Mass Spectrometry-based Proteomics Workflows Research Articles

Abstract C024: Quantitative mass spectrometry-based multi-omic profiling to overcome drug resistance in pancreatic cancer post KRAS G12D inhibition

Abstract Recently developed inhibitors targeting the KRASG12D oncoprotein are undergoing clinical trial evaluation including in patients with pancreatic adenocarcinoma (PDAC). However, resistance to such targeted therapies is a frequent occurrence, potentially undermining the long-term success of KRAS inhibitors (KRASi). To uncover the adaptive pathways and cell surface biomarkers to KRASi and to identify effective drug combinations that can overcome resistance, we employ a mass spectrometry-based quantitative temporal proteomics workflow. This approach allows us to map the proteomic changes in response to KRASi in both in vitro and in vivo PDAC models. We identify and quantify 10,898 proteins, creating the most extensive proteomic dataset related to KRASi to date. We identify shared mechanisms of both acute and long-term adaptation to KRASi across PDAC models. Utilizing this proteomic information, we are pinpointing drug combinations that pair KRASi with inhibitors of CDK4/6, AURKA, and mTOR to increase cytotoxicity and thereby reduce development of long-term resistance. Surfaceome analyses identifies cell surface proteins upregulated upon acute and long-term KRASi, including TACSTD2 (Trop-2). Antibody-drug conjugates targeting upregulated surface proteins may be a useful orthogonal approach towards preventing or circumventing KRASi resistance with testing of Trop-2 and other candidates ongoing. In summary, through mass spectrometry-based quantitative temporal proteomics, we identify proteomic adaptations to KRASi in PDAC and propose combination treatments with promising therapeutic potential. Citation Format: Qijia Yu, Nicole Sindoni, Huan Zhang, Ziyue Li, Joao A. Paulo, Andrew J. Aguirre, Joseph D. Mancias. Quantitative mass spectrometry-based multi-omic profiling to overcome drug resistance in pancreatic cancer post KRAS G12D inhibition [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C024.

Fibre-specific mitochondrial protein abundance is linked to resting and post-training mitochondrial content in the muscle of men

Analyses of mitochondrial adaptations in human skeletal muscle have mostly used whole-muscle samples, where results may be confounded by the presence of a mixture of type I and II muscle fibres. Using our adapted mass spectrometry-based proteomics workflow, we provide insights into fibre-specific mitochondrial differences in the human skeletal muscle of men before and after training. Our findings challenge previous conclusions regarding the extent of fibre-type-specific remodelling of the mitochondrial proteome and suggest that most baseline differences in mitochondrial protein abundances between fibre types reported by us, and others, might be due to differences in total mitochondrial content or a consequence of adaptations to habitual physical activity (or inactivity). Most training-induced changes in different mitochondrial functional groups, in both fibre types, were no longer significant in our study when normalised to changes in markers of mitochondrial content.

Omics markers of platelet transfusion in trauma patients.

Even in the era of the COVID-19 pandemic, trauma remains the global leading cause of mortality under the age of 49. Trauma-induced coagulopathy is a leading driver of early mortality in critically ill patients, and transfusion of platelet products is a life-saving intervention to restore hemostasis in the bleeding patient. However, despite extensive functional studies based on viscoelastic assays, limited information is available about the impact of platelet transfusion on the circulating molecular signatures in trauma patients receiving platelet transfusion. To bridge this gap, we leveraged metabolomics and proteomics approaches to characterize longitudinal plasma samples (n = 118; up to 11 time points; total samples: 759) from trauma patients enrolled in the Control Of Major Bleeding After Trauma (COMBAT) study. Samples were collected in the field, in the emergency department (ED), and at intervals up to 168 h (7 days) post-hospitalization. Transfusion of platelet (PLT) products was performed (n = 30; total samples: 250) in the ED through 24 h post-hospitalization. Longitudinal plasma samples were subjected to mass spectrometry-based metabolomics and proteomics workflows. Multivariate analyses were performed to determine omics markers of transfusion of one, two, three, or more PLT transfusions. Higher levels of tranexamic acid (TXA), inflammatory proteins, carnitines, and polyamines were detected in patients requiring PLT transfusion. Correlation of PLT units with omics data suggested sicker patients required more units and partially overlap with the population requiring transfusion of packed red blood cell products. Furthermore, platelet activation was likely increased in the most severely injured patients. Fatty acid levels were significantly lower in PLT transfusion recipients (at time of maximal transfusion: Hour 4) compared with non-recipients, while carnitine levels were significantly higher. Fatty acid levels restore later in the time course (e.g., post-PLT transfusion). The present study provides the first multi-omics characterization of platelet transfusion efficacy in a clinically relevant cohort of trauma patients. Physiological alterations following transfusion were detected, highlighting the efficacy of mass spectrometry-based omics techniques to improve personalized transfusion medicine. More specialized clinical research studies focused on PLT transfusion, including organized pre and post transfusion sample collection and limitation to PLT products only, are required to fully understand subsequent metabolomic and proteomic alterations.

