Enhancing Top-Down Proteomics of Brain Tissue with FAIMS

Mass spectrometry-intensive top-down proteomics: an update on technology advancements and biomedical applications

Proteoforms are all forms of protein molecules from the same gene because of variations at the DNA, RNA, and protein levels, e.g., alternative splicing and post-translational modifications (PTMs). Delineation of proteins in a proteoform-specific manner is crucial for understanding their biological functions. Mass spectrometry (MS)-intensive top-down proteomics (TDP) is promising for comprehensively characterizing intact proteoforms in complex biological systems. It has achieved substantial progress in technological development, including sample preparation, proteoform separations, MS instrumentation, and bioinformatics tools. In a single TDP study, thousands of proteoforms can be identified and quantified from a cell lysate. It has also been applied to various biomedical research to better our understanding of protein function in regulating cellular processes and to discover novel proteoform biomarkers of diseases for early diagnosis and therapeutic development. This review covers the most recent technological development and biomedical applications of MS-intensive TDP.

Using mass spectrometry-based methods to understand amyloid formation and inhibition of alpha-synuclein and amyloid beta

Amyloid fibrils, insoluble β-sheets structures that arise from protein misfolding, are associated with several neurodegenerative disorders. Many small molecules have been investigated to prevent amyloid fibrils from forming; however, there are currently no therapeutics to combat these diseases. Mass spectrometry (MS) is proving to be effective for studying the high order structure (HOS) of aggregating proteins and for determining structural changes accompanying protein-inhibitor interactions. When combined with native MS (nMS), gas-phase ion mobility, protein footprinting, and chemical cross-linking, MS can afford regional and sometimes amino acid spatial resolution of the aggregating protein. The spatial resolution is greater than typical low-resolution spectroscopic, calorimetric, and the traditional ThT fluorescence methods used in amyloid research today. High-resolution approaches can struggle when investigating protein aggregation, as the proteins exist as complex oligomeric mixtures of many sizes and several conformations or polymorphs. Thus, MS is positioned to complement both high- and low-resolution approaches to studying amyloid fibril formation and protein-inhibitor interactions. This review covers basics in MS paired with ion mobility, continuous hydrogen-deuterium exchange (continuous HDX), pulsed hydrogen-deuterium exchange (pulsed HDX), fast photochemical oxidation of proteins (FPOP) and other irreversible labeling methods, and chemical cross-linking. We then review the applications of these approaches to studying amyloid-prone proteins with a focus on amyloid beta and alpha-synuclein. Another focus is the determination of protein-inhibitor interactions. The expectation is that MS will bring new insights to amyloid formation and thereby play an important role to prevent their formation.

Top-down proteomics

Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.

Critical steps in an environmental metaproteomics workflow

Environmental metaproteomics is a rapidly advancing field that provides insights into the structure, dynamics, and metabolic activity of microbial communities. As the field is still maturing, it lacks consistent workflows, making it challenging for non-expert researchers to navigate. This review aims to introduce the workflow of environmental metaproteomics. It outlines the standard practices for sample collection, processing, and analysis, and offers strategies to overcome the unique challenges presented by common environmental matrices such as soil, freshwater, marine environments, biofilms, sludge, and symbionts. The review also highlights the bottlenecks in data analysis that are specific to metaproteomics samples and provides suggestions for researchers to obtain high-quality datasets. It includes recent benchmarking studies and descriptions of software packages specifically built for metaproteomics analysis. The article is written without assuming the reader's familiarity with single-organism proteomic workflows, making it accessible to those new to proteomics or mass spectrometry in general. This primer for environmental metaproteomics aims to improve accessibility to this exciting technology and empower researchers to tackle challenging and ambitious research questions. While it is primarily a resource for those new to the field, it should also be useful for established researchers looking to streamline or troubleshoot their metaproteomics experiments.

