Analytical validation considerations of multiplex mass-spectrometry-based proteomic platforms for measuring protein biomarkers

Development of a Fit-For-Purpose Multi-Marker Panel for Early Diagnosis of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) suffers from a lack of an effective diagnostic method, which hampers improvement in patient survival. Carbohydrate antigen 19-9 (CA19-9) is the only FDA-approved blood biomarker for PDAC, yet its clinical utility is limited due to suboptimal performance. Liquid chromatography-mass spectrometry (LC-MS) has emerged as a burgeoning technology in clinical proteomics for the discovery, verification, and validation of novel biomarkers. A plethora of protein biomarker candidates for PDAC have been identified using LC-MS, yet few has successfully transitioned into clinical practice. This translational standstill is owed partly to insufficient considerations of practical needs and perspectives of clinical implementation during biomarker development pipelines, such as demonstrating the analytical robustness of proposed biomarkers which is critical for transitioning from research-grade to clinical-grade assays. Moreover, the throughput and cost-effectiveness of proposed assays ought to be considered concomitantly from the early phases of the biomarker pipelines for enhancing widespread adoption in clinical settings. Here, we developed a fit-for-purpose multi-marker panel for PDAC diagnosis by consolidating analytically robust biomarkers as well as employing a relatively simple LC-MS protocol. In the discovery phase, we comprehensively surveyed putative PDAC biomarkers from both in-house data and prior studies. In the verification phase, we developed a multiple-reaction monitoring (MRM)-MS-based proteomic assay using surrogate peptides that passed stringent analytical validation tests. We adopted a high-throughput protocol including a short gradient (<10 min) and simple sample preparation (no depletion or enrichment steps). Additionally, we developed our assay using serum samples, which are usually the preferred biospecimen in clinical settings. We developed predictive models based on our final panel of 12 protein biomarkers combined with CA19-9, which showed improved diagnostic performance compared to using CA19-9 alone in discriminating PDAC from non-PDAC controls including healthy individuals and patients with benign pancreatic diseases. A large-scale clinical validation is underway to demonstrate the clinical validity of our novel panel.

Paradigm shift in biomarker translation: a pipeline to generate clinical grade biomarker candidates from DIA-MS discovery

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package. This innovative tool uses the discovery cohort analysis to select precursors, peptides, and proteins that adhere to established targeted assay criteria. TEAQ was applied to Data-Independent Acquisition MS data from plasma samples acquired on an Orbitrap™ Astral™ MS. Identified precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation from 11-point loading curves under three throughputs, to develop a resource for clinical-grade targeted assays. From a clinical cohort of individuals with inflammatory bowel disease (n=492), TEAQ successfully identified 1116 signature peptides for 327 quantifiable proteins from 1180 identified proteins. Embedding stringent selection criteria adaptable to targeted assay development into the analysis of discovery data will streamline the transition to validation and clinical studies.

Proteomic methods for the study of porcine acute phase proteins - anything new to detect?

Acute phase proteins (APPs) reflect the health status of individuals and are important tools in diagnostics, as their altered levels are a sign of disturbed homeostasis. While, in most cases, quantitation of known serum APPs is routinely performed by immunoassays, proteomics is helpful in discovery of new biomarker candidates, especially in samples other than body fluids. Besides putting APP regulation into an overall context of differentially abundant proteins, this approach can detect further details or outright new features in protein structure or specific modifications, and help understand better their function. Thus, it can show up ways to make present diagnostic assays more sensitive and/or specific, or correlate regulations of disease-specific proteins. The APP repertoire is dependent on the species. The pig is both, an important farm animal and a model animal for human diseases, due to similarities in physiology. Besides reviewing existing literature, yet unpublished examples for two-dimensional electrophoresis in connection with pig APPs highlight some of the benefits of proteomics. Of further help would be the emerging targeted proteomics, offering the possibility to determine particular isoforms or proteoforms, without the need of specific antibodies, but this method is presently scarcely used in veterinary medicine.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

FastCAT Accelerates Absolute Quantification of Proteins Using Multiple Short Nonpurified Chimeric Standards

Absolute (molar) quantification of clinically relevant proteins determines their reference values in liquid and solid biopsies. The FastCAT (for Fast-track QconCAT) method employs multiple short (<50 kDa), stable-isotope labeled chimeric proteins (CPs) composed of concatenated quantotypic (Q)-peptides representing the quantified proteins. Each CP also comprises scrambled sequences of reference (R)-peptides that relate its abundance to a single protein standard (bovine serum albumin, BSA). FastCAT not only alleviates the need to purify CP or use sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) but also improves the accuracy, precision, and dynamic range of the absolute quantification by grouping Q-peptides according to the expected abundance of the target proteins. We benchmarked FastCAT against the reference method of MS Western and tested it in the direct molar quantification of neurological markers in human cerebrospinal fluid at the low ng/mL level.

