Agenda

Single cell proteomics methods often utilize a carrier proteome added at 20-500x single cell channels to enable peptide detection, selection, and quantification. Adding excess carrier proteome may increase measurement variability and decrease measurement accuracy. We introduce an advanced data acquisition approach that utilizes a carrier proteome labeled with an offset mass chemical tag (i.e., TMT-SH) that separates the carrier proteome peptide cluster from the multiplexed single cell proteomes and enables quantification of single cells in the absence of carrier proteome reporter ions. This approach was implemented with inSeqAPI, an API program that enables facile construction of real-time data analysis methods, such that sequencing of the TMT-SH labeled carrier proteome triggers quantitative MS2/SPS-MS3 on TMT labeled single cell proteomes.

The recent introduction of real-time database searches (RTS) have drastically improved the number of peptides that can be quantified during SPS-MS3 quantification of isobaric labeled samples. Here we introduce inSeqAPI - an API based Windows application that enables the construction of custom acquisition methods on Orbitrap Eclipse mass spectrometers. inSeqAPI is unique in that it enables modular method construction complete with a number of custom available real-time filters as well as ‘.json’ method files that can be shared across instruments. Furthermore, inSeqAPI produces a full audit trail of real-time filter results for each raw file, enabling optimization and troubleshooting during method development.

We apply inSeqAPI to the analysis of single cells by implementing an offset mass triggering method at proteome scale. This approach enables a deep proteomic analysis and sensitive quantification of single cell proteomes by quantifying peptides only after they are identified. Utilizing TMT-SH as a carrier proteome removes carrier proteome reporter ions from analysis during quantitative scans. We demonstrate that this enables accurate quantification of proteins even when carrier proteome levels exceed 500x.

There is a great need in high-throughput workflows for reproducibility and precision during proteomic sample preparation for mass spectrometry-based protein quantification, and large-scale LC-MS analysis. Proteomic sample preparation consists of protein denaturation, cystine reduction and alkylation, and trypsin digestion carried out in a temperature-controlled incubator, with salts and contaminants removed prior to MS analysis. In particular, the steps around the proteolytic digestion of protein samples are prone to inconsistency. One route for reliable sample processing is the development and optimization of a workflow utilizing an automated liquid handling workstation. When combining robust liquid chromatography-mass spectrometry with either discovery or targeted methods, automated sample preparation facilitates increased throughput and reproducible quantitation of biomarker candidates.

In both data dependent (DDA) and data independent acquisition (DIA) workflows, the ability to collect high quality MS/MS at fast acquisition rates is key to maximizing peptide and protein identifications and obtaining highly reproducible quantification. Novel Zeno trap functionality can greatly improve duty cycle in the orthogonal pulsing region of a QTOF system, providing large gains in MS/MS sensitivity. This work investigates the impact of Zeno trap MS/MS sensitivity increases on protein and peptide workflows, at the very fast LC-MS run times offered by microflow chromatography.

Goal: Identify a serum biomarker(s) of hepatocellular carcinoma that originate directly from cancer tissue. Methods: Formalin-fixed human liver tissue from patients with healthy livers, cirrhotic livers, or hepatocellular carcinoma (HCC) were evaluated using N-glycan MALDI imaging mass spectrometry. In total, 239 HCC tissue samples, many which were genetically subtyped, and 145 control tissue samples were analyzed. Subsequently, serum glycoproteomics was performed and proteins identified as containing the same glycans observed in HCC tissue were further examined in five independent and cohorts consisting of 1,071 patients, 492 with liver cirrhosis and 472 with hepatocellular carcinoma, including 270 with early-stage HCC, and 51 patients treated for the HCC, either by transplant or resection. Results: Through tissue based glycan imaging, increased levels of fucosylation was observed in 96% of the HCC tissues examined. Glycan alterations in tissue correlated with the underlying genetic subtype of the cancer. The glycan most often observed in the tissue of aggressive HCC was a tetra-antennary glycan with one to three fucose residues. Using a recombinant AAL lectin with increased affinity towards fucosylated and branched glycan, we identified low molecular weight kininogen (LMWK) as a serum protein that contains tetra-antennary glycan with one to three fucose residues. A plate based lectin ELISA assay was used to test the performance of fucosylated LMWK as a biomarker of HCC. In five independent studies, a fucosylated LMWK based biomarker algorithm had a median AUC of 0.9575 in the detection of all HCC and 0.945 for early stage HCC, far exceeding the performance obtained currently used markers. Conclusions: Using a novel tissue based glycan imaging platform, we were able to identify glycan changes that occur directly in cancer tissue, identify glycans associated with specific subtypes of HCC and used this information to identify serum based biomarkers of HCC that are far superior to those currently used.

