Sciex (Canada)

We have developed an electron-activated dissociation (EAD) device with product isolation functionality for multistage tandem mass spectrometry (MSn). The EAD portion is a branched magneto-radio frequency ion trap with an electron beam source, and we attached a linear radio frequency quadrupole (RFQ) ion trap (or D trap) to one of the branches of the EAD device. Because the D trap was installed inside a strong ring permanent magnet, we made the linear quadrupole rods from para-magnetic stainless steel, which works as a magnetic shield. Using this D trap, we isolated a fragment produced by the first dissociation technique, which can be further fragmented by collision-induced dissociation (CID) or EAD. Using the new EAD-D trap, we demonstrated a near-complete structural identification of cardiolipins via an MS3 workflow with CID followed by electronically excited dissociation (EED) after isolation from the CID products. The number of double bonds, their positions, and cis/trans isomerism in each acyl group, as well as the regioisomerism of acyl chains, were fully identified; however, the only remaining structural ambiguity of the cardiolipins was the chirality at the central carbon of the central glycerol group. We identified 18 cardiolipins in Escherichia coli (E. coli) extract.

This study is significant as it discovered deep proteomic signatures for the highly aggressive and malignant metaplastic breast cancer (MCB) which is now considered a fifth subtype based upon its remarkable intra-tumoral heterogeneity that illustrates its unique cell plasticity. To efficiently analyze formalin-fixed paraffin-embedded (FFPE) breast tissues from patients with different breast cancer subtypes and disease-free individuals, we optimized a novel workflow in which we combined paraffinization and Folch extraction. We identified and confidently quantified ∼6,000 protein groups. We were able to find robust changes in extracellular matrix (ECM) with cancer, even though no ECM enrichments were performed. Interestingly, despite the relatively small human cohort size (42 patients), distinct protein signatures emerged throughout all cancer subtypes - common and unique - with remarkable statistical significance for many cancer-relevant proteins and pathways. This Pilot study indicates the hypothesis that an altered stroma can dictate epithelial tumor cell fate. We also observed that MBC was characterized by an especially immunosuppressed tumor environment. We do note the limitation of the relatively small cohort size of our study, and in the future additional patient cohorts will be needed to further validate our findings.

ABSTRACT Introduction The emergence of personalized medicine (PM) has shifted the focus of healthcare from the traditional ‘one-size-fits-all’ approach to strategies tailored to individual patients, accounting for genetic, environmental, and lifestyle factors. Acoustic ejection mass spectrometry (AEMS) is a novel technology that offers a robust and scalable platform for high-throughput MS readout. AEMS achieves analytical speeds of one sample per second while maintaining high data quality, broad compound coverage, and minimal sample preparation, making it an invaluable tool for PM. Areas covered This article explores the potential of AEMS in critical PM applications, including therapeutic drug monitoring (TDM), proteomics, metabolomics, and mass spectrometry imaging. AEMS simplifies conventional workflows by minimizing sample preparation, enhancing automation compatibility, and enabling direct analysis of complex biological matrices. Expert Opinion Integrating AEMS with orthogonal separation techniques such as differential mobility spectrometry (DMS) further addresses challenges in isomer discrimination, expanding the platform’s analytical capabilities. Additionally, the development of high-throughput data processing tools could further enable AEMS to accelerate the development of personalized medicine.

