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The multi-attribute method (MAM) was conceived as a single assay to potentially replace multiple single-attribute assays that have long been used in process development and quality control (QC) for protein therapeutics. MAM is rooted in traditional peptide mapping methods; it leverages mass spectrometry (MS) detection for confident identification and quantitation of many types of protein attributes that may be targeted for monitoring. While MAM has been widely explored across the industry, it has yet to gain a strong foothold within QC laboratories as a replacement method for established orthogonal platforms. Members of the MAM consortium recently undertook an interlaboratory study to evaluate the industry-wide status of MAM. Here we present the results of this study as they pertain to the targeted attribute analytics component of MAM, including investigation into the sources of variability between laboratories and comparison of MAM data to orthogonal methods. These results are made available with an eye toward aiding the community in further optimizing the method to enable its more frequent use in the QC environment.

The commercially available Polysorbate 80 (PS-80) is a highly heterogeneous product. It is a complex and structurally diverse mixture consisting of polymeric species containing polyoxyethylenes (POEs), fatty acid esters, with/or without a carbohydrate core. The core is primarily sorbitan, with some isosorbide and sorbitol. Depending on the sources of fatty acids and the degrees of esterification, multiple combinations of fatty acid esters are commonly observed. A number of POE intermediates, such as polyoxyethylene glycols, POE-sorbitans, POE-isosorbides, and an array of fatty acid esters from these intermediates remain in the raw material as well. The complex composition of PS-80 is difficult to control and poses a significant characterization challenge for its use in the pharmaceutical industry. Here, we present a novel solution for PS-80 characterization using ultra high-performance liquid chromatography coupled with charge-reduction high resolution mass spectrometry. Post column co-infusion of triethylamine focused the signal into mainly singly charged molecular ions and reduced the extent of in-source fragmentation, resulting in a simpler ion map and enhanced measurement of PS-80 species. The data processing workflow is designed to programmatically identify PS-80 component classes and reduce the burden of manually analyzing complex MS data. The 2-dimensional graphical representation of the data helps visualize these features. Together, these innovative methodologies enabled us to analyze components in PS-80 with unprecedented detail and shall be a useful tool to study formulation and stability of pharmaceutical preparations. The power of this approach was demonstrated by comparing the composition of PS-80 obtained from different vendors.

Host cell proteins (HCPs) are process‐related impurities derived from host organisms, which need to be controlled to ensure adequate product quality and safety. In this study, product quality attributes were tracked for several monoclonal antibodies (mAbs) under the intended storage and accelerated stability conditions. One product quality attribute not expected to be stability indicating is the N‐glycan heterogeneity profile. However, significant N‐glycan degradation was observed for one mAb under accelerated and stressed stability conditions. The root cause for this instability was attributed to hexosaminidase B (HEXB), an enzyme known to remove terminal N‐acetylglucosamine (GlcNAc). HEXB was identified by liquid chromatography–mass spectrometry (LC–MS)‐based proteomics approach to be enriched in the impacted stability batches from mAb‐1. Subsequently, enzymatic and targeted multiple reaction monitoring (MRM) MS assays were developed to support process and product characterization. A potential interaction between HEXB and mAb‐1 was initially observed from the analysis of process intermediates by proteomics among several mAbs and later supported by computational modeling. An improved bioprocess was developed to significantly reduce HEXB levels in the final drug substance. A risk assessment was conducted by evaluating the in silico immunogenicity risk and the impact on product quality. To the best of our knowledge, HEXB is the first residual HCP reported to have impact on the glycan profile of a formulated drug product. The combination of different analytical tools, mass spectrometry, and computational modeling provides a general strategy on how to study residual HCP for biotherapeutics development.

As the application for quantitative proteomics in the life sciences has grown in recent years, so has the need for more robust and generally applicable methods for quality control and calibration. The reliability of quantitative proteomics is tightly linked to the reproducibility and stability of the analytical platforms, which are typically multicomponent (e.g. sample preparation, multistep separations, and mass spectrometry) with individual components contributing unequally to the overall system reproducibility. Variations in quantitative accuracy are thus inevitable, and quality control and calibration become essential for the assessment of the quality of the analyses themselves. Toward this end, the use of internal standards cannot only assist in the detection and removal of outlier data acquired by an irreproducible system (quality control) but can also be used for detection of changes in instruments for their subsequent performance and calibration. Here we introduce a set of halogenated peptides as internal standards. The peptides are custom designed to have properties suitable for various quality control assessments, data calibration, and normalization processes. The unique isotope distribution of halogenated peptides makes their mass spectral detection easy and unambiguous when spiked into complex peptide mixtures. In addition, they were designed to elute sequentially over an entire aqueous to organic LC gradient and to have m/z values within the commonly scanned mass range (300–1800 Da). In a series of experiments in which these peptides were spiked into an enriched N-glycosite peptide fraction (i.e. from formerly N-glycosylated intact proteins in their deglycosylated form) isolated from human plasma, we show the utility and performance of these halogenated peptides for sample preparation and LC injection quality control as well as for retention time and mass calibration. Further use of the peptides for signal intensity normalization and retention time synchronization for selected reaction monitoring experiments is also demonstrated.

