Liquid Chromatography Retention Research Articles

Green HPLC Enantioseparation of Chemopreventive Chiral Isothiocyanates Homologs on an Immobilized Chiral Stationary Phase Based on Amylose tris-[(S)-α-Methylbenzylcarbamate

Sulforaphane is a chiral phytochemical with chemopreventive properties. The presence of a stereogenic sulfur atom is responsible for the chirality of the natural isothiocyanate. The key role of sulfur chirality in biological activity is underscored by studies of the efficacy of individual enantiomers as chemoprotective agents. The predominant native (R) enantiomer is active, whereas the (S) antipode is inactive or has little or no biological activity. Here we provide an enantioselective high-performance liquid chromatography (HPLC) protocol for the direct and complete resolution of sulforaphane and its chiral natural homologs with different aliphatic chain lengths between the sulfinyl sulfur and isothiocyanate group, namely iberin, alyssin, and hesperin. The chromatographic separations were carried out on the immobilized-type CHIRALPAK IH-3 chiral stationary phase with amylose tris-[(S)-methylbenzylcarbamate] as a chiral selector. The effects of different mobile phases consisting of pure alcoholic solvents and hydroalcoholic mixtures on enantiomer retention and enantioselectivity were carefully investigated. Simple and environmentally friendly enantioselective conditions for the resolution of all chiral ITCs were found. In particular, pure ethanol and highly aqueous mobile phases gave excellent enantioseparations. The retention factors of the enantiomers were recorded as the water content in the aqueous-organic modifier (methanol, ethanol, or acetonitrile) mobile phases progressively varied. U-shaped retention maps were generated, indicating a dual and competitive hydrophilic interaction liquid chromatography (HILIC) and reversed-phase liquid chromatography retention mechanism on the CHIRALPAK IH-3 chiral stationary phase. Finally, experimental chiroptical studies performed in ethanol solution showed that the (R) enantiomers were eluted before the (S) counterpart under all eluent conditions investigated.

Wideband PRM: Highly Accurate and Sensitive Method for High-Throughput Targeted Proteomics.

Targeted mass spectrometry (MS) approaches, which are powerful methods for uniquely and confidently quantifying a specific panel of proteins in complex biological samples, play a crucial role in validating and clinically translating protein biomarkers discovered through global proteomic profiling. Common targeted MS methods, such as multiple reaction monitoring (MRM) and parallel-reaction monitoring (PRM), employ specific mass spectrometric technologies to quantify protein levels by comparing the transitions of surrogate endogenous (ENDO) peptides with those of stable isotope-labeled (SIL) peptide counterparts. These methods utilizing amino acid analyzed (AAA) SIL peptides warrant sensitive and precise measurements required for targeted MS assays. Compared with MRM, PRM provides higher experimental throughput by simultaneously acquiring all transitions of the target peptides and thereby compensates for different ion suppressions among transitions of a target peptide. However, PRM still suffers different ion suppressions between ENDO and SIL peptides due to spray instability, as the ENDO and SIL peptides were monitored at different liquid chromatography (LC) retention times. Here we introduce a new targeted MS method, termed wideband PRM (WBPRM), that is designed for high-throughput targeted MS analysis. WBPRM employs a wide isolation window for simultaneous fragmentation of both ENDO and SIL peptides along with multiplexed single ion monitoring (SIM) scans for enhanced MS sensitivity of the target peptides. Compared with PRM, WBPRM was demonstrated to provide increased sensitivity, precision, and reproducibility of quantitative measurements of target peptides with increased throughput, allowing more target peptide measurements in a shortened experiment time. WBPRM is a straightforward adaptation to a manufacturer-provided MS method, making it an easily implementable technique, particularly in complex biological samples where the demand for higher precision, sensitivity, and efficiency is paramount.

Effect of Mobile Phase pH and Counterion Concentration on Retention and Selectivity for a Diol Column in Hydrophilic Interaction Liquid Chromatography

This work focuses on the effects of the mobile phase pH and the counterion concentration in buffer solution on retention in hydrophilic interaction liquid chromatography (HILIC) mode. Analytes with various acid-base properties and a silica-based Diol stationary phase were used. Retention and separation selectivity changes with ionization of the analytes and the adsorbent’s groups were discussed. It was demonstrated that the Diol phase behaved as a cation exchanger at pH 5.76 because of its residual dissociated silanols, while the phase provided almost no charge at lower pH (2.85). Separation efficiency and asymmetry factors for ionized compounds were also affected by the changes of mobile phase pH and counterion concentration. Separation conditions for the mixture of analytes of various acid-base properties were established.

