Retention time alignment of LC/MS data by a divide-and-conquer algorithm

Stop-Codon Readthrough in Therapeutic Protein Candidates Expressed from Mammalian Cells

Stop codon readthroughs were examined in 48 recombinant therapeutic protein candidates produced from multiple clones of Chinese hamster ovary cells, using peptide mapping with LC-MS/MS detection. We found that stop codon readthrough is a common phenomenon occurring in most of these candidates, with levels varying from below the detection limit of ∼0.001 % to ∼1 %. The readthrough propensity depends on the stop codon being used, as well as the nucleotides surrounding it. The amino acids misincorporated into the stop position can be well-predicted by a third-base wobble mismatch and a first-base U/G mismatch during codon recognition, i.e., tyrosine or glutamine insertion for the UAA and UAG stop codons, and tryptophan, cysteine or arginine insertion for the UGA stop codon. Data shown in this report demonstrate the importance of optimizing the DNA sequence near the stop codon, and the importance of detecting stop codon readthroughs during the development of a therapeutic product.

Utilising a Clinical Metabolomics LC-MS Study to Determine the Integrity of Biological Samples for Statistical Modelling after Long Term -80 °C Storage: A TOFI_Asia Sub-Study

Biological samples of lipids and metabolites degrade after extensive years in -80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of -80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS-DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of -80 °C storage. With receiver operator characteristic curves, sparse PLS-DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at -80 °C when producing predictive metrics from polar metabolites, more so than lipids.

Fully Unattended Online Protein Digestion and LC-MS Peptide Mapping

LC-MS based peptide mapping, i.e., proteolytic digestion followed by LC-MS/MS analysis, is the method of choice for protein primary structural characterization. Manual proteolytic digestion is usually a labor-intensive procedure. In this work, a novel method was developed for fully automated online protein digestion and LC-MS peptide mapping. The method generates LC-MS data from undigested protein samples without user intervention by utilizing the same HPLC system that performs the chromatographic separation with some additional modules. Each sample is rapidly digested immediately prior to its LC-MS analysis, minimizing artifacts that can grow over longer digestion times or digest storage times as in manual or automated offline digestion methods. In this report, we implemented the method on an Agilent 1290 Infinity II LC system equipped with a Multisampler. The system performs a complete digestion workflow including denaturation, disulfide reduction, cysteine alkylation, buffer exchange, and tryptic digestion. We demonstrated that the system is capable of digesting monoclonal antibodies and other proteins with excellent efficiency and is robust and reproducible and produces fewer artifacts than manually prepared digests. In addition, it consumes only a few micrograms of material as most of the digested sample protein is subjected to LC-MS analysis.

Observation of Heavy-Chain C-Terminal Des-GK Truncation in Recombinant and Human Endogenous IgG4

Therapeutic IgG mAbs have shown presence of three variations of their heavy chain C-termini, including the unprocessed C-terminal lysine, the processed C-terminal lysine, and C-terminal amidation. These variants are also present in endogenous human IgGs, although the level of unprocessed C-terminal lysine is very low. Here we report a new heavy-chain C-terminal variant, i.e., the des-GK truncation, which exists in both recombinant and endogenous human IgG4. The des-GK truncation was found in negligible amount in IgG1, IgG2 and IgG3 subclasses. Observation of a significant level of heavy-chain C-terminal des-GK truncation in endogenous human IgG4 suggests that low level of this variant present in therapeutic IgG4 is unlikely to be a safety concern.

Method for detecting rare differences between two LC-MS runs

LC-MS based multi-attribute methods (MAM) have drawn substantial attention due to their capability of simultaneously monitoring a large number of quality attributes of a biopharmaceutical product. For successful implementation of MAM, it is usually considered a requirement that the method is capable of detecting any new or missing peaks in the sample when compared to a control. Comparing a sample to a control for rare differences is also commonly practiced in many fields for investigational purpose. Because MS signal variability differs greatly between signals of different intensities, this type of comparison is often challenging, especially when the comparison is made without enough replicates. In this report we describe a statistical method for detecting rare differences between two very similar samples without replicate analyses. The method assumes that an overwhelming majority of components have equivalent abundance between the two samples, and signals with similar intensities have similar relative variability. By analyzing several monoclonal antibody peptide mapping datasets, we demonstrated that the method is suitable for new-peak detection for MAM as well as for other applications when rare differences between two samples need to be detected. The method greatly reduced false positive rate without a significant increase of false negative rate.