Complement multiplex testing: Concept, promises and pitfalls

Complement is a complex system. This complexity becomes more obvious when looking at complement analysis in health and disease, where one presentation can require a number of measurements to understand the full role of this cascade in the disease. The current state of clinical testing requires multiple tests to cover the whole of the complement cascade. There is a clear potential for multiplex testing to help address this need for comprehensive analysis of the state of complement deficiency, activation or inhibition. Fortunately, there are a number of potential methods for multiplex analysis, each with advantages and disadvantages that need to be considered in light of the intricacy of the complement cascade and its interconnection to other systems. Despite the complexities of such methods several groups have started utilizing multiplex analysis for research and even for diagnostic testing. The potential methods, current successes, and the type of testing that needs to be streamlined are reviewed in this text.

Targeted liquid chromatography-tandem mass spectrometry analysis of proteins: Basic principles, applications, and perspectives

Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is now the main analytical method for the identification and quantification of peptides and proteins in biological samples. In modern research, identification of biomarkers and their quantitative comparison between samples are becoming increasingly important for discovery, validation, and monitoring. Such data can be obtained following specific signals after fragmentation of peptides using multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) methods, with high specificity, accuracy, and reproducibility. In addition, these methods allow measurement of the amount of post-translationally modified forms and isoforms of proteins. This review article describes the basic principles of MRM assays, guidelines for sample preparation, recent advanced MRM-based strategies, applications and illustrative perspectives of MRM/PRM methods in clinical research and molecular biology.

Predicting Metabolite-Disease Associations Based on LightGBM Model

Metabolites have been shown to be closely related to the occurrence and development of many complex human diseases by a large number of biological experiments; investigating their correlation mechanisms is thus an important topic, which attracts many researchers. In this work, we propose a computational method named LGBMMDA, which is based on the Light Gradient Boosting Machine (LightGBM) to predict potential metabolite–disease associations. This method extracts the features from statistical measures, graph theoretical measures, and matrix factorization results, utilizing the principal component analysis (PCA) process to remove noise or redundancy. We evaluated our method compared with other used methods and demonstrated the better areas under the curve (AUCs) of LGBMMDA. Additionally, three case studies deeply confirmed that LGBMMDA has obvious superiority in predicting metabolite–disease pairs and represents a powerful bioinformatics tool.

Mass Spectrometry Techniques: Principles and Practices for Quantitative Proteomics

In the current omics-age of research, major developments have been made in technologies that attempt to survey the entire repertoire of genes, transcripts, proteins, and metabolites present within a cell. While genomics has led to a dramatic increase in our understanding of such things as disease morphology and how organisms respond to medications, it is critical to obtain information at the proteome level since proteins carry out most of the functions within the cell. The primary tool for obtaining proteome-wide information on proteins within the cell is mass spectrometry (MS). While it has historically been associated with the protein identification, developments over the past couple of decades have made MS a robust technology for protein quantitation as well. Identifying quantitative changes in proteomes is complicated by its dynamic nature and the inability of any technique to guarantee complete coverage of every protein within a proteome sample. Fortunately, the combined development of sample preparation and MS methods have made it capable of quantitatively comparing many thousands of proteins obtained from cells and organisms.

Assessment of the Potential and Limitations of Elemental Mass Spectrometry in Life Sciences for Absolute Quantification of Biomolecules Using Generic Standards

