Shotgun Lipidomic Profiling of Sebum Lipids via Photocatalyzed Paternò-Büchi Reaction and Ion Mobility-Mass Spectrometry.

Sebum lipids are composed of nonpolar lipids, and they pose challenges for mass spectrometry-based analysis due to low ionization efficiency and the existence of numerous isomers and isobars. To address these challenges, we have developed ethyl 2-oxo-2-(pyridine-3-yacetate as a charge-tagging Paternò-Büchi reagent and Michler's ketone as a highly efficient photocatalyst, achieving ∼90% conversion for C═C derivatization under 440 nm LED irradiation. This derivatization, when coupled with electrospray ionization-tandem mass spectrometry, boosts the detection of sebum lipids and pinpoints C═C location in a chain-specific fashion. Identification and quantitation of isomers are readily achieved for wax esters, a class of underexplored sebum lipids, which have C═C bonds distributed in fatty alcohol and fatty acyl chains. A shotgun analysis workflow has been developed by pairing the offline PB derivatization with cyclic ion mobility spectrometry-mass spectrometry. Besides the dominant n-10 C═C location in unsaturated wax esters, profiling of low abundance isomers, including the rarely reported n-7 and n-13 locations, is greatly enhanced due to separations of C═C diagnostic ions by ion mobility. Over 900 distinct lipid structures from human sebum lipid extract have been profiled at the chain-specific C═C level, including wax esters (500), glycerolipids (393), and cholesterol esters (22), far more exceeding previous reports. Overall, we have developed a fast and comprehensive lipidomic profiling tool for sebum samples, a type of noninvasive biofluids holding potential for the discovery of disease markers in distal organs.

Deep-profiling of phospholipidome via rapid orthogonal separations and isomer-resolved mass spectrometry.

A lipidome comprises thousands of lipid species, many of which are isomers and isobars. Liquid chromatography-tandem mass spectrometry (LC-MS/MS), although widely used for lipidomic profiling, faces challenges in differentiating lipid isomers. Herein, we address this issue by leveraging the orthogonal separation capabilities of hydrophilic interaction liquid chromatography (HILIC) and trapped ion mobility spectrometry (TIMS). We further integrate isomer-resolved MS/MS methods onto HILIC-TIMS, which enable pinpointing double bond locations in phospholipids and sn-positions in phosphatidylcholine. This system profiles phospholipids at multiple structural levels with short analysis time (<10 min per LC run), high sensitivity (nM detection limit), and wide coverage, while data analysis is streamlined using a home-developed software, LipidNovelist. Notably, compared to our previous report, the system doubles the coverage of phospholipids in bovine liver and reveals uncanonical desaturation pathways in RAW 264.7 macrophages. Relative quantitation of the double bond location isomers of phospholipids and the sn-position isomers of phosphatidylcholine enables the phenotyping of human bladder cancer tissue relative to normal control, which would be otherwise indistinguishable by traditional profiling methods. Our research offers a comprehensive solution for lipidomic profiling and highlights the critical role of isomer analysis in studying lipid metabolism in both healthy and diseased states.

Visible-Light Paternò-Büchi Reaction for Lipidomic Profiling at Detailed Structure Levels.

The Paternò-Büchi (PB) derivatization of carbon-carbon double bond (C═C) has been increasingly employed with tandem mass spectrometry to analyze unsaturated lipids. It enables the discovery of altered or uncanonical lipid desaturation metabolism, which would be otherwise undetected by conventional methods. Although highly useful, the reported PB reactions only provide moderate yield (∼30%). Herein, we aim to determine the key factors that affect the PB reactions and develop a system with improved capabilities for lipidomic analysis. An Ir(III) photocatalyst is chosen as the triplet energy donor for the PB reagent under 405 nm light irradiation, while phenylglyoxalate and its charge-tagging version, pyridylglyoxalate, are developed as the most efficient PB reagents. The above visible-light PB reaction system provides higher PB conversions than all previously reported PB reactions. Around 90% conversion can be achieved at high concentrations (>0.5 mM) for different classes of lipids but drops as the lipid concentration decreases. The visible-light PB reaction has then been integrated with shotgun and liquid chromatography-based workflows. The limits of detection for locating C═C in standard lipids of glycerophospholipids (GPLs) and triacylglycerides (TGs) are in the sub-nM to nM range. More than 600 distinct GPLs and TGs have been profiled at the C═C location level or the sn-position level from the total lipid extract of bovine liver, demonstrating that the developed method is capable of large-scale lipidomic analysis.

A Machine Learning Method for Identifying Lung Cancer Based on Routine Blood Indices: Qualitative Feasibility Study.

BACKGROUND: Liquid biopsies based on blood samples have been widely accepted as a diagnostic and monitoring tool for cancers, but extremely high sensitivity is frequently needed due to the very low levels of the specially selected DNA, RNA, or protein biomarkers that are released into blood. However, routine blood indices tests are frequently ordered by physicians, as they are easy to perform and are cost effective. In addition, machine learning is broadly accepted for its ability to decipher complicated connections between multiple sets of test data and diseases. OBJECTIVE: The aim of this study is to discover the potential association between lung cancer and routine blood indices and thereby help clinicians and patients to identify lung cancer based on these routine tests. METHODS: The machine learning method known as Random Forest was adopted to build an identification model between routine blood indices and lung cancer that would determine if they were potentially linked. Ten-fold cross-validation and further tests were utilized to evaluate the reliability of the identification model. RESULTS: In total, 277 patients with 49 types of routine blood indices were included in this study, including 183 patients with lung cancer and 94 patients without lung cancer. Throughout the course of the study, there was correlation found between the combination of 19 types of routine blood indices and lung cancer. Lung cancer patients could be identified from other patients, especially those with tuberculosis (which usually has similar clinical symptoms to lung cancer), with a sensitivity, specificity and total accuracy of 96.3%, 94.97% and 95.7% for the cross-validation results, respectively. This identification method is called the routine blood indices model for lung cancer, and it promises to be of help as a tool for both clinicians and patients for the identification of lung cancer based on routine blood indices. CONCLUSIONS: Lung cancer can be identified based on the combination of 19 types of routine blood indices, which implies that artificial intelligence can find the connections between a disease and the fundamental indices of blood, which could reduce the necessity of costly, elaborate blood test techniques for this purpose. It may also be possible that the combination of multiple indices obtained from routine blood tests may be connected to other diseases as well.

In situ growth of luminescent perovskite fibers in natural hollow templates.

Natural hollow fibers were used as templates to in situ produce thin Cs4PbX6 nanosheets on the inner walls, forming luminescent fibers that integrated the advantages of the large length of fibers and the emission tunability of perovskites, and exhibited great robustness as well for multiple applications like warning signs, anti-counterfeiting and fashion.