Romain Huguet

Tandem mass spectrometry (MS/MS) is the gold standard for intact glycopeptide identification, enabling peptide sequence elucidation and site-specific localization of glycan compositions. Beam-type collisional activation is generally sufficient for N-glycopeptides, while electron-driven dissociation is crucial for site localization in O-glycopeptides. Modern glycoproteomic methods often employ multiple dissociation techniques within a single LC-MS/MS analysis, but this approach frequently sacrifices sensitivity when analyzing multiple glycopeptide classes simultaneously. Here we explore the utility of intelligent data acquisition for glycoproteomics through real-time library searching (RTLS) to match oxonium ion patterns for on-the-fly selection of the appropriate dissociation method. By matching dissociation method with glycopeptide class, this autonomous dissociation-type selection (ADS) generates equivalent numbers of N-glycopeptide identifications relative to traditional beam-type collisional activation methods while also yielding comparable numbers of site-localized O-glycopeptide identifications relative to conventional electron transfer dissociation-based methods. The ADS approach represents a step forward in glycoproteomics throughput by enabling site-specific characterization of both N-and O-glycopeptides within the same LC-MS/MS acquisition.

Modern mass spectrometry technology allows for extensive sequencing of the ~25 kDa subunits of monoclonal antibodies (mAbs) produced by IdeS proteolysis followed by disulfide bond reduction, an approach known as middle-down mass spectrometry (MD MS). However, the spectral congestion of tandem mass spectra of large polypeptides dramatically complicates fragment ion assignment. Here, we report the development and benchmark of an MD MS strategy based on the combination of different ion fragmentation techniques with proton transfer charge reduction (PTCR) to simplify the gas-phase sequencing of mAb subunits. Applied on the liquid chromatography time scale using an Orbitrap Tribrid mass spectrometer, PTCR produces easy-to-interpret mass spectra with limited ion signal overlap. We demonstrate that the accurate estimation of the number of charges submitted to the Orbitrap mass analyzer after PTCR allows for the detection of charge-reduced product ions over a wide mass-over-charge (m/z) window with low parts per million m/z accuracy. Therefore, PTCR-based MD MS analysis increases not only sequence coverage, number of uniquely identified fragments and number of assigned complementary ion pairs, but also the general confidence in the assignment of subunit fragments. This data acquisition method can be readily applied to any class of mAbs without an apparent need for optimization, and benefits from the high resolving power of the Orbitrap mass analyzer to return sequence coverage of individual subunits exceeding 80% in a single run, and >90% when just two experiments are combined.

The high-throughput quantification of intact proteoforms using a label-free approach is typically performed on proteins in the 0-30 kDa mass range extracted from whole cell or tissue lysates. Unfortunately, even when high-resolution separation of proteoforms is achieved by either high performance liquid chromatography or capillary electrophoresis, the number of proteoforms that can be identified and quantified is inevitably limited by the inherent sample complexity. Here we benchmark label-free quantification of proteoforms of Escherichia coli by applying gas-phase fractionation (GPF) via field asymmetric ion mobility spectrometry (FAIMS). Recent advances in Orbitrap instrumentation have enabled the acquisition of high-quality intact and fragmentation mass spectra without the need for averaging time-domain transients prior to Fourier transform. The resulting speed improvements allowed for the application of multiple FAIMS compensation voltages in the same liquid chromatography-tandem mass spectrometry experiment without increasing the overall data acquisition cycle. As a result, the application of FAIMS to label-free quantification based on intact mass spectra substantially increases the number of both identified and quantified proteoforms without penalizing quantification accuracy in comparison to traditional label-free experiments that do not adopt GPF.

