Browsing by Author "McCool, Elijah N."

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Single-Shot Top-Down Proteomics with Capillary Zone Electrophoresis-Electrospray Ionization-Tandem Mass Spectrometry for Identification of Nearly 600 Escherichia coli Proteoforms
    (American Chemical Society, 2017-11-21) Lubeckyj, Rachele A.; McCool, Elijah N.; Shen, Xiaojing; Kou, Qiang; Liu, Xiaowen; Sun, Liangliang; BioHealth Informatics, School of Informatics and Computing
    Capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry (CZE-ESI-MS/MS) has been recognized as an invaluable platform for top-down proteomics. However, the scale of top-down proteomics using CZE-MS/MS is still limited due to the low loading capacity and narrow separation window of CZE. In this work, for the first time we systematically evaluated the dynamic pH junction method for focusing of intact proteins during CZE-MS. The optimized dynamic pH junction-based CZE-MS/MS approached a 1 μL loading capacity, 90 min separation window, and high peak capacity (∼280) for characterization of an Escherichia coli proteome. The results represent the largest loading capacity and the highest peak capacity of CZE for top-down characterization of complex proteomes. Single-shot CZE-MS/MS identified about 2800 proteoform-spectrum matches, nearly 600 proteoforms, and 200 proteins from the Escherichia coli proteome with spectrum-level false discovery rate (FDR) less than 1%. The number of identified proteoforms in this work is over three times higher than that in previous single-shot CZE-MS/MS studies. Truncations, N-terminal methionine excision, signal peptide removal, and some post-translational modifications including oxidation and acetylation were detected.