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps.

The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.

Uncommon posttranslational modifications in proteomics: ADP-ribosylation, tyrosine nitration, and tyrosine sulfation.

Posttranslational modifications (PTMs) are covalent modifications of proteins that modulate the structure and functions of proteins and regulate biological processes. The development of various mass spectrometry-based proteomics workflows has facilitated the identification of hundreds of PTMs and aided the understanding of biological significance in a high throughput manner. Improvements in sample preparation and PTM enrichment techniques, instrumentation for liquid chromatography-tandem mass spectrometry (LC-MS/MS), and advanced data analysis tools enhance the specificity and sensitivity of PTM identification. Highly prevalent PTMs like phosphorylation, glycosylation, acetylation, ubiquitinylation, and methylation are extensively studied. However, the functions and impact of less abundant PTMs are not as well understood and underscore the need for analytical methods that aim to characterize these PTMs. This review focuses on the advancement and analytical challenges associated with the characterization of three less common but biologically relevant PTMs, specifically, adenosine diphosphate-ribosylation, tyrosine sulfation, and tyrosine nitration. The advantages and disadvantages of various enrichment, separation, and MS/MS techniques utilized to identify and localize these PTMs are described.

Mass Spectrometry-Based Analytical Strategy for Single-Cell Proteomics.

Single-cell proteomics is a novel application area of bioanalysis aiming to characterize proteomes of isolated single cells, which in contrast to bulk cell analysis has the potential to reveal a more detailed heterogeneity of cell populations. Although several antibody-based targeted approaches have been readily available for single-cell analysis, so far only the mass spectrometry methodology can offer unbiased proteome profiling. While this strategy has only recently emerged, it has already demonstrated unparalleled analytical power quantifying >1000 proteins in single cells. Several applications of a general isobaric labeling scheme for multiplexed sample preparation and data acquisition have been outlined using various cell types and instrumentation. This chapter provides a typical example of mass spectrometry-based single-cell proteomics workflow with details about the critical steps of analysis and alternative methods useful for optimization purposes.

Quantitative Proteomics Workflow using Multiple Reaction Monitoring Based Detection of Proteins from Human Brain Tissue.

The proteomic analysis of the human brain tissue over the last decade has greatly enhanced our understanding of the brain. However, brain related disorders continue to be a major contributor of deaths around the world, necessitating the need for even greater understanding of their pathobiology. Traditional antibody-based techniques like western blotting or immunohistochemistry suffer from being low-throughput besides being labor-intensive and qualitative or semi-quantitative. Even conventional mass spectrometry-based shotgun approaches fail to provide conclusive evidence to support a certain hypothesis. Targeted proteomics approaches are largely hypothesis driven and differ from the conventional shotgun proteomics approaches that have been long in use. Multiple reaction monitoring is one such targeted approach that requires the use of a special mass spectrometer called the tandem quadrupole mass spectrometer or triple quadrupole mass spectrometer. In the current study, we have systematically highlighted the major steps involved in performing a successful tandem quadrupole mass spectrometry-based proteomics workflow using human brain tissue with an aim to introduce this workflow to a broader research community.

Quantitative proteome comparison of human hearts with those of model organisms

Delineating human cardiac pathologies and their basic molecular mechanisms relies on research conducted in model organisms. Yet translating findings from preclinical models to humans present a significant challenge, in part due to differences in cardiac protein expression between humans and model organisms. Proteins immediately determine cellular function, yet their large-scale investigation in hearts has lagged behind those of genes and transcripts. Here, we set out to bridge this knowledge gap: By analyzing protein profiles in humans and commonly used model organisms across cardiac chambers, we determine their commonalities and regional differences. We analyzed cardiac tissue from each chamber of human, pig, horse, rat, mouse, and zebrafish in biological replicates. Using mass spectrometry-based proteomics workflows, we measured and evaluated the abundance of approximately 7,000 proteins in each species. The resulting knowledgebase of cardiac protein signatures is accessible through an online database: atlas.cardiacproteomics.com. Our combined analysis allows for quantitative evaluation of protein abundances across cardiac chambers, as well as comparisons of cardiac protein profiles across model organisms. Up to a quarter of proteins with differential abundances between atria and ventricles showed opposite chamber-specific enrichment between species; these included numerous proteins implicated in cardiac disease. The generated proteomics resource facilitates translational prospects of cardiac studies from model organisms to humans by comparisons of disease-linked protein networks across species.

