Heavy Water Microdroplet Surface Enriches the Lighter Isotopologue Impurities.

Water microdroplets promote unusual chemical reactions at the air-water interface. However, the interfacial structure of water microdroplets and its potential influence on chemical processes are still enigmatic. Here, we present evidence of in-droplet fractionation of water isotopologues. Employing a sonic spray, we atomized the heavy water (D2O, 99.9 atom % D) solution of three classes of organic compounds (basic, acidic, and neutral). The analytes were predominantly desorbed from the resulting droplet surface in protonated form rather than deuterated form, as detected by mass spectrometry. This result remained unaltered upon adding formic acid-d2 (DCOOD) to the droplet. Monitoring Dakin oxidation of benzaldehyde at the surface of binary microdroplets composed of 1:1 (v/v) D2O/H218O revealed the preferred formation of phenolate-16O over phenolate-18O. Atmospheric pressure chemical ionization mass spectrometric analysis of the vapor composition in the sprayed aerosol revealed the preferential evaporation of lighter water isotopologue impurities from the surface of heavy water microdroplets. These results indicate the enrichment of lighter water isotopologue impurities (HOD/H2O) on the surface of heavy water microdroplets, implying possible future developments for water isotopologue fractionation using microdroplets.

Water Microdroplets in Air: A Hitherto Unnoticed Natural Source of Nitrogen Oxides.

Water microdroplets are widespread in the atmosphere. We report a striking observation that micron-sized water droplets obtained from zero-volt spray sources (sonic spray, humidifier, spray bottle, steamer, etc.) spontaneously generate nitrogen oxides. The mechanistic investigation through the development of custom-designed sampling sources combined with mass spectrometry and isotope labeling experiments confirmed that air nitrogen reacts with the water at the air-water interface, fixing molecular nitrogen to its oxides (NO, NO2, and N2O) and acids (HNO2 and HNO3) at trace levels without any catalyst. These reactions are attributed to the consequence of an experimentally detected feeble corona discharge (breakdown of air) at the air-water interface, likely driven by the high intrinsic electric field at the surface of water microdroplets. The extent of this corona discharge effect varies depending on the pH, salinity/impurity, size, speed, and lifetime of microdroplets in the air. Thus, this study discloses that the air-water interface of microdroplets breaks the strong chemical bond of nitrogen (N2), producing nitrogen oxides in the environment, while lightning strikes and microbial processes in soil are considered their dominant natural sources. As nitrogen oxides are toxic air pollutants, their spontaneous formation at the air-water interface should have important implications in atmospheric reactions, requiring further investigations.

Mass Spectrometric Imaging of Anionic Phospholipids Desorbed from Human Hippocampal Sections: Discrimination between Temporal and Nontemporal Lobe Epilepsies.

Temporal lobe epilepsy (TLE) is one of the most common neurological disorders, often accompanied by hippocampal sclerosis. The molecular processes underlying this epileptogenesis are poorly understood. To examine the lipid profile, 39 fresh frozen sections of the human hippocampus obtained from epilepsy surgery for TLE (n = 14) and non-TLE (control group; n = 25) patients were subjected to desorption electrospray ionization mass spectrometry imaging in the negative ion mode. In contrast to our earlier report that showed striking downregulation of positively charged phospholipids (e.g., phosphatidylcholine and phosphatidylethanolamine, etc.) in the TLE hippocampus, this study finds complementary upregulation of negatively charged phospholipids, notably, phosphatidylserine and phosphatidylglycerol. This result may point to an active metabolic pool in the TLE hippocampus that produces these anionic phospholipids at the expense of the cationic phospholipids. This metabolic shift could be due to the dysregulation of the Kennedy and CDP-DG pathways responsible for biosynthesizing these lipids. Thus, this study further opens up opportunities to investigate the molecular hallmarks and potential therapeutic targets for TLE.

Carbocation Mechanism Revelation of Molecular Iodine-Mediated Dehydrogenative Aromatization of Substituted Cyclic Ketones to Phenols.

Dehydrogenative aromatization (DA) of cyclic ketones is central to the development of functionalized aromatic precursors and hydrogen transfer-related technologies. Traditional DA strategies require precious metals with oxidants and are typically performed at high temperatures (100-150 °C) to overcome the high energy barrier of aliphatic C-H bond activation. Recently, a mild alternative approach based on I2 has been proposed to realize DA on substituted unsaturated cyclic ketones under ambient conditions. However, depending on the solvent, the product selectivity may vary between phenol ether and phenol, and the reaction mechanisms remain unclear. Herein, based on time-resolved proton nuclear magnetic resonance, DFT calculation, and mass spectrometric analyses, we established a unified mechanism to account for the product distribution. Through substrate scope and desorption electrospray ionization-mass spectrometry, we discovered the formation of a carbocation, which has been overlooked in previous studies. An expanded substrate scope study coupled with spectroscopic observation provided strong evidence to elucidate the formation mechanism and the location of the carbocation. With a renewed understanding of the mechanism, we achieved a phenolic product yield of 17-96% while controlling the selectivity. Moreover, some reactants could undergo DA in H2O, achieving 95-96% yield at below water-boiling temperature.

