MSACL 2025 Abstract

Integrated Applications of Mass Spectrometry in Clinical Diagnostics and Gut Microbial Functional Metabolite Discovery

Laura Min Xuan Chai (1), Qiang Lyu (1), Hsin-Bai Zou (1,2), Rou-An Chen (1,2), Hsin-Yi Wu1, Wei-Kai Wu (3,4), Ming-Shiang Wu (5) & Cheng-Chih Hsu (1,2)
(1) Department of Chemistry, National Taiwan University, Taipei, Taiwan (2) Leeuwenhoek Laboratories Co. Ltd, Taipei, Taiwan 3 Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan 4 Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan 5 Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan

Laura Min Xuan Chai, PhD (Presenter) Department of Chemistry, National Taiwan University

Relevant Financial Disclosures (within past 24 months, reported on Aug 19, 2025) No relevant financial relationship(s) to disclose.

INTRODUCTION:
Mass spectrometry (MS) is increasingly employed as a core technology for clinical diagnostics and molecular discovery. This abstract presents two distinct yet complementary MS-based approaches: one focuses on on-site intraoperative cancer diagnosis using ambient ionization and machine learning, and the other leverages untargeted metabolomics to identify probiotic-derived bioactive metabolites that regulate host metabolism. Here, we will be discussing on the method development process and the implications of the developed methods and findings for the clinical industry.

METHODS:
In the first study, we utilized swab touch-spray mass spectrometry (TS-MS) combined with supervised machine learning for rapid surgical margin assessment during breast cancer surgeries. Tissue margins were swabbed in vivo and analyzed within 20 min using a miniaturized mass spectrometer an Advion ExpressionL CMS , located near the operating room. The acquired metabolomic profiles were preprocessed and labeled according pathology, and multiple algorithms (LASSO, random forest, and SVM) were tested to obtain optimal predictive accuracy.

The second application focused on identifying probiotic bacterial metabolites from polyphenol-rich dietary interventions. A bioactivity-guided metabolomics workflow was applied, integrating LC-MS-based untargeted profiling, in vitro screening for anti-adipogenic activity, and in vivo metabolic phenotyping in a murine model. We identified active ingredients, which enriched in probiotic cultures and fecal samples following dietary treatment. We used germ-free mice, C57BL/6JNarl mice model and HepG2 cell model to improve our breakthrough.

RESULTS:
For the assessment of breast surgical margins , LASSO model constructed with swab TS-MS profiles achieved over 90% accuracy in predicting both testing and validation data. Notably, 3 out of 4 tumor-positive margins were correctly classified. The whole process – from sample analysis to prediction – required less than 5 minutes, demonstrating its potential for intraoperative decision-making.

In the metabolomics study, we found that lychee polyphenols alleviated high-fat diet-induced metabolic disorders and increased the abundance of Bifidobacterium. We next demonstrated that B. longum, a Bifidobacterium strain, exerts anti-obesogenic activity in both in vitro and in vivo models. To identify a bioactive metabolite produced by B. longum, we applied a fractionation approach followed by LC-MS coupled with an in vitro screening strategy to the identification of bioactive metabolite produced by Bifidobacterium. MTA was identified by in vitro screening and its anti-obesity activity in vivo. Finally, we demonstrated that MTA may prevent obesity and insulin resistance by regulating fatty acid biosynthesis, glucose utilization, and bile acid metabolism. Using multi-omics methods, we present a strategy for identifying bioactive compounds and their biological activities.

Our findings have implications. First, MTA, which is a gut microbiota-derived small molecule, may have potential for use in the development of functional foods and pharmaceuticals to treat a variety of obesity-related diseases with further research and advancement. Second, each of the polyphenols, Bifidobacterium, and MTA showed promise in promoting weight reduction in obese mice. These components could individually serve as prebiotic, probiotic, or postbiotic interventions. This finding may provide a basis for the development of innovative interventions targeting metabolic disorders using microbiome-based approaches.

CONCLUSIONS:
The two approaches mentioned demonstrates the versatility of MS technologies in solving both diagnostic and therapeutic challenges. Ambient MS coupled with machine learning and a miniaturized mass spectrometer offers rapid, low-cost and highly adaptable alternative on-site clinical use. Concurrently, metabolomics-guided discovery of gut microbial metabolites such as MTA reveals mechanistic links between diet, microbiota, and host metabolism. MTA’s involvement in lipid and methionine-related metabolic pathways, along with its anti-obesity properties, highlights its promise as a lead compound for microbiota-targeted therapeutic development. These two studies also show that mass spectrometry has demonstrated its unique capabilities in clinical diagnosis and identification of active ingredients.