IsoAsp-Quest: workflow development for isoAsp identification using database searches.

Biomarkers are detectable molecules that can reflect specific physiological states of cells, organs, and organisms and therefore be regarded as indicators for specific diseases. And the discovery of biomarkers plays an essential role in cancer management from the initial diagnosis to the final treatment regime. Practically, reliable clinical biomarkers are still limited, restricted by the suboptimal methods in biomarker discovery. Nucleic acid aptamers nowadays could be used as a powerful tool in the discovery of protein biomarkers. Nucleic acid aptamers are single-strand oligonucleotides that can specifically bind to various targets with high affinity. As artificial ssDNA or RNA, aptamers possess unique advantages compared to conventional antibodies. They can be flexible in design, low immunogenicity, relative chemical/thermos stability, as well as modifying convenience. Several SELEX (Systematic Evolution of Ligands by Exponential Enrichment) based methods have been generated recently to construct aptamers for discovering new biomarkers in different cell locations. Secretome SELEX-based aptamers selection can facilitate the identification of secreted protein biomarkers. The aptamers developed by cell-SELEX can be used to unveil those biomarkers presented on the cell surface. The aptamers from tissue-SELEX could target intracellular biomarkers. And as a multiplexed protein biomarker detection technology, aptamer-based SOMAScan can analyze thousands of proteins in a single run. In this review, we will introduce the principle and workflow of variations of SELEX-based methods, including secretome SELEX, ADAPT, Cell-SELEX and tissue SELEX. Another powerful proteome analyzing tool, SOMAScan, will also be covered. In the second half of this review, how these methods accelerate biomarker discovery in various diseases, including cardiovascular diseases, cancer and neurodegenerative diseases, will be discussed.

Muscle-invasive bladder cancer (MIBC) is an aggressive disease with limited therapeutic options. Although immunotherapies are approved for MIBC, the majority of patients fail to respond, suggesting the existence of complementary immune evasion mechanisms underscoring the need for comprehensive immune profiling of patient tumor samples. The PPARγ pathway constitutes a tumor-intrinsic mechanism underlying immune evasion in MIBC. Immune cell-infiltration is controlled by activated PPARγ, which in turn inhibits expression/secretion of inflammatory cytokines. Clinical data sets indicate that PPARγHigh impairs CD8+ T-cell infiltration and confers partial resistance to immunotherapies. Here, we report a comprehensive biomarker study aimed at identifying a single, highly multiplexed platform for profiling the tumor microenvironment in clinical-type specimens as formalin-fixed, paraffin-embedded (FFPE) samples. A cohort of PPARγHigh and PPARγLow muscle invasive bladder cancer samples were used for the technology assessments. Gene expression patterns were measured using a combination of research use only genomics and spatial genomic technologies to determine concordance including RNA-seq, spatial in situ hybridization (GeoMx™ Digital Spatial Profiler) and IO360. We determined that multiple RNA and protein-based technologies were able to accurately characterize the immune phenotypes of PPARγHigh tumors. However, the GeoMx™ technology was ideal for precious samples since it digitally quantifies target abundance while maintaining spatial resolution in only two FFPE slides. GeoMxTM DSP technology is for Research Use Only and not for use in diagnostic procedures. Citation Format: Zhaojie Zhang, Subhasree Das, Manav Korpal, Pavan Kumar, Jingjing Gong, Yan Liang, Margaret Hoang, Joseph Beecham, Victoria Rimkunas. Integrating bulk and spatial profiling technologies for the discovery of RNA and protein biomarkers in muscle invasive bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 498.

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry–based proteomics methods. We conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073–2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.

Breast cancer classification by proteomic technologies: Current state of knowledge

Proteomics

In discovery proteomics experiments, tandem mass spectrometry and data-dependent acquisition (DDA) are classically used to identify and quantify peptides and proteins through database searching. This strategy suffers from known limitations such as under-sampling and lack of reproducibility of precursor ion selection in complex proteomics samples, leading to somewhat inconsistent analytical results across large datasets. Data-independent acquisition (DIA) based on fragmentation of all the precursors detected in predetermined isolation windows can potentially overcome this limitation. DIA promises reproducible peptide and protein quantification with deeper proteome coverage and fewer missing values than DDA strategies. This approach is particularly attractive in the field of clinical biomarker discovery, where large numbers of samples must be analyzed. Here, we describe a DIA workflow for non-depleted serum analysis including a straightforward approach through which to construct a dedicated spectral library, and indications on how to optimize chromatographic and mass spectrometry analytical methods to produce high-quality DIA data and results.