Spontaneous Generation of Aryl Carbocations from Phenols in Aqueous Microdroplets: Aromatic SN1 Reactions at the Air-Water Interface.

Although phenol is stable in bulk water, we report an exceptional phenomenon in which phenol is spontaneously transformed into a phenyl carbocation (Ph+) in water microdroplets. The high electric field at the air-water interface is proposed to break the phenolic Csp2-OH bond, forming Ph+, which remains in equilibrium with phenol as deciphered by mass spectrometry. We detected up to 70% conversion of phenol to Ph+ in aqueous microdroplets, although catalyst-free activation of the phenolic Csp2-OH bond is challenging. This transformation is well tolerated by a wide range of electron-donating and -withdrawing substituents in phenolic compounds. The Ph+ in water microdroplets could be reacted with various nucleophiles (amine, pyridine, azide, thiol, carboxylic acid, alcohol, and 18O-water), yielding the ipso-substitution products of phenol through an aromatic SN1 mechanism. Despite the fleeting life of Ph+ in the bulk, this study demonstrates its unusual stability at the aqueous microdroplet surface, enabling its detection and transformation.

Mass Spectrometry Imaging of Lumpectomy Specimens Deciphers Diacylglycerols as Potent Biomarkers for the Diagnosis of Breast Cancer.

Detecting breast tumor markers with a fast turnaround time from frozen sections should foster intraoperative histopathology in breast-conserving surgery, reducing the need for a second operation. Hence, rapid label-free discrimination of the spatially resolved molecular makeup between cancer and adjacent normal breast tissue is of growing importance. We performed desorption electrospray ionization mass spectrometry imaging (DESI-MSI) of fresh-frozen excision specimens, including cancer and paired adjacent normal sections, obtained from the lumpectomy of 73 breast cancer patients. The results demonstrate that breast cancer tissue posits sharp metabolic upregulation of diacylglycerol, a lipid second messenger that activates protein kinase C for promoting tumor growth. We identified four specific sn-1,2-diacylglycerols that outperformed all other lipids simultaneously mapped by the positive ion mode DESI-MSI for distinguishing cancers from adjacent normal specimens. This result contrasts with several previous DESI-MSI studies that probed metabolic dysregulation of glycerophospholipids, sphingolipids, and free fatty acids for cancer diagnoses. A random forest-based supervised machine learning considering all detected ion signals also deciphered the highest diagnostic potential of these four diacylglycerols with the top four importance scores. This led us to construct a classifier with 100% overall prediction accuracy of breast cancer by using the parsimonious set of four diacylglycerol biomarkers only. The metabolic pathway analysis suggested that increased catabolism of phosphatidylcholine in breast cancer contributes to diacylglycerol overexpression. These results open up opportunities for mapping diacylglycerol signaling in breast cancer in the context of novel therapeutic and diagnostic developments, including the intraoperative assessment of breast cancer margin status.

Rapid Molecular Evaluation of Human Kidney Tissue Sections by In Situ Mass Spectrometry and Machine Learning to Classify the Nephrotic Syndrome.

Nephrotic syndrome (NS) is classified based on morphological changes of glomeruli in biopsied kidney tissues evaluated by time-consuming microscopy methods. In contrast, we employed desorption electrospray ionization mass spectrometry (DESI-MS) directly on renal biopsy specimens obtained from 37 NS patients to rapidly differentiate lipid profiles of three prevalent forms of NS: IgA nephropathy (n = 9), membranous glomerulonephritis (n = 7), and lupus nephritis (n = 8), along with other types of glomerular diseases (n = 13). As we noted molecular heterogeneity in regularly spaced renal tissue regions, multiple sections from each biopsy specimen were collected, providing a total of 973 samples for investigation. Using multivariate analysis, we report differential expressions of glycerophospholipids, sphingolipids, and glycerolipids among the above four classes of NS kidneys, which were otherwise overlooked in several past studies correlating lipid abnormalities with glomerular diseases. We developed machine learning (ML) models with the top 100 features using the support vector machine, which enabled us to discriminate the concerned glomerular diseases with 100% overall accuracy in the training, validation, and holdout test set. This DESI-MS/ML-based tissue analysis can be completed in a few minutes, in sharp contrast to a daylong procedure followed in the conventional histopathology of NS.