Rapid detection and identification of bacteria and other microorganisms has become a field where new analytical methods are needed. An approach, based on the use of pyrolysis coupled to mass spectrometry (Py-MS), for the detection and identification of bacteria in urine and other aqueous samples is described. In this procedure, the sample is placed on a direct insertion probe and pyrolysis is conducted directly into the ion source of the mass spectrometer. Ionization is achieved with a metastable atom bombardment source (MAB). In contrast to electron ionization, this novel ionization source, by providing discrete ionization energies, allows selective ionization and control over fragmentation. The MAB source is coupled to a time-of-flight (Tof) mass analyzer that provides high sensitivity and fast spectral acquisition. Experiments conducted with a number of uropathogenic bacteria, such as Escherichia coli, Citrobacter freundii, Proteus mirabilis and Pseudomonas aeruginosa, reveal that they provide discernable fingerprints when analyzed in urine samples by Py-MAB-Tof. Results can be obtained in minutes and the sensitivity is such that 104 bacteria on the pyrolysis probe are required for analysis.

As the application for quantitative proteomics in the life sciences has grown in recent years, so has the need for more robust and generally applicable methods for quality control and calibration. The reliability of quantitative proteomics is tightly linked to the reproducibility and stability of the analytical platforms, which are typically multicomponent (e.g. sample preparation, multistep separations, and mass spectrometry) with individual components contributing unequally to the overall system reproducibility. Variations in quantitative accuracy are thus inevitable, and quality control and calibration become essential for the assessment of the quality of the analyses themselves. Toward this end, the use of internal standards cannot only assist in the detection and removal of outlier data acquired by an irreproducible system (quality control) but can also be used for detection of changes in instruments for their subsequent performance and calibration. Here we introduce a set of halogenated peptides as internal standards. The peptides are custom designed to have properties suitable for various quality control assessments, data calibration, and normalization processes. The unique isotope distribution of halogenated peptides makes their mass spectral detection easy and unambiguous when spiked into complex peptide mixtures. In addition, they were designed to elute sequentially over an entire aqueous to organic LC gradient and to have m/z values within the commonly scanned mass range (300–1800 Da). In a series of experiments in which these peptides were spiked into an enriched N-glycosite peptide fraction (i.e. from formerly N-glycosylated intact proteins in their deglycosylated form) isolated from human plasma, we show the utility and performance of these halogenated peptides for sample preparation and LC injection quality control as well as for retention time and mass calibration. Further use of the peptides for signal intensity normalization and retention time synchronization for selected reaction monitoring experiments is also demonstrated.

Label-free quantification of high mass resolution LC-MS data has emerged as a promising technology for proteome analysis. Computational methods are required for the accurate extraction of peptide signals from LC-MS data and the tracking of these features across the measurements of different samples. We present here an open source software tool, SuperHirn, that comprises a set of modules to process LC-MS data acquired on a high resolution mass spectrometer. The program includes newly developed functionalities to analyze LC-MS data such as feature extraction and quantification, LC-MS similarity analysis, LC-MS alignment of multiple datasets, and intensity normalization. These program routines extract profiles of measured features and comprise tools for clustering and classification analysis of the profiles. SuperHirn was applied in an MS1-based profiling approach to a benchmark LC-MS dataset of complex protein mixtures with defined concentration changes. We show that the program automatically detects profiling trends in an unsupervised manner and is able to associate proteins to their correct theoretical dilution profile.

Tandem mass spectrometry (MS/MS) is frequently used in the identification of peptides and proteins. Typical proteomic experiments rely on algorithms such as SEQUEST and MASCOT to compare thousands of tandem mass spectra against the theoretical fragment ion spectra of peptides in a database. The probabilities that these spectrum-to-sequence assignments are correct can be determined by statistical software such as PeptideProphet or through estimations based on reverse or decoy databases. However, many of the software applications that assign probabilities for MS/MS spectra to sequence matches were developed using training data sets from 3D ion-trap mass spectrometers. Given the variety of types of mass spectrometers that have become commercially available over the last 5 years, we sought to generate a data set of reference data covering multiple instrumentation platforms to facilitate both the refinement of existing computational approaches and the development of novel software tools. We analyzed the proteolytic peptides in a mixture of tryptic digests of 18 proteins, named the "ISB standard protein mix", using 8 different mass spectrometers. These include linear and 3D ion traps, two quadrupole time-of-flight platforms (qq-TOF), and two MALDI-TOF-TOF platforms. The resulting data set, which has been named the Standard Protein Mix Database, consists of over 1.1 million spectra in 150+ replicate runs on the mass spectrometers. The data were inspected for quality of separation and searched using SEQUEST. All data, including the native raw instrument and mzXML formats and the PeptideProphet validated peptide assignments, are available at http://regis-web.systemsbiology.net/PublicDatasets/.

This work shows how fingerprints of mass spectral patterns from microbial isolates are affected by variations in instrumental condition, by sample environment, and by sample handling factors. It describes a novel method by which pattern distortions can be mathematically corrected for variations in factors not amenable to experimental control. One uncontrollable variable is “between-batch” differences in culture media. Another, relevant for determination of noncultured extracts, is differences between the cells’ environmental experience (e.g., starved environmental extracts versus cultured standards). The method suggests that, after a single growth cycle on a solid medium (perhaps, a selective one), pyrolysis MS spectra of microbial isolates can be algorithmically compensated and an unknown isolate identified using a spectral database defined by culture on a different (perhaps, nonselective) medium. This reduces identification time to as few as 24 h from sample collection. The concept also proposes a possible way to compensate certain noncultured, nonisolated samples (e.g., cells concentrated from urine or impacted from aerosol or semi-selectively extracted by immunoaffinity methods from heavily contaminated matrices) for identification within half an hour. Using the method, microbial mass spectra from different labs can be assembled into coherent databases similar to those routinely used to identify pure compounds. This type of data treatment is applicable for rapid detection in biowarfare and bioterror events as well as in forensic, research, and clinical laboratory contexts.

Background Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics. Results We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling. Conclusion The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.