Chiral Hydroxy Metabolite of Mebendazole: Analytical and Semi-Preparative High-Performance Liquid Chromatography Resolution and Chiroptical Properties.

Mebendazole (MBZ) is a benzimidazole carbamate anthelmintic used worldwide for the treatment and prevention of parasitic disorders in animals and humans. A large number of in vivo and in vitro studies have demonstrated that MBZ also has anticancer activity in multiple types of cancers. After oral administration, the phenylketone moiety of MBZ is rapidly reduced to the hydroxyl group to form the chiral hydroxy metabolite (MBZ-OH). To the best of our knowledge, there is no information in the literature on the stereochemical course of transformation and the anthelmintic and antitumor activity of individual enantiomers of MBZ-OH. In the present study, we describe in detail the direct HPLC resolution of MBZ-OH on a 100 mm × 4.6 mm Chirapak IG-3 column packed with 3 μm silica particles containing amylose (3-chloro-5-methylphenylcarbamate) as a selector. At 25 °C and using pure methanol as the mobile phase, the enantioseparation and resolution factors were 2.38 and 6.13, respectively. These conditions were scaled up at a semi-preparative scale using a 250 mm × 10 mm Chiralpak IG column to isolate multi-milligram amounts of both enantiomeric forms of the chiral metabolite. The chiroptical properties of the collected enantiomers were determined and, through a theoretical study, were related to the more stable conformations of MBZ-OH. The first and second eluted enantiomers were dextrorotatory and levorotatory, respectively, in dimethylformamide solution. Finally, by recording the retention factors of the enantiomers as the water content in the water-acetonitrile mobile phases was progressively varied, U-shaped retention maps were generated, indicating a dual and competitive hydrophilic interaction liquid chromatography and reversed-phase liquid chromatography retention mechanism on the Chirapak IG-3 chiral stationary phase.

Hydrophobic Sorption Properties of an Extended Series of Anionic Per- and Polyfluoroalkyl Substances Characterized by C18 Chromatographic Retention Measurement.

Partitioning from water to nonaqueous phases is an important process that controls the behavior of contaminants in the environment and biota. However, for ionic chemicals including many perfluoroalkyl and polyfluoroalkyl substances (PFAS), environmentally relevant partition coefficients cannot be predicted using the octanol/water partition coefficient, which is commonly used as a hydrophobicity indicator for neutral compounds. As an alternative, this study measured C18 liquid chromatography retention times of 39 anionic PFAS and 20 nonfluorinated surfactants using isocratic methanol/water eluent systems. By measuring a series of PFAS with different perfluoroalkyl chain lengths, retention factors at 100% water (k0) were successfully extrapolated even for long-chain PFAS. Molecular size was the most important factor determining the k0 of PFAS and non-PFAS, suggesting that the cavity formation process is the key driver for retention. Log k0 showed a high correlation with the log of partition coefficients from water to the phospholipid membrane, air/water interface, and soil organic carbon. The results indicate the potential of C18 retention factors as predictive descriptors for anionic PFAS partition coefficients and the possibility of developing a more comprehensive multiparameter model for the partitioning of anionic substances in general.

RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification.

Liquid chromatography retention times prediction can assist in metabolite identification, which is a critical task and challenge in nontargeted metabolomics. However, different chromatographic conditions may result in different retention times for the same metabolite. Current retention time prediction methods lack sufficient scalability to transfer from one specific chromatographic method to another. Therefore, we present RT-Transformer, a novel deep neural network model coupled with graph attention network and 1D-Transformer, which can predict retention times under any chromatographic methods. First, we obtain a pre-trained model by training RT-Transformer on the large small molecule retention time dataset containing 80038 molecules, and then transfer the resulting model to different chromatographic methods based on transfer learning. When tested on the small molecule retention time dataset, as other authors did, the average absolute error reached 27.30 after removing not retained molecules. Still, it reached 33.41 when no samples were removed. The pre-trained RT-Transformer was further transferred to 5 datasets corresponding to different chromatographic conditions and fine-tuned. According to the experimental results, RT-Transformer achieves competitive performance compared to state-of-the-art methods. In addition, RT-Transformer was applied to 41 external molecular retention time datasets. Extensive evaluations indicate that RT-Transformer has excellent scalability in predicting retention times for liquid chromatography and improves the accuracy of metabolite identification. The source code for the model is available at https://github.com/01dadada/RT-Transformer. The web server is available at https://huggingface.co/spaces/Xue-Jun/RT-Transformer.