Expanding Differential Ion Mobility Separations into the MegaDalton Range

Along with mass spectrometry (MS), ion mobility separations (IMS) are advancing to ever larger biomolecules. The emergence of electrospray ionization (ESI) and native MS enabled the IMS/MS analyses of proteins up to ∼100 kDa in the 1990s and whole protein complexes and viruses up to ∼10 MDa since the 2000s. Differential IMS (FAIMS) is substantially orthogonal to linear IMS based on absolute mobility K and offers exceptional resolution, unique selectivity, and steady filtering readily compatible with slower analytical methods such as electron capture or transfer dissociation (ECD/ETD). However, the associated MS stages had limited FAIMS to ions with m/z < 8000 and masses under ∼300 kDa. Here, we integrate high-definition FAIMS with the Q-Exactive Orbitrap UHMR mass spectrometer that can handle m/z up to 80,000 and MDa-size ions in the native ESI regime. In the initial evaluation, the oligomers of monoclonal antibody adalimumab (148 kDa) are size-selected up to at least the nonamers (1.34 MDa) with m/z values up to ∼17,000. This demonstrates the survival and efficient separation of noncovalent MDa assemblies in the FAIMS process, opening the door to novel analyses of the heaviest macromolecules.

Modulation of α-synuclein in vitro aggregation kinetics by its alternative splice isoforms

The misfolding and aggregation of α-synuclein is linked to a family of neurodegenerative disorders known as synucleinopathies, the most prominent of which is Parkinson's disease (PD). Understanding the aggregation process of α-synuclein from a mechanistic point of view is thus of key importance. SNCA, the gene encoding α-synuclein, comprises six exons and produces various isoforms through alternative splicing. The most abundant isoform is expressed as a 140-amino acid protein (αSyn-140), while three other isoforms, αSyn-126, αSyn-112, and αSyn-98, are generated by skipping exon 3, exon 5, or both exons, respectively. In this study, we performed a detailed biophysical characterization of the aggregation of these four isoforms. We found that αSyn-112 and αSyn-98 exhibit accelerated aggregation kinetics compared to αSyn-140 and form distinct aggregate morphologies, as observed by transmission electron microscopy. Moreover, we observed that the presence of relatively small amounts of αSyn-112 accelerates the aggregation of αSyn-140, significantly reducing the aggregation half-time. These results indicate a potential role of alternative splicing in the pathological aggregation of α-synuclein and provide insights into how this process could be associated with the development of synucleinopathies.

Precursor deconvolution error estimation: The missing puzzle piece in false discovery rate in top-down proteomics

Top-down proteomics (TDP) directly analyzes intact proteins and thus provides more comprehensive qualitative and quantitative proteoform-level information than conventional bottom-up proteomics (BUP) that relies on digested peptides and protein inference. While significant advancements have been made in TDP in sample preparation, separation, instrumentation, and data analysis, reliable and reproducible data analysis still remains one of the major bottlenecks in TDP. A key step for robust data analysis is the establishment of an objective estimation of proteoform-level false discovery rate (FDR) in proteoform identification. The most widely used FDR estimation scheme is based on the target-decoy approach (TDA), which has primarily been established for BUP. We present evidence that the TDA-based FDR estimation may not work at the proteoform-level due to an overlooked factor, namely the erroneous deconvolution of precursor masses, which leads to incorrect FDR estimation. We argue that the conventional TDA-based FDR in proteoform identification is in fact protein-level FDR rather than proteoform-level FDR unless precursor deconvolution error rate is taken into account. To address this issue, we propose a formula to correct for proteoform-level FDR bias by combining TDA-based FDR and precursor deconvolution error rate.