Expanding the substrate scope of pyrrolysyl-transfer RNA synthetase enzymes to include non-α-amino acids in vitro and in vivo

The absence of orthogonal aminoacyl-transfer RNA (tRNA) synthetases that accept non-L-α-amino acids is a primary bottleneck hindering the in vivo translation of sequence-defined hetero-oligomers and biomaterials. Here we report that pyrrolysyl-tRNA synthetase (PylRS) and certain PylRS variants accept α-hydroxy, α-thio and N-formyl-L-α-amino acids, as well as α-carboxy acid monomers that are precursors to polyketide natural products. These monomers are accommodated and accepted by the translation apparatus in vitro; those with reactive nucleophiles are incorporated into proteins in vivo. High-resolution structural analysis of the complex formed between one PylRS enzyme and a m-substituted 2-benzylmalonic acid derivative revealed an active site that discriminates prochiral carboxylates and accommodates the large size and distinct electrostatics of an α-carboxy substituent. This work emphasizes the potential of PylRS-derived enzymes for acylating tRNA with monomers whose α-substituent diverges substantially from the α-amine of proteinogenic amino acids. These enzymes or derivatives thereof could synergize with natural or evolved ribosomes and/or translation factors to generate diverse sequence-defined non-protein heteropolymers.

Process Development of a SARS-CoV-2 Nanoparticle Vaccine

One of the outcomes from the global COVID-19 pandemic caused by SARS-CoV-2 has been an acceleration of development timelines to provide treatments in a timely manner. For example, it has recently been demonstrated that the development of monoclonal antibody therapeutics from vector construction to IND submission can be achieved in five to six months rather than the traditional ten-to-twelve-month timeline using CHO cells [1], [2]. This timeline is predicated on leveraging existing, robust platforms for upstream and downstream processes, analytical methods, and formulation. These platforms also reduce; the requirement for ancillary studies such as cell line stability, or long-term product stability studies. Timeline duration was further reduced by employing a transient cell line for early material supply and using a stable cell pool to manufacture toxicology study materials. The development of non-antibody biologics utilizing traditional biomanufacturing processes in CHO cells within a similar timeline presents additional challenges, such as the lack of platform processes and additional analytical assay development. In this manuscript, we describe the rapid development of a robust and reproducible process for a two-component self-assembling protein nanoparticle vaccine for SARS-CoV-2. Our work has demonstrated a successful academia-industry partnership model that responded to the COVID-19 global pandemic quickly and efficiently and could improve our preparedness for future pandemic threats.

Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling

Retention time (RT) alignment has been important for robust protein identification and quantification in proteomics. In data-dependent acquisition mode, whereby the precursor ions are semistochastically chosen for fragmentation in MS/MS, the alignment is used in an approach termed matched between runs (MBR). MBR transfers peptides, which were fragmented and identified in one experiment, to a replicate experiment where they were not identified. Before the MBR transfer, the RTs of experiments are aligned to reduce the chance of erroneous transfers. Despite its widespread use in other areas of quantitative proteomics, RT alignment has not been applied in data analyses for protein turnover using an atom-based stable isotope-labeling agent such as metabolic labeling with deuterium oxide, D2O. Deuterium incorporation changes isotope profiles of intact peptides in full scans and their fragment ions in tandem mass spectra. It reduces the peptide identification rates in current database search engines. Therefore, the MBR becomes more important. Here, we report on an approach to incorporate RT alignment with peptide quantification in studies of proteome turnover using heavy water metabolic labeling and LC-MS. The RT alignment uses correlation-optimized time warping. The alignment, followed by the MBR, improves labeling time point coverage, especially for long labeling durations.

Alignstein: Optimal transport for improved LC-MS retention time alignment

BACKGROUND

Reproducibility of liquid chromatography separation is limited by retention time drift. As a result, measured signals lack correspondence over replicates of the liquid chromatography-mass spectrometry (LC-MS) experiments. Correction of these errors is named retention time alignment and needs to be performed before further quantitative analysis. Despite the availability of numerous alignment algorithms, their accuracy is limited (e.g., for retention time drift that swaps analytes' elution order).

RESULTS

We present the Alignstein, an algorithm for LC-MS retention time alignment. It correctly finds correspondence even for swapped signals. To achieve this, we implemented the generalization of the Wasserstein distance to compare multidimensional features without any reduction of the information or dimension of the analyzed data. Moreover, Alignstein by design requires neither a reference sample nor prior signal identification. We validate the algorithm on publicly available benchmark datasets obtaining competitive results. Finally, we show that it can detect the information contained in the tandem mass spectrum by the spatial properties of chromatograms.

CONCLUSIONS

We show that the use of optimal transport effectively overcomes the limitations of existing algorithms for statistical analysis of mass spectrometry datasets. The algorithm's source code is available at https://github.com/grzsko/Alignstein.

