Mass Spectrometry Data Processing Research Articles

The Application of a Random Forest Classifier to ToF-SIMS Imaging Data.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information on surfaces at the microscale. However, the hyperspectral nature of ToF-SIMS datasets can be challenging to analyze and interpret. Both supervised and unsupervised machine learning (ML) approaches are increasingly useful to help analyze ToF-SIMS data. Random Forest (RF) has emerged as a robust and powerful algorithm for processing mass spectrometry data. This machine learning approach offers several advantages, including accommodating nonlinear relationships, robustness to outliers in the data, managing the high-dimensional feature space, and mitigating the risk of overfitting. The application of RF to ToF-SIMS imaging facilitates the classification of complex chemical compositions and the identification of features contributing to these classifications. This tutorial aims to assist nonexperts in either machine learning or ToF-SIMS to apply Random Forest to complex ToF-SIMS datasets.

An Expedited Qualitative Profiling of Free Amino Acids in Plant Tissues Using Liquid Chromatography-Mass Spectrometry (LC-MS) in Conjunction With MS-DIAL.

The estimation of relative levels of amino acids is crucial for understanding various biological processes in plants, including photosynthesis, stress tolerance, and the uptake and translocation of nutrients. A wide range of liquid chromatography (LC; HPLC/UHPLC)-based methods is available for measuring the quantity of amino acids in plants. Additionally, the coupling of LC with mass spectrometry (MS) significantly enhanced the robustness of existing chromatographic methods used for amino acid quantification. However, accurate annotation and integration of mass peaks can be challenging for plant biologists with limited experience in analyzing MS data, especially in studies involving large datasets with multiple treatments and/or replicates. Further, there are instances when the experiment demands an overall view of the amino acids profile rather than focusing on absolute quantification. The present protocol provides a detailed LC-MS method for obtaining a qualitative amino acids profile using MS-DIAL, a versatile and user-friendly program for processing MS data. Free amino acids were extracted from the leaves of control and Tomato leaf curl Palampur virus (ToLCPalV)-infected Nicotiana benthamiana plants. Extracted amino acids were derivatized and separated using UHPLC-QTOF, with each amino acid subsequently identified by aligning mass data with a custom text library created in MS-DIAL. Further, MS-DIAL was employed for internal standard-based normalization to obtain a qualitative profile of 15 amino acids in control and virus-infected plants. The outlined method aims to simplify the processing of MS data to quickly assess any modulation in amino acid levels in plants with a higher degree of confidence.

Innovative molecular networking analysis of steroids and characterisation of the urinary steroidome

Steroids are cholesterol-derived biomolecules that play an essential role in biological processes. These substances used as growth promoters in animals are strictly regulated worldwide. Targeted assays are the conventional methods of monitoring steroid abuse, with limitations: only detect known metabolites. Metabolism leads to many potential compounds (isomers), which complicates the analysis. Thus, to overcome these limitations, non-targeted analysis offers new opportunities for a deeper understanding of metabolites related to steroid metabolism. Molecular networking (MN) appears to be an innovative strategy combining high-resolution mass spectrometry and specific data processing to study metabolic pathways. In the present study, two databases and networks of steroids were constructed to lay the foundations for the implementation of the GNPS-MN approach. Steroids of the same family were grouped together, nandrolone and testosterone were linked to other analogues. This network and associated database were then applied to a few urine samples in order to demonstrate the annotation capacity in steroidome study. The results show that MN strategy could be used to study steroid metabolism and highlight biomarkers.