GeLC-FAIMS-MS workflow for in-depth middle-down proteomics

Middle-down proteomics (MDP) is an analytical approach in which protein samples are digested with proteases such as Glu-C to generate large peptides (>3 kDa) that are analyzed by mass spectrometry (MS). This method is useful for characterizing high-molecular-weight proteins that are difficult to detect by top-down proteomics (TDP), in which intact proteins are analyzed by MS. In this study, we applied GeLC-FAIMS-MS, a multidimensional separation workflow that combines gel-based prefractionation with LC-FAIMS MS, for deep MDP. Middle-down peptides generated by optimized limited Glu-C digestion conditions were first size-fractionated by polyacrylamide gel electrophoresis, followed by C4 reversed-phase liquid chromatography separation and additional ion mobility fractionation, resulting in a significant increase in peptide length detectable by MS. In addition to global analysis, the GeLC-FAIMS-MS concept can also be applied to targeted MDP, where only proteins in the desired molecular weight range are gel-fractionated and their Glu-C digestion products are analyzed, as demonstrated by targeted analysis of integrins in exosomes. In-depth MDP achieved by global and targeted GeLC-FAIMS-MS supports the exploration of proteoform information not covered by conventional TDP by increasing the number of detectable protein groups or post-translational modifications (PTMs) and improving the sequence coverage.

Identification of proteoforms by top-down proteomics using two-dimensional low/low pH reversed-phase liquid chromatography-mass spectrometry

In top-down (TD) proteomics, efficient proteoform separation is crucial to reduce the sample complexity and increase the depth of the analysis. Here, we developed a two-dimensional low pH/low pH reversed-phase liquid chromatography separation scheme for TD proteomics. The first dimension for offline fractionation was performed using a polymeric reversed-phase (PLRP-S) column with trifluoroacetic acid as ion-pairing reagent. The second dimension, a C4 nanocolumn with formic acid as ion-pairing reagent, was coupled online with a high-field asymmetric ion mobility spectrometry (FAIMS) Orbitrap Tribrid mass spectrometer. For both dimensions several parameters were optimized, such as the adaption of the LC gradients in the second dimension according to the elution time (i.e., fraction number) in the first dimension. Avoidance of elevated temperatures and prolonged exposure to acidic conditions minimized cleavage of acid labile aspartate-proline peptide bonds. Furthermore, a concatenation strategy was developed to reduce the total measurement time. We compared our low/low pH with a previously published high pH (C4, ammonium formate)/low pH strategy and found that both separation strategies led to complementary proteoform identifications, mainly below 20 kDa, with a higher number of proteoforms identified by the low/low pH separation. With the optimized separation scheme, more than 4900 proteoforms from 1250 protein groups were identified in Caco-2 cells.

Capillary zone electrophoresis-high field asymmetric ion mobility spectrometry-tandem mass spectrometry for top-down characterization of histone proteoforms

Characterization of histone proteoforms with various post-translational modifications (PTMs) is critical for a better understanding of functions of histone proteoforms in epigenetic control of gene expression. Mass spectrometry (MS)-based top-down proteomics (TDP) is a valuable approach for delineating histone proteoforms because it can provide us with a bird's-eye view of histone proteoforms carrying diverse combinations of PTMs. Here, we present the first example of coupling capillary zone electrophoresis (CZE), ion mobility spectrometry (IMS), and MS for online multi-dimensional separations of histone proteoforms. Our CZE-high-field asymmetric waveform IMS (FAIMS)-MS/MS platform identified 366 (ProSight PD) and 602 (TopPIC) histone proteoforms from a commercial calf histone sample using a low microgram amount of histone sample as the starting material. CZE-FAIMS-MS/MS improved the number of histone proteoform identifications by about 3 folds compared to CZE-MS/MS alone (without FAIMS). The results indicate that CZE-FAIMS-MS/MS could be a useful tool for comprehensive characterization of histone proteoforms with high sensitivity.

