6:00 AM - 7:00 AM - Social Activity
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Morning Run/Walk
Meet in Hotel Lobby
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7:30 AM - 7:00 PM - Registration
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Registration and Information Desk
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8:30 AM - 9:05 AM - Plenary Session
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Donald F. Hunt Distinguished Contribution in Proteomics Award Plenary Session
Harnessing Mass Spectrometry to Help Domesticate the Human Proteome
Sponsored By: JPR
Professor Donald F. Hunt has had an enormous impact in the area of protein sequencing, biochemistry, proteomics, immunology and chromatin biology. Over the last half-century, his approach to science, technology and mentorship has produced an incredible impact that this session will celebrate. As a beneficiary of Professor Hunt's legacy, Dr. Kelleher will describe professional impact of Hunt-lab advances as we stand on Don's shoulders moving forward.
For over twenty years, the Kelleher Group has invented new methods to discover the exact forms of protein molecules in human cells. The world has come to call these "proteoforms" and Kelleher uses so-called "Top-Down" Proteomics to discover, characterize and assign function to them with increasing efficiency. The "domestication" of the human proteome via precise compositional mapping will improve the efficiency of basic and clinical research and therefore enhance diverse goals for the 21st Century, including designer organs, personalized medicine, and early detection of human disease. A recent article in Science (2022, 375: 411-418) typifies the promise and crescendo of activity in this area of proteomics, advanced consistently by Kelleher over the past 25 years.
Presented By:
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Neil Kelleher, Walter and Mary Elizabeth Glass Professor of Chemistry, Molecular Biosciences, and Medicine, Northwestern University, Chicago, IL, United States
(Bio)
Neil L. Kelleher, PhD is the Walter and Mary Glass Professor of Molecular Biosciences and professor of chemistry in the Weinberg College of Arts and Sciences. He also is director of the 50-person Proteomics Center of Excellence, Director of the Chemistry of Life Processes and a member of the Robert H. Lurie Comprehensive Cancer Center of Northwestern University. His research is focused in the areas of top-down proteomics, natural products discovery, and cancer biology. With >450 papers, Dr. Kelleher is a cross-disciplinary investigator with international impact in proteomics (the study of proteins). Together with colleagues in a research consortium (https://www.topdownproteomics.org/), this emerging approach to measure proteins with complete molecular specificity is being advanced to improve the detection and assignment of function to protein modifications and complexes. Now with an H-factor of 89, Kelleher has mentored over 52 Ph.D. students, >200 postdoctoral scholars, and >200 undergraduates. After a breakthrough Nature paper in 2011, Kelleher has continued to push the boundaries of proteomics and is currently advancing a compositional map of proteins in all cell types of the human body. This "domestication" of the human proteome via precise compositional mapping will improve the efficiency of basic and clinical research and therefore enhance diverse goals for the 21st Century, including designer organs, personalized medicine, and early detection of human disease.
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9:05 AM - 9:35 AM - Break
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Coffee Break
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9:35 AM - 10:55 AM - Parallel Sessions
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Parallel Session 01: Structural Proteomics in Disease Biology
Developing structural interactomics and its application in cell biology
Profiling human interactome is critical in understanding the molecular basis for nearly all processes of life. Over the years, we’ve advanced crosslinking mass spectrometry by developing experimental methods and software tools to identify tens of thousands of PPIs from whole cells. These data reveal numerous aspects of living systems - for example protein subcellular localizations, virus-host interactions, and architectures of suprabiomolecular machineries. Furthermore, these data offer unprecedented opportunities to profile interactome changes between tissues and disease states, providing invaluable training data for AI-based methods to identify PPI-mediating motifs, inform new protein/antibody designs and screen for drug targets.
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Fan Liu, Leibniz-Forschungsinstitut für Molekulare Pharmakologie, Berlin, Germany
(Bio)
Fan graduated from Fudan University in Shanghai with a B.Sc. in Biology. Afterwards she joined the lab of Prof. Dr. Mike Goshe at North Carolina State University and obtained her PhD in Biochemistry in 2013. Fan did her postdoc in the lab of Prof. Dr. Albert Heck (Utrecht, The Netherlands).