The Implementation of Mass Spectrometry-Based Proteomics Workflows in Clinical Routines of Acute Myeloid Leukemia: Applicability and Perspectives.

With the current reproducibility of proteome preparation workflows along with the speed and sensitivity of the mass spectrometers, the transition of the mass spectrometry (MS)-based proteomics technology from biomarker discovery to clinical implementation is under appraisal in the biomedicine community. Therefore, this technology might be implemented soon to detect well-known biomarkers in cancers and other diseases. Acute myeloid leukemia (AML) is an aggressive heterogeneous malignancy that requires intensive treatment to cure the patient. Leukemia relapse is still a major challenge even for patients who have favorable genetic abnormalities. MS-based proteomics could be of great help to both describe the proteome changes of individual patients and identify biomarkers that might encourage specific treatments or clinical strategies. Herein, we will review the advances and availability of the MS-based proteomics strategies that could already be used in clinical proteomics. However, the heterogeneity of complex diseases as AML requires consensus to recognize AML biomarkers and to establish MS-based workflows that allow their unbiased identification and quantification. Although our literature review appears promising towards the utilization of MS-based proteomics in clinical AML in a near future, major efforts are required to validate AML biomarkers and agree on clinically approved workflows.

Abstract C48: Defining mechanisms of adaptation to KRAS G12C inhibitors: Using quantitative proteomics to design combinatorial strategies in pancreatic cancer

Abstract KRAS is mutated in 95% of pancreatic ductal adenocarcinoma (PDAC) tumors and is a critical driver of PDAC initiation, survival, and proliferation. Therapeutic efforts to target KRAS directly have been largely unsuccessful, leading to the thought that KRAS was “undruggable.” Recently, KRAS G12C inhibitors (KRASi) that covalently bind the mutant cysteine have been reported and are currently being tested in clinical trials. Unfortunately, as has been demonstrated with other targeted therapies, the efficacy of targeting an oncogenic driver can be limited by both tumor heterogeneity and development of resistance. Here, we have used a mass spectrometry-based quantitative proteomics workflow to identify pathways of adaptation to KRASi and to predict cytotoxic drug combinations both in 2D and 3D cell culture conditions. We treated multiple KRAS G12C mutant tumor lines (pancreatic, lung) with KRASi, which induced a cytostatic response with subsequent re-establishment of proliferation at longer time points. We profiled the proteomic adaptations acutely and at long term using a multiplexed quantitative proteomics workflow and identified and quantified over 8000 proteins. Pathway analysis by Gene Set Enrichment Analysis (GSEA) identified common mechanisms of acute drug response among KRAS G12C mutant tumor lines such as downregulation of cell cycle/transcription and increased lipid metabolism. Long-term adaptation was associated with increased DNA repair and oxidative metabolism, but further adaptations differed between tumor cell lines, suggesting selective programs are required to reactivate proliferation. Connectivity map (Cmap) analysis identified 30 perturbagen classes (connectivity score of Cmap class>90) that positively correlated with the MiaPaCa-2 KRASi profile at 24h. We identified correlations with elements in the KRAS pathway (MEK, RAF inhibitors) and with pathways previously described to synergize with KRAS inhibition (PI3K inhibitors), validating the utility of our approach. In addition, we identified combinations with HSP90, MET and EGFR inhibitors that, when co-targeted with KRASi, were able to suppress growth in long-term assays and induced cytotoxicity. Given that recent results suggest that 3D culture conditions may be most predictive of in vivo efficacy, we selected cell lines with greater KRASi sensitivity in 3D growth conditions and performed proteomic analysis in 3D versus 2D culture. Through GSEA analysis we determined differences in the 3D versus 2D proteome basally as well as after KRASi. Finally, we identified combinations by Cmap analysis that differentially correlated with the 3D signature, including CDK4/6 inhibitors, which reduced growth in 3D conditions. Overall, we employed a proteomics platform to characterize adaptation to KRASi and identified combinatorial regimens that induce cytotoxicity with potential therapeutic utility. Citation Format: Naiara Santana-Codina, Amrita Singh Chandhoke, Qijia Yu, Beata Malachowska, Miljan Kuljanin, Mark P. Jedrychowski, Ajami Gikandi, Marcin Stanczak, David A. Scott, Wojciech Fendler, Nathanael S. Gray, Joseph D. Mancias. Defining mechanisms of adaptation to KRAS G12C inhibitors: Using quantitative proteomics to design combinatorial strategies in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr C48.