Post synthetic editing of heparan 6-O-sulfation by the human endosulfatases SULF1 and SULF2 regulates extracellular matrix remodeling, growth factor signaling, or chemokine distribution. These pathways modulate outcomes of cancer diseases and patient responses to therapy. In this study, we carried out a translational study of multiple cancer diseases which confirms the impact of the 6-O-endosulfatases on the progression of head and neck squamous cell carcinoma (HNSCC). We optimized expression of the SULF enzymes in mammalian cell lines and began characterization of their structure, activities, and function in tumor biology. We expect that the advances will facilitate detection and targeting of the enzymes in HNSCC and other malignancies.

This presentation will discuss the protein and metabolite contents of exosomes derived from pancreatic cancers. We demonstrate that these have biological activities that enhance the growth and metastatic properties of pancreatic cancer. Pancreatic cancer derived exosomes contain high levels of MUC1, and we present evidence that MUC1 may contribute to the differential packaging of exosomal contents.

Cancers and neurodegenerative diseases have an essential impact on human lives with high mortality and poor prognosis in the early stage. Several biomarkers such as CA125 and tau have been utilized to improve the prognosis of these diseases. However, the limited prediction accuracy is demanding to explore more reliable markers. Glycosylation is one of the most common post-translational modifications in mammalian cells and influences a variety of essential biological functions. Aberrant glycosylation and its isomeric distribution have been associated with many neurodegenerative diseases such as neurological disorders, Alzheimer’s disease, Parkinson’s disease, and various cancers. However, the glycomics and glycoproteomics methods for the isomeric glycans and glycopeptides for clinical samples remain a great challenge due to the complex microheterogeneity of glycoproteins. Over decades, different methods have been developed to facilitate the identification and quantitation of glycan and glycopeptide isomers. Coupling to a variety of separation techniques, mass spectrometry has been considered the most powerful tool in isomeric glycomics and glycoproteomics due to its high resolution, high sensitivity, and the ability to acquire sufficient structural information. Additionally, multiple sample preparation strategies and automated software were developed to improve the identification and quantitation accuracy and throughput for large clinical cohorts. During the past ten years, we have developed efficient methods for both glycomics and glycoproteomics isomeric analysis using C18-, porous graphitized carbon (PGC)-, mesoporous graphitized carbon (MGC)-, or micropillar array column (µPAC)-LC-MS/MS under different acquisition techniques, combined with several reliable sample derivatization and multiplexing strategies. Together, these advanced technologies and approaches enhance the pace of isomeric glycan and glycopeptide biomarker discovery. Here, we review recent glycomics and glycoproteomics methods for better isomeric characterization and their applications in cancers and neurodegenerative diseases.

Affinity capture top-down MS was used to identify prevalent SOD1 proteoforms in ALS patients and controls. The degree of toxicity or protection and their structural correlates were assessed using neurotoxicological assays and global- and peptide-based HDX-MS. These results indicate that oxidative modification of cysteine was toxic, that dissociation of the SOD1 dimer initiates toxic aggregation, and that biology prevents this toxic oxidation by protective S-thiolation. Together these inspired the development of a novel class of pharmacological chaperones, cyclic thiosulfinates, which employ S-thiolation-mediated crosslinking. Cyclic thiosulfinates have 1) drug-like properties, 2) blood-brain-barrier penetration as measured by a novel top-down pharmacodynamics assay, and 3) stabilize proteins by unprecedented amounts.

