Multi-dimensional metabolomic profiling reveals dysregulated ornithine metabolism hallmarks associated with a severe acute pancreatitis phenotype.

To reveal dysregulated metabolism hallmark that was associated with a severe acute pancreatitis (SAP) phenotype. In this study, LC-MS/MS-based targeted metabolomics was used to analyze plasma samples from 106 acute pancreatitis (AP) patients (34 mild, 38 moderate, and 34 severe) admitted within 48 hours from abdominal pain onset and 41 healthy controls. Temporal metabolic profiling was performed on days 1, 3, and 7 after admission. A random forest (RF) was performed to significantly determine metabolite differences between SAP and non-SAP (NSAP) groups. Mass spectrometry imaging (MSI) and immunohistochemistry were conducted for the examination of pancreatic metabolite and metabolic enzyme alterations, respectively, on necrosis and paracancerous tissues. Simultaneously determination of serum and pancreatic tissue metabolic alterations using an L-ornithine-induced AP model to discover metabolic commonalities. Twenty-two significant differential metabolites screened by RF were selected to build an accurate model for the prediction of SAP from NSAP (AUC = 0.955). Six of 22 markers were found by MSI with significant alterations in pancreatic lesions, reduced ornithine-related metabolites were also identified. The abnormally expressed arginase2 and ornithine transcarboxylase were further discovered in combination with time-course metabolic profiling in the SAP animal models, the decreased ornithine catabolites were found at a late stage of inflammation, but ornithine-associated metabolic enzymes were activated during the inflammatory process. The plasma metabolome of AP patients is distinctive, which shows promise for early SAP diagnosis. AP aggravation is linked to the activated ornithine metabolic pathway and its inadequate levels of catabolites in in-situ lesion.

Multi-MSIProcessor: Data Visualizing and Analysis Software for Spatial Metabolomics Research.

Mass spectrometry imaging (MSI) has emerged as a revolutionary analytical strategy in biomedical research for molecular visualization. By linking the characterization of functional metabolites with tissue architecture, it is now possible to reveal unknown biological functions of tissues. However, due to the complexity and high dimensionality of MSI data, mining bioinformatics-related peaks from batch MSI data sets and achieving complete spatially resolved metabolomics analysis remain a great challenge. Here, we propose novel MSI data processing software, Multi-MSIProcessor (MMP), which integrates the data read-in, MSI visualization, processed data preservation, and biomarker discovery functions. The MMP focuses on the AFADESI-MSI data platform but also supports mzXML and imzmL data input formats for compatibility with data generated by other MSI platforms such as MALDI/SIMS-MSI. MMP enables deep mining of batch MSI data and has flexible adaptability with the source code opened that welcomes new functions and personalized analysis strategies. Using multiple clinical biosamples with complex heterogeneity, we demonstrated that MMP can rapidly establish complete MSI analysis workflows, assess batch sample data quality, screen and annotate differential MS peaks, and obtain abnormal metabolic pathways. MMP provides a novel platform for spatial metabolomics analysis of multiple samples that could meet the diverse analysis requirements of scholars.

Time-Course Lipidomics of Ornithine-Induced Severe Acute Pancreatitis Model Reveals the Free Fatty Acids Centered Lipids Dysregulation Characteristics.

The relationship between the type and intensities of lipids of blood and pancreas and the pathological changes in the pancreas during severe acute pancreatitis (SAP) remains unclear. In our study, we employed a rat model of SAP induced through intraperitoneal ornithine injections. We collected serum and pancreas samples at various time points (0-144 h) for histopathological and biochemical assessments, followed by lipidomic analyses using LC-MS/MS or in situ mass spectrometry imaging (MSI) To discern changes over time or at specific points, we employed time-course and univariate analyses for lipid screening, respectively. Our findings indicated that the peak inflammation in the Orn-SAP model occurred within the 24-30 h timeframe, with evident necrosis emerging from 24 h onwards, followed by regeneration starting at 48 h. Time-course analysis revealed an overall decrease in glycerophospholipids (PEs, PCs, LPEs, LPCs), while CEs exhibited an increase within the pancreas. Univariate analysis unveiled a significant reduction in serum TAGs containing 46-51 carbon atoms at 24 h, and CERs in the pancreas significantly increased at 30 h, compared with 0 h. Moreover, a substantial rise in TAGs containing 56-58 carbon atoms was observed at 144 h, both in serum and pancreas. MSI demonstrated the CERs containing saturated mono-acyl chains of 16 and 18 carbon atoms influenced pancreatic regeneration. Tracing the origin of FFAs hydrolyzed from pancreatic glycerophospholipids and serum TAGs during the early stages of inflammation, as well as FFAs utilized for CEs and CERs synthesis during the repair phase, may yield valuable strategies for diagnosing and managing SAP.