In 2017 Fan joined the Leibniz-Forschungsinstitut for Molecular Pharmacology (FMP) in Berlin as a group leader for Structural Interactomics and head of the proteomics research platform. In addition to her position at the FMP, Fan is jointly appointed as Professor for Structural Interactomics at Charité - Universitätsmedizin Berlin.
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Lan Huang, University of California, Irvine, Irvine, CA, United States
(Bio)
Dr. Lan Huang is a Professor of Physiology & Biophysics in the School of Medicine, University of California, Irvine and the Director of UCI High-end Mass Spectrometry Facility. Her research focuses on developing novel, integrated mass spectrometry-based proteomic strategies to characterize macromolecular protein complexes and understand their functions, particularly those in the ubiquitin-proteasome system. During the last two decades, the Huang lab has developed a number of novel methodologies to capture, purify and quantify protein-protein interactions in living cells. She has pioneered the development of sulfoxide-containing MS-cleavable cross-linkers (e.g. DSSO), and thus established a robust cross-linking mass spectrometry (XL-MS) platform that enables the elucidation of interaction networks and structural topologies of native proteomes in vitro and in vivo. The strategies developed by her group have proven highly effective as general proteomic tools for studying protein-protein interactions and protein complexes. She has successfully translated her research findings into practical applications, receiving several patents and commercializing reagents that have made a substantial impact in the scientific community. Her lab has recently applied XL-MS technologies to clinical samples to define protein modules and network topologies of proteomes and understand their associations with human disease.
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OA01.01 | Applying Structural Proteomics to Understand the Role of Aging and Mutation in the Pathogenesis and Pathophysiology of Amyloid Diseases
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Chad Hyer, Brigham Young University
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OA01.02 | Spatial-temporal control of proteostasis and metabolons revealed by interactomics
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Lars Plate, Vanderbilt University
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Parallel Session 02: Innovative Technologies and Methods for Quantitation
Comparison of quantitation strategies of protein turnover rates
Different stable isotope labeling strategies coupled to mass spectrometry have been used to measure protein turnover rates in vivo and in vitro. Our group developed Riana (Relative isotope abundance analyzer) which performs isotopomer quantification and kinetic curve fitting in a manner compatible with various SILAC and elemental labeling approaches. Using Riana, we have compared the turnover rates of proteins across four mouse tissues, obtained from the same inbred mouse strain maintained under identical husbandry conditions, measured using either heavy water or heavy lysine as the labeling precursor. We describe an isotopomer selection strategy that can improve the depth of protein turnover quantification in vivo and in vitro.
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Edward Lau, University of Colorado-Anschutz, Aurora, CO, United States
(Bio)
I am an Assistant Professor at the Department of Medicine at the University of Colorado School of Medicine. Prior to starting my lab in Colorado, I completed my PhD at the University of California Los Angeles followed by a postdoctoral fellowship at Stanford University. Research in my group aims to understand how the spatial and temporal dynamics of the proteome are regulated in development, aging, and disease. In prior work, we have developed the protocol and software to apply heavy water and stable isotope labeled amino acids to measure the half-life of thousands of proteins across multiple mouse tissues (Hammond [...] Beynon, Lau; MCP 2022); applied machine learning methods to unravel genewise differences in mRNA-protein correlation and its regulation (Srivastava et al. PLoS Comput Bill 2022); and developed a new method to simultaneously trace the subcellular localization and turnover rates of proteins in cell culture, which is being utilized toward identifying new elements of proteostatic stress response and drug induced cardiotoxicity (Currie et al. bioRxiv 2023).