Observation of Heavy-Chain C-Terminal Amidation in Human Endogenous IgG

Therapeutic IgG mAbs expressed from Chinese hamster ovary (CHO) cells are known to contain three C-terminal variants in their heavy chains, namely, the unprocessed C-terminal lysine, the processed C-terminal lysine, and C-terminal amidation. Although the presence of C-terminal amidation in CHO-expressed IgGs is well studied, the biological impact of the variant on the safety and efficacy of biotherapeutics has not been well understood. To further our biological understanding of C-terminal amidation, we analyzed a series of IgG samples, including both endogenous human IgGs as well as recombinant IgGs of different subclasses expressed from both CHO and murine cell lines, for their heavy-chain C-terminal variants by LC-MS/MS based peptide mapping. The results demonstrate that heavy-chain C-terminal amidation is a common variant occurring in IgG of all four subclasses (IgG1, IgG2, IgG3 and IgG4). The variant is generally present in recombinant IgG mAbs expressed from CHO cell lines but not in IgG mAbs expressed from murine cell lines, whereas the IgGs expressed from murine cell lines contain a much larger amount of unprocessed C-terminal lysine. Additionally, a significant amount of heavy-chain C-terminal amidation is observed in endogenous human IgGs, indicating that small amount of the variant present in therapeutic IgGs does not pose a safety concern.

Targeted Profiling of Rodent Unconjugated Bile Acids by GC-MS to Reveal the Influence of High-Fat Diet

Unconjugated bile acids (BAs) have gained more attention than conjugated BAs in the association studies among diet, gut microbiota and diseases. Gas chromatography-mass spectrometry (GC-MS) is probably a good choice for specialized analysis of unconjugated BAs due to high separation capacity and identification convenience. However, few reports have focused on the rodent unconjugated BAs by GC-MS, and the main library for identification has not included rodent-specific BAs. We developed a GC-MS method for targeted profiling of eight main unconjugated BAs in rodent models, which showed excellent performance on sensitivity, reproducibility and accuracy. Quantitative reproducibility in terms of relative standard deviation (RSD) was in the range of 2.05%-2.91%, with detection limits of 3-55 ng/mL, quantitation limits of 9-182 ng/mL and the recovery rate of 72%-115%. All the calibration curves displayed good linearity with correlation coefficient values (R² ) more than 0.99. Using the established method, the influence of high-fat diet on the metabolism of unconjugated BAs were revealed. Significant increasing of fecal unconjugated BAs induced by high-fat diet, would be a potential risk to gut inflammation and cancer. The study provides a convenient and targeted GC-MS method for specialized profiling of rodent unconjugated BAs in physiological and pathological studies.

Non-targeted characterization of attributes affecting antibody-FcγRIIIa V158 (CD16a) binding via online affinity chromatography-mass spectrometry

Antibodies facilitate targeted cell killing by engaging with immune cells such as natural killer cells through weak binding interactions with Fcγ receptors on the cell surface. Here, we evaluate the binding affinity of the receptor FcγRIIIa V158 (CD16a) for several therapeutic antibody classes, isoforms, and Fc-fusion proteins using an immobilized receptor affinity liquid chromatography (LC) approach coupled with online mass spectrometry (MS) detection. Aglycosylated FcγRIIIa was used in the affinity chromatography and compared with published affinities using glycosylated receptors. Affinity LC-MS differentiated the IgG1 antibodies primarily according to their Fc glycosylation patterns, with highly galactosylated species having greater affinity for the immobilized receptors and thus eluting later from the column (M5< G0F < G0 afucosylated ≅ G1F < G2F). Sialylated species bound weaker to their asialylated counterparts as reported previously. High mannose glycoforms bound weaker than G0F, contrary to previously published studies using glycosylated receptors. Also, increased receptor binding affinity associated with afucosylated antibodies was not observed with the aglycosylated FcγRIIIa. This apparent difference from previous findings highlighted the importance of the glycans on the receptors for mediating stronger binding interactions. Characterization of temperature-stressed samples by LC-MS peptide mapping revealed over 200 chemical and post-translational modifications, but only the Fc glycans, deamidation of EU N325, and an unknown modification to either proline or cysteine residues of the hinge region were found to have a statistically significant impact on binding.

Abbreviations: Antibody-dependent cell-mediated cytotoxicity (ADCC), chimeric antigen receptor (CAR), Chinese hamster ovary (CHO), dithiothreitol (DTT), electrospray ionization (ESI), hydrogen-deuterium exchange (HDX), filter aided-sample preparation (FASP), Fcγ receptor (FcγR), fragment crystallizable (Fc), high-pressure liquid chromatography (HPLC), immunoglobulin G (IgG), liquid chromatography (LC), monoclonal antibody (mAb), mass spectrometry (MS), natural killer (NK), N-glycolylneuraminic acid (NGNA), N-acetylneuraminic acid (NANA), principal component analysis (PCA), surface plasmon resonance (SPR), trifluoroacetic acid (TFA), and extracted mass chromatogram (XMC).