Proteins are the primary effectors of function in biology, and thus complete knowledge of their structure and behavior is needed to decipher function. However the richness of protein structure and function goes far beyond the linear amino acid sequence dictated by the genetic code. Multigene families, alternative splicing, coding polymorphisms, and post-translational modifications, work together to create a rich variety of proteoforms, whose chemical diversity is the foundation of the biological complexes and networks that control biology. The term “proteoforms” refers to the specific molecular forms in which proteins are present in biological systems; only direct analysis of the proteoforms themselves can reveal their structures, dynamics, and localizations in biological systems. Remarkably, the current paradigm of proteomics research, “bottom-up” proteomics, is unable to identify proteoforms – rather, proteins are enzymatically digested into peptides, which are then identified, serving as surrogates for the likely presence of their parent proteins in the sample. This strategy destroys the information as to what form of the protein the peptide represents, and thus the critical information needed to identify proteoforms is lost. The entire field of Biology is thus attempting to understand life in the absence of the ability to understand the molecules that define life. This limitation of todays technology provides a “grand challenge” to the scientific community, to devise new strategies and approaches that are able to comprehensively and quantitatively reveal the full breadth of the proteome at the proteoform level. Developing the technology to accomplish this, building a comprehensive atlas of proteoforms present in human systems, and eventually deciphering the functional roles they play in normal and disease biology, comprise central elements in the quest to understand human biology.

Cell surface glycoproteins and glycans play critical roles in a range of physiological functions and disease processes, are valuable drug targets, and may be exploited as biomarkers for precision medicine. Despite their biological relevance and utility, glycoproteins and glycans are often understudied largely due to technical challenges. This presentation will describe CellSurfer and glyPAQ, new analytical platforms that enable rapid identification and quantification of cell surface glycoproteins and glycans from small sample sizes. The application of these new methodologies to address outstanding questions in cardiac physiology and disease, with an emphasis on precision medicine, will be described. CellSurfer enables routine discovery of cell surface N-glycoproteins from samples with limited availability (100-1000 μg or 300,000 – 10 million cells) with >80% specificity. Innovative bioinformatic tools expedite discovery, analysis, annotation, and candidate prioritization for downstream validation. We applied CellSurfer to cells isolated from cardiac tissue to develop the first cell type-specific maps of the cell surface N-glycoproteome of adult human cardiomyocytes from five myocardial chambers. Primary explanted cardiac fibroblasts, cardiac microvascular endothelial and coronary artery smooth muscle cells were also profiled. New cardiac cell-type specific markers emerged and novel insights into cardiac fibroblast surfaceome dynamics due to extended culturing were revealed. glyPAQ enables standardized processing of biological samples for quantitative profiling of native, reduced N- and O-glycan structures by mass spectrometry. glyPAQ is suitable for the preparation of a broad range of sample complexities, including monoclonal antibodies, cells, tissues, serum, plasma, and urine. We applied glyPAQ to human serum and tissue to inform new precision medicine strategies for patients at increased risk for cardiovascular disease due to rheumatoid arthritis and generate new insights into the impact of COVID-19 infection on the human heart.

