Untargeted HILIC-HRMS Metabolic Profiling of MIN6 Cells Under Glucose Challenge: Statistically Prioritized Hydrophilic Cellular Metabolites

Information from the abstract

Objective: This study developed a quality-control-driven HILIC-HRMS workflow for discovery-level profiling of hydrophilic cellular metabolites in MIN6 pancreatic β-cells. The principal analytical contribution was the integration of characterization of a representative standard panel with pooled-QC-based filtering of untargeted molecular features, rather than the development of HILICHRMS or MIN6 metabolomics as new technologies. Methods: MIN6 cells were maintained under a basal 3 mM glucose condition or challenged with 10 mM glucose for 30 minutes (n = 6 independent biological replicates per group). Metabolites were extracted with chilled methanol and analyzed on a ZIC-pHILIC-Q-Orbitrap platform in positive and negative ionization modes. The representative standard panel was evaluated for linearity, sensitivity, retention-time stability, and peak-area precision. Untargeted features were filtered using blanks, detection frequency, missingness, and pooled-QC reproducibility criteria. PLS-DA was treated as exploratory and was evaluated by leave-one-out cross-validation and 1,000 class-label permutations. Results: The evaluated standards showed R² values of 0.90-1.00, while limits of detection varied substantially among analytes (2-744 nM), indicating class- and compound-dependent sensitivity rather than uniformly low-nanomolar performance. In the MIN6 dataset, 298 features passed pooled- QC filtering. PCA, exploratory PLS-DA, and univariate analyses indicated glucose-associated differences in the hydrophilic metabolic profile. Ten features were statistically prioritized; exact structural assignments were retained only for authentic-standard-confirmed metabolites, whereas MS/MSsupported assignments were reported as putative annotations. Discussion: The prioritized features provide discovery-level evidence of acute glucose-associated metabolic remodeling in MIN6 cells. These features should not be interpreted as validated biomarkers or as direct mechanistic evidence of glucose-stimulated insulin secretion. Conclusion: The workflow provides an analytically characterized platform for exploratory hydrophilic cell metabolomics. Larger biological datasets, feature-level spectral reporting, targeted quantitative validation, stable-isotope tracing, and functional β-cell assays are required before the prioritized metabolites can be considered biologically or clinically validated.

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Related topics: Metabolomics and Mass Spectrometry Studies · Advanced Proteomics Techniques and Applications · Microbial Metabolic Engineering and Bioproduction