Mass spectrometry-based quantification

Utilizing proteomics to identify and optimize microalgae strains for high-quality dietary protein: a review

Algae-derived protein has immense potential to provide high-quality protein foods for the expanding human population. To meet its potential, a broad range of scientific tools are required to identify optimal algal strains from the hundreds of thousands available and identify ideal growing conditions for strains that produce high-quality protein with functional benefits. A research pipeline that includes proteomics can provide a deeper interpretation of microalgal composition and biochemistry in the pursuit of these goals. To date, proteomic investigations have largely focused on pathways that involve lipid production in selected microalgae species. Herein, we report the current state of microalgal proteome measurement and discuss promising approaches for the development of protein-containing food products derived from algae.

Quantitative Aspects of the Human Cell Proteome

The number and identity of proteins and proteoforms presented in a single human cell (a cellular proteome) are fundamental biological questions. The answers can be found with sophisticated and sensitive proteomics methods, including advanced mass spectrometry (MS) coupled with separation by gel electrophoresis and chromatography. So far, bioinformatics and experimental approaches have been applied to quantitate the complexity of the human proteome. This review analyzed the quantitative information obtained from several large-scale panoramic experiments in which high-resolution mass spectrometry-based proteomics in combination with liquid chromatography or two-dimensional gel electrophoresis (2DE) were used to evaluate the cellular proteome. It is important that even though all these experiments were performed in different labs using different equipment and calculation algorithms, the main conclusion about the distribution of proteome components (proteins or proteoforms) was basically the same for all human tissues or cells. It follows Zipf's law and has a formula N = A/x, where N is the number of proteoforms, A is a coefficient, and x is the limit of proteoform detection in terms of abundance.

3D Visualization of the Dynamic Bidirectional Talk Between ER/PR and Her2 Pathways

Background: Extensive amounts of archived formalin fixed paraffin embedded (FFPE) human tumor tissues are the ultimate resource to investigate signaling network underlying tumorigenesis in human. Yet, their usage is severely limited for lacking of suitable protein techniques. In this study, a quantitative, objective, absolute, and high throughput immunoblot method, quantitative dot blot (QDB), was explored to address this issue by investigating the putative relationship between estrogen receptor (ER)/progesterone receptor (PR) and human epidermal growth factor receptor 2 (Her2) pathways in breast cancer tumorigenesis. Methods: In this descriptive observational retrospective study, ER, PR, Her2, and Ki67 protein levels were measured absolutely and quantitatively in 852 FFPE breast cancer tissues using the QDB method. ER, PR, and Her2 levels were charted on the X, Y, and Z-axes to observe samples distribution in a 3D scatterplot. Results: A “seesaw” relationship between ER/PR and Her2 pathways was observed in ER–PR–Her2 space, characterized by the expression levels of these 3 proteins. Specimens with strong expressions of ER/PR proteins were found spreading along the ER/PR floor while those with strong Her2 expression were found wrapping around the Her2 axis. Those lacking strong expressions of all 3 proteins were found accumulating at the intersection of the ER, PR, and Her2 axes. Few specimens floated in the ER–PR–Her2 space to suggest the lack of co-expression of all 3 proteins simultaneously. Ki67 levels were found to be significantly reduced in specimens spreading in the ER–PR space. Conclusions: The unique distribution of specimens in ER–PR–Her2 space prior to any clinical intervention provided visual support of bidirectional talk between ER/PR and Her2 pathways in breast cancer specimens. Clinical interventions to suppress these 2 pathways alternatively warrant further exploration for breast cancer patients accordingly. Our study also demonstrated that the QDB method is an effective tool to analyze archived FFPE cancer specimens in biomedical research.

A panoramic view of proteomics and multiomics in precision health

Health is often qualitatively defined as a status free from disease and its quantitative definition requires finding the boundary separating health from pathological conditions. Since many complex diseases have a strong genetic component, substantial efforts have been made to sequence large-scale personal genomes; however, we are not yet able to effectively quantify health status from personal genomes. Since mutational impacts are ultimately manifested at the protein level, we envision that introducing a panoramic proteomic view of complex diseases will allow us to mechanistically understand the molecular etiologies of human diseases. In this perspective article, we will highlight key proteomic approaches to identify pathogenic mutations and map their convergent pathways underlying disease pathogenesis and the integration of omics data at multiple levels to define the borderline between health and disease.