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The Dynamics of Mass Isotopomers Reveal Metabolic Label Incorporation
The presentation will describe computational proteomics algorithms for efficient and accurate analyses of stable isotope labeled time course data to determine the turnover rates of individual proteins. The algorithms are based on analytical equations for the dynamics of the six mass isotopomers during metabolic labeling with heavy water. The equations allow 1) to determine the label enrichment from raw abundances of two mass isotopomers, 2) to simultaneously determine the label enrichment and the number of labeling sights from raw abundances of three mass isotopomers, 3) to identify the decay and growth components of each mass isotopomer during the labeling. We show that the estimation of turnover rate from partial isotope profiles reduces the effects of co-elutions and increases the number of high-quality quantified peptides in murine liver samples. The separation of growth and decaying components of a mass isotopomer identifies a data transformation needed for the accurate extraction of the turnover rates using the relative abundances of heavy mass isotopomers.
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Rovshan Sadygov, The University of Texas Medical Branch, Galveston, TX, United States
(Bio)
Rovshan G. Sadygov, Ph.D., Associate Professor, Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX. He is a bioinformatician working on the development and applications of computational tools for the analyses of mass spectral data. His lab has developed d2ome - a tool to quantify label incorporation from LC-MS data of heavy water labeled samples.
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OA02.02 | Mag-Net enrichment coupled with Evosep One and Orbitrap Astral MS enable high throughput deep plasma proteome profiling
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Lilian Heil, Thermo Fisher Scientific
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OA02.01 | Adding genomics to highly multiplex proteomics for improved predictive modeling performance
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Michael Hinterberg, SomaLogic, Inc.
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11:00 AM - 12:15 PM - Lightning Session
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Lightning Talks - Round 01
Sponsored By: QuantumSi
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1:30 PM - 3:00 PM - Poster Session
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Poster Session 01 and Exhibitor Viewing
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3:00 PM - 4:20 PM - Parallel Sessions
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Parallel Session 03: Emerging Machine Learning and AI methods in Proteomics
Approaches to Generating AI/ML-ready Data for Proteomics
A wealth of proteomics data is being generated every day to study a wide range of biomedical challenges. These datasets are often multi-modal, paired with other omics and metadata. Further, there is a community push from funders and researchers to make data FAIR (Findable, Assessable, Interoperable, Reusable). This can be challenging with multi-modal datasets, but even when achieved, often the domain knowledge needed to engage AI/ML-experts is considerable. Thus, a core challenge to engage the AI/ML community is the packaging of the data in a form that can be easily understood by non-experts, or those not involved in the project from where the data originated. Best practices for generating AI/ML-datasets will be discussed and demonstrated though our current dissemination of AI/ML-ready datasets pipeline which is amenable to new methods to improve the quality of single- and multi-omics datasets (e.g., imputation, batch correction, etc.), as well as clean production level datasets for predictive modeling to elicit biomarkers and pathway-level molecular signatures from the data.
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Bobbie-Jo Webb-Robertson, Pacific Northwest National Laboratory, Richland, WA, United States
(Bio)
Bobbie-Jo Webb-Robertson is the Director of the Biological Sciences Division with the Earth and Biological Sciences Directorate at Pacific Northwest National Laboratory. In addition, she holds joint appointments in the Department of Biomedical Engineering at Oregon Health & Science University, the Department of Pathology, Immunology and Laboratory Medicine at the University of Florida, and the Department of Biostatistics and Informatics at the University of Colorado Anschutz Medical Campus. Dr. Webb-Robertson's research focuses on the development of machine learning (ML) and statistical methods in two primary areas; improving downstream analytics from mass spectrometry derived proteomic, metabolomic and lipidomic data and machine learning driven feature extraction focused on biomarker discovery from complex heterogenous data. Her current research is largely focused on understanding the progression of type 1 diabetes. In that context she is currently leading the development of Artificial Intelligence (AI)/ML-ready data for studies of diabetes mellitus funded through the Human Islet Research Network from the National Institutes of Health, the Data Science lead for the national Pancreatic Organ Donors with diabetes (nPOD) program, and a co-I of the Diabetes Autoimmunity Study in the Young (DAISY). Dr. Webb-Robertson received a BA in mathematics from Eastern Oregon University and a ME in Statistics & Operations Research and PhD in Decision Sciences and Engineering Systems from Rensselaer Polytechnic Institute.
