Journal of the American Society for Mass Spectrometry最新文献

The growing use of branched polymers in various industrial and technological applications has prompted significant interest in understanding their properties, for which accurate structure determination is vital. This work is the first instance where the macromolecular structures of dendrimers, linear polymers, and hyperbranched polymers with analogous 2,2-bis(hydroxymethyl)propionic acid (bis-MPA) backbone groups were synthesized and analyzed via tandem mass spectrometry (MS/MS). When comparing the fragmentation pathways of these polymers, some unique and interesting patterns emerge that provide insight into the primary structures and architectures of each of these materials. As expected, the linear polymer undergoes multiple random backbone cleavages resulting in several fragment ion distributions that vary in size and end group composition. The hyperbranched polymer dissociates preferentially at branching sites; however, differently branched isomers exist for each oligomer size, thus giving rise again to several fragment distributions. In contrast, the dendrimer presents a unique fragmentation pattern comprising key fragment ions of high molecular weight; this unique characteristic stands out as a signature for identifying dendrimer structures. Overall, dendrimers, hyperbranched polymers, and linear polymers display individualized fragmentation behaviors, which are caused by differences in primary structure. As a result, tandem mass spectrometry fragmentation is a particularly useful analytical tool for distinguishing such macromolecular architectures.

Cysteine residues are crucial for the formation of conserved disulfide bonds in therapeutic monoclonal antibodies (mAbs), which are essential for their folding and structural stability. The presence of free thiols in mAbs can indicate incomplete disulfide bond formation, potentially impacting the molecule's conformational stability. Free thiol quantitation has been achieved using labeling-based strategies such as maleimide and haloalkyl derivatives at both intact and peptide levels. However, intact-level measurement only provides total free thiol levels, while peptide-level measurement is time-consuming and more prone to assay-induced artifacts. In this study, we present a novel label-free HILIC-MS method that separates free thiol species at the subunit level, followed by free thiol localization by the MS2 fragmentation pattern. This allows for facile identification and quantitation of intrachain free thiols at domain-specific resolution. Compared to bottom-up approaches, this subunit HILIC-MS method excels in simpler sample preparation and higher throughput and enables chain-specific free thiol analysis for bispecific mAbs. This method can be readily applied for screening mAb candidates with elevated levels of free thiols in early-stage developability assessment and facilitating an effective comparability evaluation of mAb samples during process development.

Native top-down mass spectrometry is a powerful approach for characterizing proteoforms and has recently been applied to provide similarly powerful insights into protein conformation. Current approaches, however, are limited such that structural insights can only be obtained for the entire conformational landscape in bulk or without any direct conformational measurement. We report a new ion-mobility-enabled method for performing native top-down MS in a conformation-specific manner. Our approach identified conformation-linked differences in backbone dissociation for the model protein calmodulin, which simultaneously informs upon proteoform variations and provides structural insights. We also illustrate that our method can be applied to protein-ligand complexes, either to identify components or to probe ligand-induced structural changes.

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

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a rapidly advancing technology for biomedical research. As spatial resolution increases, however, so do acquisition time, file size, and experimental cost, which increases the need to perform precise sampling of targeted tissue regions to optimize the biological information gleaned from an experiment and minimize wasted resources. The ability to define instrument measurement regions based on key tissue features and automatically sample these specific regions of interest (ROIs) addresses this challenge. Herein, we demonstrate a workflow using standard software that allows for direct sampling of microscopy-defined regions by MALDI IMS. Three case studies are included, highlighting different methods for defining features from common sample types─manual annotation of vasculature in human brain tissue, automated segmentation of renal functional tissue units across whole slide images using custom segmentation algorithms, and automated segmentation of dispersed HeLa cells using open-source software. Each case minimizes data acquisition from unnecessary sample regions and dramatically increases throughput while uncovering molecular heterogeneity within targeted ROIs. This workflow provides an approachable method for spatially targeted MALDI IMS driven by microscopy as part of multimodal molecular imaging studies.