Limited Proteolysis Coupled with Mass Spectrometry for Simultaneous Evaluation of a Large Number of Protein Variants for Their Impact on Conformational Stability

A stable molecular structure is important in the development of a protein candidate into a therapeutic product. A therapeutic protein often contains many different variants; some of them may have an impact on the conformational stability of the protein. Conventionally, to evaluate the impact of a variant on stability, the variant must be enriched to a reasonable purity, and then its stability characterized by chromatographic or biophysical techniques. However, it is often impractical to purify and characterize each variant in a therapeutic protein. A workflow, based on limited proteolysis followed by MS detection, was established to simultaneously assess the impact of a large number of variants on conformational stability without enrichment. Because a less stable domain is more susceptible to proteolytic degradation, conformational stability of the domain can be reported from the release rate of a proteolytic peptide. A kinetic model is established to quantitatively determine the extent of domain stabilization/destabilization of different variants. The methodology is demonstrated by examining variants known to affect the stability of immunoglobulin domains, such as different N-glycoforms, methionine oxidations, and sequence variants. With this methodology, near 100 variants may be evaluated within 2 days in a single experiment. Insights into the sequence-stability relationship will be obtained by monitoring the large number of low-level sequence variants, facilitating engineering of more stable molecules.

An evaluation of instrument types for mass spectrometry-based multi-attribute analysis of biotherapeutics

Multi-attribute methods (MAM), based on proteolytic digestion followed by liquid chromatography-mass spectrometry analysis of proteolytic peptides, have gained substantial attention in the biopharmaceutical industry for quantifying a variety of quality attributes for therapeutic proteins. Most MAM developed so far have been based on high-resolution mass spectrometers, due to their superb resolving power to distinguish analyte signals from interferences. Lower-resolution instruments, if demonstrated suitable, may further promote the adoption of the technology due to their low cost, small footprint, and ease of use. In this work, we compared the performance of a high-resolution instrument with a few low-resolution quadrupole-type instruments in quantifying a diverse set of quality attributes in a monoclonal antibody product. Different modes of operation for the quadrupole instruments, including scan mode, selected-ion monitoring and multiple-reaction monitoring, were evaluated. The high-resolution instrument has superb performance, with a quantitation limit of 0.002%. Single-quadrupole instruments in scan mode, on the other hand, provide a quantitation limit of about 1%, which may be fit-for-purpose for many routine MAM applications.

Transcriptome Analysis Reveals High Similarities between Adult Human Cardiac Stem Cells and Neural Crest-Derived Stem Cells

For the identification of a stem cell population, the comparison of transcriptome data enables the simultaneous analysis of tens of thousands of molecular markers and thus enables the precise distinction of even closely related populations. Here, we utilized global gene expression profiling to compare two adult human stem cell populations, namely neural crest-derived inferior turbinate stem cells (ITSCs) of the nasal cavity and human cardiac stem cells (hCSCs) from the heart auricle. We detected high similarities between the transcriptomes of both stem cell populations, particularly including a range of neural crest-associated genes. However, global gene expression likewise reflected differences between the stem cell populations with regard to their niches of origin. In a broader analysis, we further identified clear similarities between ITSCs, hCSCs and other adherent stem cell populations compared to non-adherent hematopoietic progenitor cells. In summary, our observations reveal high similarities between adult human cardiac stem cells and neural crest-derived stem cells from the nasal cavity, which include a shared relation to the neural crest. The analyses provided here may help to understand underlying molecular regulators determining differences between adult human stem cell populations.

Mass Spectrometry-Based Protein Footprinting for Higher-Order Structure Analysis: Fundamentals and Applications

Proteins adopt different higher-order structures (HOS) to enable their unique biological functions. Understanding the complexities of protein higher-order structures and dynamics requires integrated approaches, where mass spectrometry (MS) is now positioned to play a key role. One of those approaches is protein footprinting. Although the initial demonstration of footprinting was for the HOS determination of protein/nucleic acid binding, the concept was later adapted to MS-based protein HOS analysis, through which different covalent labeling approaches "mark" the solvent accessible surface area (SASA) of proteins to reflect protein HOS. Hydrogen-deuterium exchange (HDX), where deuterium in D2O replaces hydrogen of the backbone amides, is the most common example of footprinting. Its advantage is that the footprint reflects SASA and hydrogen bonding, whereas one drawback is the labeling is reversible. Another example of footprinting is slow irreversible labeling of functional groups on amino acid side chains by targeted reagents with high specificity, probing structural changes at selected sites. A third footprinting approach is by reactions with fast, irreversible labeling species that are highly reactive and footprint broadly several amino acid residue side chains on the time scale of submilliseconds. All of these covalent labeling approaches combine to constitute a problem-solving toolbox that enables mass spectrometry as a valuable tool for HOS elucidation. As there has been a growing need for MS-based protein footprinting in both academia and industry owing to its high throughput capability, prompt availability, and high spatial resolution, we present a summary of the history, descriptions, principles, mechanisms, and applications of these covalent labeling approaches. Moreover, their applications are highlighted according to the biological questions they can answer. This review is intended as a tutorial for MS-based protein HOS elucidation and as a reference for investigators seeking a MS-based tool to address structural questions in protein science.

