Browsing by Author "Jiang, Yuming"

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    Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry
    (American Chemical Society, 2024-06-04) Jiang, Yuming; Rex, Devasahayam Arokia Balaya; Schuster, Dina; Neely, Benjamin A.; Rosano, Germán L.; Volkmar, Norbert; Momenzadeh, Amanda; Peters-Clarke, Trenton M.; Egbert, Susan B.; Kreimer, Simion; Doud, Emma H.; Crook, Oliver M.; Yadav, Amit Kumar; Vanuopadath, Muralidharan; Hegeman, Adrian D.; Mayta, Martín L.; Duboff, Anna G.; Riley, Nicholas M.; Moritz, Robert L.; Meyer, Jesse G.; Biochemistry and Molecular Biology, School of Medicine
    Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.
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    Comprehensive Overview of Bottom-Up Proteomics using Mass Spectrometry
    (ArXiv, 2023-11-13) Jiang, Yuming; Rex, Devasahayam Arokia Balaya; Schuster, Dina; Neely, Benjamin A.; Rosano, Germán L.; Volkmar, Norbert; Momenzadeh, Amanda; Peters-Clarke, Trenton M.; Egbert, Susan B.; Kreimer, Simion; Doud, Emma H.; Crook, Oliver M.; Yadav, Amit Kumar; Vanuopadath, Muralidharan; Mayta, Martín L.; Duboff, Anna G.; Riley, Nicholas M.; Moritz, Robert L.; Meyer, Jesse G.; Biochemistry and Molecular Biology, School of Medicine
    Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics.
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    Genomic Signature for Initial Brain Metastasis Velocity (iBMV) in Non-Small-Cell Lung Cancer Patients: The Elusive Biomarker to Predict the Development of Brain Metastases?
    (MDPI, 2025-03-15) Glynn, Sarah E.; Lanier, Claire M.; Choi, Ariel R.; D'Agostino, Ralph, Jr.; Farris, Michael; Abdulhaleem, Mohammed; Wang, Yuezhu; Smith, Margaret; Ruiz, Jimmy; Lycan, Thomas; Petty, William Jeffrey; Cramer, Christina K.; Tatter, Stephen B.; Laxton, Adrian W.; White, Jaclyn J.; Su, Jing; Whitlow, Christopher T.; Soto-Pantoja, David R.; Xing, Fei; Jiang, Yuming; Chan, Michael; Helis, Corbin A.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Background/Objectives: No prior studies have attempted to identify a biomarker for initial brain metastasis velocity (iBMV), with limited studies attempting to correlate genomic data with the development of brain metastases. Methods: Patients with non-small-cell lung cancer (NSCLC) who underwent next-generation sequencing (NGS) were identified in our departmental database. iBMV was calculated by dividing the number of BMs by the interval of time between primary cancer and BM diagnosis. Two-sample t-testing was used to identify mutations statistically associated with iBMV (p < 0.1). A value of +1 was assigned to each mutation with a positive association ("deleterious genes"), and a value of -1 to each with an inverse association ("protective genes"). The sum of these values was calculated to define iBMV risk scores of -1, 0 and 1. Pearson correlation test was used to determine the association between iBMV risk score and calculated iBMV, and a competing risk analysis assessed for death as a competing risk to the development of BMs. Results: A total of 312 patients were included in the analysis, 218 of whom (70%) developed brain metastases. "Deleterious genes" included ARID1A, BRAF, CDK4, GNAQ, MLH1, MSH6, PALB2, RAD51D, RB1 and TSC1; "protective genes" included ARAF, IDH1, MYC, and PTPN11. iBMV risk scores of 1, 0 and -1, predicted an 88%, 61% and 65% likelihood of developing a BM (p < 0.01). A competing risk analysis found a significant association between iBMV risk scores of 1 vs. 0 and 1 vs. -1, and the likelihood of developing a BM using death as a competing risk. Overall survival (OS) at 1 and 2 years for patients with iBMV risk scores of 1, 0 and -1 was 72% vs. 84% vs. 85% and 46% vs. 69% vs. 70% (p < 0.02). Conclusions: Development of a genomic signature for iBMV via non-invasive liquid biopsy appears feasible in NSCLC patients. Patients with a positive iBMV risk score were more likely to develop brain metastases. Validation of this signature could lead to a biomarker with the potential to guide treatment recommendations and surveillance schedules.