Proteome analysis using machine learning approaches and its applications to diseases

With the tremendous developments in the fields of biological and medical technologies, huge amounts of data are generated in the form of genomic data, images in medical databases or as data on protein sequences, and so on. Analyzing this data through different tools sheds light on the particulars of the disease and our body's reactions to it, thus, aiding our understanding of the human health. Most useful of these tools is artificial intelligence and deep learning (DL). The artificially created neural networks in DL algorithms help extract viable data from the datasets, and further, to recognize patters in these complex datasets. Therefore, as a part of machine learning, DL helps us face all the various challenges that come forth during protein prediction, protein identification and their quantification. Proteomics is the study of such proteins, their structures, features, properties and so on. As a form of data science, Proteomics has helped us progress excellently in the field of genomics technologies. One of the major techniques used in proteomics studies is mass spectrometry (MS). However, MS is efficient with analysis of large datasets only with the added help of informatics approaches for data analysis and interpretation; these mainly include machine learning and deep learning algorithms. In this chapter, we will discuss in detail the applications of deep learning and various algorithms of machine learning in proteomics.

Developing a growing cancer profile database based on quantitative analysis of protein biomarkers in formalin-fixed paraffin-embedded specimens

Over a century of clinical practice has led to the accumulation of millions of archived formalin fixed paraffin embedded (FFPE) cancer specimens with detailed medical records worldwide. Absolute quantitation of clinical protein biomarkers in these FFPE specimens allows individual specimens to be profiled at the population level, with the absolute nature of the measurements enabling the continuous processing of archived FFPE specimens over the time. A continuously growing cancer patient profile database is proposed here to support "big data" profiling of these protein biomarkers alone or in combination, enabling next-generation retrospective-prospective analytics into the field of clinical diagnostics.

Bioinformatics Resources for Plant Abiotic Stress Responses: State of the Art and Opportunities in the Fast Evolving -Omics Era

Abiotic stresses are among the principal limiting factors for productivity in agriculture. In the current era of continuous climate changes, the understanding of the molecular aspects involved in abiotic stress response in plants is a priority. The rise of -omics approaches provides key strategies to promote effective research in the field, facilitating the investigations from reference models to an increasing number of species, tolerant and sensitive genotypes. Integrated multilevel approaches, based on molecular investigations at genomics, transcriptomics, proteomics and metabolomics levels, are now feasible, expanding the opportunities to clarify key molecular aspects involved in responses to abiotic stresses. To this aim, bioinformatics has become fundamental for data production, mining and integration, and necessary for extracting valuable information and for comparative efforts, paving the way to the modeling of the involved processes. We provide here an overview of bioinformatics resources for research on plant abiotic stresses, describing collections from -omics efforts in the field, ranging from raw data to complete databases or platforms, highlighting opportunities and still open challenges in abiotic stress research based on -omics technologies.

Environmental proteomic studies: closer step to understand bacterial biofilms

Advancement in proteome analytical techniques and the development of protein databases have been helping to understand the physiology and subtle molecular mechanisms behind biofilm formation in bacteria. This review is to highlight how the evolving proteomic approaches have revealed fundamental molecular processes underlying the formation and regulation of bacterial biofilms. Based on the survey of research reports available on differential expression of proteins in biofilms of bacterial from wide range of environments, four important cellular processes viz. metabolism, motility, transport and stress response that contribute to formation of bacterial biofilms are discussed. This review might answer how proteins related to these cellular processes contribute significantly in stabilizing biofilms of different bacteria in diverse environmental conditions.

Infrared spectroscopic characterization of monocytic microvesicles (microparticles) released upon lipopolysaccharide stimulation

Microvesicles (MVs) are involved in cell-cell interactions, including disease pathogenesis. Nondestructive Fourier-transform infrared (FTIR) spectra from MVs were assessed as a technique to provide new biochemical insights into a LPS-induced monocyte model of septic shock. FTIR spectroscopy provided a quick method to investigate relative differences in biomolecular content of different MV populations that was complementary to traditional semiquantitative omics approaches, with which it is difficult to provide information on relative changes between classes (proteins, lipids, nucleic acids, carbohydrates) or protein conformations. Time-dependent changes were detected in biomolecular contents of MVs and in the monocytes from which they were released. Differences in phosphatidylcholine and phosphatidylserine contents were observed in MVs released under stimulation, and higher relative concentrations of RNA and α-helical structured proteins were present in stimulated MVs compared with MVs from resting cells. FTIR spectra of stimulated monocytes displayed changes that were consistent with those observed in the corresponding MVs they released. LPS-stimulated monocytes had reduced concentrations of nucleic acids, α-helical structured proteins, and phosphatidylcholine compared with resting monocytes but had an increase in total lipids. FTIR spectra of MV biomolecular content will be important in shedding new light on the mechanisms of MVs and the different roles they play in physiology and disease pathogenesis.-Lee, J., Wen, B., Carter, E. A., Combes, V., Grau, G. E. R., Lay, P. A. Infrared spectroscopic characterization of monocytic microvesicles (microparticles) released upon lipopolysaccharide stimulation.