Impact of Fc N-glycan sialylation on IgG structure

Human IgG antibodies containing terminal alpha 2,6-linked sialic acid on their Fc N-glycans have been shown to reduce antibody-dependent cell-mediated cytotoxicity and possess anti-inflammatory properties. Although terminal sialylation on complex N-glycans can happen via either an alpha 2,3-linkage or an alpha 2,6-linkage, sialic acids on human serum IgG Fc are almost exclusively alpha 2,6-linked. Recombinant IgGs expressed in Chinese hamster ovary (CHO) cells, however, have sialic acids through alpha 2,3-linkages because of the lack of the alpha 2,6-sialyltransferase gene. The impact of different sialylation linkages to the structure of IgG has not been determined. In this work, we investigated the impact of different types of sialylation to the conformational stability of IgG through hydrogen/deuterium exchange (HDX) and limited proteolysis experiments. When human-derived and CHO-expressed IgG1 were analyzed by HDX, sialic acid-containing glycans were found to destabilize the CH2 domain in CHO-expressed IgG, but not human-derived IgG. When structural isomers of sialylated glycans were chromatographically resolved and identified in the limited proteolysis experiment, we found that only alpha 2,3-linked sialic acid on the 6-arm (the major sialylated glycans in CHO-expressed IgG1) destabilizes the CH2 domain, presumably because of the steric effect that decreases the glycan-CH2 domain interaction. The alpha 2,6-linked sialic acid on the 3-arm (the major sialylated glycan in human-derived IgG), and the alpha 2,3-linked sialic acid on the 3-arm, do not have this destabilizing effect.

Reliable LC-MS Multiattribute Method for Biotherapeutics by Run-Time Response Calibration

A major challenge of a mass-spectrometry-based quantitative multiattribute method (MAM) for biotherapeutics is its high variability between instruments. For reproducible attribute measurements, not only is a similar instrument model required, but the instruments must also be tuned to the same condition. This poses great long-term challenges, considering the rapid development of new instrumentations. In addition, differences in digestion efficiency, peptide recovery, and artificial modifications during sample preparation also contribute to variability between laboratories. To overcome these challenges, new mathematical methods are developed to calculate the attribute abundance in the sample, using the reference standard (RS) material as calibrant. Most quality attributes in the RS remain constant throughout the life of the standard, and therefore, the RS can serve as a calibrant to correct for the difference between instruments or sample preparation procedures. Because RS data are usually collected in a MAM assay, no additional work is required from the analyst. Data from a large number of attributes demonstrated that these methodologies greatly reduced instrument-to-instrument and sample preparation variabilities. With these methodologies, a consistent instrument model and sample preparation procedure is no longer a requirement. As a result, changes in digestion procedure and advances in instrumentations will not significantly affect the assay result.

Recent advancements, challenges, and practical considerations in the mass spectrometry-based analytics of protein biotherapeutics: A viewpoint from the biosimilar industry

The extensive analytical characterization of protein biotherapeutics, especially of biosimilars, is a critical part of the product development and registration. High-resolution mass spectrometry became the primary analytical tool used for the structural characterization of biotherapeutics. Its high instrumental sensitivity and methodological versatility made it possible to use this technique to characterize both the primary and higher-order structure of these proteins. However, even by using high-end instrumentation, analysts face several challenges with regard to how to cope with industrial and regulatory requirements, that is, how to obtain accurate and reliable analytical data in a time- and cost-efficient way. New sample preparation approaches, measurement techniques and data evaluation strategies are available to meet those requirements. The practical considerations of these methods are discussed in the present review article focusing on hot topics, such as reliable and efficient sequencing strategies, minimization of artefact formation during sample preparation, quantitative peptide mapping, the potential of multi-attribute methodology, the increasing role of mass spectrometry in higher-order structure characterization and the challenges of MS-based identification of host cell proteins. On the basis of the opportunities in new instrumental techniques, methodological advancements and software-driven data evaluation approaches, for the future one can envision an even wider application area for mass spectrometry in the biopharmaceutical industry.