Practical examination of flow rate effects and influence of the stationary phase water layer on peak shape and retention in Hydrophilic Interaction Liquid Chromatography.

The effect of flow velocity on retention and peak shape of neutral, acidic and basic probe compounds was studied using seven different UHPLC hydrophilic interaction chromatography (HILIC) columns. Surprisingly on some columns, the retention factor k was found to vary somewhat with flow velocity, due to the combined effects of pressure and of frictional heating on retention. The selectivity of different HILIC columns was much greater than typically found with RP columns. The volume of the water layer on the HILIC columns was measured using the toluene exclusion procedure. For the neutral solute uridine, a good correlation was found between the volume of the water layer and retention, indicating the likely domination of a partition mechanism. For the ionogenic solutes, the correlation was generally poor, due to the presence of strong additional mechanisms such as ionic retention and repulsion. Reduced van Deemter plots for uridine showed a negative correlation between the reduced b coefficient and the volume of the water layer, which can be attributed to reduced surface diffusion in this viscous layer. Once again, the behaviour of ionic solutes was complex on some columns making detailed interpretation difficult.

Modelling and predicting liquid chromatography retention time for PFAS with no-code machine learning

Machine learning is increasingly popular and promising in environmental science due to its potential in solving various environmental problems, particularly with simple code-free tools.

BART: A transferable liquid chromatography retention time library for bile acids

Identification of unknown bile acids, especially the distinguishment between isomers, requires retention times of a large number of reference standards, which are often not commercially available. Meanwhile, published retention information cannot be directly transferred across labs due to the differences between liquid chromatography (LC) systems, such as different extra column volume and dwell volume. To improve this situation, a transferrable retention time library for bile acids named BART was developed. BART was composed of isocratic retention models of 272 bile acids and a software tool to predict their gradient retention times on various LC systems. The isocratic retention times of bile acids were acquired on a Waters BEH C18 column with mobile phases of acidic ammonium acetate buffer and acetonitrile, and fit to the quadratic solvent strength model (QSSM). Segmented linear gradient retention times were calculated with holdup time (t0), dwell time (tD) and actual gradient profile corrected using 21 bile acid calibration standards. In addition to the reference system where the isocratic retention times were acquired, this approach has been validated on four other LC-MS systems in four labs with two gradient methods. Average root mean square errors (RMSE) between predicted and experimental retention times were 0.052 and 0.054 min for the two gradients tested, which were 9-fold more accurate than referring to a static retention time library. The library is freely available at https://bafinder.github.io/.

Experimental design and re-parameterization of the Neue-Kuss model for accurate and precise prediction of isocratic retention factors from gradient measurements in reversed phase liquid chromatography

The present work describes a re-parameterization of the Neue Kuss (NK) model for describing retention in liquid chromatography, and this re-parameterized model is used to fit a large set of isocratic retention measurements with improved convergence properties relative to the original parameterization of the model. Next, an experimental design for retention measurements using mobile phase gradient elution conditions is proposed for the purpose of obtaining accurate and precise NK parameters. Simulated retention data for mobile phase gradient elution conditions with two different levels of noise, as well as an essentially zero noise level were fit with the re-parameterized model. The results showed that the re-parameterized fits yielded average (absolute value) prediction errors for the parameters at the highest noise level of 7.2 % for S1,ref, 18 % for S2,ref and 6.2 % for kref (the re-parameterized NK model parameters). These errors were significantly smaller than those for the original parameterization of the NK model, where the errors were 23 % for S1, 25 % for S2 and 160 % for kw (the original NK model parameters). Furthermore, isocratic retention factors predicted using these model parameters were found to have an average magnitude of error of 0.51 % for the re-parameterized model, as opposed to 6800 % for the model with the original parameterization. A further test of this approach was carried out for independent experimental measurements for five solutes on a C18 column. The average magnitude of error of the isocratic retention factors predicted from parameters obtained from fits of gradient data was 1.6 %, provided that the range of organic solvent compositions that the solute sampled in the mobile phase gradient experiments was consistent with the isocratic experiments. These results indicate that the re-parameterization of the NK model allows for significant improvements in the fitting process, and that the proposed experimental design allows for NK parameters to be extracted from mobile phase gradient experiments, with prediction accuracies of isocratic retention factors on the order of 1-2 %.

