Agenda

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.

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.

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.

Biological functions are reflected in the natural variation of proteome configurations across individual cells. Single-cell proteomics methods may decode this variation and empower inference of biological mechanisms with minimal assumptions. This promise is beginning to be realized by sensitive and scalable mass spectrometry methods. Specifically, prioritized single-cell mass spectrometry analysis (pSCoPE) allows for consistent and sensitive analysis of thousands of proteins of biological interest, and multiplexed data independent acquisition methods (plexDIA) afford high throughput and data completeness. These methods have allowed us to interpret protein covariation in different biological systems, including primary macrophages and melanoma cells expressing markers for drug-resistance priming. The focus of the talk will be on conceptual innovations and strategies for data acquisition and interpretation that make single-cell protein analysis accessible, robust and highly quantitative.

Purpose: The low cancer specificity of Prostate-Specific Antigen (PSA), the principal blood biomarker for prostate cancer detection, leads to a high frequency of unwarranted prostate biopsies. To alleviate this deficit, we executed a proteomic discovery study seeking PSA reflex signatures using the Proteograph™ Product Suite, a multi-nanoparticle-based deep plasma/serum proteomics workflow (Seer, Inc.), with timsTOF Pro mass spectrometry (Bruker) to interrogate over 900 serum specimens from patients referred for biopsy based on elevated PSA and/or abnormal digital rectal exam.

Methods: Our study followed rigorous design principles including uniform collection, randomization and blinding of specimens, all of which were processed with the Proteograph Product Suite. Liquid chromatography-mass spectrometry (LC-MS) analyses leveraged the Bruker timsTOF Pro MS platform utilizing 30-minute reversed-phase chromatography and a label-free dia-PASEF (data independent acquisition - parallel accumulation serial fragmentation) data acquisition method. Peptides deriving from a chosen subset of specimens designed to maximize peptide diversity were pooled, fractionated and analyzed using DDA (data-dependent acquisition)-PASEF to build a spectral reference library.

Results: The DIA-NN algorithm was employed through the cloud-based Proteograph Analysis Suite (PAS) to search LC-MS data. Leveraging our study-specific spectral library proteomic depth achieved was approximately 3600 protein groups identified (median) per patient serum specimen and more than 4400 in at least 25% of samples across the study. Customized machine learning workflows and the SeerML pipeline were used to identify and assess diagnostic power of new proteomic signatures.

Conclusions: Our data demonstrate the remarkable proteomic depth achievable in a scaled patient serum specimen discovery study using a combination of Proteograph and timsTOF platforms. This significant effort revealed serum proteomic signatures with increased diagnostic performance over that provided by PSA blood measurement and traditional patient-associated metadata alone, providing a foundation for future clinical tests aimed to reduce the current high frequency of unnecessary prostate biopsy.

Advances in DNA sequencing and editing technologies have revolutionized our understanding of the molecular basis of many human diseases. However, many disease-relevant genes encode proteins that are poorly characterized and/or are considered “undruggable”, hindering our understanding of disease mechanisms and translating this knowledge into new therapies. Chemical probes offer a valuable way to directly interrogate the function and disease-relevance of proteins and can also serve as valuable leads for drug development, yet most proteins in the human proteome lack small-molecule ligands that can serve as probes. More generally, the boundaries, if any, on the ligandability, and therefore potential druggability, across native proteomes remains poorly understood. In this seminar, I will describe our lab’s efforts to develop powerful chemical proteomic strategies to broadly map ligandable proteins directly in cells, and how this information can be advanced into useful chemical probes to investigate protein function.

Deregulation of kinase function in cell signaling pathways is implicated in numerous cancers. In response, kinase inhibitors (KIs) have been developed to interact with these kinases for highly specific treatment. Though nearly 50 KIs have been FDA-approved, KI monotherapy is seldom curative, likely owing to tumor heterogeneity and acquired resistance. In response, effective combination therapies must be tailored to known resistance mechanisms to efficiently engage with their targets and exploit cellular vulnerabilities. However, standard drug screening tools (e.g., plasma analysis, western blot) are bulk in nature, and no established technology exists to quantify KI target engagement, concomitant with local protein expression, while assessing tumor response heterogeneity. To address these shortcomings, our group has (1) developed protocols to fluorescently label KIs (and other small molecule therapeutics) that mimic the native drug, (2) advanced a novel intracellular paired agent imaging (iPAI) platform to quantify drug target availability (DTA) with these fluorescent KIs, and (3) established and validated a highly multiplexed immunostaining strategy utilizing DNA barcoded antibodies, enabling in situ cyclic immunofluorescence (cyCIF) imaging. In this project, we combine these three complementary innovations into a fluorescence imaging platform we call TRIPODD (Therapeutic Response Imaging through Proteomics and Optical Drug Distribution and binding).Deregulation of kinase function in cell signaling pathways is implicated in numerous cancers. In response, kinase inhibitors (KIs) have been developed to interact with these kinases for highly specific treatment. Though nearly 50 KIs have been FDA-approved, KI monotherapy is seldom curative, likely owing to tumor heterogeneity and acquired resistance. In response, effective combination therapies must be tailored to known resistance mechanisms to efficiently engage with their targets and exploit cellular vulnerabilities. However, standard drug screening tools (e.g., plasma analysis, western blot) are bulk in nature, and no established technology exists to quantify KI target engagement, concomitant with local protein expression, while assessing tumor response heterogeneity. To address these shortcomings, our group has (1) developed protocols to fluorescently label KIs (and other small molecule therapeutics) that mimic the native drug, (2) advanced a novel intracellular paired agent imaging (iPAI) platform to quantify drug target availability (DTA) with these fluorescent KIs, and (3) established and validated a highly multiplexed immunostaining strategy utilizing DNA barcoded antibodies, enabling in situ cyclic immunofluorescence (cyCIF) imaging. In this project, we combine these three complementary innovations into a fluorescence imaging platform we call TRIPODD (Therapeutic Response Imaging through Proteomics and Optical Drug Distribution and binding).