Browsing by Author "Sun, Liangliang"

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    Capillary zone electrophoresis-tandem mass spectrometry with activated ion electron transfer dissociation for large-scale top-down proteomics
    (Springer, 2019-12) McCool, Elijah N.; Basharat, Abdul Rehman; Liu, Xiaowen; Coon, Joshua J.; Sun, Liangliang; BioHealth Informatics, School of Informatics and Computing
    Capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS) has been recognized as an efficient approach for top-down proteomics recently for its high-capacity separation and highly sensitive detection of proteoforms. However, the commonly used collision-based dissociation methods often cannot provide extensive fragmentation of proteoforms for thorough characterization. Activated ion electron transfer dissociation (AI-ETD), that combines infrared photoactivation concurrent with ETD, has shown better performance for proteoform fragmentation than higher energy-collisional dissociation (HCD) and standard ETD. Here, we present the first application of CZE-AI-ETD on an Orbitrap Fusion Lumos mass spectrometer for large-scale top-down proteomics of Escherichia coli (E. coli) cells. CZE-AI-ETD outperformed CZE-ETD regarding proteoform and protein identifications (IDs). CZE-AI-ETD reached comparable proteoform and protein IDs with CZE-HCD. CZE-AI-ETD tended to generate better expectation values (E values) of proteoforms than CZE-HCD and CZE-ETD, indicating a higher quality of MS/MS spectra from AI-ETD respecting the number of sequence-informative fragment ions generated. CZE-AI-ETD showed great reproducibility regarding the proteoform and protein IDs with relative standard deviations less than 4% and 2% (n = 3). Coupling size exclusion chromatography (SEC) to CZE-AI-ETD identified 3028 proteoforms and 387 proteins from E. coli cells with 1% spectrum level and 5% proteoform-level false discovery rates. The data represents the largest top-down proteomics dataset using the AI-ETD method so far. Single-shot CZE-AI-ETD of one SEC fraction identified 957 proteoforms and 253 proteins. N-terminal truncations, signal peptide cleavage, N-terminal methionine removal, and various post-translational modifications including protein N-terminal acetylation, methylation, S-thiolation, disulfide bonds, and lysine succinylation were detected.
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    Deep top-down proteomics revealed significant proteoform-level differences between metastatic and nonmetastatic colorectal cancer cells
    (American Association for the Advancement of Science, 2022) McCool, Elijah N.; Xu, Tian; Chen, Wenrong; Beller, Nicole C.; Nolan, Scott M.; Hummon, Amanda B.; Liu, Xiaowen; Sun, Liangliang; BioHealth Informatics, School of Informatics and Computing
    Understanding cancer metastasis at the proteoform level is crucial for discovering previously unknown protein biomarkers for cancer diagnosis and drug development. We present the first top-down proteomics (TDP) study of a pair of isogenic human nonmetastatic and metastatic colorectal cancer (CRC) cell lines (SW480 and SW620). We identified 23,622 proteoforms of 2332 proteins from the two cell lines, representing nearly fivefold improvement in the number of proteoform identifications (IDs) compared to previous TDP datasets of human cancer cells. We revealed substantial differences between the SW480 and SW620 cell lines regarding proteoform and single amino acid variant (SAAV) profiles. Quantitative TDP unveiled differentially expressed proteoforms between the two cell lines, and the corresponding genes had diversified functions and were closely related to cancer. Our study represents a pivotal advance in TDP toward the characterization of human proteome in a proteoform-specific manner, which will transform basic and translational biomedical research.
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    Deep Top-Down Proteomics Using Capillary Zone Electrophoresis-Tandem Mass Spectrometry: Identification of 5700 Proteoforms from the Escherichia coli Proteome
    (American Chemical Society, 2018-05-01) McCool, Elijah N.; Lubeckyj, Rachele A.; Shen, Xiaojing; Chen, Daoyang; Kou, Qiang; Liu, Xiaowen; Sun, Liangliang; BioHealth Informatics, School of Informatics and Computing
    Capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS) has been recognized as a useful tool for top-down proteomics. However, its performance for deep top-down proteomics is still dramatically lower than widely used reversed-phase liquid chromatography (RPLC)-MS/MS. We present an orthogonal multidimensional separation platform that couples size exclusion chromatography (SEC) and RPLC based protein prefractionation to CZE-MS/MS for deep top-down proteomics of Escherichia coli. The platform generated high peak capacity (∼4000) for separation of intact proteins, leading to the identification of 5700 proteoforms from the Escherichia coli proteome. The data represents a 10-fold improvement in the number of proteoform identifications compared with previous CZE-MS/MS studies and represents the largest bacterial top-down proteomics data set reported to date. The performance of the CZE-MS/MS based platform is comparable to the state-of-the-art RPLC-MS/MS based systems in terms of the number of proteoform identifications and the instrument time.