Inductively coupled plasma-mass spectrometry (ICP-MS) has been widely used in Life Sciences for the absolute quantification of biomolecules without specific standards, assuming the same response for generic compounds including complex biomolecules. However, contradictory results have been published on this regard. We present the first critical statistical comparison of the ICP-MS response factors obtained for 14 different relevant S-containing biomolecules (three peptides, four proteins, one amino acid, two cofactors, three polyethylene glycol (PEG) derivatives, and sulfate standard), covering a wide range of hydrophobicities and molecular sizes. Two regular flow nebulizers and a total consumption nebulizer (TCN) were tested. ICP-MS response factors were determined though calibration curves, and isotope dilution analysis was used to normalize the results. No statistical differences have been found for low-molecular-weight biocompounds, PEGs, and nonhydrophobic peptides using any of the nebulizers tested. Interestingly, while statistical differences were still found negligible (96-104%) for the proteins and hydrophobic peptide using the TCN, significantly lower response factors (87-40%) were obtained using regular flow nebulizers. Such differential behavior seems to be related mostly to hydrophobicity and partially to the molecular weight. Findings were validated using IDA in intact and digested bovine serum albumin solutions using the TCN (98 and 100%, respectively) and the concentric nebulizer (73 and 97%, respectively). Additionally, in the case of a phosphoprotein, results were corroborated using the P trace in parallel to the S trace used along the manuscript. This work seems to suggest that ICP-MS operated with regular nebulizers can offer absolute quantification using generic standards for most biomolecules except proteins and hydrophobic peptides.

The power of proteomics to monitor senescence-associated secretory phenotypes and beyond: toward clinical applications

INTRODUCTION

Cellular senescence is a rapidly growing field with potential relevance for the treatment of multiple human diseases. In the last decade, cellular senescence and the senescence-associated secretory phenotype (SASP) have emerged as central drivers of aging and many chronic diseases, including cancer, neurodegeneration, heart disease and osteoarthritis. Major efforts are underway to develop drugs that selectively eliminate senescent cells (senolytics) or alter the SASP (senomorphics) to treat age-related diseases in humans. The translation of senescence-targeting therapies into humans is still in early stages. Nonetheless, it is clear that proteomic approaches will facilitate the discovery of important SASP proteins, development of senescence- and SASP-derived biomarkers, and identification of therapeutic targets for senolytic and senomorphic drugs.

AREAS COVERED

We review recent proteomic studies of cellular senescence and their translational relevance and, particularly, characterization of the secretory phenotype and preclinical development of biomarkers (from 2008-2020, PubMed). We focus on emerging areas, such as the heterogeneity of senescent cells and the SASP, extracellular vesicles released by senescent cells, and validating biomarkers of aging in vivo.

EXPERT OPINION

Proteomic and multi-omic approaches will be important for the development of senescence-based biomarkers to facilitate and monitor future therapeutic interventions that target senescent cells.

An Overview on G Protein-coupled Receptor-induced Signal Transduction in Acute Myeloid Leukemia

BACKGROUND

Acute Myeloid Leukemia (AML) is a genetically heterogeneous disease characterized by uncontrolled proliferation of precursor myeloid-lineage cells in the bone marrow. AML is also characterized by patients with poor long-term survival outcomes due to relapse. Many efforts have been made to understand the biological heterogeneity of AML and the challenges to develop new therapies are therefore enormous. G Protein-coupled Receptors (GPCRs) are a large attractive drug-targeted family of transmembrane proteins, and aberrant GPCR expression and GPCR-mediated signaling have been implicated in leukemogenesis of AML. This review aims to identify the molecular players of GPCR signaling, focusing on the hematopoietic system, which are involved in AML to help developing novel drug targets and therapeutic strategies.

METHODS

We undertook an exhaustive and structured search of bibliographic databases for research focusing on GPCR, GPCR signaling and expression in AML.

RESULTS AND CONCLUSION

Many scientific reports were found with compelling evidence for the involvement of aberrant GPCR expression and perturbed GPCR-mediated signaling in the development of AML. The comprehensive analysis of GPCR in AML provides potential clinical biomarkers for prognostication, disease monitoring and therapeutic guidance. It will also help to provide marker panels for monitoring in AML. We conclude that GPCR-mediated signaling is contributing to leukemogenesis of AML, and postulate that mass spectrometrybased protein profiling of primary AML cells will accelerate the discovery of potential GPCR related biomarkers for AML.

Cancer Proteomics and the Elusive Diagnostic Biomarkers

Despite progress in genomic and proteomic technology and applications, the validation of cancer biomarkers of use as clinical early detection diagnostics has remained elusive. As described in this brief viewpoint, there are now recognized to be many types of clinical biomarkers and proteomic analyses, particularly when combined with other 'omic analyses, have been effective in many such biomarker identifications. However, in the area of early diagnosis of cancers, the problems associated with the conversion from identification to diagnostic have largely not been overcome. Notably, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) of the National Cancer Institute (NCI), has been particularly successful in refining the analytical steps needed to tackle this challenging issue and has provided positive insight into how to solve many of the underlying problems. The potential for developing clinical diagnostics for early detection of highly lethal cancers and possible new therapeutic strategies through proteomic analyses, as seen through these CPTAC successes, is more promising than ever.