Targeted label-free quantification of interleukin-8 in PMA-activated U937 cell secretome by nanoLC-ESI-MS/MS-sSRM

Monocytes are a part of the innate immune system. Their differentiation into macrophages changes their cellular proteome and secretome. Particularly secretome components such as cytokines are crucial for immune response and inflammation in many diseases. Differentiation of human lymphoma cell line U937 can be triggered by phorbol 12-myristate 13-acetate (PMA). Screening of the cytokine release in U937 upon PMA stimulation by cytometric bead array almost exclusively showed interleukin-8 (IL-8). Next, a label-free nanoLC-ESI-MS/MS-sSRM method for quantification of IL-8 in the cell secretome was established and applied to monitor the time kinetics of PMA treatment in different concentrations. Targeted secretome analysis was achieved by scheduled SRM-MS using one proteotypic peptide as precursor ion and four mass transitions. Label-free quantification was performed by external calibration using IL-8 standard. Validation results indicated that the method was suited for the quantification of IL-8 in the secretome. The maximal IL-8 release of 62.4 ng/mL was observed after incubating cells treated by 50 ng/mL PMA for 48 h. The method can now be used for quantification of IL-8 release from different cells under various conditions. Furthermore, it can be easily expanded to other secreted proteins detected by untargeted screening methods.

Authentication of shrimp muscle in complex foodstuff by in-solution digestion and high-resolution mass spectrometry

A method for shrimp muscle identification in complex foods is required to safeguard the shrimp-allergic population.

The Proteomic Characterization of Plasma or Serum from HIV-Infected Patients

Proteomics holds great promise for uncovering disease-related markers and mechanisms in human disorders. Recent advances have led to efficient, sensitive, and reproducible methods to quantitate the proteome in biological samples. Here we describe the techniques for processing, running, and analyzing samples from HIV-infected plasma or serum through quantitative mass spectroscopy.

Plant responses to ambient temperature fluctuations and water-limiting conditions: A proteome-wide perspective

BACKGROUND

Every year, environmental stresses such as limited water and nutrient availability, salinity, and temperature fluctuations inflict significant losses on crop yields across the globe. Recently, developments in analytical techniques, e.g. mass spectrometry, have led to great advances towards understanding how plants respond to environmental stresses. These processes are mediated by many molecular pathways and, at least partially, via proteome-environment interactions.

SCOPE OF REVIEW

This review focuses on the current state of knowledge about interactions between the plant proteome and the environment, with a special focus on drought and temperature responses of plant proteome dynamics, and subcellular and organ-specific compartmentalization, in Arabidopsis thaliana and crop species.

MAJOR CONCLUSIONS

Correct plant development under non-optimal conditions requires complex self-protection mechanisms, many of them common to different abiotic stresses. Proteome analyses of plant responses to temperature and drought stresses have revealed an intriguing interplay of modifications, mainly affecting the photosynthetic machinery, carbohydrate metabolism, and ROS activation and scavenging. Imbalances between transcript-level and protein-level regulation observed during adaptation to abiotic stresses suggest that many of the regulatory processes are controlled at translational and post-translational levels; proteomics is thus essential in revealing important regulatory networks.

GENERAL SIGNIFICANCE

Because information from proteomic data extends far beyond what can be deduced from transcriptome analysis, the results of proteome studies have substantially deepened our understanding of stress adaptation in plants; this is clearly a prerequisite for designing strategies to improve the yield and quality of crops grown under unfavorable conditions brought about by ongoing climatic change. This article is part of a Special Issue entitled: Plant Proteomics--a bridge between fundamental processes and crop production, edited by Dr. Hans-Peter Mock.

Making proteomics data accessible and reusable: current state of proteomics databases and repositories

Compared to other data-intensive disciplines such as genomics, public deposition and storage of MS-based proteomics, data are still less developed due to, among other reasons, the inherent complexity of the data and the variety of data types and experimental workflows. In order to address this need, several public repositories for MS proteomics experiments have been developed, each with different purposes in mind. The most established resources are the Global Proteome Machine Database (GPMDB), PeptideAtlas, and the PRIDE database. Additionally, there are other useful (in many cases recently developed) resources such as ProteomicsDB, Mass Spectrometry Interactive Virtual Environment (MassIVE), Chorus, MaxQB, PeptideAtlas SRM Experiment Library (PASSEL), Model Organism Protein Expression Database (MOPED), and the Human Proteinpedia. In addition, the ProteomeXchange consortium has been recently developed to enable better integration of public repositories and the coordinated sharing of proteomics information, maximizing its benefit to the scientific community. Here, we will review each of the major proteomics resources independently and some tools that enable the integration, mining and reuse of the data. We will also discuss some of the major challenges and current pitfalls in the integration and sharing of the data.