Amino acid misincorporation in recombinant proteins

Proteins provide the molecular basis for cellular structure, catalytic activity, signal transduction, and molecular transport in biological systems. Recombinant protein expression is widely used to prepare and manufacture novel proteins that serve as the foundation of many biopharmaceutical products. However, protein translation bioprocesses are inherently prone to low-level errors. These sequence variants caused by amino acid misincorporation have been observed in both native and recombinant proteins. Protein sequence variants impact product quality, and their presence can be exacerbated through cellular stress, overexpression, and nutrient starvation. Therefore, the cell line selection process, which is used in the biopharmaceutical industry, is not only directed towards maximizing productivity, but also focuses on selecting clones which yield low sequence variant levels, thereby proactively avoiding potentially inauspicious patient safety and efficacy outcomes. Here, we summarize a number of hallmark studies aimed at understanding the mechanisms of amino acid misincorporation, as well as exacerbating factors, and mitigation strategies. We also describe key advances in analytical technologies in the identification and quantification of sequence variants, and some practical considerations when using LC-MS/MS for detecting sequence variants.

Computational methods and challenges in hydrogen/deuterium exchange mass spectrometry

Hydrogen/Deuterium exchange (HDX) has been applied, since the 1930s, as an analytical tool to study the structure and dynamics of (small) biomolecules. The popularity of using HDX to study proteins increased drastically in the last two decades due to the successful combination with mass spectrometry (MS). Together with this growth in popularity, several technological advances have been made, such as improved quenching and fragmentation. As a consequence of these experimental improvements and the increased use of protein-HDXMS, large amounts of complex data are generated, which require appropriate analysis. Computational analysis of HDXMS requires several steps. A typical workflow for proteins consists of identification of (non-)deuterated peptides or fragments of the protein under study (local analysis), or identification of the deuterated protein as a whole (global analysis); determination of the deuteration level; estimation of the protection extent or exchange rates of the labile backbone amide hydrogen atoms; and a statistically sound interpretation of the estimated protection extent or exchange rates. Several algorithms, specifically designed for HDX analysis, have been proposed. They range from procedures that focus on one specific step in the analysis of HDX data to complete HDX workflow analysis tools. In this review, we provide an overview of the computational methods and discuss outstanding challenges. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:649-667, 2017.

Robust alignment of chromatograms by statistically analyzing the shifts matrix generated by moving window fast Fourier transform cross-correlation

Retention time shift is one of the most challenging problems during the preprocessing of massive chromatographic datasets. Here, an improved version of the moving window fast Fourier transform cross-correlation algorithm is presented to perform nonlinear and robust alignment of chromatograms by analyzing the shifts matrix generated by moving window procedure. The shifts matrix in retention time can be estimated by fast Fourier transform cross-correlation with a moving window procedure. The refined shift of each scan point can be obtained by calculating the mode of corresponding column of the shifts matrix. This version is simple, but more effective and robust than the previously published moving window fast Fourier transform cross-correlation method. It can handle nonlinear retention time shift robustly if proper window size has been selected. The window size is the only one parameter needed to adjust and optimize. The properties of the proposed method are investigated by comparison with the previous moving window fast Fourier transform cross-correlation and recursive alignment by fast Fourier transform using chromatographic datasets. The pattern recognition results of a gas chromatography mass spectrometry dataset of metabolic syndrome can be improved significantly after preprocessing by this method. Furthermore, the proposed method is available as an open source package at https://github.com/zmzhang/MWFFT2.

Current state-of-the-art of nontargeted metabolomics based on liquid chromatography-mass spectrometry with special emphasis in clinical applications

Metabolomics, as a part of systems biology, has been widely applied in different fields of life science by studying the endogenous metabolites. The development and applications of liquid chromatography (LC) coupled with high resolution mass spectrometry (MS) greatly improve the achievable data quality in non-targeted metabolic profiling. However, there are still some emerging challenges to be covered in LC-MS based metabolomics. Here, recent approaches about sample collection and preparation, instrumental analysis, and data handling of LC-MS based metabolomics are summarized, especially in the analysis of clinical samples. Emphasis is put on the improvement of analytical techniques including the combination of different LC columns, isotope coded derivatization methods, pseudo-targeted LC-MS method, new data analysis algorithms and structural identification of important metabolites.