PowerNovo: de novo peptide sequencing via tandem mass spectrometry using an ensemble of transformer and BERT models

The primary objective of analyzing the data obtained in a mass spectrometry-based proteomic experiment is peptide and protein identification, or correct assignment of the tandem mass spectrum to one amino acid sequence. Comparison of empirical fragment spectra with the theoretical predicted one or matching with the collected spectra library are commonly accepted strategies of proteins identification and defining of their amino acid sequences. Although these approaches are widely used and are appreciably efficient for the well-characterized model organisms or measured proteins, they cannot detect novel peptide sequences that have not been previously annotated or are rare. This study presents PowerNovo tool for de novo sequencing of proteins using tandem mass spectra acquired in a variety of types of mass analyzers and different fragmentation techniques. PowerNovo involves an ensemble of models for peptide sequencing: model for detecting regularities in tandem mass spectra, precursors, and fragment ions and a natural language processing model, which has a function of peptide sequence quality assessment and helps with reconstruction of noisy sequences. The results of testing showed that the performance of PowerNovo is comparable and even better than widely utilized PointNovo, DeepNovo, Casanovo, and Novor packages. Also, PowerNovo provides complete cycle of processing (pipeline) of mass spectrometry data and, along with predicting the peptide sequence, involves the peptide assembly and protein inference blocks.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3.

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

How Much Storage Precision Can Be Lost: Guidance for Near-Lossless Compression of Untargeted Metabolomics Mass Spectrometry Data.

Several lossy compressors have achieved superior compression rates for mass spectrometry (MS) data at the cost of storage precision. Currently, the impacts of precision losses on MS data processing have not been thoroughly evaluated, which is critical for the future development of lossy compressors. We first evaluated different storage precision (32 bit and 64 bit) in lossless mzML files. We then applied 10 truncation transformations to generate precision-lossy files: five relative errors for intensities and five absolute errors for m/z values. MZmine3 and XCMS were used for feature detection and GNPS for compound annotation. Lastly, we compared Precision, Recall, F1 - score, and file sizes between lossy files and lossless files under different conditions. Overall, we revealed that the discrepancy between 32 and 64 bit precision was under 1%. We proposed an absolute m/z error of 10-4 and a relative intensity error of 2 × 10-2, adhering to a 5% error threshold (F1 - scores above 95%). For a stricter 1% error threshold (F1 - scores above 99%), an absolute m/z error of 2 × 10-5 and a relative intensity error of 2 × 10-3 were advised. This guidance aims to help researchers improve lossy compression algorithms and minimize the negative effects of precision losses on downstream data processing.

Technical Note: mzML and imzML Libraries for Processing Mass Spectrometry Data with the High-Performance Programming Language Julia.

Julia combines the virtues of high-level and low-level programming languages: The code is human-readable, and the performance of the created binaries competes with machine-orientated compilers. Thus, Julia is popular in "Big Data" sciences. Reading mass spectrometry (MS) data with Julia was impossible until now due to missing libraries. Here, we present a Julia library for importing mass spectrometry (MS) data in HUPO standard mzML and imzML formats and demonstrate its function with direct and ambient ionization MS, liquid chromatography-MS, and MS imaging data on standard platforms (Windows, Linux, and Mac OS). The processing speed of Julia for reading imzML MS imaging files was up to 214 times faster than the comparable code in R. Julia can remove bottlenecks for computationally demanding tasks in large-scale MS-Omics and MS imaging data processing workflows and supports their agile development. In addition, time-critical and complex data evaluation tasks become possible, such as following the real-time monitoring of biological processes and pattern recognition in large MS imaging projects. Our mzML/imzML libraries and code examples are available under the terms of the MIT license from https://github.com/CINVESTAV-LABI/julia_mzML_imzML.

An integrated approach for studying the metabolic profiling of herbal medicine in mice using high-resolution mass spectrometry and metabolomics data processing tools

