In-Gel Nonspecific Proteolysis for Elucidating Glycoproteins: A Method for Targeted Protein-Specific Glycosylation Analysis in Complex Protein Mixtures

Determining protein-specific glycosylation in protein mixtures remains a difficult task. A common approach is to use gel electrophoresis to isolate the protein followed by glycan release from the identified band. However, gel bands are often composed of several proteins. Hence, release of glycans from specific bands often yields products not from a single protein but a composite. As an alternative, we present an approach whereby glycans are released with peptide tags allowing verification of glycans bound to specific proteins. We term the process in-gel nonspecific proteolysis for elucidating glycoproteins (INPEG). INPEG combines rapid gel separation of a protein mixture with in-gel nonspecific proteolysis of protein bands followed by tandem mass spectrometry (MS) analysis of the resulting N- and O-glycopeptides. Here, in-gel digestion is shown for the first time with nonspecific and broad specific proteases such as Pronase, proteinase K, pepsin, papain, and subtilisin. Tandem MS analysis of the resulting glycopeptides separated on a porous graphitized carbon (PGC) chip was achieved via nanoflow liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (nano-LC/Q-TOF MS). In this study, rapid and automated glycopeptide assignment was achieved via an in-house software (Glycopeptide Finder) based on a combination of accurate mass measurement, tandem MS data, and predetermined protein identification (obtained via routine shotgun analysis). INPEG is here initially validated for O-glycosylation (κ casein) and N-glycosylation (ribonuclease B). Applications of INPEG were further demonstrated for the rapid determination of detailed site-specific glycosylation of lactoferrin and transferrin following gel separation and INPEG analysis on crude bovine milk and human serum, respectively

Isomer-Specific LC/MS and LC/MS/MS Profiling of the Mouse Serum N‑Glycome Revealing a Number of Novel Sialylated N‑Glycans

Mice are the premier mammalian models for studies of human physiology and disease, bearing extensive biological similarity to humans with far fewer ethical, economic, or logistic complications. To facilitate glycomic studies based on the mouse model, we comprehensively profiled the mouse serum N-glycome using isomer-specific nano-LC/MS and -LC/MS/MS. N-Glycans were identified by accurate mass MS and structurally elucidated by MS/MS. Porous graphitized carbon nano-LC was able to separate out nearly 300 N-linked glycan compounds (including isomers) from just over 100 distinct N-linked glycan compositions. Additional MS/MS structural analysis was performed on a number of novel N-glycans, revealing the structural characteristics of modifications such as dehydration, O-acetylation, and lactylation. Experimental findings were combined with known glycobiology to generate a theoretical library of all biologically possible mouse serum N-glycan compositions. The library may be used for automated identification of complex mixtures of mouse N-glycans, with possible applications to a wide range of mouse-related research endeavors, including pharmaceutical drug development and biomarker discovery

Isomer-Specific LC/MS and LC/MS/MS Profiling of the Mouse Serum N‑Glycome Revealing a Number of Novel Sialylated N‑Glycans

Mice are the premier mammalian models for studies of human physiology and disease, bearing extensive biological similarity to humans with far fewer ethical, economic, or logistic complications. To facilitate glycomic studies based on the mouse model, we comprehensively profiled the mouse serum N-glycome using isomer-specific nano-LC/MS and -LC/MS/MS. N-Glycans were identified by accurate mass MS and structurally elucidated by MS/MS. Porous graphitized carbon nano-LC was able to separate out nearly 300 N-linked glycan compounds (including isomers) from just over 100 distinct N-linked glycan compositions. Additional MS/MS structural analysis was performed on a number of novel N-glycans, revealing the structural characteristics of modifications such as dehydration, O-acetylation, and lactylation. Experimental findings were combined with known glycobiology to generate a theoretical library of all biologically possible mouse serum N-glycan compositions. The library may be used for automated identification of complex mixtures of mouse N-glycans, with possible applications to a wide range of mouse-related research endeavors, including pharmaceutical drug development and biomarker discovery