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    Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry
    (American Chemical Society, 2022) Chen, Wenrong; McCool, Elijah N.; Sun, Liangliang; Zang, Yong; Ning, Xia; Liu, Xiaowen; BioHealth Informatics, School of Informatics and Computing
    Reversed-phase liquid chromatography (RPLC) and capillary zone electrophoresis (CZE) are two primary proteoform separation methods in mass spectrometry (MS)-based top-down proteomics. Proteoform retention time (RT) prediction in RPLC and migration time (MT) prediction in CZE provide additional information for accurate proteoform identification and quantification. While existing methods are mainly focused on peptide RT and MT prediction in bottom-up MS, there is still a lack of methods for proteoform RT and MT prediction in top-down MS. We systematically evaluated eight machine learning models and a transfer learning method for proteoform RT prediction and five models and the transfer learning method for proteoform MT prediction. Experimental results showed that a gated recurrent unit (GRU)-based model with transfer learning achieved a high accuracy (R = 0.978) for proteoform RT prediction and that the GRU-based model and a fully connected neural network model obtained a high accuracy of R = 0.982 and 0.981 for proteoform MT prediction, respectively.
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    Identification and Quantification of Proteoforms by Mass Spectrometry
    (Wiley, 2019-05) Schaffer, Leah V.; Millikin, Robert J.; Miller, Rachel M.; Anderson, Lissa C.; Fellers, Ryan T.; Ge, Ying; Kelleher, Neil L.; LeDuc, Richard D.; Liu, Xiaowen; Payne, Samuel H.; Sun, Liangliang; Thomas, Paul M.; Tucholski, Trisha; Wang, Zhe; Wu, Si; Wu, Zhijie; Yu, Dahang; Shortreed, Michael R.; Smith, Lloyd M.; BioHealth Informatics, School of Informatics and Computing
    A proteoform is a defined form of a protein derived from a given gene with a specific amino acid sequence and localized post-translational modifications. In top-down proteomic analyses, proteoforms are identified and quantified through mass spectrometric analysis of intact proteins. Recent technological developments have enabled comprehensive proteoform analyses in complex samples, and an increasing number of laboratories are adopting top-down proteomic workflows. In this review, we outline some recent advances and discuss current challenges and future directions for the field.
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    Large-Scale Qualitative and Quantitative Top-Down Proteomics Using Capillary Zone Electrophoresis-Electrospray Ionization-Tandem Mass Spectrometry with Nanograms of Proteome Samples
    (Springer, 2019-04-09) Lubeckyj, Rachele A.; Basharat, Abdul Rehman; Shen, Xiaojing; Liu, Xiaowen; Sun, Liangliang; BioHealth Informatics, School of Informatics and Computing
    Capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry (CZE-ESI-MS/MS) has attracted attention recently for top-down proteomics because it can achieve highly efficient separation and very sensitive detection of proteins. However, separation window and sample loading volume of CZE need to be boosted for a better proteome coverage using CZE-MS/MS. Here, we present an improved CZE-MS/MS system that achieved a 180-min separation window and a 2-μL sample loading volume for top-down characterization of protein mixtures. The system obtained highly efficient separation of proteins with nearly one million theoretical plates for myoglobin and enabled baseline separation of three different proteoforms of myoglobin. The CZE-MS/MS system identified 797±21 proteoforms and 258±7 proteins (n=2) from an Escherichia coli (E. coli) proteome sample in a single run with only 250 ng of proteins injected. The system still identified 449±40 proteoforms and 173±6 proteins (n=2) from the E. coli sample when only 25 ng of proteins were injected per run. Single-shot CZE-MS/MS analyses of zebrafish brain cerebellum (Cb) and optic tectum (Teo) regions identified 1 730±196 proteoforms (n=3) and 2 024±255 proteoforms (n=3), respectively, with only 500-ng proteins loaded per run. Label-free quantitative top-down proteomics of zebrafish brain Cb and Teo regions revealed significant differences between Cb and Teo regarding the proteoform abundance. Over 700 proteoforms from 131 proteins had significantly higher abundance in Cb compared to Teo, and these proteins were highly enriched in several biological processes, including muscle contraction, glycolytic process, and mesenchyme migration.
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    Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry
    (MyJove Corporation, 2018-10-24) McCool, Elijah N.; Lubeckyj, Rachele; Shen, Xiaojing; Kou, Qiang; Liu, Xiaowen; Sun, Liangliang; Computer and Information Science, School of Science
    Capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry (CZE-ESI-MS/MS) has been recognized as a useful tool for top-down proteomics that aims to characterize proteoforms in complex proteomes. However, the application of CZE-MS/MS for large-scale top-down proteomics has been impeded by the low sample-loading capacity and narrow separation window of CZE. Here, a protocol is described using CZE-MS/MS with a microliter-scale sample-loading volume and a 90-min separation window for large-scale top-down proteomics. The CZE-MS/MS platform is based on a linear polyacrylamide (LPA)-coated separation capillary with extremely low electroosmotic flow, a dynamic pH-junction-based online sample concentration method with a high efficiency for protein stacking, an electro-kinetically pumped sheath flow CE-MS interface with extremely high sensitivity, and an ion trap mass spectrometer with high mass resolution and scan speed. The platform can be used for the high-resolution characterization of simple intact protein samples and the large-scale characterization of proteoforms in various complex proteomes. As an example, a highly efficient separation of a standard protein mixture and a highly sensitive detection of many impurities using the platform is demonstrated. As another example, this platform can produce over 500 proteoform and 190 protein identifications from an Escherichia coli proteome in a single CZE-MS/MS run.