Analysis of exposure to traditional Chinese medicine (TCM) in vivo based on mass spectrometry is helpful for the screening of effective ingredients of TCM and the development of new drugs. The method of screening biomarkers through metabolomics technology is a nontargeted research method to explore the differential components between two sets of biological samples. By taking this advantage, this study aims to takes Forsythia suspensa, which is a TCM also known as Lian Qiao (LQ), as the research object and to study its in vivo exposure by using metabolomics technology. By comparing the significant differences between biological samples before and after administration, it could be focused on the components that were significantly upregulated, where a complete set of analysis strategies for nontargeted TCM in vivo exposure mass spectrometry was established. Furthermore, the threshold parameters for peak extraction, parameter selection during statistical data analysis, and sample concentration multiples in this method have also been optimized. More interestingly, by using the established analysis strategy, we found 393 LQ-related chemical components in mice after administration, including 102 prototypes and 291 LQ-related metabolites, and plotted their metabolic profiles in vivo. In short, this study has obtained a complete mass spectrum of LQ exposure in mice in vivo for the first time, which provides a reference for research on the active ingredients of LQ in vivo. More importantly, compared with other methods, the analysis strategy of nontargeted exposure of TCM in vivo-based mass spectrometry, constructed by using this research method, has good universality and does not require self-developed postprocessing software. It is worth mentioning that, for the identification and characterization of trace amounts of metabolites in vivo, this analysis strategy has no discrimination and has a detection capability similar to that of highly exposed components.

An In Silico Database for Automated Feature Identification of High-Resolution Tandem Mass Spectrometry 13C-Trimethylation Enhancement Using Diazomethane (13C-TrEnDi)-Modified Lipid Data.

13C-Trimethylation enhancement using diazomethane (13C-TrEnDi) is a chemical derivatization technique that uses 13C-labeled diazomethane to increase mass spectrometry (MS) signal intensities for phosphatidylcholine (PC) and phosphatidylethanolamine (PE) lipid classes, both of which are of major interest in biochemistry. In silico mass spectrometry databases have become mainstays in lipidomics experiments; however, 13C-TrEnDi-modified PC and PE species have altered m/z and fragmentation patterns from their native counterparts. To build a database of 13C-TrEnDi-modified PC and PE species, a lipid extract from nutritional yeast was derivatized and fragmentation spectra of modified PC and PE species were mined using diagnostic fragmentation filtering by searching 13C-TrEnDi-modified headgroups with m/z 199 (PC) and 202 (PE). Identities of 25 PC and 10 PE species were assigned after comparing to predicted masses from the Lipid Maps Structure Database with no false positive identifications observed; neutral lipids could still be annotated after derivatization. Collision energies from 16 to 52 eV were examined, resulting in three additional class-specific fragment ions emerging, as well as a combined sn-1/sn-2 fragment ion, allowing sum-composition level annotations to be assigned. Using the Lipid Blast templates, a NIST-compatible 13C-TrEnDi database was produced based on fragmentation spectra observed at 36 eV and tested on HEK 293T cell lipid extracts, identifying 47 PC and 24 PE species, representing a 1.8-fold and 2.2-fold increase in annotations, respectively. The 13C-TrEnDi database is freely available, MS vendor-independent, and widely compatible with MS data processing pipelines, increasing the throughput and accessibility of TrEnDi for lipidomics applications.

Differential Gene Expression Analysis of Human Ovarian Follicular Cumulus and Mural Granulosa Cells Under the Influence of Insulin in IVF Ovulatory Women and Polycystic Ovary Syndrome Patients Through Network Analysis