MetSigDis: a manually curated resource for the metabolic signatures of diseases

Complex diseases cannot be understood only on the basis of single gene, single mRNA transcript or single protein but the effect of their collaborations. The combination consequence in molecular level can be captured by the alterations of metabolites. With the rapidly developing of biomedical instruments and analytical platforms, a large number of metabolite signatures of complex diseases were identified and documented in the literature. Biologists' hardship in the face of this large amount of papers recorded metabolic signatures of experiments' results calls for an automated data repository. Therefore, we developed MetSigDis aiming to provide a comprehensive resource of metabolite alterations in various diseases. MetSigDis is freely available at http://www.bio-annotation.cn/MetSigDis/. By reviewing hundreds of publications, we collected 6849 curated relationships between 2420 metabolites and 129 diseases across eight species involving Homo sapiens and model organisms. All of these relationships were used in constructing a metabolite disease network (MDN). This network displayed scale-free characteristics according to the degree distribution (power-law distribution with R2 = 0.909), and the subnetwork of MDN for interesting diseases and their related metabolites can be visualized in the Web. The common alterations of metabolites reflect the metabolic similarity of diseases, which is measured using Jaccard index. We observed that metabolite-based similar diseases are inclined to share semantic associations of Disease Ontology. A human disease network was then built, where a node represents a disease, and an edge indicates similarity of pair-wise diseases. The network validated the observation that linked diseases based on metabolites should have more overlapped genes.

Standardization approaches in absolute quantitative proteomics with mass spectrometry

Mass spectrometry-based approaches have enabled important breakthroughs in quantitative proteomics in the last decades. This development is reflected in the better quantitative assessment of protein levels as well as to understand post-translational modifications and protein complexes and networks. Nowadays, the focus of quantitative proteomics shifted from the relative determination of proteins (ie, differential expression between two or more cellular states) to absolute quantity determination, required for a more-thorough characterization of biological models and comprehension of the proteome dynamism, as well as for the search and validation of novel protein biomarkers. However, the physico-chemical environment of the analyte species affects strongly the ionization efficiency in most mass spectrometry (MS) types, which thereby require the use of specially designed standardization approaches to provide absolute quantifications. Most common of such approaches nowadays include (i) the use of stable isotope-labeled peptide standards, isotopologues to the target proteotypic peptides expected after tryptic digestion of the target protein; (ii) use of stable isotope-labeled protein standards to compensate for sample preparation, sample loss, and proteolysis steps; (iii) isobaric reagents, which after fragmentation in the MS/MS analysis provide a final detectable mass shift, can be used to tag both analyte and standard samples; (iv) label-free approaches in which the absolute quantitative data are not obtained through the use of any kind of labeling, but from computational normalization of the raw data and adequate standards; (v) elemental mass spectrometry-based workflows able to provide directly absolute quantification of peptides/proteins that contain an ICP-detectable element. A critical insight from the Analytical Chemistry perspective of the different standardization approaches and their combinations used so far for absolute quantitative MS-based (molecular and elemental) proteomics is provided in this review.

Identifying diseases-related metabolites using random walk

Background

Metabolites disrupted by abnormal state of human body are deemed as the effect of diseases. In comparison with the cause of diseases like genes, these markers are easier to be captured for the prevention and diagnosis of metabolic diseases. Currently, a large number of metabolic markers of diseases need to be explored, which drive us to do this work.

Methods

The existing metabolite-disease associations were extracted from Human Metabolome Database (HMDB) using a text mining tool NCBO annotator as priori knowledge. Next we calculated the similarity of a pair-wise metabolites based on the similarity of disease sets of them. Then, all the similarities of metabolite pairs were utilized for constructing a weighted metabolite association network (WMAN). Subsequently, the network was utilized for predicting novel metabolic markers of diseases using random walk.

Results

Totally, 604 metabolites and 228 diseases were extracted from HMDB. From 604 metabolites, 453 metabolites are selected to construct the WMAN, where each metabolite is deemed as a node, and the similarity of two metabolites as the weight of the edge linking them. The performance of the network is validated using the leave one out method. As a result, the high area under the receiver operating characteristic curve (AUC) (0.7048) is achieved. The further case studies for identifying novel metabolites of diabetes mellitus were validated in the recent studies.

