Yutong Jin

Single-cell proteomics has emerged as a powerful method to characterize cellular phenotypic heterogeneity and the cell-specific functional networks underlying biological processes. However, significant challenges remain in single-cell proteomics for the analysis of proteoforms arising from genetic mutations, alternative splicing, and post-translational modifications. Herein, we have developed a highly sensitive functionally integrated top-down proteomics method for the comprehensive analysis of proteoforms from single cells. We applied this method to single muscle fibers (SMFs) to resolve their heterogeneous functional and proteomic properties at the single cell level. Notably, we have detected single-cell heterogeneity in large proteoforms (>200 kDa) from the SMFs. Using SMFs obtained from three functionally distinct muscles, we found fiber-to-fiber heterogeneity among the sarcomeric proteoforms which can be related to the functional heterogeneity. Importantly, we reproducibly detected multiple isoforms of myosin heavy chain (~223 kDa), a motor protein that drives muscle contraction, with high mass accuracy to enable the classification of individual fiber types. This study represents the first “single-cell” top-down proteomics analysis that captures single muscle cell heterogeneity in large proteoforms and establishes a direct relationship between sarcomeric proteoforms and muscle fiber types, highlighting the potential of top-down proteomics for uncovering the molecular underpinnings of cell-to-cell variation in complex systems. Significance Statement: Single-cell technologies are revolutionizing biology and molecular medicine by allowing direct investigation of the biological variability among individual cells. Top-down proteomics is uniquely capable of dissecting biological heterogeneity at the intact protein level. Herein, we develop a highly sensitive single-cell top-down proteomics method to reveal diverse molecular variations in large proteins (>200 kDa) among individual single muscle cells. Our results both reveal and characterize the differences in protein post-translational modifications and isoform expression possible between individual muscle cells. We further integrate functional properties with proteomics and accurately measure myosin isoforms for individual muscle fiber type classification. Our study highlights the potential of top-down proteomics for understanding how single-cell protein heterogeneity contributes to cellular functions.

Background: The clinical significance of KRAS exon 3/4 mutations in colorectal cancer (CRC) remains unclear. We aimed to assess the prognostic value of KRAS exons 3 and 4 mutations to determine the necessity for their testing.

Metabolomics—the endpoint of the omics cascade—is increasingly recognized as a preferred method for understanding the ultimate responses of biological systems to stress. Flow injection electrospray (FIE) mass spectrometry (MS) has advantages for untargeted metabolic fingerprinting due to its simplicity and capability for high-throughput screening but requires a high-resolution mass spectrometer to resolve metabolite features. In this study, we developed and validated a high-throughput and highly reproducible metabolomics platform integrating FIE with ultrahigh-resolution Fourier transform ion cyclotron resonance (FTICR) MS for analysis of both polar and nonpolar metabolite features from plasma samples. FIE-FTICR MS enables high-throughput detection of hundreds of metabolite features in a single mass spectrum without a front-end separation step. Using plasma samples from genetically identical obese mice with or without type 2 diabetes (T2D), we validated the intra and intersample reproducibility of our method and its robustness for simultaneously detecting alterations in both polar and nonpolar metabolite features. Only 5 min is needed to acquire an ultra-high resolution mass spectrum in either a positive or negative ionization mode. Approximately 1000 metabolic features were reproducibly detected and annotated in each mouse plasma group. For significantly altered and highly abundant metabolite features, targeted tandem MS (MS/MS) analyses can be applied to confirm their identity. With this integrated platform, we successfully detected over 300 statistically significant metabolic features in T2D mouse plasma as compared to controls and identified new T2D biomarker candidates. This FIE-FTICR MS-based method is of high throughput and highly reproducible with great promise for metabolomics studies toward a better understanding and diagnosis of human diseases.

Top-down mass spectrometry (MS) is a powerful tool for identification and comprehensive characterization of proteoforms arising from alternative splicing, sequence variation, and post-translational modifications. However, the complex dataset generated from top-down MS experiments requires multiple sequential data processing steps to successfully interpret the data for identifying and characterizing proteoforms. One critical step is the deconvolution of the complex isotopic distribution that arises from naturally occurring isotopes. Multiple algorithms are currently available to deconvolute top-down mass spectra, resulting in different deconvoluted peak lists with varied accuracy compared to true positive annotations. In this study, we have designed a machine learning strategy that can process and combine the peak lists from different deconvolution results. By optimizing clustering results, deconvolution results from THRASH, TopFD, MS-Deconv, and SNAP algorithms were combined into consensus peak lists at various thresholds using either a simple voting ensemble method or a random forest machine learning algorithm. For the random forest algorithm which had better predictive performance, the consensus peak lists on average could achieve a recall value (true positive rate) of 0.60 and a precision value (positive predictive value) of 0.78. It outperforms the single best algorithm which only achieved a recall value of 0.47, and a precision value of 0.58. This machine learning strategy enhanced the accuracy and confidence in protein identification during database search by accelerating detection of true positive peaks while filtering out false positive peaks. Thus, this method show promise in enhancing proteoform identification and characterization for high-throughput data analysis in top-down proteomics.

