from biological data thanks to MS/MS experiments for metabolites not available in our chemical library. By these means, 384 metabolites corresponding to 1484 annotated features (659 in negative ion mode and 825 in positive ion mode) were characterized in human urine samples. Of these metabolites, 192 and 66 were formally and putatively identified, respectively, and 54 are reported in human urine for the first time. These lists of features could be used by other laboratories to annotate their ESI-MS metabolomics data sets

Enantioselective Reduction of Noncovalent Complexes of Amino Acids with Cu^II via Resonant Collision-Induced Dissociation: Collision Energy, Activation Duration Effects, and RRKM Modeling

Formation of noncovalent complexes is one of the approaches to perform chiral analysis with mass spectrometry. Enantiomeric distinction of amino acids (AAs) based on the relative rate constants of competitive fragmentations of quaternary copper complexes is an efficient method for chiral differentiation. Here, we studied the complex [CuII,(Phe,PhG,Pro-H)]+ (m/z 493) under resonant collision-induced dissociation conditions while varying the activation time. The precursor ion can yield two main fragments through the loss of the non-natural AA phenylglycine (PhG): the expected product ion [CuII,(Phe,Pro-H)]+ (m/z 342) and the reduced product ion [CuI,(Phe,Pro)]+ (m/z 343). Enantioselective reduction describes the difference in relative abundance of these ions, which depends on the chirality of the precursor ion: the formation of the reduced ion m/z 343 is favored in homochiral complexes (DDD) compared to heterochiral complexes (such as LDD). Energy-resolved mass spectrometry data show that reduction, which arises from rearrangement, is favored at a low collision energy (CE) and long activation time (ActT), whereas direct cleavage preferentially occurs at a high CE and short ActT. These results were confirmed with kinetic modeling based on RRKM theory. For this modeling, it was necessary to set a pre-exponential factor as a reference, so that the E0 values obtained are relative values. Interestingly, these simulations showed that the critical energy E0 required to form the reduced ion is comparable in both homochiral and heterochiral complexes. However, the formation of product ion m/z 342 through direct cleavage is associated with a lower E0 in heterochiral complexes. Consequently, enantioselectivity would not be caused by enhanced reduction in homochiral complexes but rather by direct cleavage being favored in heterochiral complexes

Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer

Metabolic profiles of biofluids obtained by atmospheric pressure ionization mass spectrometry-based technologies contain hundreds to thousands of features, most of them remaining unknown or at least not characterized in analytical systems. We report here on the annotation of the human adult urinary metabolome and metabolite identification from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics data sets. Features of biological interest were first of all annotated using the ESI-MS database of the laboratory. They were also grouped, thanks to software tools, and annotated using public databases. Metabolite identification was achieved using two complementary approaches: (i) formal identification by matching chromatographic retention times, mass spectra, and also product ion spectra (if required) of metabolites to be characterized in biological data sets to those of reference compounds and (ii) putative identification from biological data thanks to MS/MS experiments for metabolites not available in our chemical library. By these means, 384 metabolites corresponding to 1484 annotated features (659 in negative ion mode and 825 in positive ion mode) were characterized in human urine samples. Of these metabolites, 192 and 66 were formally and putatively identified, respectively, and 54 are reported in human urine for the first time. These lists of features could be used by other laboratories to annotate their ESI-MS metabolomics data sets

Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer

Metabolic profiles of biofluids obtained by atmospheric pressure ionization mass spectrometry-based technologies contain hundreds to thousands of features, most of them remaining unknown or at least not characterized in analytical systems. We report here on the annotation of the human adult urinary metabolome and metabolite identification from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics data sets. Features of biological interest were first of all annotated using the ESI-MS database of the laboratory. They were also grouped, thanks to software tools, and annotated using public databases. Metabolite identification was achieved using two complementary approaches: (i) formal identification by matching chromatographic retention times, mass spectra, and also product ion spectra (if required) of metabolites to be characterized in biological data sets to those of reference compounds and (ii) putative identification from biological data thanks to MS/MS experiments for metabolites not available in our chemical library. By these means, 384 metabolites corresponding to 1484 annotated features (659 in negative ion mode and 825 in positive ion mode) were characterized in human urine samples. Of these metabolites, 192 and 66 were formally and putatively identified, respectively, and 54 are reported in human urine for the first time. These lists of features could be used by other laboratories to annotate their ESI-MS metabolomics data sets

Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer

Metabolic profiles of biofluids obtained by atmospheric pressure ionization mass spectrometry-based technologies contain hundreds to thousands of features, most of them remaining unknown or at least not characterized in analytical systems. We report here on the annotation of the human adult urinary metabolome and metabolite identification from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics data sets. Features of biological interest were first of all annotated using the ESI-MS database of the laboratory. They were also grouped, thanks to software tools, and annotated using public databases. Metabolite identification was achieved using two complementary approaches: (i) formal identification by matching chromatographic retention times, mass spectra, and also product ion spectra (if required) of metabolites to be characterized in biological data sets to those of reference compounds and (ii) putative identification from biological data thanks to MS/MS experiments for metabolites not available in our chemical library. By these means, 384 metabolites corresponding to 1484 annotated features (659 in negative ion mode and 825 in positive ion mode) were characterized in human urine samples. Of these metabolites, 192 and 66 were formally and putatively identified, respectively, and 54 are reported in human urine for the first time. These lists of features could be used by other laboratories to annotate their ESI-MS metabolomics data sets

Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer

Metabolic profiles of biofluids obtained by atmospheric pressure ionization mass spectrometry-based technologies contain hundreds to thousands of features, most of them remaining unknown or at least not characterized in analytical systems. We report here on the annotation of the human adult urinary metabolome and metabolite identification from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics data sets. Features of biological interest were first of all annotated using the ESI-MS database of the laboratory. They were also grouped, thanks to software tools, and annotated using public databases. Metabolite identification was achieved using two complementary approaches: (i) formal identification by matching chromatographic retention times, mass spectra, and also product ion spectra (if required) of metabolites to be characterized in biological data sets to those of reference compounds and (ii) putative identification from biological data thanks to MS/MS experiments for metabolites not available in our chemical library. By these means, 384 metabolites corresponding to 1484 annotated features (659 in negative ion mode and 825 in positive ion mode) were characterized in human urine samples. Of these metabolites, 192 and 66 were formally and putatively identified, respectively, and 54 are reported in human urine for the first time. These lists of features could be used by other laboratories to annotate their ESI-MS metabolomics data sets

Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer

Metabolic profiles of biofluids obtained by atmospheric pressure ionization mass spectrometry-based technologies contain hundreds to thousands of features, most of them remaining unknown or at least not characterized in analytical systems. We report here on the annotation of the human adult urinary metabolome and metabolite identification from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics data sets. Features of biological interest were first of all annotated using the ESI-MS database of the laboratory. They were also grouped, thanks to software tools, and annotated using public databases. Metabolite identification was achieved using two complementary approaches: (i) formal identification by matching chromatographic retention times, mass spectra, and also product ion spectra (if required) of metabolites to be characterized in biological data sets to those of reference compounds and (ii) putative identification from biological data thanks to MS/MS experiments for metabolites not available in our chemical library. By these means, 384 metabolites corresponding to 1484 annotated features (659 in negative ion mode and 825 in positive ion mode) were characterized in human urine samples. Of these metabolites, 192 and 66 were formally and putatively identified, respectively, and 54 are reported in human urine for the first time. These lists of features could be used by other laboratories to annotate their ESI-MS metabolomics data sets

Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer

Metabolic profiles of biofluids obtained by atmospheric pressure ionization mass spectrometry-based technologies contain hundreds to thousands of features, most of them remaining unknown or at least not characterized in analytical systems. We report here on the annotation of the human adult urinary metabolome and metabolite identification from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics data sets. Features of biological interest were first of all annotated using the ESI-MS database of the laboratory. They were also grouped, thanks to software tools, and annotated using public databases. Metabolite identification was achieved using two complementary approaches: (i) formal identification by matching chromatographic retention times, mass spectra, and also product ion spectra (if required) of metabolites to be characterized in biological data sets to those of reference compounds and (ii) putative identification from biological data thanks to MS/MS experiments for metabolites not available in our chemical library. By these means, 384 metabolites corresponding to 1484 annotated features (659 in negative ion mode and 825 in positive ion mode) were characterized in human urine samples. Of these metabolites, 192 and 66 were formally and putatively identified, respectively, and 54 are reported in human urine for the first time. These lists of features could be used by other laboratories to annotate their ESI-MS metabolomics data sets

Energy-Resolved Ion Mobility Spectrometry: Composite Breakdown Curves for Distinguishing Isomeric Product Ions

Identification of lipopeptides (LpAA) synthesized from bacteria involves the study of structural characterization. Twenty LpAA have been characterized using commercial tandem high-resolution mass spectrometers in negative electrospray, employing nonresonant excitation in “RF only” collision cells and generally behave identically. However, [LpAA-H]− (AA = Asp or Glu) shows surprising fragmentation pathways, yielding a complementary fatty acid carboxylate and dehydrated amino acid fragment anions. In this study, the dissociation mechanisms of [C12Glu-H]− were determinate using energy-resolved mass spectrometry (ERMS). Product ion breakdown profiles are, generally, unimodal with full width at half-maximum (fwhm) increasing as product ion m/z ratios decrease, except for the two product ions of interest (fatty acid carboxylate and dehydrated glutamate) characterized by broad and composite profiles. Such behavior was already shown for other ions using a custom-built guided ion beam mass spectrometer. In this study, we investigate the meaning of these particular profiles from an ERMS breakdown, using fragmentation mechanisms depending on the collision energy. ERMS on line with ion mobility spectrometry (IMS), here called ER-IMS, provides a way to probe such questions. Broad or composite profiles imply that the corresponding product ions may be generated by two (or more) pathways, resulting in common or isomeric product ion structures. ER-IMS analysis indicates that the fatty acid carboxylate product ion is produced with a common structure through different pathways, while dehydrated glutamate has two isomeric forms depending on the mechanism involved. Drift time values correlate with the calculated collision cross section that confirms the product ion structures and fragmentation mechanisms