GEN-MKT-18-7897-A
Aug 10, 2017 | Blogs, Life Science Research, Metabolomics | 0 comments
I recently had the opportunity to catch up with Baljit Ubhi to discuss the top questions you’re asking in regards to using Microflow HILIIC Chromatography for Targeted Metabolomics. Here’s what Bal said:
Many of the metabolites of interest in the study of metabolomics are extremely polar and therefore often unable to be analyzed through traditional coupling of reversed phase (RP) chromatography and mass spectrometry. Also to detect and quantify key metabolites from pathways of biochemical importance, samples must be run on both reversed phase and normal phase, in negative and positive ion modes requiring a total of four injections.
We have implemented hydrophilic chromatography (HILIC) with microflow and mass spectrometry to develop a method for screening over 300 polar metabolites. HILIC allows separation by partitioning of analytes between an aqueous enriched layer of a polar stationary phase. HILIC conditions typically use high organic, with a moderate amount of a salt (i.e. 20-100mM ammonium formate or acetate, pH 4.4 or 5.5. respectively). This method deviates by having 20 mM ammonium hydroxide in both mobile phases to provide constant pH of 9.0 during the chromatographic separation. The high pH deprotonates the stationary phase and allows for better selectivity of the polar metabolites. The method is multiple reaction monitoring (MRM) with positive/negative polarity switching allowing the collection of these key metabolites in a single injection!
While microflow has become increasingly popular for many applications, microflow for metabolomics has not been readily employed because the typically used (aqueous) sample solvent does not allow for injecting larger volumes of samples without sacrificing chromatographic resolution. However, by simply reconstituting the sample in an organic solvent (95% acetonitrile, pH 9), we were able to inject up to 5 µL onto the microLC column, while maintaining excellent peak shape.
The microflow Luna-NH2 HILIC chromatography provides excellent chromatographic separation of polar, hydrophilic metabolites. The cross-linked aminopropyl phase gives a slightly different selectivity than the traditional amide phase; it gives higher coverage of the metabolome when compared to the amide functionality. This allows for improved sensitivity with signal-to-noise (S/N) improvement of up to 60X and up to 50% higher coverage of the metabolome than traditional analytical approaches (see table above).
As an analytical strategy, middle-down mass spectrometry (MS) workflows characterize biotherapeutic proteins by analyzing large, digested protein fragments or defined subunits, rather than fully intact proteins (top-down) or digested peptides (bottom-up). A middle-down strategy combines the strengths of top-down and bottom-up approaches by delivering high sequence coverage and structural specificity while maintaining relatively simple sample preparation. In practice, middle-down analysis enables accurate mass measurement, rapid sequence confirmation, and localization of key post-translational modifications (PTMs) on protein subunits that are directly relevant to product quality.
In biopharmaceutical development, sequence variants (SV) are considered an inherent risk of producing complex proteins in living systems. Sequence variants are unintended changes to the amino acid sequence of a biotherapeutic and can be caused by errors in transcription or translation in the host cell, or cell culture and process conditions. Detailed analysis of SVs is important in process and product development to ensure the drug’s safety and efficacy. Even low‑level sequence variants can have significant implications for product quality, safety, and efficacy, making their accurate detection and characterization a critical requirement across development, process optimization, and regulatory submission.
CE‑SDS remains a cornerstone assay for characterizing fragmentation, aggregation, and product‑related impurities in therapeutic proteins. UV detection has been the long‑standing standard. However, it frequently struggles with baseline noise, limited sensitivity for minor fragments, and subjective integration.
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