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Multi-Omic Integration with Machine Learning
Measuring multiple omics layers from the same biological sample, often called "multi-omics", is becoming more common. Integration of data from multiple omics layers promises to reveal a clearer picture of the biological state. However, knowledge extraction through the integration of multi-omics data remains challenging, often requiring manual interpretation thereby limiting scalability. In this presentation I will describe our recent work that enables discovery of connections between molecules in different omic layers through a combination of machine learning and model interpretation.
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Jesse Meyer, Cedars-Sinai Medical Center, Los Angeles, CA, United States
(Bio)
Jesse G. Meyer is an Assistant Professor at Cedars Sinai Medical Center in Los Angeles, California. He received his Bachelor of Science degree in Biochemistry in his home state at the University of Minnesota. He then moved to the University of California San Diego to get his PhD in the Chemistry Department. He was a postdoctoral fellow at the Buck Institute for Research on Aging and then the University of Wisconsin Madison. He was named among the Rising Stars in Proteomics and Metabolomics by the Journal of Proteome Research in 2021 and received the ASMS Research Award in 2023. His group does research on human disease by developing and applying techniques at the interface of omics and data science.
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OA03.01 | Experimental design of quantitative proteomic experiments by simulating data from models aware of prior knowledge of biomolecular networks
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Vartika Tewari, Northeastern University
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OA03.02 | The development of MSFragger-DDA+ and MSFragger-DIA for multiplexed DDA and DIA data analysis
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Fengchao Yu, University of Michigan
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Parallel Session 04: The Omics of Aging and Age Related Diseases
Multidimensional proteomics identifies molecular trajectories of cellular aging and rejuvenation
The declining capacity of cells to maintain a functional proteome is a major driver of cellular dysfunction and decreased fitness in aging. Here we assess the impact of aging on multiple proteome dimensions, which are reflective of function, across the replicative lifespan of Saccharomyces cerevisiae. We quantified protein abundance, protein turnover, protein thermal stability, and protein phosphorylation in mother yeast cells and their derived progeny at different ages. We find progressive and cumulative proteomic alterations that are reflective of dysregulation of complex assemblies, mitochondrial remodeling, post-translational activation of the AMPK/Snf1 energy sensor in mother cells, and an overall shift from biosynthetic to energy-metabolic processes. Our multidimensional proteomic study systematically corroborates previous findings of asymmetric segregation and daughter cell rejuvenation, and extends these concepts to protein complexes, protein phosphorylation, and activation of signaling pathways. Lastly, profiling age-dependent proteome changes in a caloric restriction model of yeast provided mechanistic insights into longevity, revealing minimal remodeling of energy-metabolic pathways, improved mitochondrial maintenance, ameliorated protein biogenesis, and decreased stress responses. Taken together, our study provides thousands of age-dependent molecular events that can be used to gain a holistic understanding of mechanisms of aging.
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Judit Villen, University of Washington, Seattle, WA, United States
(Bio)
Judit Villen is a Professor in the Department of Genome Sciences at the University of Washington in Seattle. She earned her PhD in Chemistry from the University of Barcelona developing peptide vaccines. She was a postdoctoral fellow at Harvard Medical School developing phosphoproteomic methods and applications. She has a track record of creative, interdisciplinary, technology-driven, and collaborative biomedical research. Research in her laboratory focuses on the organization and function of the proteome. They develop mass spectrometry-based proteomics technologies and apply these technologies to study cellular signaling in cancer, metabolic diseases, and aging. Beyond signaling studies, her lab has invented a high throughput, non-genetic, mutagenesis technology to study the impact of amino acid substitutions on protein function on a proteome-wide scale using mass spectrometry, with the goal of accelerating variant interpretation.