Viral spike proteins are in general heavily glycosylated proteins that bind host receptors to facilitate host cell invasion. These spike proteins are often the primary immunogens utilized to develop neutralizing antibodies and vaccines. Furthermore, the host cell surface receptors themselves are also glycoproteins. Thus, a molecular level understanding of the glycosylation of the spike protein protein of viruses and of the cell surface receptors of hosts are key to understanding initial steps of the infection cycle as well as developing therapeutics. In this talk, we will primarily focus on the SARS-CoV-2 Spike protein and the human angiotensin converting enzyme 2 (ACE2) receptor but will also refer to other spike glycoproteins such as that found on HIV-1. The SARS-CoV-2 virus is responsible for the COVID-19 pandemic that has ravaged the world population for the last 2+ years. This betacoronavirus utilizes a heavy glycosylated trimer spike protein to bind to the ACE2 glycoprotein to facilitate host cell entry. We utilized a glycomics-informed glycoproteomic approach to determine site-specific microheterogeneity at all 22 sites of N-linked glycosylation for a stabilized recombinant trimer Spike mimetic immunogen and for a soluble version of human ACE2. When combined with bioinformatics and molecular dynamic simulations, our results illuminate roles for glycans in masking viral epitopes as well as directly participating in modulating the viral spike-host receptor interaction.

Mass spectrometry imaging (MSI) is a powerful technique for studying the localization of lipids, metabolites, and proteins in biological samples. We have developed nanospray desorption electrospray ionization (nano-DESI), an ambient ionization technique that relies on localized liquid extraction of analyte molecules from the sample into a liquid bridge formed between two glass capillaries. The extracted analytes are transferred to a mass spectrometer inlet and ionized by electrospray ionization. Nano-DESI enables quantitative imaging of biomolecules in fully hydrated samples with minimal or no sample pre-treatment. Recent developments in the nano-DESI MSI instrumentation have enabled quantitative imaging of lipids, metabolites, and proteins in tissues with high sensitivity and spatial resolution down to 8-10 microns using finely pulled capillaries. Furthermore, we have developed a microfluidic nano-DESI probe, which greatly simplifies the experimental setup and demonstrates similar performance to the capillary probe. Correlative omics imaging of small biomolecules and proteins provides unique insights into biochemical pathways in biological systems.

Rapid evolution of statistical and machine learning methods and techniques has dramatically improved our ability to analyze and interpret mass spectrometry-based experiments. This talk will discuss strategies for leveraging modern statistical and machine learning techniques for interpretation of mass spectrometry-based imaging (MSI) experiments of biological tissues. We will demonstrate that approaches adapted to specifically account for the properties of MSI data improve both the accuracy and the interpretability of the analyses, as compared to the standard off-the-shelf methods. We will overview the implementations of such specialized methods in the open-source software Cardinal, and illustrate these methods in case studies.

The histopathological assessment of tumors is critical for the diagnostic process, and directly affects patients' prognosis and treatment. Proteogenomic analysis, albeit slower and more expensive, can provide additional information that can be used to select more effective treatments. Here, we apply a customized multi-resolution deep convolutional neural network, Panoptes, to H&E-stained histopathological images of tumors to predict cell of origin, clinical features, mutations, pathway-level molecular signatures and tumor status. The model achieves high accuracy and generalizes well on independent datasets. Our results suggest that it is possible to use deep learning aid pathologists in providing proteogenomic information from H&E-stained histopathological images without the need for additional analysis.

With over 240 mammalian genome assemblies available through open-source repositories like DNAzoo, Zoonomia, and the Earth Biogenome Project, scientists are now able to interrogate chromosomal rearrangements, positive gene selection, copy number, and deletions across mammalian taxa like never before. Differences in genomic architecture due to evolutionary pressure result in much of the phenotypic diversity witnessed today and give rise to unique physiological adaptations that have become the topic of study for comparative physiologists. Some of these unique physiological adaptations hold promise for medical researchers interested in understanding ischemia/reperfusion injury (e.g. dive response in marine mammals) and uremia tolerance (hibernation in bears) amongst others.  Although genome information is useful for understanding evolutionary relationships and physiological adaptations, differences in gene copy number or positive gene selection is difficult to translate into protein abundance, especially in biofluids. For this reason, parallel reaction monitoring was utilized to quantify the serum protein, pantetheinase, in 44 different mammals to determine whether a high abundance of serum pantetheinase was unique to diving marine mammals. Although levels could not be predicted from comparative genomics, diving marine mammals in general had relatively high levels of pantetheinase. In addition, the high pantetheinase phenotype also defined the ungulates (odd and even-toed), a previously unreported observation. To expedite hypothesis testing across taxa the Comparative Protein Aggregator Resource (CoMPARe) was initiated in 2018 to examine differences between mammalian taxa. This ongoing venture is collecting proteomic data from serum across 52 mammalian species. Ranked comparisons are being made following “humanization” of the proteomes to capture information that is not predicted from genomes. Exploration of the proteome adds another layer of information to describe interrelationships amongst mammals and will help to define the landscape of comparative mammalian physiology from which biomimicry can be employed to extract novel solutions for humans and advance industry.

