A targeted proteomics and metabolomics mass spectrometry

This proposal will provide new capabilities and take advantage of the advances in mass spectrometric (MS) instrumentation, methodologies, and computational capabilities to move into clinical analysis. MS-based ‘omics’ research will provide a spectrum of analyses that will range from pre-clinical discovery to applied diagnostics to interrogate clinically relevant samples. These ‘omic” analyses will address specific clinical questions or needs. MS continues to play an increasingly important role in molecular-level research, and is central to ‘omics’ research, i.e., proteomics, metabolomics, lipidomics, etc. and are crucial to cancer research as they inform on the mechanisms that drive the oncogenic phenotype. We have developed and implemented thirteen targeted panels in metabolomics and lipidomics, for investigators in UT-MDACC, UT Health-SA, UT Health-Houston, Rice University, Baylor College of Medicine, Texas A&M University (College Station), UT-Southwestern Medical Center, UTMB, and our collaborators at the TREC institutions of Texas Tech University and UT-El Paso (see attached letters of support). The existing CPRIT facility at UTMB is utilized by over 110 PIs in Texas including 39 external to UTMB, highlighting progress in our goals for being a Texas-wide resource. We contributed to acquiring over $200,000,000 in new federal, foundation, and industrial grants in our 5 years as a CPRIT Core and aided in the generation of data for over 51 peer-reviewed manuscripts in high impact peer-reviewed journals (see attached tables). The proposed Bruker timsTOF Ultra2 will enable us to expand our capabilities into the emerging fields of immunopeptidomics, clinical proteomics, and single cell proteomics while simultaneously improving the sensitivity, throughput, and depth of analysis of our existing assays. For example, the proposed LCMS platform is capable of analyzing 10x more proteomics samples per day, while achieving a depth of quantified proteins that is >2-fold higher than our current instrumentation. Improvements in LCMS instruments’ sensitivity and speed are essential to deepening our understanding of signaling biology within biochemical pathways, that are out of reach with our current instrumentation. Specifically, a wider net is needed to identify posttranslationally modified (PTM) signaling molecules that drive disease and therapeutic resistance. Enhanced detection, identification and quantitation of PTMs such as phosphorylation, ubiquitylation, acetylation, sumoylation, and glycosylation are paramount. The serial enrichment and analysis strategies we have developed would provide global proteome and numerous PTMs from the same biological sample, thereby facilitating multi-omic integration while sparing precious patient material at unprecedented depths. Another important goal is to implement protocols and assays for high-throughput immunopeptidomics from plasma. Immunopeptidomics is crucial role in identifying targets for cancer immunotherapy and vaccine development. Immunotherapies that harness the ability of T cells to recognize aberrant cells have shown great clinical potential with personalized cancer vaccines and cell therapies. State-of-the-art MS instrumentation, protocols, automation and operators enable the robust identification of tens of thousands of HLA I immunopeptides from a single donor. Single cell proteomics (SCP) capabilities will be achievable in our improved proposed MS core. In recent years, SCP has advanced significantly, enabling the analysis of thousands of proteins within single mammalian cells. This progress has been driven by advances in instrumentation, assay development, and innovations in separation techniques. These new technologies collectively contribute to improved sensitivity, throughput, and reproducibility. Cutting-edge mass spectrometry platforms with enhanced data acquisition approaches are essential to enhancing data quality. All of these recent technological advances in SCP analyses, will be used to probe spatial proteomics with single-cell resolution which will aid in understanding cellular interactions/communication that drive the neoplastic phenotypes. New data analysis platforms aided by machine learning and artificial intelligence algorithms will further enhance the data generated with the new LCMS platforms and further bolster oncological research. In our hands, all of these enhanced technologies will open new opportunities in oncology biomarker discovery and anti-neoplastic drug development and pave the way for more efficient and comprehensive oncologic analyses across sample types. Approval of this proposal will fortify researchers across the State of Texas (and beyond) to advance medical research and therapeutic innovations in treatment which5 ultimately will benefit the patient stricken with the disease. UTMB has committed to continued support of the facility as highlighted in the attached letter from our CRO.