LC-MS/MS peptide mapping with automated data processing for routine profiling of N-glycans in immunoglobulins

Protein N-Glycan analysis is traditionally performed by high pH anion exchange chromatography (HPAEC), reversed phase liquid chromatography (RPLC), or hydrophilic interaction liquid chromatography (HILIC) on fluorescence-labeled glycans enzymatically released from the glycoprotein. These methods require time-consuming sample preparations and do not provide site-specific glycosylation information. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) peptide mapping is frequently used for protein structural characterization and, as a bonus, can potentially provide glycan profile on each individual glycosylation site. In this work, a recently developed glycopeptide fragmentation model was used for automated identification, based on their MS/MS, of N-glycopeptides from proteolytic digestion of monoclonal antibodies (mAbs). Experimental conditions were optimized to achieve accurate profiling of glycoforms. Glycan profiles obtained from LC-MS/MS peptide mapping were compared with those obtained from HPAEC, RPLC, and HILIC analyses of released glycans for several mAb molecules. Accuracy, reproducibility, and linearity of the LC-MS/MS peptide mapping method for glycan profiling were evaluated. The LC-MS/MS peptide mapping method with fully automated data analysis requires less sample preparation, provides site-specific information, and may serve as an alternative method for routine profiling of N-glycans on immunoglobulins as well as other glycoproteins with simple N-glycans.

A flexible statistical model for alignment of label-free proteomics data--incorporating ion mobility and product ion information

Background

The goal of many proteomics experiments is to determine the abundance of proteins in biological samples, and the variation thereof in various physiological conditions. High-throughput quantitative proteomics, specifically label-free LC-MS/MS, allows rapid measurement of thousands of proteins, enabling large-scale studies of various biological systems. Prior to analyzing these information-rich datasets, raw data must undergo several computational processing steps. We present a method to address one of the essential steps in proteomics data processing - the matching of peptide measurements across samples.

Results

We describe a novel method for label-free proteomics data alignment with the ability to incorporate previously unused aspects of the data, particularly ion mobility drift times and product ion information. We compare the results of our alignment method to PEPPeR and OpenMS, and compare alignment accuracy achieved by different versions of our method utilizing various data characteristics. Our method results in increased match recall rates and similar or improved mismatch rates compared to PEPPeR and OpenMS feature-based alignment. We also show that the inclusion of drift time and product ion information results in higher recall rates and more confident matches, without increases in error rates.

Conclusions

Based on the results presented here, we argue that the incorporation of ion mobility drift time and product ion information are worthy pursuits. Alignment methods should be flexible enough to utilize all available data, particularly with recent advancements in experimental separation methods.

LC-MS alignment in theory and practice: a comprehensive algorithmic review

Liquid chromatography-mass spectrometry is widely used for comparative replicate sample analysis in proteomics, lipidomics and metabolomics. Before statistical comparison, registration must be established to match corresponding analytes from run to run. Alignment, the most popular correspondence approach, consists of constructing a function that warps the content of runs to most closely match a given reference sample. To date, dozens of correspondence algorithms have been proposed, creating a daunting challenge for practitioners in algorithm selection. Yet, existing reviews have highlighted only a few approaches. In this review, we describe 50 correspondence algorithms to facilitate practical algorithm selection. We elucidate the motivation for correspondence and analyze the limitations of current approaches, which include prohibitive runtimes, numerous user parameters, model limitations and the need for reference samples. We suggest and describe a paradigm shift for overcoming current correspondence limitations by building on known liquid chromatography-mass spectrometry behavior.

Nonlinear alignment of chromatograms by means of moving window fast Fourier transfrom cross-correlation

The preprocessing of chromatograms is essential to modern chromatography for further qualitative and quantitative analysis, especially when chromatographic instruments are used for herb products analysis involving large number of samples. To accurately compare and analyze the obtained chromatograms, it is necessary to preprocess, especially align retention time shifts. Here moving window fast Fourier transform (FFT) cross-correlation is introduced to perform nonlinear alignment of high-throughput chromatograms. Since elution characteristics of chromatograms will produce local similarity in retention time shifts, moving window procedure seems to be a better substitute of segmentation steps. The retention time shifts can be calculated and accelerated by FFT cross-correlation. The artifacts can be detected and eliminated from the retention time shifts profile since the continuity of moving window procedure. The proposed method is demonstrated in comparison with recursive alignment by FFT on chromatographic datasets from herb products analysis. It is shown that the proposed method can address nonlinear retention time shift problem in chromatograms with the simple moving window procedure, which will not introduce segments size optimization problem. In additional, the parameters are intuitive and easy to adjust, which makes it off-the-shelf toolbox for alignment of chromatograms.

