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PubMed2025

Mass spectrometry-based top-down proteomics for proteoform profiling of protein coronas.

The protein corona is a layer of biomolecules-primarily proteins-that adsorbs to nanoparticle (NP) surfaces in biological fluids. If the purpose of the NP is therapeutic, this can have a profound effect on its biological activity and function in vivo. Protein corona formation can also be exploited for diagnostic purposes and to differentially enrich proteins for biomarker discovery. For all of these applications, it is useful to determine which proteins, and which specific proteoforms, bind to different types of NP. The traditional mass spectrometry (MS)-based bottom-up proteomics does not accurately identify specific proteoforms within the protein corona. This limitation impedes the nanomedicine field's ability to precisely predict the biological fate and pharmacokinetics of nanomedicines and their effectiveness in early-stage biomarker discovery and disease detection because many different proteoforms of the same gene could exist in the corona, and they have divergent biological functions. Here, we describe how to use capillary zone electrophoresis (CZE)-MS-based top-down proteomics to characterize the proteoform landscape of the protein corona. Our procedures detail the recovery of intact proteoforms from NP surfaces by using detergent-assisted proteoform elution and the measurement of these proteoforms by using CZE-tandem MS (MS/MS) and CZE-high-field asymmetric waveform ion mobility spectrometry (FAIMS)-MS/MS. The entire workflow is completed within 3-4 d. Using this protocol, hundreds of proteoforms from the protein corona of polystyrene NPs can be identified. Distinct protein corona proteoform profiles were observed from NPs with different physicochemical properties. The addition of FAIMS is beneficial for more in-depth proteoform characterization.

Proteomics↗

PubMed2025

ProteoformDB: A Built-In Application to Generate Proteoform Database.

Proteins play essential functions through their complex regulations on cell-type-specific expression, localization, and molecular complexes. Protein complexity is further enhanced by proteoforms, which are the diverse molecular forms that each gene can produce through genomic alterations, transcriptional variations, translational regulations, and protein modifications. Profiling of proteoforms is a promising method for gaining a deeper understanding of the role of proteins in biological pathways and disease mechanisms. Here, we developed ProteoformDB, an application tool for generating proteoform databases, and we cataloged a total of over one million unique single-site human proteoforms. We showed that ProteoformDB can serve as a valuable resource to document the experimentally identified proteoforms in a database, supporting protein characterization in quantitative proteomics for both total protein abundances and modified protein forms.

Humans↗

PubMed2026

A Draft Map of E. coli Proteoforms.

Top-down proteomics (TDP) enables direct characterization of intact proteoforms, providing protein-level insights into molecular diversity arising from post-translational modifications and sequence variations. Despite this advantage, proteome coverage in TDP remains limited relative to bottom-up proteomics (BUP). To expand coverage, we developed an integrated multidimensional approach combining sequential protein extraction, size-exclusion chromatography (SEC) fractionation, and capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS) and reversed-phase liquid chromatography (RPLC)-MS/MS. This approach identified 743 proteoform families and 10,613 proteoforms from E. coli cells through hundreds of MS runs. By incorporating previous E. coli TDP data sets from our group, we identified 14,932 proteoforms from 985 proteoform families, covering 43% of the E. coli proteome. The data represent the highest proteome coverage of cells by MS-based TDP, creating a draft map of E. coli proteoforms. The results offer strong evidence that MS-based TDP can reach high proteome coverage.

Escherichia coli↗

PubMed2026

Tau proteoforms as plasma biomarkers in Alzheimer's disease: mechanisms, measurement, and medicine.

INTRODUCTION: Blood-based tau proteoforms have emerged as specific, scalable biomarkers of Alzheimer's pathology, addressing the limitations of symptom-based diagnosis, neuroimaging, and invasive cerebrospinal fluid (CSF) testing. This review synthesizes advances in tau phosphorylation and truncation biology, evaluates translation from CSF to plasma with state-of-the-art proteomics, and outlines the analytical standards and cross-matrix calibration needed for clinical adoption. AREAS COVERED: We conducted a literature search in PubMed and Google Scholar. We reviewed studies published between January 2005 and September 2025 investigating tau proteoforms in Alzheimer's disease. EXPERT OPINION: Blood-based tau proteoforms are poised to move Alzheimer's diagnostics from specialized imaging to accessible frontline testing, with plasma p-tau217 approaching positron emission tomography (PET) and CSF performance and multi-analyte panels with glial fibrillary acidic protein (GFAP) or neurofilament light (NfL) improving differential diagnosis while reducing invasiveness and cost. Building on the first FDA-cleared plasma assay (Lumipulse G p-tau217/Aβ1-42 Ratio) in May 2025, we anticipate a dual pathway over the next decade in which referral centers use high-plex mass spectrometry (MS) panels for phosphoforms and truncations, while primary care adopts automated high-throughput immunoassays (e.g. chemiluminescent enzyme immunoassay (CLEIA)) for triage, supported by harmonized standard operating procedures (SOPs), cross-matrix calibration, and robust reference materials.