A comprehensive characterization of biotherapeutics, mandated by regulatory authorities, requires analyses of a protein drug at multiple structure levels. Such multilevel characterization can be performed by mass spectrometry (MS), with established conventional MS-based assays of product quality attributes (PQAs) comprising intact protein and subunit middle-up MS with analytes resolved on a C4 column, and bottom-up peptide mapping with analytes resolved on a C18 column. Recent advances in MS have facilitated the increasing use of middle-down analysis, expanding the qualitative analytical capability of MS for protein characterization. Recent studies using less-retentive reversed-phase LC in bottom-up MS also offer an opportunity for streamlining equipment configuration to a single-column LC-MS setup for multilevel characterization of therapeutic proteins. In this study, we developed a robust middle-down LC-MS method on a ZenoTOF 7600 and evaluated a C4 LC-MS setup for the characterization of NISTmAb, RG7221 bispecific antibody (bsAb), and Fc-fusion etanercept by intact protein, subunit middle-up/down, and bottom-up analyses. Successful multilevel characterization of the analytes using C4 LC-MS was demonstrated; notably, high sequence coverage and comprehensive post-translational modification profiling, including the mapping of all 13 O- and 3 N-glycosylation sites on etanercept in a single run, were obtained by bottom-up C4 LC-MS. This is also the first report on middle-down analysis of the major etanercept TNFR and Fc subunit glycoforms. A streamlined single-column LC-MS setup will enable more robust and efficient MS workflows for PQA assessment and simplify the integration of an LC-MS analyzer as a process analytical technology instrument for biopharma applications.

Ambient mass spectrometry (MS) technologies have been applied to spatial metabolomic profiling of various samples in an attempt to both increase analysis speed and reduce the length of sample preparation. Recent studies, however, have focused on improving the spatial resolution of ambient approaches. Finer resolution requires greater analysis times and commensurate computing power for more sophisticated data analysis algorithms and larger data sets. Higher resolution provides a more detailed molecular picture of the sample; however, for some applications, this is not required. A liquid microjunction surface sampling probe (LMJ-SSP) based MS platform combined with unsupervised multivariant analysis based hyperspectral visualization is demonstrated for the metabolomic analysis of marine bacteria from the genus Pseudoalteromonas to create a rapid and robust spatial profiling workflow for microbial natural product screening. In our study, metabolomic profiles of different Pseudoalteromonas species are quickly acquired without any sample preparation and distinguished by unsupervised multivariant analysis. Our robust platform is capable of automated direct sampling of microbes cultured on agar without clogging. Hyperspectral visualization-based rapid spatial profiling provides adequate spatial metabolite information on microbial samples through red-green-blue (RGB) color annotation. Both static and temporal metabolome differences can be visualized by straightforward color differences and differentiating m/z values identified afterward. Through this approach, novel analogues and their potential biosynthetic pathways are discovered by applying results from the spatial navigation to chromatography-based metabolome annotation. In this current research, LMJ-SSP is shown to be a robust and rapid spatial profiling method. Unsupervised multivariant analysis based hyperspectral visualization is proven straightforward for facile/rapid data interpretation. The combination of direct analysis and innovative data visualization forms a powerful tool to aid the identification/interpretation of interesting compounds from conventional metabolomics analysis.

Interests in covalent drugs have grown in modern drug discovery as they could tackle challenging targets traditionally considered "undruggable". The identification of covalent binders to target proteins typically involves directly measuring protein covalent modifications using high-resolution mass spectrometry. With a continually expanding library of compounds, conventional mass spectrometry platforms such as LC-MS and SPE-MS have become limiting factors for high-throughput screening. Here, we introduce a prototype high-resolution acoustic ejection mass spectrometry (AEMS) system for the rapid screening of a covalent modifier library comprising ∼10,000 compounds against a 50 kDa-sized target protein─Werner syndrome helicase. The screening samples were arranged in a 1536-well format. The sample buffer containing high-concentration salts was directly analyzed without any cleanup steps, minimizing sample preparation efforts and ensuring protein stability. The entire AEMS analysis process could be completed within a mere 17 h. An automated data analysis tool facilitated batch processing of the sample data and quantitation of the formation of various covalent protein-ligand adducts. The screening results displayed a high degree of fidelity, with a Z' factor of 0.8 and a hit rate of 2.3%. The identified hits underwent orthogonal testing in a biochemical activity assay, revealing that 75% were functional antagonists of the target protein. Notably, a comparative analysis with LC-MS showcased the AEMS platform's low risk of false positives or false negatives. This innovative platform has enabled robust high-throughput covalent modifier screening, featuring a 10-fold increase in library size and a 10- to 100-fold increase in throughput when compared with similar reports in the existing literature.