Recent progress in urinary proteome analysis for prostate cancer diagnosis and management

Prostate cancer (PCa) is fifth leading cause of cancer-associated deaths in men worldwide. Although the application of the serum prostate-specific antigen (PSA) screening test resulted in an increase in the PCa diagnosed cases, it demonstrated a negligible benefit regarding the associated mortality. Treatment options vary, with active surveillance to be preferable for patients with low-risk PCa and therapy of advanced castration-resistant PCa to rely on α-emitters and cytotoxic chemotherapy. Although recent developments have led to the approval of novel drugs for the treatment of castration-resistant PCa, the optimal sequence and timing of medication have not been yet determined. New screening modalities could improve the discriminatory accuracy between tumors with favorable clinical prognosis. Implementation of proteomic-based biomarkers appears to be a promising improvement, which could enable a more accurate diagnosis, guide treatment and improve patient outcome. Reviewed here are urinary proteome-based approaches for detection of PCa and patient management.

The biological basis of degenerative disc disease: proteomic and biomechanical analysis of the canine intervertebral disc

INTRODUCTION

In the present study, we sought to quantify and contrast the secretome and biomechanical properties of the non-chondrodystrophic (NCD) and chondrodystrophic (CD) canine intervertebral disc (IVD) nucleus pulposus (NP).

METHODS

We used iTRAQ proteomic methods to quantify the secretome of both CD and NCD NP. Differential levels of proteins detected were further verified using immunohistochemistry, Western blotting, and proteoglycan extraction in order to evaluate the integrity of the small leucine-rich proteoglycans (SLRPs) decorin and biglycan. Additionally, we used robotic biomechanical testing to evaluate the biomechanical properties of spinal motion segments from both CD and NCD canines.

RESULTS

We detected differential levels of decorin, biglycan, and fibronectin, as well as of other important extracellular matrix (ECM)-related proteins, such as fibromodulin and HAPLN1 in the IVD NP obtained from CD canines compared with NCD canines. The core proteins of the vital SLRPs decorin and biglycan were fragmented in CD NP but were intact in the NP of the NCD animals. CD and NCD vertebral motion segments demonstrated significant differences, with the CD segments having less stiffness and a more varied range of motion.

CONCLUSIONS

The CD NP recapitulates key elements of human degenerative disc disease. Our data suggest that at least some of the compromised biomechanical properties of the degenerative disc arise from fibrocartilaginous metaplasia of the NP secondary to fragmentation of SLRP core proteins and associated degenerative changes affecting the ECM. This study demonstrates that the degenerative changes that naturally occur within the CD NP make this animal a valuable animal model with which to study IVD degeneration and potential biological therapeutics.

Cancer proteomics: developments in technology, clinical use and commercialization

In the last two decades, advances in genomic, transcriptomic and proteomic methods have enabled us to identify and classify cancers by their molecular profiles. Many anticipate that a molecular taxonomy of cancer will not only lead to more effective subtyping of cancers but also earlier diagnoses, more informative prognoses and more targeted treatments. This article reviews recent technological developments in the field of proteomics, recent discoveries in proteomic cancer biomarker research and trends in clinical use. Readers are also informed of examples of successful commercialization, and the future of proteomics in cancer diagnostics.

Viewing the proteome: how to visualize proteomics data?

Proteomics has become one of the main approaches for analyzing and understanding biological systems. Yet similar to other high-throughput analysis methods, the presentation of the large amounts of obtained data in easily interpretable ways remains challenging. In this review, we present an overview of the different ways in which proteomics software supports the visualization and interpretation of proteomics data. The unique challenges and current solutions for visualizing the different aspects of proteomics data, from acquired spectra via protein identification and quantification to pathway analysis, are discussed, and examples of the most useful visualization approaches are highlighted. Finally, we offer our ideas about future directions for proteomics data visualization.

Five-plex isotope dimethyl labeling for quantitative proteomics

Stable isotope dimethyl labeling, a widely used method for quantitative proteomics, was extended to five channels for the first time. Comprehensive proteome and phosphoproteome quantification validated the high quantification accuracy and throughput of this five-plex method.

