Digital Microfluidics and Magnetic Bead-Based Intact Proteoform Elution for Quantitative Top-down Nanoproteomics of Single C.†elegans Nematodes.

While most nanoproteomics approaches for the analysis of low-input samples are based on bottom-up proteomics workflows, top-down approaches enabling proteoform characterization are still underrepresented. Using mammalian cell proteomes, we established a facile one-pot sample preparation protocol based on protein aggregation on magnetic beads and intact proteoform elution using 40 % formic acid. Performed on a digital microfluidics device, the workflow enabled sensitive analyses of single Caenorhabditis elegans nematodes, thereby increasing the number of proteoform identifications compared to in-tube sample preparation by 46 %. Label-free quantification of single nematodes grown under different conditions allowed to identify changes in the abundance of proteoforms not distinguishable by bottom-up proteomics. The presented workflow will facilitate proteoform-directed analysis on samples of limited availability.

Validation of Top-Down Proteomics Data by Bottom-Up-Based N-Terminomics Reveals Pitfalls in Top-Down-Based Terminomics Workflows.

Bottom-up proteomics (BUP)-based N-terminomics techniques have become standard to identify protein N-termini. While these methods rely on the identification of N-terminal peptides only, top-down proteomics (TDP) comes with the promise to provide additional information about post-translational modifications and the respective C-termini. To evaluate the potential of TDP for terminomics, two established TDP workflows were employed for the proteome analysis of the nematode Caenorhabditis elegans. The N-termini of the identified proteoforms were validated using a BUP-based N-terminomics approach. The TDP workflows used here identified 1658 proteoforms, the N-termini of which were verified by BUP in 25% of entities only. Caveats in both the BUP- and TDP-based workflows were shown to contribute to this low overlap. In BUP, the use of trypsin prohibits the detection of arginine-rich or arginine-deficient N-termini, while in TDP, the formation of artificially generated termini was observed in particular in a workflow encompassing sample treatment with high acid concentrations. Furthermore, we demonstrate the applicability of reductive dimethylation in TDP to confirm biological N-termini. Overall, our study shows not only the potential but also current limitations of TDP for terminomics studies and also presents suggestions for future developments, for example, for data quality control, allowing improvement of the detection of protein termini by TDP.

MSTopDiff: A Tool for the Visualization of Mass Shifts in Deconvoluted Top-Down Proteomics Data for the Database-Independent Detection of Protein Modifications.

Top-down proteomics analyzes intact proteoforms with all of their post-translational modifications and genetic and RNA splice variants. In addition, modifications introduced either deliberately or inadvertently during sample preparation, that is, via oxidation, alkylation, or labeling reagents, or through the formation of noncovalent adducts (e.g., detergents) further increase the sample complexity. To facilitate the recognition of protein modifications introduced during top-down analysis, we developed MSTopDiff, a software tool with a graphical user interface written in Python, which allows one to detect protein modifications by calculating and visualizing mass differences in top-down data without the prerequisite of a database search. We demonstrate the successful application of MSTopDiff for the detection of artifacts originating from oxidation, formylation, overlabeling during isobaric labeling, and adduct formation with cations or sodium dodecyl sulfate. MSTopDiff offers several modes of data representation using deconvoluted MS1 or MS2 spectra. In addition to artificial modifications, the tool enables the visualization of biological modifications such as phosphorylation and acetylation. MSTopDiff provides an overview of the artificial and biological modifications in top-down proteomics samples, which makes it a valuable tool in quality control of standard workflows and for parameter evaluation during method development.

Improved Identification of Proteoforms in Top-Down Proteomics Using FAIMS with Internal CV Stepping.

In top-down (TD) proteomics, prefractionation prior to mass spectrometric (MS) analysis is a crucial step for both the high confidence identification of proteoforms and increased proteome coverage. In addition to liquid-phase separations, gas-phase fractionation strategies such as field asymmetric ion mobility spectrometry (FAIMS) have been shown to be highly beneficial in TD proteomics. However, so far, only external compensation voltage (CV) stepping has been demonstrated for TD proteomics, i.e., single CVs were applied for each run. Here, we investigated the use of internal CV stepping (multiple CVs per acquisition) for single-shot TD analysis, which has huge advantages in terms of measurement time and the amount of sample required. In addition, MS parameters were optimized for the individual CVs since different CVs target certain mass ranges. For example, small proteoforms identified mainly with more negative CVs can be identified with lower resolution and number of microscans than larger proteins identified primarily via less negative CVs. We investigated the optimal combination and number of CVs for different gradient lengths and validated the optimized settings with the low-molecular-weight proteome of CaCo-2 cells obtained using a range of different sample preparation techniques. Compared to measurements without FAIMS, both the number of identified protein groups (+60-94%) and proteoforms (+46-127%) and their confidence were significantly increased, while the measurement time remained identical. In total, we identified 684 protein groups and 2675 proteoforms from CaCo-2 cells in less than 24 h using the optimized multi-CV method.

Quantitative Top-Down Proteomics by Isobaric Labeling with Thiol-Directed Tandem Mass Tags.

While identification-centric (qualitative) top-down proteomics (TDP) has seen rapid progress in the recent past, the quantification of intact proteoforms within complex proteomes is still challenging. The by far mostly applied approach is label-free quantification, which, however, provides limited multiplexing capacity, and its use in combination with multidimensional separation is encountered with a number of problems. Isobaric labeling, which is a standard quantification approach in bottom-up proteomics, circumvents these limitations. Here, we introduce the application of thiol-directed isobaric labeling for quantitative TDP. For this purpose, we analyzed the labeling efficiency and optimized tandem mass spectrometry parameters for optimal backbone fragmentation for identification and reporter ion formation for quantification. Two different separation schemes, gel-eluted liquid fraction entrapment electrophoresis × liquid chromatography-mass spectrometry (LC-MS) and high/low-pH LC-MS, were employed for the analyses of either Escherichia coli (E. coli) proteomes or combined E. coli/yeast samples (two-proteome interference model) to study potential ratio compression. While the thiol-directed labeling introduces a bias in the quantifiable proteoforms, being restricted to Cys-containing proteoforms, our approach showed excellent accuracy in quantification, which is similar to that achievable in bottom-up proteomics. For example, 876 proteoforms could be quantified with high accuracy in an E. coli lysate. The LC-MS data were deposited to the ProteomeXchange with the dataset identifier PXD026310.

Shedding light on both ends: An update on analytical approaches for N- and C-terminomics.

Though proteases were long regarded as nonspecific degradative enzymes, over time, it was recognized that they also hydrolyze peptide bonds very specifically with a limited substrate pool. This irreversible posttranslational modification modulates the fate and activity of many proteins, making proteolytic processing a master switch in the regulation of e.g., the immune system, apoptosis and cancer progression. N- and C-terminomics, the identification of protein termini, has become indispensable in elucidating protease substrates and therefore protease function. Further, terminomics has the potential to identify yet unknown proteoforms, e.g. formed by alternative splicing or the recently discovered alternative ORFs. Different strategies and workflows have been developed that achieve higher sensitivity, a greater depth of coverage or higher throughput. In this review, we summarize recent developments in both N- and C-terminomics and include the potential of top-down proteomics which inherently delivers information on both ends of analytes in a single analysis.