ABSTRACT Introduction High-throughput mass spectrometry that could deliver > 10 times faster sample readout speed than traditional LC-based platforms has emerged as a powerful analytical technique, enabling the rapid analysis of complex biological samples. This increased speed of MS data acquisition has brought a critical demand for automatic data processing capabilities that should match or surpass the speed of data acquisition. Those data processing capabilities should serve the different requirements of drug discovery workflows. Areas covered This paper introduced the key steps of the automatic data processing workflows for high-throughput MS technologies. Specific examples and requirements are detailed for different drug discovery applications. Expert opinion The demand for automatic data processing in high-throughput mass spectrometry is driven by the need to keep pace with the accelerated speed of data acquisition. The seamless integration of processing capabilities with LIMS, efficient data review mechanisms, and the exploration of future features such as real-time feedback, automatic method optimization, and AI model training is crucial for advancing the drug discovery field. As technology continues to evolve, the synergy between high-throughput mass spectrometry and intelligent data processing will undoubtedly play a pivotal role in shaping the future of high-throughput drug discovery applications.

RationaleMolecular imaging of samples using mass spectrometric techniques, such as matrix‐assisted laser desorption ionization or desorption electrospray ionization, requires the sample surface to be even/flat and sliced into thin sections (c. 10 μm). Furthermore, sample preparation steps can alter the analyte composition of the sample. The liquid microjunction‐surface sampling probe (LMJ‐SSP) is a robust sampling interface that enables surface profiling with minimal sample preparation. In conjunction with a conductance feedback system, the LMJ‐SSP can be used to automatically sample uneven specimens.MethodsA sampling stage was built with a modified 3D printer where the LMJ‐SSP is attached to the printing head. This setup can scan across flat and even surfaces in a predefined pattern (“static sampling mode”). Uneven samples are automatically probed in “conductance sampling mode” where an electric potential is applied and measured at the probe. When the probe contacts the electrically grounded sample, the potential at the probe drops, which is used as a feedback signal to determine the optimal position of the probe for sampling each location.ResultsThe applicability of the probe/sensing system was demonstrated by first examining the strawberry tissue using the “static sampling mode.” Second, porcine tissue samples were profiled using the “conductance sampling mode.” With minimal sample preparation, an area of 11 × 15 mm was profiled in less than 2 h. From the obtained results, adipose areas could be distinguished from non‐adipose parts. The versatility of the approach was further demonstrated by directly sampling the bacteria colonies on agar and resected human kidney (intratumoral hemorrhage) specimens with thicknesses ranging from 1 to 4 mm.ConclusionThe LMJ‐SSP in conjunction with a conductive feedback system is a powerful tool that allows for fast, reproducible, and automated assessment of uneven surfaces with minimal sample preparation. This setup could be used for perioperative assessment of tissue samples, food screening, and natural product discovery, among others.

Mass spectrometry imaging (MSI) has been widely used to discover natural products (NPs) from underexplored microbiological sources. However, the technique is limited by incompatibility with complicated/uneven surface topography and labor-intensive sample preparation, as well as lengthy compound profiling procedures. Here, liquid micro-junction surface sampling probe (LMJ-SSP)-based MSI is used for rapid profiling of natural products from Gram-negative marine bacteria Pseudoalteromonas on nutrient agar media without any sample preparation. A conductance-based autosampling platform with 1 mm spatial resolution and an innovative multivariant analysis-driven method was used to create one hyperspectral image for the sampling area. NP discovery requires general spatial correlation between m/z and colony location but not highly precise spatial resolution. The hyperspectral image was used to annotate different m/z by straightforward color differences without the need to directly interrogate the spectra. To demonstrate the utility of our approach, the rapid analysis of Pseudoalteromonas rubra DSM6842, Pseudoalteromonas tunicata DSM14096, Pseudoalteromonas piscicida JCM20779, and Pseudoalteromonas elyakovii ATCC700519 cultures was directly performed on Agar. Various natural products, including prodiginine and tambjamine analogues, were quickly identified from the hyperspectral image, and the dynamic extracellular environment was shown with compound heatmaps. Hyperspectral visualization-based MSI is an efficient and sensitive strategy for direct and rapid natural product profiling from different Pseudoalteromonas strains.