In situ metabolomic analysis of osteonecrosis of the femoral head (ONFH) using MALDI MSI.

Osteonecrosis of the femoral head (ONFH) is a common orthopedic disease characterized by disability and deformity. To better understand ONFH at molecular level and to explore the possibility of early diagnosis, instead of diagnosis based on macroscopic spatial characteristics, a matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) method was developed for ONFH disease for the first time. The most challenging step for ONFH MSI is to deal with human bone tissues which are much harder than the other biological samples studied by the reported MSI studies. In this work, the MSI sectioning method of hard bone tissues was established using tender acids and a series of test criteria. Small-molecule metabolites, such as lipids and amino acids, were detected in bone sections, realizing the in situ detection of spatial distribution of biometabolites. By comparing the distribution of metabolites from different regions of normal femoral head, ONFH bone tissue (ONBT), and adjacent ONFH bone tissue (ANBT), the whole process of femoral head from normal stage to necrosis was monitored and visualized at molecular level. Moreover, this developed MSI method was used for metabolomics study of ONFH. 72 differential metabolites were identified, suggesting that disturbances in energy metabolism and lipid metabolism affected the normal life activities of osteoblasts and osteoclasts. This study provides new perspectives for future pathological studies of ONFH.

Tracing the migration and transformation of metabolites in xylem during wood growth by mass spectrometry imaging.

Metabolites in the xylem experience several migration and transformation processes during tree growth. Their composition and distributions can reflect the environment that the wood lived through. Herein, a matrix-assisted laser desorption/ionization mass spectrometry imaging method was developed to investigate the migration and transformation of metabolites in the xylem during heartwood formation and after mechanical injury. The thickness of the wood slice, the type of matrix and its manner of deposition were optimized to improve ionization response and spatial resolution. The mass difference correlation (MDC) data processing method was proposed to improve the efficiency of compound identification, in which the compounds were classified by their molecular weight. The compound species was identified by results calculated using MDC and the experimental results from MS/MS. The directly identified metabolites, whose type and number were found to be quite different between sapwood and heartwood, demonstrated the transformation and migration of metabolites from sapwood to heartwood. Additionally, two kinds of resins produced from different positions were identified by MSI simultaneously, even though their heterogeneous distribution was not visible in optical images. The origin and type of the two resins were deduced from the identified compounds and their molecular distribution. This work provides a method to directly reveal metabolite migration and transformation mechanisms in xylem during wood growth.

Dissecting diazirine photo-reaction mechanism for protein residue-specific cross-linking and distance mapping.

While photo-cross-linking (PXL) with alkyl diazirines can provide stringent distance restraints and offer insights into protein structures, unambiguous identification of cross-linked residues hinders data interpretation to the same level that has been achieved with chemical cross-linking (CXL). We address this challenge by developing an in-line system with systematic modulation of light intensity and irradiation time, which allows for a quantitative evaluation of diazirine photolysis and photo-reaction mechanism. Our results reveal a two-step pathway with mainly sequential generation of diazo and carbene intermediates. Diazo intermediate preferentially targets buried polar residues, many of which are inaccessible with known CXL probes for their limited reactivity. Moreover, we demonstrate that tuning light intensity and duration enhances selectivity towards polar residues by biasing diazo-mediated cross-linking reactions over carbene ones. This mechanistic dissection unlocks the full potential of PXL, paving the way for accurate distance mapping against protein structures and ultimately, unveiling protein dynamic behaviors.

Analysis of Metabolite Distribution in Rat Liver of High-Fat Model by Mass Spectrometry Imaging.

Hyperlipidemia is a medical condition characterized by elevated levels of blood lipids, especially triglycerides (TG). However, it remains unclear whether TG levels remain consistently elevated throughout the entire developmental stage of the high-lipid state. In our animal experiment, we found that TG levels were significantly higher in the early stage of the high-lipid model but significantly decreased at the 14th week of the late stage, reaching levels similar to those of the control group. This suggests that TG levels in the high-lipid model are not always higher than those of the control group. To determine the reason for this observation, we used in situ mass spectrometry imaging (MSI) to detect the distribution of metabolites in the liver of rats. The metabolite distribution of the control rats at different stages was significantly different from that of the model rats, and the high-lipid model differed significantly from the control rats. We identified nine functional metabolites that showed differences throughout the period, namely, PA(20:3-OH/i-21:0), PA(20:4-OH/22:6), PG(20:5-OH/i-16:0), PG(22:6-2OH/i-13:0), PG(O-18:0/20:4), PGP(18:3-OH/i-12:0), PGP(PGJ2/i-15:0), SM(d18:0/18:1-2OH), and TG(14:0/14:0/16:0), among which TG was most significantly correlated with hyperlipidemia and high lipid. This study is unique in that it used MSI to reveal the changes in metabolites in situ, showing the distribution of different metabolites or the same metabolite in liver tissue. The findings highlight the importance of considering the animal's age when using TG as a biomarker for hyperlipidemia. Additionally, the MSI images of the liver in the high-lipid model clearly indicated the distribution and differences of more significant metabolites, providing valuable data for further research into new biomarkers and mechanisms of hyperlipidemia. This new pathway of in situ, visualized, and data-rich metabolomics research provides a more comprehensive understanding of the characteristics of high lipid and its implications for disease prevention and treatment.

Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis.

Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism-related pathway. Untargeted lipidomics was carried out in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we identified nine lipids (lysophosphatidylcholines 16:0, 18:0, and 20:4; phosphatidylcholines 16:0-18:1, 16:0-18:2, 18:0-18:1, 18:0-18:2, and 16:0-22:6; and triglycerides 16:0-18:1-18:1) as the features most important for early-stage cancer detection. Using these nine features, we developed a liquid chromatography-mass spectrometry (MS)-based targeted assay using multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low-dose computed tomography and a prospective clinical cohort containing 109 participants, the assay reached more than 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization MS imaging confirmed that the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. This method, designated as Lung Cancer Artificial Intelligence Detector, may be useful for early detection of lung cancer or large-scale screening of high-risk populations for cancer prevention.