High-Throughput Analyses of Therapeutic Antibodies Using High-Field Asymmetric Waveform Ion Mobility Spectrometry Combined with SampleStream and Intact Protein Mass Spectrometry

Intact protein mass spectrometry (MS) coupled with liquid chromatography was applied to characterize the pharmacokinetics and stability profiles of therapeutic proteins. However, limitations from chromatography, including throughput and carryover, result in challenges with handling large sample numbers. Here, we combined intact protein MS with multiple front-end separations, including affinity capture, SampleStream, and high-field asymmetric waveform ion mobility spectrometry (FAIMS), to perform high-throughput and specific mass measurements of a multivalent antibody with one antigen-binding fragment (Fab) fused to an immunoglobulin G1 (IgG1) antibody. Generic affinity capture ensures the retention of both intact species 1Fab-IgG1 and the tentative degradation product IgG1. Subsequently, the analytes were directly loaded into SampleStream, where each injection occurs within ∼30 s. By separating ions prior to MS detection, FAIMS further offered improvement in signal-overnoise by ∼30% for denatured protein MS via employing compensation voltages that were optimized for different antibody species. When enhanced FAIMS transmission of 1Fab-IgG1 was employed, a qualified assay was established for spiked-in serum samples between 0.1 and 25 μg/mL, resulting in ∼10% accuracy bias and precision coefficient of variation. Selective FAIMS transmission of IgG1 as the degradation surrogate product enabled more sensitive detection of clipped species for intact 1Fab-IgG1 at 5 μg/mL in serum, generating an assay to measure 1Fab-IgG1 truncation between 2.5 and 50% with accuracy and precision below 20% bias and coefficient of variation. Our results revealed that the SampleStream-FAIMS-MS platform affords high throughput, selectivity, and sensitivity for characterizing therapeutic antibodies from complex biomatrices qualitatively and quantitatively.

Orbitrap Mass Spectrometry and High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) Enable the in-Depth Analysis of Human Serum Proteoforms

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, truncations, 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 that cannot recapitulate the original proteoform content of samples. Clinical laboratories have been slow to adopt top-down analysis, also due to higher sample handling requirements. In this study, we describe a straightforward protocol for intact proteoform sample preparation based on the depletion of albumin and immunoglobulins, followed by simplified protein fractionation 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) to further simplify serum proteoform mixtures. This LC-FAIMS-MS2 method led to the identification of over 1000 serum proteoforms < 30 kDa, outperforming traditional LC-MS2 data acquisition and more than doubling the number of proteoforms identified in previous studies.

Top-Down Proteomics of Mouse Islets With Beta Cell CPE Deletion Reveals Molecular Details in Prohormone Processing

Altered prohormone processing, such as with proinsulin and pro-islet amyloid polypeptide (proIAPP), has been reported as an important feature of prediabetes and diabetes. Proinsulin processing includes removal of several C-terminal basic amino acids and is performed principally by the exopeptidase carboxypeptidase E (CPE), and mutations in CPE or other prohormone convertase enzymes (PC1/3 and PC2) result in hyperproinsulinemia. A comprehensive characterization of the forms and quantities of improperly processed insulin and other hormone products following Cpe deletion in pancreatic islets has yet to be attempted. In the present study we applied top-down proteomics to globally evaluate the numerous proteoforms of hormone processing intermediates in a β-cell-specific Cpe knockout mouse model. Increases in dibasic residue-containing proinsulin and other novel proteoforms of improperly processed proinsulin were found, and we could classify several processed proteoforms as novel substrates of CPE. Interestingly, some other known substrates of CPE remained unaffected despite its deletion, implying that paralogous processing enzymes such as carboxypeptidase D (CPD) can compensate for CPE loss and maintain near normal levels of hormone processing. In summary, our quantitative results from top-down proteomics of islets provide unique insights into the complexity of hormone processing products and the regulatory mechanisms.

