Spectral Precision: Recent Advances in Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry for Pathogen Detection and Resistance Profiling

Abstract

With the global rise in antimicrobial resistance (AMR), rapid and reliable microbial diagnostics have become more critical than ever. Traditional culture-based and molecular diagnostic techniques often fall short in terms of speed, cost-efficiency, or scalability, particularly in resource-limited settings. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has emerged as a transformative tool in clinical microbiology. Its unparalleled speed and accuracy in microbial identification, along with expanding applications in AMR profiling, make it a leading candidate for next-generation diagnostic workflows. This review aims to provide a comprehensive update on recent advances in MALDI-TOF MS, focusing on its technological evolution, clinical applications, and future potential in microbial diagnostics and resistance detection. We conducted a critical synthesis of peer-reviewed literature published over the last decade, with emphasis on innovations in sample preparation, instrumentation, data interpretation, and clinical integration. Key developments in AMR detection, including growth-based assays, resistance biomarker profiling, and machine learning-driven spectral analysis, are discussed. MALDI-TOF MS is increasingly deployed not only in clinical laboratories but also in environmental surveillance, food safety, and military biodefense. Despite challenges such as database variability and limited access in low-income regions, it remains a cornerstone of modern microbial diagnostics and holds promise for future integration into global AMR surveillance systems.

Keywords: MALDI–TOF MS; antibiotic resistance; machine learning; microbial diagnostics.

Figure 1

Schematic workflows for microbial identification using MALDI–TOF MS across three organism groups. (A) Bacterial workflow: from clinical specimen collection and nutrient agar culture to protein extraction and mass spectral analysis using a bacterial reference database. (B) Mycobacterial workflow: incorporating heat inactivation, centrifugation, ethanol-formic acid extraction, matrix application, crystallization, and spectral matching against a mycobacterial-specific library. (C) Fungal workflow: covering cultivation on selective media, direct or extended protein extraction, and analysis using a fungal-specific spectral database. Each workflow reflects the unique biological and diagnostic requirements of the respective microorganism.

Figure 2

Schematic representation of AMR detection workflow using the MALDI–TOF MS approach. The process involves sample preparation from bacterial isolates, acquisition of mass spectra with or without antibiotic exposure, and analysis of spectral patterns. Sensitive strains show typical protein profiles, while resistant strains exhibit distinct spectral features such as metabolite peaks, mass shifts, or specific AMR-associated peaks. These differences enable rapid identification and resistance profiling based on mass spectrometry data.

Figure 3

Workflow for predicting AMR using MALDI–TOF MS and ML integration. (A) Protein spectra acquisition using the MALDI–TOF VITEK MS system (bioMérieux). (B) Antimicrobial susceptibility testing (AST) to generate phenotypic resistance profiles. (C) Spectral preprocessing: normalization and extraction of peaks between 2000 and 10,000 Da. (D) Feature selection Via recursive elimination to isolate key spectral features. (E) Classification using ML models (logistic regression, random forest, support vector machine, transfer learning), optimized by tenfold cross-validation. (F) Performance evaluation with AUROC, AUPRC, balanced accuracy, and F1 score; SHAP analysis for identifying key features driving resistance prediction.