Revised descriptors for polycyclic aromatic and related hydrocarbons for the prediction of environmental properties using the solvation parameter model

Revised descriptors for twenty-five polycyclic aromatic and related hydrocarbons (PAHs) forming a component of the Wayne State University (WSU) descriptor database are provided for use with the solvation parameter model. The descriptors are determined by the Solver method using experimental data for calibrated gas-liquid and reversed-phase liquid chromatographic retention factors and liquid-liquid partition constants in totally organic biphasic systems. The characteristic solvation properties of the PAHs are accounted for mainly by the additional dispersion interactions (E descriptor) and dipole-type interactions (S descriptor) resulting from the availability of easily polarizable electrons that complement typical dispersion interactions for saturated hydrocarbons. The descriptors afford acceptable prediction of the water-air partition constant (average absolute deviation AAD = 0.17, n = 22), octanol-air partition constant (AAD = 0.12, n = 20), and water-octanol partition constant (AAD = 0.10, n = 23). A two-parameter model containing only the V and B descriptors provides an unbiased prediction of aqueous solubility for the PAHs with an AAD = 0.26 (n = 22). The descriptors estimated by convenient chromatographic and partition constant measurements are demonstrated to be a viable alternative to the experimental determination of environmental properties otherwise only available by tedious, expensive, and low data throughput experimental techniques.

Comparison of ZIC-HILIC Columns for the Simultaneous Analysis of Antiviral Drugs in Dosage Forms by Hydrophilic Interaction Liquid Chromatography

Background:: Antiviral drugs are vital since many viruses can produce fatal infections, as we have recently seen with the COVID-19 pandemic. Antiviral drugs can battle viruses at multiple stages of their life cycles, including neuraminidase, nucleic acid synthesis, protease, and virion fusion or entry [1]. Objective:: Antiviral drugs have poor retention in reversed-phase liquid chromatography, which makes it difficult to analyze a mixture of antiviral medications using high-performance liquid chromatography. Using zwitterionic hydrophilic interaction liquid chromatography (ZICHILIC), the paper highlights the simultaneous quantification of three antiviral drugs as active constituents in pharmaceutical formulations. Moreover, the influence of the length of the spacer between the two charges in two stationary phases on the retention behavior of antiviral drugs has been discussed. Methods:: Two homemade stationary phases (ZIC1-HILIC and ZIC5-HILIC) were utilized for the qualitative and quantitative analysis of three antiviral drugs, and UV was used as the detector. Several chromatographic conditions were examined, such as the organic modifier concentration, buffer concentration, and pH value. Results:: After optimizing the parameters, the devised method was applied to analyse three antiviral medications quantitatively. The initial results demonstrated the current procedure for separating and determining these three antiviral drugs to be sensitive, robust, and effective. Consequently, the present method has shown excellent repeatability, a broad linear range (0.1- 16.5 μgml-1), and excellent sensitivity (LOD 0.04-0.072 μgml-1). The RSD value of the method was less than 1. Conclusion:: A mixed mode of hydrophilic and ion exchange interactions was the predominant mode of antiviral medications with two ZIC-HILIC stationary phases. The ZIC5-HILIC stationary phase had a lower detection and limit of quantitation for three antiviral drugs and a prolonged retention time compared to the ZIC1-HILIC stationary phase with a shorter chain length.

Liquid chromatographic retention time prediction models to secure and improve the feature annotation process in high-resolution mass spectrometry

The development of quantitative structure-retention relationship (QSRR) models has, until recently, required an adequate selection of molecular descriptors necessarily obtained based on a known chemical structure. However, these complex descriptors are not always available nor calculable when the high-resolution mass spectrometry (HRMS) annotation process is underway. Depending on the level of annotation, many structures or even various molecular formulas could be candidates. To secure and improve the annotation process and to save time, a QSRR model (using only 0D molecular descriptors) to predict retention times in reverse-phase liquid chromatography (RPLC) based on the molecular formula was developed, and a general QSRR annotation-based methodology was also proposed.

Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction

Implementing effective environmental management strategies requires a comprehensive understanding of the chemical composition of environmental pollutants, particularly in complex mixtures. Utilizing innovative analytical techniques, such as high-resolution mass spectrometry and predictive retention index models, can provide valuable insights into the molecular structures of environmental contaminants. Liquid Chromatography-High-Resolution Mass Spectrometry is a powerful tool for the identification of isomeric structures in complex samples. However, there are some limitations that can prevent accurate isomeric structure identification, particularly in cases where the isomers have similar mass and fragmentation patterns. Liquid chromatographic retention, determined by the size, shape, and polarity of the analyte and its interactions with the stationary phase, contains valuable 3D structural information that is vastly underutilized. Therefore, a predictive retention index model is developed which is transferrable to LC-HRMS systems and can assist in the structural elucidation of unknowns. The approach is currently restricted to carbon, hydrogen, and oxygen-based molecules <500 g mol−1. The methodology facilitates the acceptance of accurate structural formulas and the exclusion of erroneous hypothetical structural representations by leveraging retention time estimations, thereby providing a permissible tolerance range for a given elemental composition and experimental retention time. This approach serves as a proof of concept for the development of a Quantitative Structure-Retention Relationship model using a generic gradient LC approach. The use of a widely used reversed-phase (U)HPLC column and a relatively large set of training (101) and test compounds (14) demonstrates the feasibility and potential applicability of this approach for predicting the retention behaviour of compounds in complex mixtures. By providing a standard operating procedure, this approach can be easily replicated and applied to various analytical challenges, further supporting its potential for broader implementation.

Machine learning liquid chromatography retention time prediction model augments the dansylation strategy for metabolite analysis of urine samples

Herein, a standalone software equipped with a graphic user interface (GUI) is developed to predict liquid chromatography mass spectrometry (LC–MS) retention times (RTs) of dansylated metabolites. Dansylation metabolomics strategy developed by Li et al. narrows down a vast chemical space of metabolites into the metabolites containing amines and phenolic hydroxyls. Combined with differential isotope labeling, e.g., 12C-reagent labeled individual samples spiked with a 13C-reagent labeled reference or pooled sample, LC–MS analysis of the dansylated samples enables accurate relative quantification of all labeled metabolites. Herein, the LC–RTs for dansylated metabolites are predicted using an artificial neural network (ANN) machine-learning model. For the ANN modeling, 315 dansylated urine metabolites obtained from the DnsID database are used. The ANN LC–RT prediction model was reliable, with a mean absolute deviation of 0.74 min for the 30 min LC run. In the RT model, a deviation of more than 2 min was observed in only 3.2% of the total 315 metabolites, while a deviation of 1.5 min or more was observed in 11% of the metabolites. Furthermore, it was found that the LC–RT prediction was also reliable even for metabolites containing both amine and phenolic functional groups that can undergo dansylation on either one of the two functional groups, resulting in the generation of two isomeric forms. This RT-prediction model is embedded into a user-friendly GUI and can be used for identifying nontargeted dansylated metabolites with unknown RTs, along with accurate mass measurements. Furthermore, it is demonstrated that the developed software can help identify metabolites from a urine sample of an anonymous healthy pregnant woman.

Heterogenous adsorption mechanisms for describing enantioselective retention in normal-phase liquid chromatography

In this study, the enantioselective retention behaviors of methyl mandelate (MM) and benzoin (B) were investigated using Chiralpak IB as a sorbent and ethanol, 1-propanol, and 1-butanol as solvent modifiers in the normal-phase mode. For both MM and B, similar chiral recognition mechanisms were observed, potentially involving at least two types of chiral adsorption sites. With a retention model describing local retention behaviors, an enantioselectivity model based on a three-site model was proposed to describe the data. Fitted parameters were also used to analyze the contributions of each type of adsorption site to the apparent retention behavior. Combining the local retention model with the three-site model provided a qualitative and quantitative explanation for the correlation between modifier concentration and enantioselectivity. Overall, our results indicated that heterogeneous adsorption mechanisms are a key aspect in understanding enantioselective retention behaviors. Distinct local adsorption sites contribute differently to apparent retention behaviors, with these contributions being influenced by the mobile phase composition to varying degrees. Hence, enantioselectivity changes with variations in modifier concentration.

Using Machine Learning To Predict Partition Coefficient (Log P) and Distribution Coefficient (Log D) with Molecular Descriptors and Liquid Chromatography Retention Time.