Humans↗

PubMed2025

Proteoform profiling of endogenous single cells from rat hippocampus at scale.

We perform intact proteoform profiling of 10,809 endogenous single cells from the rat hippocampus using single-cell proteoform imaging mass spectrometry (scPiMS). scPiMS directly extracts whole proteins and demonstrates high throughput for MS-based single-cell proteomics compared with existing approaches. We develop an informatics workflow dedicated to this datatype and use it to assign neurons, astrocytes or microglia cell types according to their proteoform signatures.

Animals↗

PubMed2024

Top-Down Proteomics Identifies Plasma Proteoform Signatures of Liver Cirrhosis Progression.

Cirrhosis, advanced liver disease, affects 2 to 5 million Americans. While most patients have compensated cirrhosis and may be fairly asymptomatic, many decompensate and experience life-threatening complications such as gastrointestinal bleeding, confusion (hepatic encephalopathy), and ascites, reducing life expectancy from 12 to less than 2 years. Among patients with compensated cirrhosis, identifying patients at high risk of decompensation is critical to optimize care and reduce morbidity and mortality. Therefore, it is important to preferentially direct them towards specialty care which cannot be provided to all patients with cirrhosis. We used discovery top-down proteomics to identify differentially expressed proteoforms (DEPs) in the plasma of patients with progressive stages of liver cirrhosis with the ultimate goal to identify candidate biomarkers of disease progression. In this pilot study, we identified 209 DEPs across three stages of cirrhosis (compensated, compensated with portal hypertension, and decompensated), of which 115 derived from proteins enriched in the liver at a transcriptional level and discriminated the three stages of cirrhosis. Enrichment analyses demonstrated DEPs are involved in several metabolic and immunological processes known to be impacted by cirrhosis progression. We have preliminarily defined the plasma proteoform signatures of cirrhosis patients, setting the stage for ongoing discovery and validation of biomarkers for early diagnosis, risk stratification, and disease monitoring.

Humans↗

PubMed2025

Programmed ribosomal frameshifting during PLEKHM2 mRNA decoding generates a constitutively active proteoform that supports myocardial function.

Programmed ribosomal frameshifting is a process where a proportion of ribosomes change their reading frame on an mRNA. While frameshifting is commonly used by viruses, very few phylogenetically conserved examples are known in nuclear encoded genes. Here, we report a +1 frameshifting event during decoding of the human gene PLEKHM2 that provides access to a second internally overlapping ORF. The new carboxyl-terminal domain of this frameshift protein forms an α helix, which relieves PLEKHM2 from autoinhibition and allows it to move to the tips of cells without activation by ARL8. Reintroducing both the canonically translated and frameshifted protein are necessary to restore normal contractile function of PLEKHM2 knockout cardiomyocytes, demonstrating the necessity of frameshifting for normal cardiac activity.

Frameshifting, Ribosomal↗

PubMed2025

Uncovering hidden protein modifications with native top-down mass spectrometry.

Protein modifications drive dynamic cellular processes by modulating biomolecular interactions, yet capturing these modifications within their native structural context remains a significant challenge. Native top-down mass spectrometry promises to preserve the critical link between modifications and interactions. However, current methods often fail to detect uncharacterized or low-abundance modifications, limiting insights into proteoform diversity. To address this gap, we introduce precise and accurate Identification Of Native proteoforms (precisION), an interactive end-to-end software package that leverages a robust, data-driven fragment-level open search to detect, localize and quantify 'hidden' modifications within intact protein complexes. Applying precisION to four therapeutically relevant targets-PDE6, ACE2, osteopontin (SPP1) and a GABA transporter (GAT1)-we discover undocumented phosphorylation, glycosylation and lipidation, and resolve previously uninterpretable density in an electron cryo-microscopy map of GAT1. As an open-source software package, precisION offers an intuitive means for interpreting complex protein fragmentation data. This tool will empower the community to unlock the potential of native top-down mass spectrometry, advancing integrative structural biology, molecular pathology and drug development.

Mass Spectrometry↗

PubMed2025

High-Sensitivity Top-Down Proteomics Reveals Enhanced Maturation of Micropatterned Induced Pluripotent Stem Cell-Derived Cardiomyocytes.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are increasingly used for disease modeling, drug discovery, and precision medicine, yet their utility is often limited by their immature phenotype. One promising maturation strategy involves using micropatterned substrates that mimic native cardiomyocytes' organizational growth and stiffness. However, the maturity of this model has not fully been assessed, and there is currently no method to extract proteins from micropatterned hiPSC-CMs for top-down proteomic analysis. Herein, we present a high-sensitivity protein extraction protocol for top-down proteomic analysis of hiPSC-CMs. Through this method, we assessed the maturation of micropatterned hiPSC-CMs compared to traditional monoculture and coculture monolayers at the proteoform level. We found that micropatterned hiPSC-CMs display molecular signatures of cardiomyocyte maturation including increased expression of ventricular myosin light chain isoforms, reduced expression of the fetal troponin T isoform, and decreased phosphorylation of alpha-tropomyosin. This high-sensitivity approach enables robust top-down proteomics from limited, heterogeneous cell populations and identifies the micropattern hiPSC-CM as a more adult-like CM model, broadening the utility of structured culture systems for cardiac disease modeling and translational research. Source data for this manuscript are available via MassIVE repository at massive.ucsd.edu with identifier: MSV000097864.

