Probing serum N-glycan patterns for rapid and precise detection of Crohn's disease

Rapid quantitative analysis of urinary nicotine metabolites via a high-performance nanoconcentrator

Tobacco use is a widespread public health issue. The detection of smoke metabolites in body fluids using in vitro assays can offer a rapid, efficient and non-invasive approach to measure human exposure to smoke and assess its associated health risks, enabling the development of more impactful follow-up strategies. In this study, we prepared a high-performance nanoconcentrator to construct a rapid magnetic response-based integrated enrichment and detection platform, ELDI-TOF-MS, for the purpose of enriching nicotine metabolites in conjunction with successive LDI-TOF-MS analysis. Concurrently functioning as both a pre-enrichment unit and an LDI-TOF-MS matrix, the nanoconcentrator possesses a number of advantageous properties, including a rapid magnetic response within 1 s, a substantial specific surface area as high as 862 m2 g-1, high UV-vis absorption capacity, and high reproducibility in MS analysis. With these advantages, the nanoconcentrator-based ELDI-TOF-MS platform demonstrates high sensitivity and linearity for low concentration samples, rendering it suitable for both qualitative and quantitative analysis. Also, ELDI-TOF-MS responds to samples at low concentrations down to 2.5 ng mL-1. For the analysis of two metabolites of nicotine, cotinine and norcotinine, the concentrations can be enriched by 2700 and 260 times. Furthermore, the rapid and efficient process facilitates batch analysis of up to 20 samples, with each batch requiring only 40 min. It is anticipated that the platform will enhance clinical efforts to detect nicotine use and augment the depth and breadth of testing.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2021-2022

The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well-established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing.

Efficient extraction via titanium organic frameworks facilitates in-depth profiling of urinary exosome metabolite fingerprints

Urinary exosome metabolite analysis has demonstrated notable advantages in uncovering disease status, yet its potential in decoding the intricacies of clear cell renal cell carcinoma (ccRCC) remains untapped. To address this, a core-shell magnetic titanium organic framework was designed to capture urinary exosomes and assist laser desorption/ionization mass spectrometry (LDI MS) to decipher the exosomal metabolic profile of ccRCC, with high sensitivity, throughput, and speed. A total of 492 urinary exosome metabolite fingerprints (UEMFs) from 176 samples were extracted for exploring the differences between ccRCC and healthy individuals. Leveraging machine learning algorithms, the exosomal metabolic profile was disclosed, achieving accurate differentiation and prediction of ccRCC patients versus healthy individuals, with an accuracy exceeding 97.3%. Furthermore, an optimized algorithm panel comprising five key features demonstrated consistent and high diagnosing accuracy rates of over 94.0% both in the training and blind test sets for ccRCC, underscoring the remarkable effectiveness and superiority of this strategy in ccRCC detection. This study not only refines the LDI MS method for metabolite analysis in urinary exosomes but also introduces a promising technical approach for unraveling the mysteries of ccRCC.

Exploring serum N-glycome patterns as candidate non-invasive biomarkers in inguinal hernia

Introduction

Although inguinal hernia (IH) is prevalent in elderly males, research on its specific diagnostic biomarkers is limited. Protein N-glycosylation is one of the most important and ubiquitous post-translational modifications and often results in a remarkable heterogeneity of protein glycoforms. Protein N-glycosylation often changes in a disease and holds great potential for discovering non-invasive biomarkers. This study aimed to gain insights into total serum protein N-glycosylation of IH to identify candidate non-invasive biomarkers for diagnosis and subtype classification of IH.

Methods

Linkage-specific sialylation derivatization combined with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry detection was used to analyze serum protein N-glycosylation patterns in IH patients and healthy controls.

Results

IH patients had abnormal glycan fucosylation and sialylation compared to healthy controls (HC), of which two glycan traits representing linkage-specific sialylation within monoantennary glycans showed high potential as diagnostic biomarkers for IH with an area under the curve (AUC) of 0.75. Additionally, serum N-glycans were different between indirect IH and direct IH in glycosylation features, namely complexity, fucosylation, galactosylation, sialylation, and α2,6-linked sialylation. Four distinctive glycans between the two subtypes showed good performance with AUC >0.8, suggesting that these glycan traits have potential as biomarkers for subtype classification.