Automated Assignments of N- and O‑Site Specific Glycosylation with Extensive Glycan Heterogeneity of Glycoprotein Mixtures

Site-specific glycosylation (SSG) of glycoproteins remains a considerable challenge and limits further progress in the areas of proteomics and glycomics. Effective methods require new approaches in sample preparation, detection, and data analysis. While the field has advanced in sample preparation and detection, automated data analysis remains an important goal. A new bioinformatics approach implemented in software called GP Finder automatically distinguishes correct assignments from random matches and complements experimental techniques that are optimal for glycopeptides, including nonspecific proteolysis and high mass resolution liquid chromatography/tandem mass spectrometry (LC/MS/MS). SSG for multiple N- and O-glycosylation sites, including extensive glycan heterogeneity, was annotated for single proteins and protein mixtures with a 5% false-discovery rate, generating hundreds of nonrandom glycopeptide matches and demonstrating the proof-of-concept for a self-consistency scoring algorithm shown to be compliant with the target-decoy approach (TDA). The approach was further applied to a mixture of N-glycoproteins from unprocessed human milk and O-glycoproteins from very-low-density-lipoprotein (vLDL) particles

Spatially-Resolved Exploration of the Mouse Brain Glycome by Tissue Glyco-Capture (TGC) and Nano-LC/MS

Tissue glyco-capture (TGC), a highly sensitive MS-compatible method for extraction of glycans from tissue, was combined with structure-specific nano-LC/MS for sensitive and detailed profiling of the mouse brain glycome. Hundreds of glycan structures were directly detected by accurate mass MS and structurally elucidated by MS/MS, revealing the presence of novel glycan motifs such as antennary fucosylation, sulfation, and glucuronidation that are potentially associated with cellular signaling and adhesion. Microgram-level sensitivity enabled glycomic analysis of specific regions of the brain, as demonstrated on not only brain sections (with a one-dimensional spatial resolution of 20 μm) but also isolated brain structures (e.g., the hippocampus). Reproducibility was extraordinarily high (<i>R</i> > 0.98) for both method and instrumental replicates. The pairing of TGC with structure-specific nano-LC/MS was found to be an exceptionally powerful platform for qualitative and quantitative exploration of the brain glycome

Differentiation of Cancer Cell Origin and Molecular Subtype by Plasma Membrane N‑Glycan Profiling

In clinical settings, biopsies are routinely used to determine cancer type and grade based on tumor cell morphology, as determined via histochemical or immunohistochemical staining. Unfortunately, in a significant number of cases, traditional biopsy results are either inconclusive or do not provide full subtype differentiation, possibly leading to inefficient or ineffective treatment. Glycomic profiling of the cell membrane offers an alternate route toward cancer diagnosis. In this study, isomer-sensitive nano-LC/MS was used to directly obtain detailed profiles of the different N-glycan structures present on cancer cell membranes. Membrane N-glycans were extracted from cells representing various subtypes of breast, lung, cervical, ovarian, and lymphatic cancer. Chip-based porous graphitized carbon nano-LC/MS was used to separate, identify, and quantify the native N-glycans. Structure-sensitive N-glycan profiling identified hundreds of glycan peaks per cell line, including multiple isomers for most compositions. Hierarchical clusterings based on Pearson correlation coefficients were used to quickly compare and separate each cell line according to originating organ and disease subtype. Based simply on the relative abundances of broad glycan classes (e.g., high mannose, complex/hybrid fucosylated, complex/hybrid sialylated, etc.), most cell lines were readily differentiated. More closely related cell lines were differentiated based on several-fold differences in the abundances of individual glycans. Based on characteristic N-glycan profiles, primary cancer origins and molecular subtypes could be distinguished. These results demonstrate that stark differences in cancer cell membrane glycosylation can be exploited to create an MS-based biopsy, with potential applications toward cancer diagnosis and direction of treatment