Myocytes, Cardiac↗

PubMed2024

Deciphering nanoparticle protein coronas by capillary isoelectric focusing-mass spectrometry-based top-down proteomics.

The nanoparticle (NP) protein corona significantly influences the outcome of nanomedicine. We present the first example of top-down proteomics (TDP) measurement of the protein corona using capillary isoelectric focusing-mass spectrometry, identifying seventy proteoforms of 16 cancer-related genes. This technique has the potential to revolutionize our understanding of the protein corona and advance nanomedicine.

Proteomics↗

PubMed2026

Clinical proteomics in inborn errors of metabolism: from biomarker discovery to implementation.

INTRODUCTION: Inborn errors of metabolism (IEMs) are rare, heterogeneous disorders traditionally diagnosed through genetic testing, enzyme assays, and metabolite measurements. However, these tools often do not fully explain phenotypic variability, organ involvement, disease progression, or treatment response. Clinical proteomics provides a complementary functional layer by capturing changes in protein abundance, proteoforms, post-translational modifications (PTM), and biological pathways, offering insights beyond genotype- and metabolite-based approaches. AREAS COVERED: This review examines the role of high-resolution mass spectrometry and computational proteomics in biomarker discovery and clinical decision-making for IEMs. It focuses on their contribution to diagnosis, variant interpretation, patient stratification, and treatment monitoring. Disease-specific applications are discussed, with the strongest evidence in lysosomal storage disorders, mitochondrial diseases, congenital disorders of glycosylation, and selected neurodegenerative or renal metabolic conditions. The literature search was performed in PubMed, Scopus, Web of Science, and Google Scholar, covering peer-reviewed articles available up to 2026, with emphasis on methodological advances and translational applications in clinical proteomics for IEMs. EXPERT OPINION: Proteomics will not replace established diagnostic tools, but it can help address clinically actionable questions in selected contexts. Translation into clinical practice will require standardized workflows, multicenter validation, clinically anchored endpoints, and integration with other omics approaches.

Humans↗

PubMed2026

Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics.

MOTIVATION: Mass spectrometry-based proteomics allows studying all proteins of a sample on a molecular level. However, mass spectra are noisy and contain complex patterns, making them inherently challenging to analyze with algorithmic approaches. In terms of the protein sequence landscape, most recent bottom-up MS-based proteomics studies consider either a diverse pool of post-translational modifications, employ large databases-as in metaproteomics or proteogenomics, study multiple isoforms of proteins, include unspecific cleavage sites or even combinations thereof. All this makes peptide and protein identifications challenging. RESULTS: Here, we present a foundation model, called yHydra, that jointly embeds spectra and peptides. This allows us to implement various downstream tasks and search modes in Euclidean space. We implement an open search which allows querying multiple ten-thousands of spectra against millions of peptides. Furthermore, we implement an error-tolerant search for identifying additional proteoforms that are not included in off-the-shelf reference proteomes. Our foundation model provides meaningful embeddings, as we interpret learned peptide embeddings in comparison to the peptide's physico-chemical properties. Hydra's open search, assigns delta masses to each identification which allows to unrestrictedly characterize post-translational modifications. The error-tolerant mode of yHydra can be used as post-processing to existing search engines or as a standalone. yHydra is evaluated on several real life data sets for the identification of modified peptide sequences and shows up to 25% increase in peptide identification at constant false discovery rate compared to the current state-of-the-art. AVAILABILITY AND IMPLEMENTATION: Code is available on Gitlab: https://gitlab.com/dacs-hpi/yHydra, and https://gitlab.com/dacs-hpi/yHydra_train.

Proteomics↗

PubMed2025

Establishing a Top-Down Proteomics Platform on a Time-of-Flight Instrument with Electron-Activated Dissociation.

Top-down proteomics is the study of intact proteins and their post-translational modifications with mass spectrometry. Historically, this field is more challenging than its bottom-up counterpart because the species are much bigger and have a larger number of possible combinations of sequences and modifications; thus, there is a great need for technological development. With improvements in instrumentation and a multiplicity of fragmentation modes available, top-down proteomics is quickly gaining in popularity with renewed attention on increasing confidence in identification and quantification. Here, we systematically evaluated the Sciex ZenoTOF 7600 system for top-down proteomics, applying standards in the field to validate the platform and further experimenting with its capabilities in electron-activated dissociation and post-translational modification site localization. The instrument demonstrated robustness in standard proteins for platform QC, as aided by zeno trapping. We were also able to apply this to histone post-translational modifications, achieving high sequence coverage that allowed PTM's site localization across protein sequences with optimized EAD fragmentation. We demonstrated the ability to analyze proteins spanning the mass range and included analysis of glycosylated proteins. This is a reference point for future top-down proteomics experiments to be conducted on the ZenoTOF 7600 system.

Proteomics↗