Next generation of food allergen quantification using mass spectrometric systems

Food allergies are increasing worldwide and becoming a public health concern. Food legislation requires detailed declarations of potential allergens in food products and therefore an increased capability to analyze for the presence of food allergens. Currently, antibody-based methods are mainly utilized to quantify allergens; however, these methods have several disadvantages. Recently, mass spectrometry (MS) techniques have been developed and applied to food allergen analysis. At present, 46 allergens from 11 different food sources have been characterized using different MS approaches and some specific signature peptides have been published. However, quantification of allergens using MS is not routinely employed. This review compares the different aspects of food allergen quantification using advanced MS techniques including multiple reaction monitoring. The latter provides low limits of quantification for multiple allergens in simple or complex food matrices, while being robust and reproducible. This review provides an overview of current approaches to analyze food allergens, with specific focus on MS systems and applications.

Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development

Biomarker research is continuously expanding in the field of clinical proteomics. A combination of different proteomic-based methodologies can be applied depending on the specific clinical context of use. Moreover, current advancements in proteomic analytical platforms are leading to an expansion of biomarker candidates that can be identified. Specifically, mass spectrometric techniques could provide highly valuable tools for biomarker research. Ideally, these advances could provide with biomarkers that are clinically applicable for disease diagnosis and/ or prognosis. Unfortunately, in general the biomarker candidates fail to be implemented in clinical decision making. To improve on this current situation, a well-defined study design has to be established driven by a clear clinical need, while several checkpoints between the different phases of discovery, verification and validation have to be passed in order to increase the probability of establishing valid biomarkers. In this review, we summarize the technical proteomic platforms that are available along the different stages in the biomarker discovery pipeline, exemplified by clinical applications in the field of bladder cancer biomarker research.

iTRAQ as a method for optimization: enhancing peptide recovery after gel fractionation

At the dawn of a new era in label-free quantitation on high-resolution MS instruments, classical methods such as iTRAQ continue to provide very useful insights in comparative proteomics. The potential to multiplex samples makes this reporter-based labeling technique highly suited for method optimization as demonstrated here by a set of standard series. Instead of studying ratios of annotated proteins, we propose an alternative method, based on the analysis of the average reporter ratios of all the spectra from a sample or a large distinct subset herein. This strategy circumvents the bias, associated with the annotation and iTRAQ quantitation, leading to increased adequacy in measuring yield differences between workflows. As gel electrophoresis prior to MS analysis is highly beneficial, for example, as a fractionation step, the approach was applied to evaluate the influence of several parameters of the established in-gel digestion protocol. We quantified the negative effect of SYPRO Ruby staining and the positive effect of gel fixation prior to digestion on peptide yield. Finally, we emphasize the benefits of adding CaCl2 and ACN to a tryptic in-gel digest, resulting in an up to tenfold enhanced peptide recovery and fewer trypsin missed cleavages.

Dual LC-MS platform for high-throughput proteome analysis

We describe a dual-column interface for parallel chromatography to improve throughput during LC-MS experimentation. The system employs a high-voltage switch to operate two capillary column/nanospray emitters fixed at the MS orifice. Sequentially loading one column while operating the second nearly doubles the LC-MS duty cycle. Given the innate run-to-run variation of a nanospray LC-MS (12% RSD peak area; 2% retention time), the intercolumn variability of the platform showed no meaningful difference for proteome analysis, with equal numbers of proteins and peptides identified per column. Applied to GeLC analysis of an E. coli extract, throughput was increased using one of three methods: doubling the number of replicates, increasing the LC gradient length, or sectioning the gel into twice as many fractions. Each method increased the total number of identifications as well as detection throughput (number of peptides/proteins identified per hour). The greatest improvement was achieved by doubling the number of gel slices (10 vs 5). Analysis on the dual column platform provided a 26% increase in peptides identified per hour (24% proteins). This translates into ~50% more total proteins and peptides identified in the experiment using the dual LC-MS platform.

Proteomic Workflows for Biomarker Identification Using Mass Spectrometry - Technical and Statistical Considerations during Initial Discovery

Identification of biomarkers capable of differentiating between pathophysiological states of an individual is a laudable goal in the field of proteomics. Protein biomarker discovery generally employs high throughput sample characterization by mass spectrometry (MS), being capable of identifying and quantifying thousands of proteins per sample. While MS-based technologies have rapidly matured, the identification of truly informative biomarkers remains elusive, with only a handful of clinically applicable tests stemming from proteomic workflows. This underlying lack of progress is attributed in large part to erroneous experimental design, biased sample handling, as well as improper statistical analysis of the resulting data. This review will discuss in detail the importance of experimental design and provide some insight into the overall workflow required for biomarker identification experiments. Proper balance between the degree of biological vs. technical replication is required for confident biomarker identification.