Quantitative Time-Course Analysis of Osmotic and Salt Stress in Arabidopsis thaliana Using Short Gradient Multi-CV FAIMSpro BoxCar DIA

A major limitation when undertaking quantitative proteomic time-course experimentation is the tradeoff between depth-of-analysis and speed-of-analysis. In high complexity and high dynamic range sample types, such as plant extracts, balance between resolution and time is especially apparent. To address this, we evaluate multiple compensation voltage (CV) high field asymmetric waveform ion mobility spectrometry (FAIMSpro) settings using the latest label-free single-shot Orbitrap-based DIA acquisition workflows for their ability to deeply quantify the Arabidopsis thaliana seedling proteome. Using a BoxCarDIA acquisition workflow with a -30 -50 -70 CV FAIMSpro setting, we were able to consistently quantify >5000 Arabidopsis seedling proteins over a 21-min gradient, facilitating the analysis of ∼42 samples per day. Utilizing this acquisition approach, we then quantified proteome-level changes occurring in Arabidopsis seedling shoots and roots over 24 h of salt and osmotic stress, to identify early and late stress response proteins and reveal stress response overlaps. Here, we successfully quantify >6400 shoot and >8500 root protein groups, respectively, quantifying nearly ∼9700 unique protein groups in total across the study. Collectively, we pioneer a short gradient, multi-CV FAIMSpro BoxCarDIA acquisition workflow that represents an exciting new analysis approach for undertaking quantitative proteomic time-course experimentation in plants.

Deletion of Carboxypeptidase E in β-Cells Disrupts Proinsulin Processing but Does Not Lead to Spontaneous Development of Diabetes in Mice

Carboxypeptidase E (CPE) facilitates the conversion of prohormones into mature hormones and is highly expressed in multiple neuroendocrine tissues. Carriers of CPE mutations have elevated plasma proinsulin and develop severe obesity and hyperglycemia. We aimed to determine whether loss of Cpe in pancreatic β-cells disrupts proinsulin processing and accelerates development of diabetes and obesity in mice. Pancreatic β-cell-specific Cpe knockout mice (βCpeKO; Cpefl/fl x Ins1Cre/+) lack mature insulin granules and have elevated proinsulin in plasma; however, glucose-and KCl-stimulated insulin secretion in βCpeKO islets remained intact. High-fat diet-fed βCpeKO mice showed weight gain and glucose tolerance comparable with those of Wt littermates. Notably, β-cell area was increased in chow-fed βCpeKO mice and β-cell replication was elevated in βCpeKO islets. Transcriptomic analysis of βCpeKO β-cells revealed elevated glycolysis and Hif1α-target gene expression. On high glucose challenge, β-cells from βCpeKO mice showed reduced mitochondrial membrane potential, increased reactive oxygen species, reduced MafA, and elevated Aldh1a3 transcript levels. Following multiple low-dose streptozotocin injections, βCpeKO mice had accelerated development of hyperglycemia with reduced β-cell insulin and Glut2 expression. These findings suggest that Cpe and proper proinsulin processing are critical in maintaining β-cell function during the development of hyperglycemia.

ARTICLE HIGHLIGHTS

Carboxypeptidase E (Cpe) is an enzyme that removes the carboxy-terminal arginine and lysine residues from peptide precursors. Mutations in CPE lead to obesity and type 2 diabetes in humans, and whole-body Cpe knockout or mutant mice are obese and hyperglycemic and fail to convert proinsulin to insulin. We show that β-cell-specific Cpe deletion in mice (βCpeKO) does not lead to the development of obesity or hyperglycemia, even after prolonged high-fat diet treatment. However, β-cell proliferation rate and β-cell area are increased, and the development of hyperglycemia induced by multiple low-dose streptozotocin injections is accelerated in βCpeKO mice.

Coupling High-Field Asymmetric Waveform Ion Mobility Spectrometry with Capillary Zone Electrophoresis-Tandem Mass Spectrometry for Top-Down Proteomics