Prof. Villen has been a recipient of an NIH K99/R00 Pathway to Independence award, an Ellison Foundation New Scholar award, the ASMS Research award, and the US HUPO Robert Cotter award. She has been named a "Highly cited researcher" by Thomson Reuters/Clarivate.
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Proteomics in Geroscience: A quest for blood biomarkers of aging
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Toshiko Tanaka, NIH, Baltimore, MD, United States
(Bio)
Dr. Tanaka is a Staff Scientist at the Translational Gerontology Branch, Longitudinal Study Section of the National Institute on Aging. Her research focuses on the identification of aging biomarkers using -omics data including genetics, proteomics and metabolomics. Using omics data, Dr. Tanaka has developed measures of biological age, including proteomic clocks. One of the aims of her research is to finetune these -omics based clocks to better predict health trajectories. A secondary aim is to understand what factors that influence the pace of aging measured by epigenetic and proteomic clocks.
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OA04.01 | Looking for Plasma Biomarker Candidates for Alzheimer's Disease - Comparing CSF and Plasma from Alzheimer's and Mild Cognitive Impairment Patients
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Hanno Steen, Boston Children's Hospital and Harvard Medical School
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OA04.02 | Identifying Cognition Associated Protein Structural Changes in a Rodent Model of Aging Using Limited-Proteolysis Mass Spectrometry
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Haley Tarbox, Johns Hopkins University
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4:30 PM - 5:50 PM - Parallel Sessions
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Parallel Session 05: Serendipity in Proteomics (ECR Session)
Extending In-Cell Fast Photochemical Oxidation of Proteins (IC-FPOP) for Structural Studies in Patient Samples
In recent years, protein footprinting coupled with mass spectrometry has been used to identify protein-protein interaction sites and regions of conformational change through modification of solvent accessible sites in proteins. The footprinting method, fast photochemical oxidation of proteins (FPOP), utilizes hydroxyl radicals to modify these solvent accessible sites. To date, FPOP has been used in vitro on relatively pure protein systems. We have further extended the FPOP method for in vivo analysis of proteins. This will allow for study of proteins in their native cellular environment and be especially useful for the study of membrane proteins which can be difficult to purify for in vitro studies. A major application of the in vivo method is for proteome-wide structural biology. To this end, we have further developed the method for patient samples, specifically peripheral blood mononuclear cells (PBMCs). We have optimized several parameters for labeling of proteins in these patient samples. This method have the potential to become a powerful tool in the structural biology toolbox.
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Lisa Jones, UCSD, San Diego, CA, United States
(Bio)
Lisa M. Jones is the Chancellor's Associate Endowed Chair of Chemistry and Biochemistry at the University of California San Diego. She received her PhD in Chemistry from Georgia State University. She received postdoctoral training in structural virology at the University of Alabama-Birmingham and in MS-based protein footprinting at Washington University in St. Louis. Her research is focused on extending the protein footprinting method fast photochemical oxidation of proteins (FPOP) coupled with mass spectrometry into complex model systems. Her lab has extended the method for in-cell analysis to provide structural information across the proteome. She has further developed the method for in vivo analysis in C. elegans, an animal model for human disease. Her lab aims to understand the biological causes of health disparities in cancer and other diseases. She also has a passion for increasing diversity in STEM and participates in several outreach initiatives to achieve this.
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Preparation, Perspiration, Inspiration, and Luck lead to Biochemical Discovery.
The process of discovery requires stepping into the unknown and trying to learn something new. In my experience good notes and thoughtful meditation on results have resulted in the most insightful discoveries. This suggests a recipe to succeed in proteomics and any research-based career. I will present a couple of specific examples from my career and try to outline principles that lead to success. Interestingly, my recipe for success incorporates the essence of a couple frequently repeated idioms. 1. A well-ordered and thoughtful experiment will teach you something regardless of whether it supports your hypothesis. 2. Just because you failed doesn't mean you are a failure. 3. Luck favors the prepared. In my experience the wealth of information and opportunity for confusion versus discovery in a proteomics experiment increases the value of these ideas.
