Alkanes and [B12X12]2− (X = Cl, Br) are both stable compounds which are difficult to functionalize. Here we demonstrate the formation of a boron−carbon bond between these substances in a two-step process. Fragmentation of [B12X12]2− in the gas phase generates highly reactive [B12X11]− ions which spontaneously react with alkanes. The reaction mechanism was investigated using tandem mass spectrometry and gas-phase vibrational spectroscopy combined with electronic structure calculations. [B12X11]− reacts by an electrophilic substitution of a proton in an alkane resulting in a B−C bond formation. The product is a dianionic [B12X11CnH2n+1]2− species, to which H+ is electrostatically bound. High-flux ion soft landing was performed to codeposit [B12X11]− and complex organic molecules (phthalates) in thin layers on surfaces. Molecular structure analysis of the product films revealed that C−H functionalization by [B12X11]− occurred in the presence of other more reactive functional groups. This observation demonstrates the utility of highly reactive fragment ions for selective bond formation processes and may pave the way for the use of gas-phase ion chemistry for the generation of complex molecular structures in the condensed phase.

Seven synthesized G-lignin oligomer model compounds (ranging in size from dimers to an octamer) with 5-5 and/or β-O-4 linkages, and three synthesized S-lignin model compounds (a dimer, trimer, and tetramer) with β-O-4 linkages, were evaporated and deprotonated using negative-ion mode ESI in a linear quadrupole ion trap/Fourier transform ion cyclotron resonance mass spectrometer. The collision-activated dissociation (CAD) fragmentation patterns (obtained in MS and MS experiments, respectively) for the negative ions were studied to develop a procedure for sequencing unknown lignin oligomers. On the basis of the observed fragmentation patterns, the measured elemental compositions of the most abundant fragment ions, and quantum chemical calculations, the most important reaction pathways and likely mechanisms were delineated. Many of these reactions occur via charge-remote fragmentation mechanisms. Deprotonated compounds with only β-O-4 linkages, or both 5-5 and β-O-4 linkages, showed major 1,2-eliminations of neutral compounds containing one, two, or three aromatic rings. The most likely mechanisms for these reactions are charge-remote Maccoll and retro-ene eliminations resulting in the cleavage of a β-O-4 linkage. Facile losses of HO and CHO were also observed for all deprotonated model compounds, which involve a previously published charge-driven mechanism. Characteristic "ion groups" and "key ions" were identified that, when combined with their CAD products (MS experiments), can be used to sequence unknown oligomers.

The reactivity of a carbon‐centered σ,σ,σ,σ‐type singlet‐ground‐state tetraradical containing two meta‐benzyne moieties was examined in the gas phase. Surprisingly, the tetraradical showed higher reactivity than its individual meta‐benzyne counterparts. The reactivity of meta‐benzynes is controlled by their (calculated) distortion energy ΔE2.3, singlet–triplet spitting ΔES–T, and electron affinity (EA2.3) of the meta‐benzyne moiety at the transition state geometry for hydrogen‐atom abstraction reactions. The addition of a second meta‐benzyne moiety to a meta‐benzyne does not significantly change EA2.3. However, ΔE2.3 is substantially decreased for both meta‐benzyne moieties in the tetraradical, and this explains their higher reactivities. The decrease in ΔE2.3 for each meta‐benzyne moiety in the tetraradical is rationalized by stabilizing spin–spin coupling between one radical site in each meta‐benzyne moiety. Therefore, spin–spin coupling between the meta‐benzyne moieties in this tetraradical increases its reactivity, whereas spin–spin coupling within each meta‐benzyne moiety decreases its reactivity.

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in‐depth understanding of cancer‐associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three‐dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI‐based cancer studies. The adoption of MSI in clinical research and for single‐cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

The dismally low survival rate of ovarian cancer patients diagnosed with high-grade serous carcinoma (HGSC) emphasizes the lack of effective screening strategies. One major obstacle is the limited knowledge of the underlying mechanisms of HGSC pathogenesis at very early stages. Here, we present the first 10-month time-resolved serum metabolic profile of a triple mutant (TKO) HGSC mouse model, along with the spatial lipidome profile of its entire reproductive system. A high-coverage liquid chromatography mass spectrometry-based metabolomics approach was applied to longitudinally collected serum samples from both TKO (n = 15) and TKO control mice (n = 15), tracking metabolome and lipidome changes from premalignant stages to tumor initiation, early stages, and advanced stages until mouse death. Time-resolved analysis showed specific temporal trends for 17 lipid classes, amino acids, and TCA cycle metabolites, associated with HGSC progression. Spatial lipid distributions within the reproductive system were also mapped via ultrahigh-resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry and compared with serum lipid profiles for various lipid classes. Altogether, our results show that the remodeling of lipid and fatty acid metabolism, amino acid biosynthesis, TCA cycle and ovarian steroidogenesis are critical components of HGSC onset and development. These metabolic alterations are accompanied by changes in energy metabolism, mitochondrial and peroxisomal function, redox homeostasis, and inflammatory response, collectively supporting tumorigenesis.