ABSTRACT Background Polycystic ovarian syndrome (PCOS) is a commonly occurring reproductive disorder among the reproductive-aged women. Its global occurrence varies based on diagnostic guidelines, ethnicities, and locations of concern. Insulin resistance (IR) is commonly observed around 65–70% of women diagnosed with PCOS, representing a prevalent association. Consequently, the study was designed with an objective of illustrating the effect of insulin on mural and cumulus granulosa cells (GCs) of PCOS patients in comparison to normal ovulating women. Methodology This study is a case–control design, wherein a total of 80 participants were recruited meeting criterion of inclusion and exclusion, divided into 8 groups with each group consisting of 10 samples. The process involves the isolation and culturing of mural granulosa cells (MGC) and cumulus granulosa cells (CGC) with and without exposure to insulin. The proteins released by untreated GCs and insulin-treated GCs were extracted, and complex protein mixtures were digested with trypsin, followed by tandem mass spectrometry analysis and data processing using bioinformatics. Results We found 595 proteins in both control and PCOS samples, of which 310 were contributed by MGCs and 285 by CGCs. The PCOS MGCs expressed 20%, both the normal MGCs and CGCs have equal representation of 16% by each, whereas the PCOS CGCs proteins contributed 15% of the total of the proteomic expression. However, the poor expression observed with the Insulin exposure, the Insulin treated PCOS CGCs contributes 13%, PCOS MGCs contributes 8%. The normal MGCs upon the Insulin treatment give 8% then and there only 4% of proteins expressed by normal CGCs after Insulin treatment. The Venn analysis widened on their precise expression topographies. The examination of strings exhibited important protein–protein interaction pathways. Conclusion This is a pioneering investigation aimed to establish the link between hyperinsulinemia in localized follicular GCs and PCOS mechanisms by comparing them to control group. The examination of various attributes, mechanisms, and traits shown by genes and proteins in individuals with PCOS compared to control populations, alongside the investigation of the dynamics of these genes and proteins following exposure to insulin, holds promise for the formulation of novel hypotheses and strategies in the identification of new biomarkers.

An efficient approach addressing the chemical complexity of Jiawei Fangji Huangqi decoction by integrating ultra-high-performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry and intelligent data processing workflows.

A challenge in the quality control of traditional Chinese medicine is the systematic multicomponent characterization of the compound formulae. Jiawei Fangji Huangqi, a modified form of Fangji Huangqi, is a prescription comprising seven herbal medicines. To address the chemical complexity of the Jiawei Fangji Huangqi decoction, we integrated ion mobility-quadrupole time-of-flight high-definition MSE coupled to ultra-high-performance liquid chromatography and intelligent data processing workflows available in the UNIFI software package. Good chromatographic separation was achieved on CORTECS UPLC T3 column within 52min, and high-accuracy MS2 data were acquired using high-definition MSE in the negative and positive modes. A chemical library of 1250 compounds was created and incorporated into the UNIFI software to enable automatic peak annotation of the high-definition MSE data. We identified or tentatively characterize 430 compounds in the Jiawei Fangji Huangqi decoction. The potential superiority of high-definition MSE over conventional MS data acquisition approaches was revealed in its spectral quality (MS2 ), differentiation of isomers, separation of coeluting compounds, and target mass coverage. The multiple components of the Jiawei Fangji Huangqi decoction were elucidated, offering insight into its improved pharmacological action compared with that of the Fangji Huangqi formula.

Guided analysis of ambient ionization mass spectrometry data with the MQ_Assistant.

Ambient ionization mass spectrometry (AIMS) delivers realistic data from samples in their native state. In addition, AIMS methods reduce time and costs for sample preparation and have less environmental impact. However, AIMS data are often complex and require substantial processing before interpretation. We developed an interactive R script for guided mass spectrometry (MS) data processing. The "MQ_Assistant" is based on MALDIquant, a popular R package for MS data processing. In each step, the user can try and preview the effect of chosen parameters before deciding on the values with the best result and proceeding to the next stage. The outcome of the MQ_Assistant is a feature matrix that can be further analyzed in R and statistics tools such as MetaboAnalyst. Using 360 AIMS example spectra, we demonstrate the step-by-step processing for creating a feature matrix. In addition, we show how to visualize the results of three biological replicates of a plant-microbe interaction between Arabidopsis and Trichoderma as a heatmap using R and upload them to MetaboAnalyst. The final parameter set can be saved for reuse in MALDIquant workflows of similar data. The MQ_Assistant helps novices and experienced users to develop workflows for (AI)MS data processing. The interactive procedure supports the quick finding of appropriate settings. These parameters can be exported and reused in future projects. The stepwise operation with visual feedback also suggests the use of the MQ_Assistant in education.

Mzapy: An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format.