In time-of-flight secondary ion mass spectrometry (ToF-SIMS), multivariate analysis (MVA) methods such as principal component analysis (PCA) are routinely employed to differentiate spectra. However, additional insights can often be gained by comparing processes, where each process is characterized by its own start and end spectra, such as when identical samples undergo slightly different treatments or when slightly different samples receive the same treatment. This study proposes a strategy to compare such processes by decomposing the loading vectors associated with them, which highlights differences in the relative behavior of the peaks. This strategy identifies key information beyond what is captured by the loading vectors or the end spectra alone. While PCA is widely used, partial least-squares discriminant analysis (PLS-DA) serves as a supervised alternative and is the preferred method for deriving process-related loading vectors when classes are narrowly separated. The effectiveness of the decomposition strategy is demonstrated using artificial spectra and applied to a ToF-SIMS materials science case study on the photodegradation of N719 dye, a common dye in photovoltaics, on a mesoporous TiO₂ anode. The study revealed that the photodegradation process varies over time, and the resulting fragments have been identified accordingly. The proposed methodology, applicable to both labeled (supervised) and unlabeled (unsupervised) spectral data, can be seamlessly integrated into most modern mass spectrometry data analysis workflows to automatically generate a list of peaks whose relative behavior varies between two processes, and is particularly effective in identifying subtle differences between highly similar physicochemical processes.

High resolution mass spectrometry images are of increasing importance in biological applications, such as the study of tissues and single cells. Two promising techniques for this are matrix-enhanced secondary ion mass spectrometry (ME-SIMS) and matrix-assisted laser desorption/ionization (MALDI). For both techniques, the sample of interest must be coated with a matrix prior to analysis, and analytes must migrate into the matrix. The mechanisms involved in this migration and the factors that influence the migration are poorly understood, which lead to difficulties with reproducibility. In this work, a sublimation matrix coater with an effusion cell and sample cooling was developed and built in-house for controlled physical vapor deposition. In this system, sample transfer between the coater and mass spectrometer is possible without breaking vacuum, which facilitates the study of environmental influences on analyte migration. The influence of exposure to ambient air on the migration of two analytes (a lipid and a peptide), which were coated with the matrix α-cyano-4-hydroxycinnamic acid (CHCA), was studied using 3D-SIMS imaging. Although the distribution of analyte in the matrix changed very little after 21 h of storage in vacuum, significant redistribution of the analyte was observed after exposure to ambient air. The magnitude of the effect was greater for the lipid than for the peptide. Further work is needed to determine the role of humidity in the redistribution process and the impact of analyte redistribution on MALDI measurements.

Rapid equilibrium dialysis (RED) is predominantly used for the characterization of drug absorption, distribution, metabolism, and excretion (ADME) properties in plasma and biological fluids. We describe herein improvements in the use of RED in conjunction with mass spectrometry (RED-MS) to enable robust binding affinity measurements of small molecules for recombinant proteins and complexes from a single dialysis data set. The affinities calculated from RED-MS correlated well with measurements by both surface plasmon resonance (SPR) and affinity selection mass spectrometry (AS-MS). The method was particularly useful for quantifying the binding of small molecules to large protein complexes that were not amendable by common biophysical characterization techniques. Compound pooling and integration with automated liquid handling increased assay throughput and enabled the analysis of hundreds of measurements per week. RED-MS offers a viable option for measuring compound binding in solution and may facilitate small molecule affinity optimization toward difficult-to-drug protein complexes.

Existing analytical techniques are being improved or applied in new ways to profile the tissue microenvironment (TME) to better understand the role of cells in disease research. Fully understanding the complex interactions between cells of many different types and functions is often slowed by the intense data analysis required. Multiplexed Ion Beam Imaging (MIBI) has been developed to simultaneously characterize 50+ cell types and their functions within the TME with a subcellular spatial resolution, but this results in complex data sets that are challenging to qualitatively analyze. Deep Learning (DL) techniques were used to build the MIBIsight workflow, which can process images containing thousands of cells into easily digestible reports and plots to enable researchers to easily summarize data sets in a study and make informed conclusions. Here we present the three types of DL models that have been trained with annotated MIBI images that have been pathologist validated as well as the associated workflow for the evolution of raw mass spectral data into actionable reports and plots.