Capillary zone electrophoresis-tandem mass spectrometry (CZE-MS/MS) has emerged as an essential technique for top-down proteomics (TDP), providing superior separation efficiency and high detection sensitivity for proteoform analysis. Here, we aimed to further enhance the performance of CZE-MS/MS for TDP via coupling online gas-phase proteoform fractionation using high-field asymmetric waveform ion mobility spectrometry (FAIMS). When the compensation voltage (CV) of FAIMS was changed from -50 to 30 V, the median mass of identified proteoforms increased from less than 10 kDa to about 30 kDa, suggesting that FAIMS can efficiently fractionate proteoforms by their size. CZE-FAIMS-MS/MS boosted the number of proteoform identifications from a yeast sample by nearly 3-fold relative to CZE-MS/MS alone. It particularly benefited the identification of relatively large proteoforms, improving the number of proteoforms in a mass range of 20-45 kDa by 6-fold compared to CZE-MS/MS alone. FAIMS fractionation gained nearly 20-fold better signal-to-noise ratios of randomly selected proteoforms than no FAIMS. We expect that CZE-FAIMS-MS/MS will be a useful tool for further advancing the sensitivity and coverage of TDP. This work shows the first example of coupling CE with ion mobility spectrometry (IMS) for TDP.

Improved Label-Free Quantification of Intact Proteoforms Using Field Asymmetric Ion Mobility Spectrometry

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.

High-Field Asymmetric Waveform Ion Mobility Spectrometry: Practical Alternative for Cardiac Proteome Sample Processing

Heart tissue sample preparation for mass spectrometry (MS) analysis that includes prefractionation reduces the cellular protein dynamic range and increases the relative abundance of nonsarcomeric proteins. We previously described "IN-Sequence" (IN-Seq) where heart tissue lysate is sequentially partitioned into three subcellular fractions to increase the proteome coverage more than a single direct tissue analysis by mass spectrometry. Here, we report an adaptation of the high-field asymmetric ion mobility spectrometry (FAIMS) coupled to mass spectrometry, and the establishment of a simple one step sample preparation coupled with gas-phase fractionation. The FAIMS approach substantially reduces manual sample handling, significantly shortens the MS instrument processing time, and produces unique protein identification and quantification approximating the commonly used IN-Seq method in less time.

TopPICR: A Companion R Package for Top-Down Proteomics Data Analysis

Top-down proteomics is the analysis of proteins in their intact form without proteolysis, thus preserving valuable information about post-translational modifications, isoforms, and proteolytic processing. However, it is still a developing field due to limitations in the instrumentation, difficulties with the interpretation of complex mass spectra, and a lack of well-established quantification approaches. TopPIC is one of the popular tools for proteoform identification. We extended its capabilities into label-free proteoform quantification by developing a companion R package (TopPICR). Key steps in the TopPICR pipeline include filtering identifications, inferring a minimal set of protein accessions explaining the observed sequences, aligning retention times, recalibrating measured masses, clustering features across data sets, and finally compiling feature intensities using the match-between-runs approach. The output of the pipeline is an MSnSet object which makes downstream data analysis seamlessly compatible with packages from the Bioconductor project. It also provides the capability for visualizing proteoforms within the context of the parent protein sequence. The functionality of TopPICR is demonstrated on top-down LC-MS/MS data sets of 10 human-in-mouse xenografts of luminal and basal breast tumor samples.

Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform

Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e., functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and posttranslational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanodroplet processing in one pot for trace samples-based sample preparation system and an laser capture microdissection-based cell isolation system. We improved the nanodroplet processing in one pot for trace samples sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed laser capture microdissection-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of top-down mass spectrometry-based proteoform identification and characterization and TDPortal identifications to match with features from protein mass extractor. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential posttranslational modification-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

Seeing the complete picture: proteins in top-down mass spectrometry

Top-down protein mass spectrometry can provide unique insights into protein sequence and structure, including precise proteoform identification and study of protein–ligand and protein–protein interactions. In contrast with the commonly applied bottom-up approach, top-down approaches do not include digestion of the protein of interest into small peptides, but instead rely on the ionization and subsequent fragmentation of intact proteins. As such, it is fundamentally the only way to fully characterize the composition of a proteoform. Here, we provide an overview of how a top-down protein mass spectrometry experiment is performed and point out recent applications from the literature to the reader. While some parts of the top-down workflow are broadly applicable, different research questions are best addressed with specific experimental designs. The most important divide is between studies that prioritize sequence information (i.e., proteoform identification) versus structural information (e.g., conformational studies, or mapping protein–protein or protein–ligand interactions). Another important consideration is whether to work under native or denaturing solution conditions, and the overall complexity of the sample also needs to be taken into account, as it determines whether (chromatographic) separation is required prior to MS analysis. In this review, we aim to provide enough information to support both newcomers and more experienced readers in the decision process of how to answer a potential research question most efficiently and to provide an overview of the methods that exist to answer these questions.