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John Price, BYU, Provo, UT, United States
(Bio)
John Price grew up farming and ranching in rural Idaho. He was always inspired by how the world restarted every spring by producing new life. He studied Chemistry and Biochemistry at Utah State University in order to understand the mechanisms that produced these amazing results. Starting as an undergraduate he worked with Dr. Lisa Berreau to create synthetic models of enzyme active sites using novel small molecule chelators of metal atoms. His graduate work at Pennsylvania State University used entire enzymes.
Here he studied the steps of oxygen activation on iron atoms in dioxygenase enzymes with Drs. J. Martin Bollinger and Carsten Krebs. His success there led to an ambitious project applying kinetic approaches to the study of prion protein aggregates in the brain with Stanley Prusiner at the University of California San Francisco. At UCSF, he published the first proteome scale measurement of in vivo protein turnover. This showed that regulation of protein turnover occurred at the level of the tissue, multiprotein complex, and individual sequence. He was recruited away from UCSF to develop some intellectual property and start a research and development division at a small biotech company. After finishing the commercial development, he decided to continue research into the regulation of protein homeostasis as a faculty member in the Chemistry and Biochemistry Department at Brigham Young University.
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OA05.01 | Tapping Into the Cell Surfaceome to Identify and Target Senescent Cells In Vivo
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Reema Banarjee, National Institute on Aging, NIH
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OA05.02 | Characterizing HDAC6 as a Drug Target in High-Grade Serous Ovarian Cancer Treatment
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Jolene Duda, University of Minnesota, United States
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Parallel Session 06: Advances in Single-Cell MS
Single-nucleus proteomics identifies regulators of subcellular protein transport in LPS-stimulated macrophages
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Jason Derks
(Bio)
Jason Derks received his undergraduate education from UC Santa Barbara and his PhD from Northeastern University. His doctoral work, completed in the laboratory of Prof. Nikolai Slavov, focused on increasing the throughput of bulk and single cell proteomics. As a postdoc in the Slavov laboratory, Jason is interested in applying these developments to associate functional and proteomic variability at high throughput to gain mechanistic insights which can be experimentally tested. Specifically, his postdoctoral work has applied single nucleus proteomics to identify proteins which regulate nucleocytoplasmic transport in LPS-stimulated macrophages.
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Single-Cell Resolved Analysis of the Aortic Proteome in Marfan Syndrome
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Sarah Parker, Cedars-Sinai Medical Center, Los Angeles, CA, United States
(Bio)
Sarah Parker is an Associate Professor in the Department of Cardiology at Cedars-Sinai Medical Center, where she is also the co-director of the proteomics and metabolomics core facility in the Board of Governors Innovation Center. Her research utilizes proteomic techniques, including emerging single-cell and spatial proteomics, to elucidate mechanisms and biomarkers of aortic aneurysm, atherosclerosis, cardiovascular and other diseases.
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OA06.01 | Improved Sample Preparation, Separations and Data Acquisition for Single-Cell Proteomics
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Ryan Kelly, Brigham Young University
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OA06.02 | Thermal Inkjetting Enabled Label Free Proteomics for Tracking Outcomes of Genetic Manipulations and Engineered Proteins in Individual Cells
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Stanislau Stanisheuski, Oregon State University, United States
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6:00 PM - 7:00 PM - Evening Workshops
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Evening Workshop: Live VMO Video Competition
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Evening Workshop: CPTAC
Proteomic Data Commons: Bridging Omics to Function Through Comprehensive Proteomic Data and Multi-Omics Integration Analysis
This workshop will focus on using the PDC for comparative multi-omics analysis using or referencing large datasets accessible through its portal.