Analysis of ion mobility spectrometry (IMS) data has been challenging and limited the full utility of these measurements. Unlike liquid chromatography-mass spectrometry, where a plethora of tools with well-established algorithms exist, the incorporation of the additional IMS dimension requires upgrading existing computational pipelines and developing new algorithms to fully exploit the advantages of the technology. We have recently reported MZA, a new and simple mass spectrometry data structure based on the broadly supported HDF5 format and created to facilitate software development. While this format is inherently supportive of application development, the availability of core libraries in popular programming languages with standard mass spectrometry utilities will facilitate fast software development and broader adoption of the format. To this end, we present a Python package, mzapy, for efficient extraction and processing of mass spectrometry data in the MZA format, especially for complex data containing ion mobility spectrometry dimension. In addition to raw data extraction, mzapy contains supporting utilities enabling tasks including calibration, signal processing, peak finding, and generating plots. Being implemented in pure Python and having minimal and largely standardized dependencies makes mzapy uniquely suited to application development in the multiomics domain. The mzapy package is free and open-source, includes comprehensive documentation, and is structured to support future extension to meet the evolving needs of the MS community. The software source code is freely available at https://github.com/PNNL-m-q/mzapy.

Warning signs from the crypt: Aberrant protein glycosylation marks opportunities for early colorectal cancer detection.

Colorectal cancer (CRC) remains a leading cause of cancer-related deaths despite being the most preventable and treatable forms of cancer when caught early through screening. There is an unmet need for novel screening approaches with improved accuracy, less invasiveness, and reduced costs. In recent years, evidence has accumulated around particular biological events that happen during the adenoma to carcinoma transition, especially focusing on precancerous immune responses in the colonic crypt. Protein glycosylation plays a central role in driving those responses and recently numerous reports have been published on how aberrant protein glycosylation, both in colonic tissue and on circulating glycoproteins reflects these precancerous developments. The complex field of glycosylation, which exceeds complexity of proteins by several orders of magnitude, can now be studied primarily because of the availability of new high-throughput technologies like mass spectrometry and artificial-intelligence (AI)-powered data processing. This has now opened new avenues to studying novel biomarkers for CRC screening.This review summarizes the early events taking place from the normal colon mucosa towards adenoma and adenocarcinoma formation and associated critical protein glycosylation phenomena both on tissue level and in the circulation. These insights will help establish an understanding in the interpretation of novel CRC detection modalities that involve high throughput glycomics.

PepTraq: a toolbox for in silico data mining and fast sequence filtering.

The development of de novo sequencing tools has led to the massive production of genomes and transcriptomes from many unconventional animal models. To tackle this huge flow of data, PepTraq brings together many functionalities generally scattered in multiple tools, so that sequences can be filtered on the basis of multiple criteria. It is particularly suitable for the identification of non-annotated transcripts, re-annotation, extraction of secretomes, neuropeptidomes, targeted search for peptides and proteins, preparing specific proteomics/peptidomics fasta files for mass spectrometry (MS) applications, MS data processing, etc. PepTraq is developed in Java, and is available as a desktop application that can be downloaded from https://peptraq.greyc.fr . It is also available as a web application at the same URL for processing small files (10-20MB). The source code is open under a CeCILL-B licence.

Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils.

spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications to spectrum plotting from online data repositories and assisting data analysis tasks for dozens of other projects. Here, we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.

Shapley Value as a Quality Control for Mass Spectra of Human Glioblastoma Tissues

The automatic processing of high-dimensional mass spectrometry data is required for the clinical implementation of ambient ionization molecular profiling methods. However, complex algorithms required for the analysis of peak-rich spectra are sensitive to the quality of the input data. Therefore, an objective and quantitative indicator, insensitive to the conditions of the experiment, is currently in high demand for the automated treatment of mass spectrometric data. In this work, we demonstrate the utility of the Shapley value as an indicator of the quality of the individual mass spectrum in the classification task for human brain tumor tissue discrimination. The Shapley values are calculated on the training set of glioblastoma and nontumor pathological tissues spectra and used as feedback to create a random forest regression model to estimate the contributions for all spectra of each specimen. As a result, it is shown that the implementation of Shapley values significantly accelerates the data analysis of negative mode mass spectrometry data alongside simultaneous improving the regression models’ accuracy.