Heterogeneity in skeletal muscle contraction time, peak power output, and resistance to fatigue, among others, is necessary to accommodate the wide range of functional demands imposed on the body. Underlying this functional heterogeneity are a myriad of differences in the myofilament protein isoform expression and post-translational modifications; yet, characterizing this heterogeneity remains challenging. Herein, we have utilized top-down liquid chromatography (LC)-mass spectrometry (MS)-based proteomics to characterize myofilament proteoform heterogeneity in seven rat skeletal muscle tissues including vastus lateralis, vastus medialis, vastus intermelius, rectus femoris, soleus, gastrocnemius, and plantaris. Top-down proteomics revealed myofilament proteoforms varied greatly across the seven different rat skeletal muscle tissues. Subsequently, we quantified and characterized myofilament proteoforms using online LC-MS. We have comprehensively characterized the fast and slow skeletal troponin I isoforms, which demonstrates the ability of top-down MS to decipher isoforms with high sequence homology. Taken together, we have shown that top-down proteomics can be used as a robust and high-throughput method to characterize the molecular heterogeneity of myofilament proteoforms from various skeletal muscle tissues.

Antibody-drug conjugates (ADCs) are designed to combine the target specificity of monoclonal antibodies and potent cytotoxin drugs to achieve better therapeutic outcomes. Comprehensive evaluation of the quality attributes of ADCs is critical for drug development but remains challenging due to heterogeneity of the construct. Currently, peptide mapping with reversed-phase liquid chromatography (RPLC) coupled to mass spectrometry (MS) is the predominant approach to characterize ADCs. However, it is suboptimal for sequence characterization and quantification of ADCs because it lacks a comprehensive view of coexisting variants and suffers from varying ionization effects of drug-conjugated peptides compared to unconjugated counterparts. Here, we present the first middle-down RPLC-MS analysis of both cysteine (Adcetris; BV) and lysine (Kadcyla; T-DM1) conjugated ADCs at the subunit level (∼25 kDa) with electron transfer dissociation (ETD). We successfully achieved high-resolution separation of subunit isomers arising from different drug conjugation and subsequently localized the conjugation sites. Moreover, we obtained a comprehensive overview of the microvariants associated with each subunits and characterized them such as oxidized variants with different sites. Furthermore, we observed relatively high levels of conjugation near complementarity-determining regions (CDRs) from the heavy chain but no drug conjugation near CDRs of light chain (Lc) from lysine conjugated T-DM1. Based on the extracted ion chromatograms, we accurately measured average drug to antibody ratio (DAR) values and relative occupancy of drug-conjugated subunits. Overall, the middle-down MS approach enables the evaluation of multiple quality attributes including DAR, positional isomers, conjugation sites, occupancy, and microvariants, which potentially opens up a new avenue to characterize ADCs.

Top-down mass spectrometry (MS)-based proteomics has become a powerful tool for comprehensive characterization of intact proteins. However, due to the high complexity of the proteome, highly effective separation of intact proteins from complex mixtures prior to MS analysis remains challenging. Monolithic columns have shown great promise for intact protein separation due to their high permeability, low backpressure, and fast mass transfer. Herein, for the first time, we developed bridged hybrid bis(triethoxysilyl)ethylene (BTSEY) monolith with C8 functional groups (C8@BTSEY) for highly effective protein separation and coupled it to high-resolution MS for identification of intact proteins from complex protein mixtures. We have optimized mobile phase conditions of our monolith-based reverse-phase chromatography (RPC) for online liquid chromatography (LC)-MS analysis and evaluated separation reproducibility of the C8@BTSEY column. We further assessed the chromatographic performance of this column by separating a complex protein mixture extracted from swine heart tissue. Using our monolithic column (i.d. 100 μm x 35 cm), we separated over 300 proteoforms (up to 104 kDa) from 360 ng of protein mixture in an 80-min one-dimensional (1D) LC run. The highly effective separation and recovery of intact proteins from this monolithic column allowed unambiguous identification of ~100 proteoforms including a large protein, αactinin2 (103.77 kDa), by online 1D LC-MS/MS analysis, for the first time. As demonstrated, this C8@BTSEY column is reproducible and effective in separation of intact proteins, which shows high promise for top-down proteomics.

The pharmaceutical industry’s interest in monoclonal antibodies (mAbs) and their derivatives has spurred rapid growth in the commercial and clinical pipeline of these effective therapeutics. The complex micro-heterogeneity of mAbs requires in-depth structural characterization for critical quality attribute assessment and quality assurance. Currently, mass spectrometry (MS)-based methods are the gold standard in mAb analysis, primarily with a bottom-up approach in which immunoglobulins G (IgGs) and their variants are digested into peptides to facilitate the analysis. Comprehensive characterization of IgGs and the micro-variants remains challenging at the proteoform level. Here, we used both top-down and middle-down MS for in-depth characterization of a human IgG1 using ultra-high resolution Fourier transform MS. Our top-down MS analysis provided characteristic fingerprinting of the IgG1 proteoforms at unit mass resolution. Subsequently, the tandem MS analysis of intact IgG1 enabled the detailed sequence characterization of a representative IgG1 proteoform at the intact protein level. Moreover, we used the middle-down MS analysis to characterize the primary glycoforms and micro-variants. Micro-variants such as low-abundance glycoforms, C-terminal glycine clipping, and C-terminal proline amidation were characterized with bond cleavages higher than 44% at the subunit level. By combining top-down and middle-down analysis, 76% of bond cleavage (509/666 amino acid bond cleaved) of IgG1 was achieved. Taken together, we demonstrated the combination of top-down and middle-down MS as powerful tools in the comprehensive characterization of mAbs.