Study of a novel agent for TCA precipitated proteins washing - comprehensive insights into the role of ethanol/HCl on molten globule state by multi-spectroscopic analyses

Sample preparation for mass spectrometry-based proteomics is a key step for ensuring reliable data. In gel-free experimental workflows, protein purification often starts with a precipitation stage using trichloroacetic acid (TCA). In presence of TCA, proteins precipitate in a stable molten globule state making the pellet difficult to solubilize in aqueous buffer for proteolytic digestion and MS analysis. In this context, the objective of this work was to study the suitability of a novel agent, ethanol/HCl, for the washing of TCA-precipitated proteins. This method optimized the recovery of proteins in aqueous buffer (50 to 96%) while current organic solvents led to losses of material. Following a mechanistic study, the effect of ethanol/HCl on the conformation of TCA-precipitated proteins was investigated. It was shown that the reagent triggered the unfolding of TCA-stabilized molten globule into a reversible intermediate, characterized by a specific Raman signature, which favored protein subsequent resolubilization.Finally, the efficiency of ethanol/HCl for the washing of TCA-precipitated proteins extracted from a biofilm, a soil or a mouse liver was demonstrated (data available via ProteomeXchange with identifier PXD008110). Being versatile and simple, it could be of great interest to include an ethanol/HCl wash-step to produce high-quality protein extracts. SignificanceIn mass spectrometry-based proteomics workflows, proteins precipitation and/or washing usually involves the use of acetone. In fact, this solvent is effective for removing both biological interferences (e.g. lipids) and chemicals employed in protein extraction/purification protocols (e.g. TCA, SDS). However, the use of acetone can lead to significant protein losses. Moreover, when proteins are precipitated with TCA, the acetone-treated precipitate remains hard to disperse, leading to poor resolubilization of proteins in aqueous buffers. Here, we investigated the use of ethanol/HCl for washing TCA-precipitated proteins, with the aim to produce high-quality protein extracts which can be directly analyzed by LC-MS. An opening study on standard solutions showed that ethanol/HCl led to reduced losses of proteins compared to usual solvents (i.e. acetone and ethanol). This reagent also enabled a better solubilization of proteins in aqueous buffer that is necessary for their direct trypsin digestion and LC-HRMS analysis. A mechanistic study, performed through several spectroscopic analyses (LC-HRMS, Raman, spectrofluorometry), showed that treatment with ethanol/HCl induced conformational changes of TCA-precipitated proteins. Finally, we compared the efficiency of ethanol/HCl to published protocols for the washing of protein extracts from three different complex samples (i.e. soil, biofilm, and mouse liver). Our results demonstrated that ethanol/HCl is a valuable alternative to previous protein washing methods and, therefore could become a useful tool in mass spectrometry-based proteomics workflows for various applications (e.g. clinical research, chemical biology, environmental metaproteomics…).

Optimized Nonlinear Gradients for Reversed-Phase Liquid Chromatography in Shotgun Proteomics

Reversed-phase liquid chromatography has become the preferred method for separating peptides in most of the mass spectrometry-based proteomics workflows of today. In the way the technique is typically applied, the peptides are released from the chromatography column by the gradual addition of an organic buffer according to a linear function. However, when applied to complex peptide mixtures, this approach leads to unequal spreads of the peptides over the chromatography time. To address this, we investigated the use of nonlinear gradients, customized for each setup at hand. We developed an algorithm to generate optimized gradient functions for shotgun proteomics experiments and evaluated it for two data sets consisting each of four replicate runs of a human complex sample. Our results show that the optimized gradients produce a more even spread of the peptides over the chromatography run, while leading to increased numbers of confident peptide identifications. In addition, the list of peptides identified using nonlinear gradients differed considerably from those found with the linear ones, suggesting that such gradients can be a valuable tool for increasing the proteome coverage of mass spectrometry-based experiments.