Conclusions

We first reported the serum N-glycomic features of IH patients. Furthermore, we identified several potential biomarkers for the diagnosis and subtype classification of IH. These findings can deepen the understanding of IH.

Resol/triblock copolymer composite-guided smart fabrication of carbonized mesopores for efficiently decoding exosomal glycans

Soft-template carbonized mesopores were developed for the purpose of enriching urinary exosomal glycans through organic-organic self-assembly using block copolymers and resol precursors. With a high surface area of 229 m² g^-1, a small pore size of 3.1 nm, and a significant amount of carbon that specifically interacts with oligosaccharides in glycans, this carbonized mesopore material exhibits high selectivity and low limits of detection (5 ng μL^-1) towards glycans. Our analysis of complex urine samples from healthy volunteers and bladder carcinoma patients successfully profiled 48 and 56 exosomal glycans, respectively, and 16 of them were significantly changed. Moreover, one upregulated bisecting N-acetylglucosamine (GlcNAc)-type glycan with core fucose, two upregulated and two downregulated terminal-sialylated glycans were revealed to be linked to bladder carcinoma. This approach is of significant importance for understanding diseases that arise from protein glycosylation mutations, and it may contribute to the development of novel diagnostic and therapeutic strategies for bladder carcinoma.

Integrating age, BMI, and serum N-glycans detected by MALDI mass spectrometry to classify suspicious mammogram findings as benign lesions or breast cancer

While mammograms are the standard tool for breast cancer screening, there remains challenges for mammography to effectively distinguish benign lesions from breast cancers, leading to many unnecessary biopsy procedures. A blood-based biomarker could provide a minimally invasive supplemental assay to increase the specificity of breast cancer screening. Serum N-glycosylation alterations have associations with many cancers and several of the clinical characteristics of breast cancer. The current study utilized a high-throughput mass spectrometry workflow to identify serum N-glycans with differences in intensities between patients that had a benign lesion from patients with breast cancer. The overall N-glycan profiles of the two patient groups had no differences, but there were several individual N-glycans with significant differences in intensities between patients with benign lesions and ductal carcinoma in situ (DCIS). Many N-glycans had strong associations with age and/or body mass index, but there were several of these associations that differed between the patients with benign lesions and breast cancer. Accordingly, the samples were stratified by the patient's age and body mass index, and N-glycans with significant differences between these subsets were identified. For women aged 50-74 with a body mass index of 18.5-24.9, a model including the intensities of two N-glycans, 1850.666 m/z and 2163.743 m/z, age, and BMI were able to clearly distinguish the breast cancer patients from the patients with benign lesions with an AUROC of 0.899 and an optimal cutoff with 82% sensitivity and 84% specificity. This study indicates that serum N-glycan profiling is a promising approach for providing clarity for breast cancer screening, especially within the subset of healthy weight women in the age group recommended for mammograms.

Exosome Metabolic Patterns on Aptamer-Coupled Polymorphic Carbon for Precise Detection of Early Gastric Cancer

Gastric cancer (GC) presents high mortality worldwide because of delayed diagnosis. Currently, exosome-based liquid biopsy has been applied in diagnosis and monitoring of diseases including cancers, whereas disease detection based on exosomes at the metabolic level is rarely reported. Herein, the specific aptamer-coupled Au-decorated polymorphic carbon (CoMPC@Au-Apt) is constructed for the capture of urinary exosomes from early GC patients and healthy controls (HCs) and the subsequent exosome metabolic pattern profiling without extra elution process. Combining with machine learning algorithm on all exosome metabolic patterns, the early GC patients are excellently discriminated from HCs, with an accuracy of 100% for both the discovery set and blind test. Ulteriorly, three key metabolic features with clear identities are determined as a biomarker panel, obtaining a more than 90% diagnostic accuracy for early GC in the discovery set and validation set. Moreover, the change law of the key metabolic features along with GC development is revealed through making a comparison among HCs and GC at early stage and advanced stage, manifesting their monitoring ability toward GC. This work illustrates the high specificity of exosomes and the great prospective of exosome metabolic analysis in disease diagnosis and monitoring, which will promote exosome-driven precision medicine toward practical clinical application.