Blood serum and plasma are arguably the most commonly analyzed clinical samples, with dozens of proteins serving as validated biomarkers for various human diseases. Top-down proteomics may provide additional insights into disease etiopathogenesis since this approach focuses on protein forms, or proteoforms, originally circulating in blood, potentially providing access to information about relevant post-translational modifications, truncation, single amino acid substitutions and many other sources of protein variation. However, the vast majority of proteomic studies on serum and plasma are carried out using peptide-centric, bottom-up approaches which cannot recapitulate the original proteoform content of samples. Lengthy sample preparation and the need for extensive prefractionation to mitigate proteoform dynamic range issues are likely factors preventing clinical laboratories from routinely performing top-down experiments. In this study, we describe a straightforward protocol for intact proteoform sample preparation based on depletion of albumin and immunoglobulins followed by simplified fractionation of remaining serum proteins via polyacrylamide gel electrophoresis. After molecular weight-based fractionation, we supplemented the traditional liquid chromatography tandem mass spectrometry (LC-MS2) data acquisition with high-field asymmetric waveform ion mobility spectrometry (FAIMS), which served as an additional separation dimension to further simplify serum proteoforms mixtures. This LC-FAIMS-MS2 method led to the identification of over 1,000 serum proteoforms <30 kDa using a reduced number of experiments, more than doubling the number of proteoforms identified in previous studies.

Non-target screening (NTS) based on the combination of liquid chromatography coupled to high-resolution mass spectrometry has become the key method to identify organic micro-pollutants (OMPs) in water samples. However, a large number of compounds remains unidentified with current NTS approaches due to poor quality fragmentation spectra generated by suboptimal fragmentation methods. Here, the potential of the alternative fragmentation technique ultraviolet photodissociation (UVPD) to improve identification of OMPs in water samples was investigated. A diverse set of water-relevant OMPs was selected based on k-means clustering and unsupervised artificial neural networks. The selected OMPs were analyzed using an Orbitrap Fusion Lumos equipped with UVPD. Therewith, information-rich MS2 fragmentation spectra of compounds that fragment poorly with higher-energy collisional dissociation (HCD) could be attained. Development of an R-based data analysis workflow and user interface facilitated the characterization and comparison of HCD and UVPD fragmentation patterns. UVPD and HCD generated both unique and common fragments, demonstrating that some fragmentation pathways are specific to the respective fragmentation method, while others seem more generic. Application of UVPD fragmentation to the analysis of surface water enabled OMP identification using existing HCD spectral libraries. However, high-throughput applications still require optimization of informatics workflows and spectral libraries tailored to UVPD.

We report on the combination of nanodroplet sample preparation, ultra-low-flow nanoLC, high-field asymmetric ion mobility spectrometry (FAIMS), and the latest-generation Orbitrap Eclipse Tribrid mass spectrometer for greatly improved single-cell proteome profiling. FAIMS effectively filtered out singly charged ions for more effective MS analysis of multiply charged peptides, resulting in an average of 1056 protein groups identified from single HeLa cells without MS1-level feature matching. This is 2.3 times more identifications than without FAIMS and a far greater level of proteome coverage for single mammalian cells than has been previously reported for a label-free study. Differential analysis of single microdissected motor neurons and interneurons from human spinal tissue indicated a similar level of proteome coverage, and the two subpopulations of cells were readily differentiated based on single-cell label-free quantification.

The analysis of monoclonal antibodies (mAbs) by a middle-down approach is a growing field that attracts the attention of many researchers and biopharma companies. Usually, liquid fractionation techniques are used to separate mAbs polypeptides chains before mass spectrometry (MS) analysis. Gas-phase fractionation techniques such as high-field asymmetric waveform ion mobility spectrometry (FAIMS) can replace liquid-based separations and reduce both analysis time and cost. Here, we present a rapid FAIMS tandem MS method capable of characterizing the polypeptide sequence of mAbs light (Lc) and heavy (Hc) chains in an unprecedented, easy, and fast fashion. This new method uses commercially available instruments and takes ∼ 24 minutes —40-60% faster than regular LC-MS/MS analysis — to acquire fragmentation data using different dissociation methods.