Novel Isotope-Labeled Derivatization Strategy for the Simultaneous Analysis of Fatty Acids and Fatty Alcohols and Its Application in Idiopathic Inflammatory Myopathies and Pancreatic Cancer.

Fatty acids (FAs) and fatty alcohols (FOHs) are essential compounds for maintaining life. Due to the inherent poor ionization efficiency, low abundance, and complex matrix effect, such metabolites are challenging to precisely quantify and explore deeply. In this study, a pair of novel isotope derivatization reagents known as d0/d5-1-(2-oxo-2-(piperazin-1-yl) ethyl) pyridine-1-ium (d0/d5-OPEPI) were designed and synthesized, and an in-depth screening strategy for FAs and FOHs was established based on d0/d5-OPEPI coupled with liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS/MS). Using this approach, a total of 332 metabolites were identified and annotated (some of the FAs and FOHs were reconfirmed by standards). Our results demonstrated that OPEPI labeling could significantly enhance the MS response of FAs and FOHs via the introduction of permanently charged tags. The detection sensitivities of FAs were increased by 200-2345-fold compared with the nonderivatization method. At the same time, for FOHs, due to the absence of ionizable functional groups, sensitive detection was achieved utilizing OPEPI derivatization. One-to-one internal standards were provided by using d5-OPEPI labeling to minimize the errors in quantitation. Moreover, the method validation results showed that the method was stable and reliable. Finally, the established method was successfully applied to the study of the FA and FOH profiles of two heterogeneous severe clinical disease tissues. This study would improve our understanding of the pathological and metabolic mechanisms of FAs and FOHs for inflammatory myopathies and pancreatic cancer and also prove the generality and accuracy of the developed analytical method for complex samples.

Enhanced On-Tissue Chemical Derivatization with Hydrogel Assistance for Mass Spectrometry Imaging.

The improvement of on-tissue chemical derivatization for mass spectrometry imaging (MSI) of low-abundance and/or poorly ionizable functional molecules in biological tissue without delocalization is challenging. Here, we developed a novel hydrogel-assisted chemical derivatization (HCD) approach coupled with airflow-assisted desorption electrospray ionization (AFADESI)-MSI, allowing for enhanced visualization of inaccessible molecules in biological tissues. The derivatization reagent Girard's P (GP) reagent was creatively packaged into a hydrogel to form HCD blocks that have reactivity to carbonyl compounds as well as the feasibility of "cover/uncover" contact mode with tissue sections. The HCD blocks provided a favorable liquid microenvironment for the derivatization reaction and reduced matrix effects from derivatization reagents and tissue without obvious molecular migration, thus improving the derivatization efficiency. With this methodology, unusual carbonyl metabolites, including 166 fatty aldehydes (FALs) and 100 oxo fatty acids (FAs), were detected and visualized in rat brain, kidney, and liver tissue. This study provides a new approach to enhance chemical labeling for in situ tissue submetabolome profiling and improves our knowledge of the molecular histology and complex metabolism of biological tissues.

Hair growth predicts a depression-like phenotype in rats as a mirror of stress traceability.

As a subjective mood-related disorder with an unclear mechanism, depression has many problems in its diagnosis, which offers great space and value for research. At present, the methods commonly used to judge whether an animal model of depression has been established are mainly by biochemical index detection and behavioral tests, both of which inevitably cause stress in animals. Stress-induced hair growth inhibition has been widely reported in humans and animals. The simplicity of collecting hair samples and the observable state of hair growth has significant advantages; we tried to explore whether the parameters related to hair growth could be used as auxiliary indicators to evaluate a depression model in animals. The length and weight of the hair were calculated. Correlation analysis was conducted between the depressive behavioral results and the glucocorticoid levels in hair and serum. Learned helplessness combined with chronic restraint stress, and chronic unpredictable stress in the animal were detectable by superficial observation, weight ratio, and length of hair, and follicular development scores were significantly reduced compared to the control. The hair growth parameters of rats with depression, the rise in corticosterone, and the corresponding changes in behavioral parameters were significantly correlated. The neurotrophic factors, glucocorticoid-receptor (GR), brain-derived neurotrophic factor (BDNF), fibroblast growth factor 2 (FGF2), and fibroblast growth factor 5 (FGF5), are associated with depression and hair growth. Significant differences were detected between the stress and control groups, suggesting that the mechanism underlying the stress-phenomenon inhibition of hair growth may be related to growth factor mediation.