Graph-based peak alignment algorithms for multiple liquid chromatography-mass spectrometry datasets

Liquid chromatography coupled to mass spectrometry (LC-MS) is the dominant technological platform for proteomics. An LC-MS analysis of a complex biological sample can be visualized as a 'map' of which the positional coordinates are the mass-to-charge ratio (m/z) and chromatographic retention time (RT) of the chemical species profiled. Label-free quantitative proteomics requires the alignment and comparison of multiple LC-MS maps to ascertain the reproducibility of experiments or reveal proteome changes under different conditions. The main challenge in this task lies in correcting inevitable RT shifts. Similar, but not identical, LC instruments and settings can cause peptides to elute at very different times and sometimes in a different order, violating the assumptions of many state-of-the-art alignment tools. To meet this challenge, we developed LWBMatch, a new algorithm based on weighted bipartite matching. Unlike existing tools, which search for accurate warping functions to correct RT shifts, we directly seek a peak-to-peak mapping by maximizing a global similarity function between two LC-MS maps. For alignment tasks with large RT shifts (>500 s), an approximate warping function is determined by locally weighted scatterplot smoothing of potential matched features, detected using a novel voting scheme based on co-elution. For validation, we defined the ground truth for alignment success based on tandem mass spectrometry identifications from sequence searching. We showed that our method outperforms several existing tools in terms of precision and recall, and is capable of aligning maps from different instruments and settings.

AVAILABILITY

Available at https://sourceforge.net/projects/rt-alignment/.

Discovery of undefined protein cross-linking chemistry: a comprehensive methodology utilizing 18O-labeling and mass spectrometry

Characterization of protein cross-linking, particularly without prior knowledge of the chemical nature and site of cross-linking, poses a significant challenge, because of their intrinsic structural complexity and the lack of a comprehensive analytical approach. Toward this end, we have developed a generally applicable workflow-XChem-Finder-that involves four stages: (1) detection of cross-linked peptides via (18)O-labeling at C-termini; (2) determination of the putative partial sequences of each cross-linked peptide pair using a fragment ion mass database search against known protein sequences coupled with a de novo sequence tag search; (3) extension to full sequences based on protease specificity, the unique combination of mass, and other constraints; and (4) deduction of cross-linking chemistry and site. The mass difference between the sum of two putative full-length peptides and the cross-linked peptide provides the formulas (elemental composition analysis) for the functional groups involved in each cross-linking. Combined with sequence restraint from MS/MS data, plausible cross-linking chemistry and site were inferred, and ultimately confirmed, by matching with all data. Applying our approach to a stressed IgG2 antibody, 10 cross-linked peptides were discovered and found to be connected via thioethers originating from disulfides at locations that had not been previously recognized. Furthermore, once the cross-link chemistry was revealed, a targeted cross-link search yielded 4 additional cross-linked peptides that all contain the C-terminus of the light chain.

Tools for label-free peptide quantification

The increasing scale and complexity of quantitative proteomics studies complicate subsequent analysis of the acquired data. Untargeted label-free quantification, based either on feature intensities or on spectral counting, is a method that scales particularly well with respect to the number of samples. It is thus an excellent alternative to labeling techniques. In order to profit from this scalability, however, data analysis has to cope with large amounts of data, process them automatically, and do a thorough statistical analysis in order to achieve reliable results. We review the state of the art with respect to computational tools for label-free quantification in untargeted proteomics. The two fundamental approaches are feature-based quantification, relying on the summed-up mass spectrometric intensity of peptides, and spectral counting, which relies on the number of MS/MS spectra acquired for a certain protein. We review the current algorithmic approaches underlying some widely used software packages and briefly discuss the statistical strategies for analyzing the data.

Automated precursor ion exclusion during LC-MS/MS data acquisition for optimal ion identification

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is widely used for characterizing multiple samples of complex mixtures with similar compositions. This article addresses a data acquisition strategy for collecting a maximal number of unique, high-quality MS/MS during LC-MS/MS analysis of multiple samples. Based on the concept that a component only needs to be identified once when analyzing multiple samples with similar compositions, an automated intersample data-dependent acquisition strategy was developed. The strategy is based on precursor ion exclusion (PIE) and is implemented in MassAnalyzer in an automated fashion for Thermo Scientific (San Jose, CA, USA) mass spectrometers. In this method, MassAnalyzer submits one sample at a time to the sample queue. After data acquisition of each sample, MassAnalyzer automatically analyzes the data to generate a PIE list based on the MS/MS precursor ions, merges this list with the list generated from previous runs, adds the list to the MS method file, and submits the next sample to the queue. The PIE list contains both m/z value and time window for each precursor ion, and is generated intelligently so that if an MS/MS is insufficient for identifying the peak of interest, it will be collected again near the top of the peak in the next run. Therefore, the strategy maximizes both quality and the number of unique MS/MS. When automated PIE was used to acquire LC-MS/MS data of an antibody tryptic digest and a soy hydrolysate sample, the number of identified ions increased by 52% and 93%, respectively, compared with data acquired without using PIE.