Conclusion

In this paper, we presented a novel method for prioritizing metabolite-disease pairs. The superior performance validates its reliability for exploring novel metabolic markers of diseases.

Retrospective Proteomic Analysis of a Novel, Cancer Metastasis-Promoting RGD-Containing Peptide

Patients who undergo surgical extirpation of a primary liver carcinoma followed by radiotherapy and chemotherapy leading to complete remission are nevertheless known to develop cancerous metastases 3-10 years later. We retrospectively examined the blood sera collected over 8 years from 30 patients who developed bone metastases after the complete remission of liver cancer to identify serum proteins showing differential expression compared to patients without remission. We detected a novel RGD (Arg-Gly-Asp)-containing peptide derived from the C-terminal portion of fibrinogen in the sera of metastatic patients that appeared to control the EMT (epithelial-mesenchymal transition) of cancer cells, in a process associated with miR-199a-3p. The RGD peptide enhanced new blood vessel growth and increased vascular endothelial growth factor levels when introduced into fertilized chicken eggs. The purpose of this study was to enable early detection of metastatic cancer cells using the novel RGD peptide as a biomarker, and thereby develop new drugs for the treatment of metastatic cancer.

Integrative analysis for the discovery of lung cancer serological markers and validation by MRM-MS

Non-small-cell lung cancer (NSCLC) constitutes approximately 80% of all diagnosed lung cancers, and diagnostic markers detectable in the plasma/serum of NSCLC patients are greatly needed. In this study, we established a pipeline for the discovery of markers using 9 transcriptome datasets from publicly available databases and profiling of six lung cancer cell secretomes. Thirty-one out of 312 proteins that overlapped between two-fold differentially expressed genes and identified cell secretome proteins were detected in the pooled plasma of lung cancer patients. To quantify the candidates in the serum of NSCLC patients, multiple-reaction-monitoring mass spectrometry (MRM-MS) was performed for five candidate biomarkers. Finally, two potential biomarkers (BCHE and GPx3; AUC = 0.713 and 0.673, respectively) and one two-marker panel generated by logistic regression (BCHE/GPx3; AUC = 0.773) were identified. A validation test was performed by ELISA to evaluate the reproducibility of GPx3 and BCHE expression in an independent set of samples (BCHE and GPx3; AUC = 0.630 and 0.759, respectively, BCHE/GPx3 panel; AUC = 0.788). Collectively, these results demonstrate the feasibility of using our pipeline for marker discovery and our MRM-MS platform for verifying potential biomarkers of human diseases.

Prostate cancer proteomics: Current trends and future perspectives for biomarker discovery

The clinical and fundamental research in prostate cancer - the most common urological cancer in men - is currently entering the proteomic and genomic era. The focus has switched from one single marker (PSA) to panels of biomarkers (including proteins involved in ribosomal function and heat shock proteins). Novel genetic markers (such as Transmembrane protease serine 2 (TMPRSS2)-ERG fusion gene mRNA) or prostate cancer gene 3 (PCA3) had already entered the clinical practice, raising the question whether subsequent protein changes impact the evolution of the disease and the response to treatment. Proteomic technologies such as MALDI-MS, SELDI-MS, i-TRAQ allow a qualitative/quantitative analysis of the proteome variations, in both serum and tumor tissue. A new trend in prostate cancer research is proteomic analysis of prostasomes (prostate-specific exosomes), for the discovery of new biomarkers. This paper provides an update of novel clinical tests used in research and clinical diagnostic, as well as of potential tissue or fluid biomarkers provided by extensive proteomic research data.

Comparison of capillary electrophoresis and capillary liquid chromatography coupled to mass spectrometry for the analysis of transthyretin in human serum