Many wild mammals are adapted to tolerate "extreme" environmental and physiological challenges, such as prolonged fasting, hypoxia, and rapid skin regeneration, among others. Understanding the molecular basis of natural tolerance in wild mammals may provide insights into the treatment of human pathologies such as obesity and skin disorders. The northern elephant seal is an excellent non-model "model system" for studies of natural adaptation to prolonged fasting, catastrophic molting, and hypoxia in mammals. We examined changes in skeletal muscle, adipose tissue, skin, and plasma proteomes of elephant seals over 5 weeks of fasting associated with rapid molting and identified enzymes, signaling molecules, and transcriptional regulators of these processes, which are potential targets for further studies of human disease. Our findings include changes in apolipoprotein composition that may underlie maintenance of naturally high HDL levels and resistance to oxidative stress during fasting and alterations in extracellular matrix composition and abundance of metabolic enzymes that may support rapid skin regeneration during catastrophic molting.

In recent years, protein footprinting coupled with mass spectrometry has been extensively used to analyze the higher order structure (HOS) of proteins. These methods have been successfully 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-cell and 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 these methods is for proteome-wide structural biology. In one such application, we used in-cell FPOP (IC-FPOP) to identify on and off targets of the cancer drug methotrexate in leukemia cells. By obtaining structural information on proteins across the proteome, we were able to distinguish structural changes that occur in response to drug treatment. We have further extended the FPOP method for analysis in C. elegans, a member of the nematode family. This allows us to study protein structure directly in animal model for human disease. These methods have the potential to become a powerful tool in the structural biology toolbox.

In South Carolina (SC), there is a disproportionate increase in female breast cancer death rates in black women (BW) compared to white women (WW) (30.1 and 21.2/100,000 respectively, years 2000-2019). Historical accounts of the SC slave trade report that the majority of the enslaved African originated from the sub-Saharan West African Coastal regions. After slavery, these Africans remained in relative isolation within communities along the south eastern coastal islands, called Sea Islands (SIs). The West African origins of the Sea Islanders predispose this diasporic population to higher breast cancer risk and development of more aggressive breast cancers. Contemporary literature reports that stromal collagen differences are predictive of breast cancer progression and survival. We hypothesized that collagen stroma variations between SI BW and WW may be involved in disproportionate SC breast cancer outcomes. The study investigated collagen stromal variations by targeted collagen tissue imaging proteomics in breast cancer tumor, normal adjacent tissue, normal adjacent lymph, and metastatic lymph tissue. Newly diagnosed patients were from documented SI geographic regions (BW n=10; WW n=21). Collagen peptide peak intensities were analyzed using Area Under the Receiving Operating Curve, p-value <0.01 to determine differentiating signatures between BW and WW. Intriguingly, the largest variation when comparing by race occurred in the lymph nodes. Normal lymph tissue showed 83/1377 significantly different collagen peptides while the metastatic lymph tissue showed significant changes in 74 collagen peptides, t-test p-value ≤0.001. Certain peptides were significant both metastatic and normal lymph tissue, whereas others were uniquely significant in either metastatic or normal lymph. This study suggests that there may be an ancestry-dependent immune involvement to metastatic breast cancer that could contribute to higher breast cancer mortality rates in BW from SC SI regions. Additional studies are in progress investigating collagen stroma and immune system involvement in West African-origin breast cancer.