High-field asymmetric waveform ion mobility spectrometry for xylene isomer separation assisted by helium-chemical modifiers

We propose a combined helium-chemical modifier method for a faster and more convenient separation and detection of xylene isomers. The method employs high-field asymmetric waveform ion mobility spectrometry (FAIMS) to investigate the separation and identification of three xylene isomers. A homemade hollow needle-ring ion source was used, and five chemical modifiers, represented by methanol, ethanol, acetone, ethyl acetate, and acetic acid, were doped into the xylene target analytes to observe the separation and identification of the three isomers. This was based on the fact that the addition of helium and the increase of the RF voltage could no longer improve the resolution of the field asymmetric waveform ion mobility spectrometry system. The experimental results at an RF field voltage of 15 kV cm^-1 showed that the spectral peak shifts of o-, m-, and p-xylene in a normal nitrogen environment were -0.21, -0.21, and -0.24 V, respectively. o-Xylene showed a spectral peak of -1.33 V after the addition of helium; however, the separation was not evident. The FAIMS spectrum of xylene showed multiple cluster ion peaks upon addition of the chemical modifiers on top of helium. The alcohol chemical modifiers caused three spectral peaks, with the best effect for methanol, and the characteristic ion peak positions of -7.16, -6.90, and -6.01 V for o-, m-, and p-xylene, respectively. The separation using proton-based chemical modifiers was confirmed to be stronger than that using non-proton-based chemical modifiers, and appropriate volume fractions of chemical modifiers provided a better separation of the target analytes. This study introduces a novel concept and method for the separation and identification of xylene isomers.

Deciphering combinatorial post-translational modifications by top-down mass spectrometry

Post-translational modifications (PTMs) create vast structural and functional diversity of proteins, ultimately modulating protein function and degradation, influencing cellular signaling, and regulating transcription. The combinatorial patterns of PTMs increase the heterogeneity of proteins and further mediates their interactions. Advances in mass spectrometry-based proteomics have resulted in identification of thousands of proteins and allowed characterization of numerous types and sites of PTMs. Examination of intact proteins, termed the top-down approach, offers the potential to map protein sequences and localize multiple PTMs on each protein, providing the most comprehensive cataloging of proteoforms. This review describes some of the dividends of using mass spectrometry to analyze intact proteins and showcases innovative strategies that have enhanced the promise of top-down proteomics for exploring the impact of combinatorial PTMs in unsurpassed detail.

Size-Based Proteome Fractionation through Polyacrylamide Gel Electrophoresis Combined with LC-FAIMS-MS for In-Depth Top-Down Proteomics

The combination of liquid chromatography (LC) and gas-phase separation by field-asymmetric ion mobility spectrometry (FAIMS) is a powerful proteoform separation system for top-down proteomics. Here, we present an in-depth top-down proteomics workflow, GeLC-FAIMS-MS, in which a molecular-weight-based proteome fractionation approach using SDS-polyacrylamide gel electrophoresis is performed prior to LC-FAIMS-MS. Since individual bands and their corresponding mass ranges require different compensating voltages (CVs), the MS parameters for each gel band and CV were optimized to increase the number and reliability of proteoform identifications further. We developed an easy-to-implement and inexpensive procedure combining the earlier established Passively Eluting Proteins from Polyacrylamide gels as Intact species (PEPPI) protocol with an optimized Anion-Exchange disk-assisted Sequential sample Preparation (AnExSP) method for the removal of stains and SDS. The protocol was compared with a methanol-chloroform-water (MCW)-based protein precipitation protocol. The results show that the PEPPI-AnExSP procedure is better suited for the identification of low-molecular-weight proteoforms, whereas the MCW-based protocol showed advantages for higher-molecular-weight proteoforms. Moreover, complementary results were observed with the two methods in terms of hydrophobicity and isoelectric points of the identified proteoforms. In total, 8500 proteoforms could be identified in a human proteome standard, showing the effectiveness of the gel-based sample fractionation approaches in combination with LC-FAIMS-MS.

