MXene-based functionalized platforms for high-performance MALDI-TOF MS: application in early-stage bloodstream infection biomarker screening - Quan, Wenjia; Qiu, Yingpo; Yang, Lv; Wang, Hao (Frontiers in Bioengineering and Biotechnology, 2026)
Introduction This study developed a MALDI-TOF MS metabolomics analysis method based on MXene nanomaterial functionalization platform for early diagnosis of bloodstream infections (BSI). Currently, BSI detection mainly relies on methods such as blood culture, PCR, and single biomarkers (such as PCT, CRP), which have problems such as long detection time, low sensitivity, and insufficient specificity. Therefore, it is urgent to establish a high-throughput detection technology that is fast, sensitive, and capable of multidimensional analysis.MethodThis study synthesized and characterized MXene nanomaterials, and utilized their ultra-high specific surface area and controllable surface functional groups to construct MXene matrices, significantly improving the enrichment and ionization efficiency of serum metabolites. We used this platform to perform metabolic profiling analysis on 50 BSI positive samples and 50 non BSI control samples, and analyzed the data using principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and heatmaps.ResultThe platform achieved an area under the curve (AUC) of 0.981, a sensitivity of 92%, and a specificity of 96% in BSI diagnosis, demonstrating superior performance compared to traditional single biomarkers. Further screening identified multiple potential metabolic markers (M/Z=203.64, 206.75, 218.67, 220.70), all of which had AUCs higher than 0.969. DiscussionThis study not only confirmed the application potential of MXene in mass spectrometry, but also provided a highly sensitive and high-throughput metabonomics technology platform for early screening of infectious diseases. This progress is expected to promote the transformation of BSI diagnosis from single indicator detection to multidimensional metabolic fingerprint analysis.