Capillary electrophoresis and capillary liquid chromatography coupled to mass spectrometry (CE-MS and CapLC-MS, respectively) are nowadays very suitable techniques for the separation and characterization of intact proteins in biological fluids. In this paper, we compare the performance of both techniques for the analysis of transthyretin (TTR), which is a homotetrameric protein (relative molecular mass (Mr) ∼56,000) involved in different types of amyloidosis. Furthermore, it is also presented a novel sample pretreatment based on immunoprecipitation (IP) using Protein A Ultrarapid Agarose™ (UAPA) magnetic beads (MBs) to purify TTR from serum samples. This novel IP based on MBs allowed the detection of TTR monomeric proteoforms that were not possible to analyze by conventional IP in solution. In addition, UAPA MBs provided many other desirable advantages including higher selectivity and minimal unspecific binding of other proteins. CE-MS and CapLC-MS were applied to analyze serum samples from healthy controls and familial amyloidotic polyneuropathy type I (FAP-I) patients, who suffered from the most common hereditary systemic amyloidosis. Both techniques allowed detecting the same TTR proteoforms, including the mutant TTR (Met 30) variant (variation in relative molecular mass (ΔMr) was +32.07, from wild-type TTR). Migration/retention times and relative quantitation of the different proteoforms were similar and reproducible in both cases, but the limits of detection (LODs) achieved by CE-MS were slightly lower (2-2.5-fold). Some other differences were also found on separation selectivity (migration orders and separation of antibody), peak efficiency, total analysis time, calibration ranges and experimental Mr accuracy.

Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics

Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technologies include MS to define protein sequence, protein:protein interactions, and protein PTMs. Key enabling technologies for proteomics are bioinformatic pipelines to identify, quantitate, and summarize these events. The Trans-Proteomics Pipeline (TPP) is a robust open-source standardized data processing pipeline for large-scale reproducible quantitative MS proteomics. It supports all major operating systems and instrument vendors via open data formats. Here, we provide a review of the overall proteomics workflow supported by the TPP, its major tools, and how it can be used in its various modes from desktop to cloud computing. We describe new features for the TPP, including data visualization functionality. We conclude by describing some common perils that affect the analysis of MS/MS datasets, as well as some major upcoming features.

Quantitative mass spectrometric profiling of cancer-cell proteomes derived from liquid and solid tumors

In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic biomarkers. However, methods for quantitative proteomic characterization of patient-derived tumors and in particular their cellular subpopulations are largely lacking. Here we describe an experimental set-up that allows quantitative analysis of proteomes of cancer cell subpopulations derived from either liquid or solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry followed by bioinformatic data analysis. To enrich specific cellular subsets, liquid tumors are first immunophenotyped by flow cytometry followed by FACS-sorting; for solid tumors, laser-capture microdissection is used to purify specific cellular subpopulations. In a second step, proteins are extracted from the purified cells and subsequently combined with a tumor-specific, SILAC-labeled spike-in standard that enables protein quantification. The resulting protein mixture is subjected to either gel electrophoresis or Filter Aided Sample Preparation (FASP) followed by tryptic digestion. Finally, tryptic peptides are analyzed using a hybrid quadrupole-orbitrap mass spectrometer, and the data obtained are processed with bioinformatic software suites including MaxQuant. By means of the workflow presented here, up to 8,000 proteins can be identified and quantified in patient-derived samples, and the resulting protein expression profiles can be compared among patients to identify diagnostic proteomic signatures or potential drug targets.

Quantifying protein measurands by peptide measurements: where do errors arise?

Clinically actionable quantification of protein biomarkers by mass spectrometry (MS) requires analytical performance in concordance with quality specifications for diagnostic tests. Laboratory-developed tests should, therefore, be validated in accordance with EN ISO 15189:2012 guidelines for medical laboratories to demonstrate competence and traceability along the entire workflow, including the selected standardization strategy and the phases before, during, and after proteolysis. In this study, bias and imprecision of a previously developed MS method for quantification of serum apolipoproteins A-I (Apo A-I) and B (Apo B) were thoroughly validated according to Clinical and Laboratory Standards Institute (CLSI) guidelines EP15-A2 and EP09-A3, using 100 patient sera and either stable-isotope labeled (SIL) peptides or SIL-Apo A-I as internal standard. The systematic overview of error components assigned sample preparation before the first 4 h of proteolysis as major source (∼85%) of within-sample imprecision without external calibration. No improvement in imprecision was observed with the use of SIL-Apo A-I instead of SIL-peptides. On the contrary, when the use of SIL-Apo A-I was combined with external calibration, imprecision improved significantly (from ∼9% to ∼6%) as a result of the normalization for matrix effects on linearity. A between-sample validation of bias in 100 patient sera further supported the presence of matrix effects on digestion completeness and additionally demonstrated specimen-specific biases associated with modified peptide sequences or alterations in protease activity. In conclusion, the presented overview of bias and imprecision components contributes to a better understanding of the sources of errors in MS-based protein quantification and provides valuable recommendations to assess and control analytical quality in concordance with the requirements for clinical use.