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    Native Proteomics in Discovery Mode Using Size-Exclusion Chromatography–Capillary Zone Electrophoresis–Tandem Mass Spectrometry
    (ACS, 2018-09) Shen, Xiaojing; Kou, Ruiqiong; Yang, Zhichang; Chen, Daoyang; Liu, Xiaowen; Hong, Heedeok; Sun, Liangliang; BioHealth Informatics, School of Informatics and Computing
    Native proteomics aims to characterize complex proteomes under native conditions and ultimately produces a full picture of endogenous protein complexes in cells. It requires novel analytical platforms for high-resolution and liquid-phase separation of protein complexes prior to native mass spectrometry (MS) and MS/MS. In this work, size exclusion chromatography (SEC)-capillary zone electrophoresis (CZE)-MS/MS was developed for native proteomics in discovery mode, resulting in the identification of 144 proteins, 672 proteoforms, and 23 protein complexes from the Escherichia coli proteome. The protein complexes include four protein homodimers, 16 protein-metal complexes, two protein-[2Fe-2S] complexes, and one protein-glutamine complex. Half of them have not been reported in the literature. This work represents the first example of online liquid-phase separation-MS/MS for characterization of a complex proteome under the native condition, offering the proteomics community an efficient and simple platform for native proteomics.
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    Pilot Evaluation of the Long-Term Reproducibility of Capillary Zone Electrophoresis-Tandem Mass Spectrometry for Top-Down Proteomics of a Complex Proteome Sample
    (American Chemical Society, 2024) Sadeghi, Seyed Amirhossein; Chen, Wenrong; Wang, Qianyi; Wang, Qianjie; Fang, Fei; Liu, Xiaowen; Sun, Liangliang; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Mass spectrometry (MS)-based top-down proteomics (TDP) has revolutionized biological research by measuring intact proteoforms in cells, tissues, and biofluids. Capillary zone electrophoresis-tandem MS (CZE-MS/MS) is a valuable technique for TDP, offering a high peak capacity and sensitivity for proteoform separation and detection. However, the long-term reproducibility of CZE-MS/MS in TDP remains unstudied, which is a crucial aspect for large-scale studies. This work investigated the long-term qualitative and quantitative reproducibility of CZE-MS/MS for TDP for the first time, focusing on a yeast cell lysate. Over 1000 proteoforms were identified per run across 62 runs using one linear polyacrylamide (LPA)-coated separation capillary, highlighting the robustness of the CZE-MS/MS technique. However, substantial decreases in proteoform intensity and identification were observed after some initial runs due to proteoform adsorption onto the capillary inner wall. To address this issue, we developed an efficient capillary cleanup procedure using diluted ammonium hydroxide, achieving high qualitative and quantitative reproducibility for the yeast sample across at least 23 runs. The data underscore the capability of CZE-MS/MS for large-scale quantitative TDP of complex samples, signaling its readiness for deployment in broad biological applications. The MS RAW files were deposited in ProteomeXchange Consortium with the data set identifier of PXD046651.
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    Quantitative proteomics reveals the dynamic proteome landscape of zebrafish embryos during the maternal-to-zygotic transition
    (Elsevier, 2024-05-08) Fang, Fei; Chen, Daoyang; Basharat, Abdul Rehman; Poulos, William; Wang, Qianyi; Cibelli, Jose B.; Liu, Xiaowen; Sun, Liangliang; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Maternal-to-zygotic transition (MZT) is central to early embryogenesis. However, its underlying molecular mechanisms are still not well described. Here, we revealed the expression dynamics of 5,000 proteins across four stages of zebrafish embryos during MZT, representing one of the most systematic surveys of proteome landscape of the zebrafish embryos during MZT. Nearly 700 proteins were differentially expressed and were divided into six clusters according to their expression patterns. The proteome expression profiles accurately reflect the main events that happen during the MZT, i.e., zygotic genome activation (ZGA), clearance of maternal mRNAs, and initiation of cellular differentiation and organogenesis. MZT is modulated by many proteins at multiple levels in a collaborative fashion, i.e., transcription factors, histones, histone-modifying enzymes, RNA helicases, and P-body proteins. Significant discrepancies were discovered between zebrafish proteome and transcriptome profiles during the MZT. The proteome dynamics database will be a valuable resource for bettering our understanding of MZT.