During preclinical evaluations of drug candidates, several physicochemical (p-chem) properties are measured and employed as metrics to estimate drug efficacy in vivo. Two such p-chem properties are the octanol-water partition coefficient, Log P, and distribution coefficient, Log D, which are useful in estimating the distribution of drugs within the body. Log P and Log D are traditionally measured using the shake-flask method and high-performance liquid chromatography. However, it is challenging to measure these properties for species that are very hydrophobic (or hydrophilic) owing to the very low equilibrium concentrations partitioned into octanol (or aqueous) phases. Moreover, the shake-flask method is relatively time-consuming and can require multistep dilutions as the range of analyte concentrations can differ by several orders of magnitude. Here, we circumvent these limitations by using machine learning (ML) to correlate Log P and Log D with liquid chromatography (LC) retention time (RT). Predictive models based on four ML algorithms, which used molecular descriptors and LC RTs as features, were extensively tested and compared. The inclusion of RT as an additional descriptor improves model performance (MAE = 0.366 and R2 = 0.89), and Shapley additive explanations analysis indicates that RT has the highest impact on model accuracy.

Exploration of the effects of chloride ions on the analysis of polar compounds at low concentrations by hydrophilic interaction liquid chromatography coupled to a charged aerosol detector: Application to tromethamine.

In this study, we discuss the origin of the slightly increased response of the charged aerosol detector when low-concentration polar drugs formulated with sodium chloride are analyzed by hydrophilic interaction liquid chromatography coupled to the charged aerosol detector. In the case of tromethamine mixed with saline solutions, we investigated several levels including the mobile phase, sample matrix, and detection. We show that the analysis of the rich-salted sample results in both interactions with the mobile phase modifiers and the stationary phase during the run time. With 150mM NaCl as a compounding solution, a slight increase in the tromethamine peak area was observed(<5.5%). Our study suggests that chloride ions in excess sequentially interact firstly with the counterions from the organic modifiers and secondly with the analyte via the stationary phase and the contribution of hydrophilic interaction liquid chromatography retention mechanisms. Because of these effects, the hydrophilic interaction liquid chromatography-charged aerosol detector analysis of drugs in saline solutions requires particular attention, and a correction factor for quantitative purposes that accounts for formulation ions remains appropriate.

Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data

Structural annotation of small molecules in biological samples remains a key bottleneck in untargeted metabolomics, despite rapid progress in predictive methods and tools during the past decade. Liquid chromatography–tandem mass spectrometry, one of the most widely used analysis platforms, can detect thousands of molecules in a sample, the vast majority of which remain unidentified even with best-of-class methods. Here we present LC-MS2Struct, a machine learning framework for structural annotation of small-molecule data arising from liquid chromatography–tandem mass spectrometry (LC-MS2) measurements. LC-MS2Struct jointly predicts the annotations for a set of mass spectrometry features in a sample, using a novel structured prediction model trained to optimally combine the output of state-of-the-art MS2 scorers and observed retention orders. We evaluate our method on a dataset covering all publicly available reversed-phase LC-MS2 data in the MassBank reference database, including 4,327 molecules measured using 18 different LC conditions from 16 contributors, greatly expanding the chemical analytical space covered in previous multi-MS2 scorer evaluations. LC-MS2Struct obtains significantly higher annotation accuracy than earlier methods and improves the annotation accuracy of state-of-the-art MS2 scorers by up to 106%. The use of stereochemistry-aware molecular fingerprints improves prediction performance, which highlights limitations in existing approaches and has strong implications for future computational LC-MS2 developments.

A Data Set of Ion Mobility Collision Cross Sections and Liquid Chromatography Retention Times from 71 Pyridylaminated N-Linked Oligosaccharides

Determination of the glycan structure is an essential step in understanding structure–function relationships of glycans and glycoconjugates including biopharmaceuticals. Mass spectrometry, because of its high sensitivity and mass resolution, is an excellent means of analyzing glycan structures. We previously proposed a method for rapid and precise identification of N-glycan structures by ultraperformance liquid chromatography-connected ion mobility mass spectrometry (UPLC/IM-MS). To substantiate this methodology, we here examine 71 pyridylaminated (PA-) N-linked oligosaccharides including isomeric pairs. A data set on collision drift times, retention times, and molecular mass was collected for these PA-oligosaccharides. For standardization of the observables, LC retention times were normalized into glucose units (GU) using pyridylaminated α-1,6-linked glucose oligomers as reference, and drift times in IM-MS were converted into collision cross sections (CCS). To evaluate the CCS value of each PA-oligosaccharide, we introduced a CCS index which is defined as a CCS ratio of a target PA-glycan to the putative standard PA-glucose oligomer of the same m/z. We propose a strategy for practical structural analysis of N-linked glycans based on the database of m/z, CCS index, and normalized retention time (GU).