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.

FLASHIda enables intelligent data acquisition for top-down proteomics to boost proteoform identification counts

The detailed analysis and structural characterization of proteoforms by top-down proteomics (TDP) has gained a lot of interest in biomedical research. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complexity of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. Here, we present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In an analysis of E. coli lysate, FLASHIda increases the number of unique proteoform level identifications from 800 to 1500 or generates a near-identical number of identifications in one third of the instrument time when compared to standard DDA mode. Furthermore, FLASHIda enables sensitive mapping of post-translational modifications and detection of chemical adducts. As a software extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.

Data acquisition suitable for top-down proteomics (TDP) has the potential to significantly improve proteoform analysis. Here, the authors present FLASHIda, an intelligent online data acquisition algorithm for TDP that nearly doubles the number of proteoform-level identifications in complex samples.

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.

SUBCELLULAR TRANSCRIPTOMICS & PROTEOMICS: A COMPARATIVE METHODS REVIEW

The internal environment of cells is molecularly crowded, which requires spatial organization via subcellular compartmentalization. These compartments harbor specific conditions for molecules to perform their biological functions, such as coordination of the cell cycle, cell survival, and growth. This compartmentalization is also not static, with molecules trafficking between these subcellular neighborhoods to carry out their functions. For example, some biomolecules are multifunctional, requiring an environment with differing conditions or interacting partners, and others traffic to export such molecules. Aberrant localization of proteins or RNA species has been linked to many pathological conditions, such as neurological, cancer, and pulmonary diseases. Differential expression studies in transcriptomics and proteomics are relatively common, but the majority have overlooked the importance of subcellular information. In addition, subcellular transcriptomics and proteomics data do not always colocate because of the biochemical processes that occur during and after translation, highlighting the complementary nature of these fields. In this review, we discuss and directly compare the current methods in spatial proteomics and transcriptomics, which include sequencing- and imaging-based strategies, to give the reader an overview of the current tools available. We also discuss current limitations of these strategies as well as future developments in the field of spatial -omics.

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Subcellular information of protein and RNA give insights into molecular function.

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This review discusses strategies available to measure subcellular information.

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Hybridization of methods shows promise for exploring the composition of organelles.

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Advances are aiding understanding of the organisation and dynamics of cells.

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.

Ion Mobility Spectrometry of Superheated Macromolecules at Electric Fields up to 500 Td

Since its inception in 1980s, differential or field asymmetric waveform ion mobility spectrometry (FAIMS) has been implemented at or near ambient gas pressure. We recently developed FAIMS at 15-30 Torr with mass spectrometry and utilized it to analyze amino acids, isomeric peptides, and protein conformers. The separations broadly mirrored those at atmospheric pressure, save for larger proteins that (as predicted) exhibited dipole alignment at ambient but not low pressure. Here we reduce the pressure down to 4.7 Torr, allowing normalized electric fields up to 543 Td-double the maximum in prior FAIMS or IMS studies of polyatomic ions. Despite the collisional heating to ∼1000 °C at the waveform peaks, the proteins of size from ubiquitin to albumin survived intact. The dissociation of macromolecules in FAIMS appears governed by the average ion temperature over the waveform cycle, unlike the isomerization controlled by the peak temperature. The global separation trends in this "superhot" regime extend those at moderately low pressures, with distinct conformers and no alignment as theorized. Although the scaling of the compensation voltage with the field fell below cubic at lower fields, the resolving power increased and the resolution of different proteins or charge states substantially improved.

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.