Post-translational modifications of pancreatic fluid proteins collected via the endoscopic pancreatic function test (ePFT)

Early diagnosis of chronic pancreatitis by mass spectrometry-based proteomics may result in therapies to retard or modify disease progression. We aimed to identify differences in posttranslational modifications (PTMs) in pancreatic fluid proteins from individuals with chronic pancreatitis (n=9) and non-pancreatitis controls (n=9). We collected proteomic data from pancreatic fluid using mass spectrometry techniques. We performed database searches with emphasis on PTMs using ProteinPilot. We compared the frequency of specific PTMs in pancreatic fluid between cohorts and also to those identified in bile, gastroduodenal fluid, urine, and pancreatic duct and stellate cell lysates. We identified 97 PTMs in endoscopically-collected pancreatic fluid, of which 11 were identified exclusively in one cohort and 9 others were significantly different in frequency between cohorts. Comparing pancreatic fluid with other specimens revealed differences in specific PTM frequencies, indicating that the identified PTMs were not merely artifacts of sample processing. We determined PTMs of proteins extracted from pancreatic fluid which differed in frequency in chronic pancreatitis patients verses controls. Such PTMs may serve as biomarker candidates of chronic pancreatitis upon validation with larger cohorts. The analysis of the PTM profile of pancreatic fluid proteins offers an alternative method to standard protein-based biomarker discovery. The early diagnosis of chronic pancreatitis is paramount in developing strategies to modify, retard, or halt disease progression. In the present study, we compared post-transitional modifications (PTMs) of proteins extracted from pancreatic fluid of chronic pancreatitis patients verses a control cohort. With many mass spectrometry-based proteomics workflows aimed to identify and quantify proteins, data for PTMs typically comes gratis, in that such data are collected during protein sequencing and, as such, require only downstream bioinformatics processing. We identified a total of 20 PTMs which were exclusive to or significantly different between cohorts. Upon validation with larger cohorts and enrichment of these PTMs may serve as biomarker candidates of chronic pancreatitis. PTM profiling of pancreatic fluid proteins is complementary to standard protein-based biomarker discovery, and may be readily applied to studies of pancreatic disease. This article is part of a Special Issue entitled: Posttranslational Protein modifications in biology and Medicine.

Differential Proteomic Analysis of Renal Cell Carcinoma Tissue Interstitial Fluid

Renal cell carcinoma (RCC), the most common type of kidney cancer, currently has no biomarker of clinical utility. The present study utilized a mass spectrometry-based proteomics workflow for identifying differentially abundant proteins in RCC by harvesting shed and secreted proteins from the tumor microenvironment through sampling tissue interstitial fluid (TIF) from radical nephrectomies. Matched tumor and adjacent normal kidney (ANK) tissues were collected from 10 patients diagnosed with clear cell RCC. One-hundred thirty-eight proteins were identified with statistically significant differential abundances derived by spectral counting in tumor TIF when compared to ANK TIF. Among those proteins with elevated abundance in tumor TIF, nicotinamide n-methyltransferase (NNMT) and enolase 2 (ENO2) were verified by Western blot and selected reaction monitoring (SRM). The presence of ENO2 and thrombospondin-1 (TSP1) were verified as present and at elevated abundance in RCC patient serum samples as compared to a pooled standard control by enzyme-linked immunosorbent assay (ELISA), recapitulating the relative abundance increase in RCC as compared with ANK TIF.

HSPVdb—the Human Short Peptide Variation Database for improved mass spectrometry-based detection of polymorphic HLA-ligands

T cell epitopes derived from polymorphic proteins or from proteins encoded by alternative reading frames (ARFs) play an important role in (tumor) immunology. Identification of these peptides is successfully performed with mass spectrometry. In a mass spectrometry-based approach, the recorded tandem mass spectra are matched against hypothetical spectra generated from known protein sequence databases. Commonly used protein databases contain a minimal level of redundancy, and thus, are not suitable data sources for searching polymorphic T cell epitopes, either in normal or ARFs. At the same time, however, these databases contain much non-polymorphic sequence information, thereby complicating the matching of recorded and theoretical spectra, and increasing the potential for finding false positives. Therefore, we created a database with peptides from ARFs and peptide variation arising from single nucleotide polymorphisms (SNPs). It is based on the human mRNA sequences from the well-annotated reference sequence (RefSeq) database and associated variation information derived from the Single Nucleotide Polymorphism Database (dbSNP). In this process, we removed all non-polymorphic information. Investigation of the frequency of SNPs in the dbSNP revealed that many SNPs are non-polymorphic “SNPs”. Therefore, we removed those from our dedicated database, and this resulted in a comprehensive high quality database, which we coined the Human Short Peptide Variation Database (HSPVdb). The value of our HSPVdb is shown by identification of the majority of published polymorphic SNP- and/or ARF-derived epitopes from a mass spectrometry-based proteomics workflow, and by a large variety of polymorphic peptides identified as potential T cell epitopes in the HLA-ligandome presented by the Epstein–Barr virus cells.Electronic supplementary materialThe online version of this article (doi:10.1007/s00251-010-0497-1) contains supplementary material, which is available to authorized users.