Mass spectrometry based clinical proteomics has emerged as a powerful tool for high-throughput protein profiling and biomarker discovery. Recent improvements in mass spectrometry technology have boosted the potential of proteomic studies in biomedical research. However, the complexity of the proteomic expression introduces new statistical challenges in summarizing and analyzing the acquired data. Statistical methods for optimally processing proteomic data are currently a growing field of research. In this paper we present simple, yet appropriate methods to preprocess, summarize and analyze high-throughput MALDI-FTICR mass spectrometry data, collected in a case-control fashion, while dealing with the statistical challenges that accompany such data. The known statistical properties of the isotopic distribution of the peptide molecules are used to preprocess the spectra and translate the proteomic expression into a condensed data set. Information on either the intensity level or the shape of the identified isotopic clusters is used to derive summary measures on which diagnostic rules for disease status allocation will be based. Results indicate that both the shape of the identified isotopic clusters and the overall intensity level carry information on the class outcome and can be used to predict the presence or absence of the disease.

Mass spectrometry-based unbiased analysis of the full complement of secretory peptides is expected to facilitate the identification of unknown biologically active peptides. However, tandem MS sequencing of endogenous peptides in their native form has proven difficult because they show size heterogeneity and contain multiple internal basic residues, the characteristics not found in peptide fragments produced by in vitro digestion. Endogenous peptides remain largely unexplored by electron transfer dissociation (ETD), despite its widespread use in bottom-up proteomics. We used ETD, in comparison to collision induced dissociation (CID), to identify endogenous peptides derived from secretory granules of a human endocrine cell line. For mass accuracy, both MS and tandem MS were analyzed on an Orbitrap. CID and ETD, performed in different LC-MS runs, resulted in the identification of 795 and 569 unique peptides (ranging from 1000 to 15000 Da), respectively, with an overlap of 397. Peptides larger than 3000 Da accounted for 54% in CID and 46% in ETD identifications. Although numerically outperformed by CID, ETD provided more extensive fragmentation, leading to the identification of peptides that are not reached by CID. This advantage was demonstrated in identifying a new antimicrobial peptide from neurosecretory protein VGF (non-acronymic), VGF[554-577]-NH2, or in differentiating nearly isobaric peptides (mass difference less than 2 ppm) that arise from alternatively spliced exons of the gastrin-releasing peptide gene. CID and ETD complemented each other to add to our knowledge of the proteolytic processing sites of proteins implicated in the regulated secretory pathway. An advantage of the use of both fragmentation methods was also noted in localization of phosphorylation sites. These findings point to the utility of ETD mass spectrometry in the global study of endogenous peptides, or peptidomics.

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers.

Immunoaffinity-mass spectrometry (IA-MS) is an emerging analytical genre with several advantages for profiling and determination of protein biomarkers. Because IA-MS combines affinity capture, analogous to ligand binding assays (LBAs), with mass spectrometry (MS) detection, this platform is often described using the term hybrid methods. The purpose of this report is to provide an overview of the principles of IA-MS and to demonstrate, through application, the unique power and potential of this technology. By combining target immunoaffinity enrichment with the use of stable isotope-labeled internal standards and MS detection, IA-MS achieves high sensitivity while providing unparalleled specificity for the quantification of protein biomarkers in fluids and tissues. In recent years, significant uptake of IA-MS has occurred in the pharmaceutical industry, particularly in the early stages of clinical development, enabling biomarker measurement previously considered unattainable. By comparison, IA-MS adoption by CLIA laboratories has occurred more slowly. Current barriers to IA-MS use and opportunities for expanded adoption are discussed. The path forward involves identifying applications for which IA-MS is the best option compared with LBA or MS technologies alone. IA-MS will continue to benefit from advances in reagent generation, more sensitive and higher throughput MS technologies, and continued growth in use by the broader analytical community. Collectively, the pursuit of these opportunities will secure expanded long-term use of IA-MS for clinical applications.