G protein-coupled receptors (GPCRs) represent the largest family of signaling receptors and drug targets. Activation of GPCRs can initiate complex signaling networks from different cellular compartments. Depending on the cellular location and the signaling pathways activated, GPCRs can elicit different cellular effects. This capacity may be exploited in drug discovery by designing ligands selectively activating specific receptor-mediated signaling pathways or target receptors at selected cellular locations. However, the efforts to create pathway or location specific ligands is limited by our understanding of GPCR-based signaling and trafficking mechanisms. One reason for this is the lack of technologies that can capture the full complexity of GPCR activity.

Recently, we developed a proximity biotin labeling method for GPCRs combining the engineered peroxidase APEX with quantitative mass spectrometry (GPCR-APEX). GPCR-APEX simultaneously captures proximal protein interaction networks and cellular location of the activated receptor with high temporal resolution. While this represents a powerful unbiased method to profile agonist-selective effects on receptor interaction networks and cellular localization, a major limitation of its application has been to deconvolve this information from the proximity labeling datasets.

Here, we present a novel computational framework which predicts time and agonist dependent subcellular location of the receptor and quantitatively deconvolutes the effect of receptor location and proximal interactors in the proximity labeling data. As our model system for developing the framework, we selected the mu-opioid receptor (MOR). Activating MOR with chemically distinct agonists, we show pronounced differences in the proximal proteome of MOR elicited by their distinct capacity to initiate receptor trafficking. Moreover, we discovered two novel G protein associated proteins–KCTD12 and EYA4. We show that recruitment of KCTD12 and EYA4 is dependent on Gɑi activity and suggest that KCTD12 and EYA4 form a buffer system for G protein signaling by interacting directly with the Gβ/γ and Gɑ subunits, respectively.

Mass spectrometry based proteomic technologies are indispensable for small molecule drug discovery programs.  For example, proteomics and chemical proteomics are used, along with other technologies, to identify protein targets from well-defined phenotypic readouts. Intact protein mass spectrometry (MS) is performed to ensure the appropriate protein constructs are made, define PTM status, and can be used in covalent inhibitor programs as a screening method.  Chemical proteomic experiments are conducted to assess both on-target and off-target engagement.  This presentation will present practical examples and insights into how proteomic technologies are used throughout the drug discovery pipeline.  Also, featured will be the development and incorporation of a new technology for intact protein and protein characterization studies, infrared matrix assisted laser desorption ionization mass spectrometry IR-MALDESI-MS.

The ongoing COVID-19 pandemic caused by SARS-CoV-2 has brought fresh attention to the enormous global health risks posed by RNA viruses and the need for broad antiviral strategies. Our focus is on host-targeted therapeutics by expanding our understanding of common host processes critical for virus infections. Comparative mass spectrometry-based interactomics is a powerful approach to identify and sensitively compare the enrichment of shared and unique host cell dependencies exploited by virus proteins. We coupled affinity purification to TMTpro-multiplexed quantitative proteomics to profile the host cell interactions of several nonstructural proteins from SARS, non-pathogenic coronavirus strains, as well as newly emerging SARS-CoV-2 variants. We identified pan-strain interactions with mitochondria-associated endoplasmic reticulum (ER) membrane factors and ER proteostasis pathways. As RNA virus replication and assembly frequently occurs at the ER membrane, viruses extensively remodel this organelle and the ER proteostasis network requiring precise modulation of the unfolded protein response (UPR). We found divergent roles for two nonstructural proteins, nsp4 and nsp3, in activating and suppressing the UPR to finetune its activity. To disrupt these host ER proteostasis dependencies during viral propagation, we can take advantage of recently developed small-molecule ER proteostasis regulator compounds. We discovered that such compounds are broadly effective at inhibiting infections with flaviruses (dengue and Zika) and we are currently exploring their use against human and mouse coronavirus strains. These results open up a broadly applicable therapeutic strategy to target essential host ER protein folding processes to impair viral infections.