The speakers will cover:
Unlocking the Power of CPTAC Pan-Cancer Proteogenomics Data with LinkedOmicsKB
Connecting Tumor Histopathology Images with Molecular Features Using Multi-Resolution Deep Learning Models
A cloud-based computational platform for automated proteogenomic data analysis
The workshop will be hosted and moderated by Xu Zhang, PhD, Program Officer, NCI Office of Cancer Clinical Proteomics Research (OCCPR) within the Division of Cancer Treatment & Diagnosis (DCTD).
Presented By:
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Bing Zhang
(Bio)
Dr. Bing Zhang is a Cancer Prevention and Research Institute of Texas (CPRIT) Scholar, McNair Medical Institute Scholar, and Professor of Molecular and Human Genetics in Baylor College of Medicine. He is an internationally recognized leader in computational cancer proteogenomics, with a focus on developing informatics solutions that help translate cancer genomic and proteomic data into biological and clinical insights.
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David Fenyo, NYU School of Medicine
(Bio)
Dr. David Fenyö received a PhD in Physics from Uppsala University in Sweden and after switching to computational biology, he did a postdoc at the Rockefeller University, co-founded a bioinformatics company and worked at GE Healthcare. He has over 35 years of experience with all aspects of biomedical data analysis in both academia and industry and his work has resulted in over 250 scientific publications. In 2010 he joined NYU School of Medicine where he is currently Professor of Biochemistry and Molecular Pharmacology, Director for the Ph.D. program in Systems and Computational Biomedicine and the Master's program in Biomedical Informatics. His research focuses on applying data science methods to analyze quantitative data, model biological systems, and predict patient outcome and select treatments. His efforts to integrate data from multiple technologies-including mass spectrometry, sequencing, microscopy, and electronic health records-have provided a wide array of powerful tools to discover and verify biomarkers and therapeutic targets in cancer.
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D. R. Mani , Broad Institute / MIT and Harvard
(Bio)
D. R. Mani is Director of Computational Proteomics in the Proteomics Platform at the Broad Institute of MIT and Harvard. For over two decades, he has been applying computational pattern recognition, machine learning, signal processing, and statistical data analysis to the analysis of omics data generated from a wide range of bio-assays, including mass spectrometry-based proteomics and gene expression profiling. His research has focused on the design and implementation of innovative algorithms to enable proteogenomic analysis, immunopeptidomics, pattern-based discovery of proteomic biomarker candidates, evaluation of data quality, assessment of variability and reproducibility in mass spectrometry-based assays, and data visualization. Mani is a principal investigator for the National Cancer Institute Clinical Proteomics Tumor Analysis Consortium (NCI-CPTAC) Proteogenomic Data Analysis Center (PGDAC) at the Broad, he has been leading multi-omic data analysis for almost all projects in the Broad Proteomics Platform. He has a Ph.D. in computer science from the University of Pennsylvania and a M.S. in biostatistics from the Harvard School of Public Health.
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Xu Zhang, NCI Office of Cancer Clinical Proteomics Research (OCCPR) within the Division of Cancer Treatment & Diagnosis (DCTD)
(Bio)
Dr. Xu Zhang is a Program Manager in the Office of Cancer Clinical Proteomics Research (OCCPR) at the National Cancer Institute (NCI), National Institutes of Health (NIH). She provides scientific expertise in Proteomics Data Science/Data Management, manages and oversees the grants and contracts that support proteomics data analysis, informatics and software tools, and data management activities for NCI's Clinical Proteomic Tumor Analysis Consortium (CPTAC), International Cancer Proteogenome Consortium (ICPC), and the Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) network. Dr. Zhang has extensive experience in the proteogenomic field, especially in proteomics data management.
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7:15 PM - 8:30 PM - ECR Elevator Pitches & Speed Dating
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ECR Elevator Pitches & Speed Dating
Show up for a fun evening of "musical chairs" elevator pitches! Following a brief presentation about how to create an exciting and engaging elevator pitch, you will get the chance to practice your elevator pitch with fellow ECRs in an active speed networking rotation.
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