Human saliva is an attractive body fluid for disease diagnosis and prognosis because saliva testing is simple, safe, low-cost and noninvasive. Comprehensive analysis and identification of the proteomic content in human whole and ductal saliva will not only contribute to the understanding of oral health and disease pathogenesis, but also form a foundation for the discovery of saliva protein biomarkers for human disease detection. In this article, we have summarized the proteomic technologies for comprehensive identification of proteins in human whole and ductal saliva. We have also discussed potential quantitative proteomic approaches to the discovery of saliva protein biomarkers for human oral and systemic diseases. With the fast development of mass spectrometry and proteomic technologies, we are enthusiastic that saliva protein biomarkers will be developed for clinical diagnosis and prognosis of human diseases in the future.

The field of proteomics seeks to define, on a global scale, the levels, activities, regulation, and interaction of proteins in a biological sample. Proteomics is analogous to transcriptomics—the global analysis of mRNA transcripts that arise from the expression of genes in the genome—although the former is considerably more complicated. Whereas the human genome comprises approximately 30,000 genes, there are likely over 100,000 unique proteins in the human proteome due to the multiple ways each gene can be transcribed and translated into proteins by cellular machinery. Because proteins are fundamental components of all living cells, including enzymes, hormones, and antibodies, they are constantly in flux as the body takes in food, metabolizes it, and stores or burns energy. Furthermore, different proteins are produced and expressed at different developmental stages of an organism’s life cycle, from the moment of conception throughout the aging process. Consequently, proteomics is a dynamic, challenging research area. One of the essential tools being used to meet this challenge is mass spectrometry (MS), which is the focal point of the National Center for Toxicogenomics (NCT) Mass Spectrometry Group. There are a large number of proteins in a system at any one time, and they are always changing as an organism eats and metabolizes food, exercises, and sleeps. The proteome—all the proteins expressed in a living system—is therefore unique to the cell or tissue under study. The more narrowly one can define where the proteins are localized within the system (such as in a specific tissue or location in a cell), the easier it is to characterize the specific proteins and quantify their varying levels of expression. Proteomics research at the NCT focuses largely on changes in an organism’s proteome in response to an event such as exposure to an environmental toxicant, in order to advance understanding of how people might respond to chemical exposures in their environment. The Mass Spectrometry Group, led by NIEHS principal investigator Kenneth B. Tomer, employs high-throughput techniques, including MS, to examine hundreds or thousands of protein changes in a large number of samples. Because proteomics techniques produce large amounts of data, sophisticated analysis tools are used to decipher the results and identify key changes in select protein biomarkers that convey valuable information about exposure to harmful chemicals. The MS facility performs analyses for the intramural NIEHS research community, and Tomer also conducts his own collaborative research projects as part of the NCT Proteomics Group.

The SELDI-TOF MS Approach to Proteomics: Protein Profiling and Biomarker Identification

Summary of United States Human Proteome Organisation (HUPO) Symposium Entitled “Standardized Clinical Proteomics Platforms”

In high-throughput proteomics the development of computational methods and novel experimental strategies often rely on each other. In certain areas, mass spectrometry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass spectra. Particularly, although there are numerous situations in which a mixture tandem mass spectrum can contain fragment ions from two or more peptides, nearly all database search tools still make the assumption that each tandem mass spectrum comes from one peptide. Common examples include mixture spectra from co-eluting peptides in complex samples, spectra generated from data-independent acquisition methods, and spectra from peptides with complex post-translational modifications. We propose a new database search tool (MixDB) that is able to identify mixture tandem mass spectra from more than one peptide. We show that peptides can be reliably identified with up to 95% accuracy from mixture spectra while considering only a 0.01% of all possible peptide pairs (four orders of magnitude speedup). Comparison with current database search methods indicates that our approach has better or comparable sensitivity and precision at identifying single-peptide spectra while simultaneously being able to identify 38% more peptides from mixture spectra at significantly higher precision.