Tidy Data Neatly Resolves Mass-Spectrometry's Ragged Arrays

Mass spectrometry (MS) is a powerful tool for measuring biomolecules, but the data produced is often difficult to handle computationally because it is stored as a ragged array. In R, this format is typically encoded in complex S4 objects built around environments, requiring an extensive background in R to perform even simple tasks. However, the adoption of tidy data [@wickham2014] provides an alternate data structure that is highly intuitive and works neatly with base R functions and common packages, as well as other programming languages. Here, we discuss the current state of R-based MS data processing, the convenience and challenges of integrating tidy data techniques into MS data processing, and present [RaMS](https://CRAN.R-project.org/package=RaMS), a package that produces tidy representations of MS data.

Mass spectrometry-based glycoprofiling of biopharmaceuticals by using an automated data processing tool: SimGlycan®.

The therapeutic and immunological properties of biopharmaceuticals are governed by the glycoforms contained in them. Thus, bioinformatics tools capable of performing comprehensive characterization of glycans are significantly important to the biopharma industry. The primary structural elucidation of glycans using mass spectrometry is tricky and tedious in terms of spectral interpretation. In this study, the biosimilars of a therapeutic monoclonal antibody and an Fc-fusion protein with moderate and heavy glycosylation, respectively, were employed as representative biopharmaceuticals for released glycan analysis using liquid chromatography-tandem mass spectrometry instead of conventional mass spectrometry-based analysis. SimGlycan® is a software with proven ability to process tandem MS data for released glycans could identify eight additional glycoforms in Fc-fusion protein biosimilar, which were not detected during mass spectrometry analysis of released glycans or glyco-peptide mapping of the same molecule. Thus, liquid chromatography-tandem mass spectrometry analysis of released glycans not only complements conventional liquid chromatography-mass spectrometry-based glycan profiling but can also identify additional glycan structures that may otherwise be omitted during conventional liquid chromatography-tandem mass spectrometry based analysis of mAbs. The mass spectrometry data processing tools, such as PMI Byos™, SimGlycan® , etc., can display pivotal analytical capabilities in automated liquid chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry-based glycan analysis workflows, especially for high-throughput structural characterization of glycoforms in biopharmaceuticals.

Comprehensive N-Glycan Mapping using Parallel Reaction Monitoring LC-MS/MS.

Glycan composition can impact a biotherapeutic's safety and efficacy. For example, changes in the relative abundance of different glycan attributes like afucosylation, galactosylation or high-mannose content can change the properties or functions of a monoclonal antibody (mAb). While established methods can effectively characterize major glycan species in biotherapeutic drug products, there is still a need for more sensitive and specific methods that can effectively monitor low abundance species which may impact mAb function. Glycans released from two mAbs, adalimumab and trastuzumab, were derivatized with Rapifluor-MS™. Glycans were separated using HILIC and detected using either fluorescence (FLD) or mass spectrometry (MS). A parallel reaction monitoring (PRM) workflow was used for the MS analysis. FLD analysis identified 18 and 19 glycan peaks in adalimumab and trastuzumab, respectively. Glycan identities were determined using MS-analysis and a high number of FLD peaks containing co-eluting glycan species were observed. PRM analysis quantified 38 and 39 glycan species in adalimumab and trastuzumab, respectively, and the increase in glycans that could be identified was due to superior sensitivity and selectivity compared to FLD. Notably, many low abundance glycans identified by PRM included species that were not reported in other studies. PRM also offered several additional advantages; unique structural features could be identified using the collected MS/MS spectra and de-coupling MS acquisition and data processing simplified the transfer of methods between instruments. The results established PRM as a precise, informative tool for glycan analysis and quantitation.