Identification Of Low-abundance Proteins Research Articles

Study on interaction with high-abundant blood proteins and identification of low-abundant proteins to 5-phenyl-1-(p-tolyl)-1 H-1,2,3-triazole by serum proteomics

In this study, a comprehensive investigation was undertaken to elucidate a simple triazole compound, 5-phenyl-1-(p-tolyl)-1 H-1,2,3-triazole (PPTT), its interactions with high-abundant proteins and identification of low-abundant proteins by serum proteomics. Employing a combination of spectroscopic techniques and computational chemistry, the interactions between PPTT and three high-abundance blood globular proteins, namely human serum albumin (HSA), human immunoglobulin G (HIgG), and hemoglobin (BHb), were explored, thereby ascertaining their binding constants and thermodynamic parameters at the molecular level. Subsequently, based on the differential proteomics, utilizing two-dimensional gel electrophoresis (2-DE) in conjunction with matrix-assisted laser desorption time-of-flight mass spectrometry (MALDI-TOF-MS), the research team isolated and identified differentially expressed low-abundance proteins in human blood serum samples following exposure to PPTT. The results showed that there were twenty highly expressed proteins identified from blood serum samples intervened by PPTT. Combining bioinformatics techniques, these proteins were classified, providing preliminary insights like preproprotein or precursors inhibiting the activity of elastase, defending and regulating the immune system, carrying lipid, and other functions into their biological functionalities. One of the differential proteins, apolipoprotein A-1 (ApoA-1) protein, was selected as a possible target to explore the mechanism of action of PPTT intervention on the related signaling pathways involved in human hepatocellular carcinomas(Hep G2) cells. These research findings offer scientifically sound guidance for further in-depth exploration, development, and application of the 1,2,3-triazole compound.

Nanoparticle-Protein Corona-Based Tissue Proteomics for the Aging Mouse Proteome Atlas.

Highly abundant proteins present in biological fluids and tissues significantly interfere with low-abundance protein identification by mass spectrometry (MS), limiting proteomic depth and hindering protein biomarker discovery. Herein, to enhance the coverage of tissue proteomics, we developed a nanoparticle-protein corona (NP-PC)-based method for the aging mouse proteome atlas. Based on this method, we investigated the complexity of life process of 5 major organs, including the heart, liver, spleen, lungs, and kidneys, from 4 groups of mice at different ages. Compared with the conventional strategy, NP-PC-based proteomics significantly increased the number of identified protein groups in the heart (from 3007 to 3927; increase of 30.6%), liver (from 2982 to 4610; increase of 54.6%), spleen (from 5047 to 7351; increase of 45.7%), lungs (from 4984 to 6903; increase of 38.5%), and kidneys (from 3550 to 5739; increase of 61.7%), and we identified a total of 10 104 protein groups. The overall data indicated that 3-week-old mice showed more differences compared with the other three age groups. The proteins of amino acid-related metabolism were increased in aged mice compared with those in the 3-week-old mice. Protein-related infections were increased in the spleen of the aged mice. Interestingly, the spliceosome-related pathway significantly changed from youth to elders in the liver, spleen, and lungs, indicating the vital role of the spliceosome during the aging process. Our established aging mouse organ proteome atlas provides comprehensive insights into understanding the aging process, and it may help in prevention and treatment of age-related diseases.

Identification of low-abundance proteins in the royal jelly using the Osborne classification method

Royal jelly (RJ) is recognized as healthy food, with a high content of proteins. These proteins play important roles in honeybee caste and human health, but the proteomic analysis of low-abundance proteins in RJ has long been a challenge. Herein, we used the Osborne classification method to separate the RJ proteins of Xinjiang black bees into various fractions. The globulin, ethanol-soluble protein, and glutelin fractions were further separated by SDS-PAGE, and proteomic analysis was carried out by LC-MS/MS and searched against the UniProt database. A total of 23 secretory proteins were identified by proteomic analysis, in which 7 proteins were identified for the first time in RJ. The Osborne classification method combining one-dimensional gel electrophoresis-based proteomic analysis allows the identification of low-abundance proteins in the RJ and greatly extends the knowledge about the components and functions of RJ proteins. The raw data are available via ProteomeXchange with the identifier PXD023315. SignificanceThis study makes an important contribution to the research of the components and functions of low-abundance royal jelly proteins for the following reasons:1.The Osborne method was applied to fractionize the royal jelly proteins, which benefits the identification of low-abundance proteins.2.23 secretory proteins were identified from the globulins, the ethanol-soluble proteins, and the glutelins of the RJ, and there were 7 proteins identified for the first time in royal jelly.3.This study extends the knowledge about the components and functions of royal jelly proteins.

Comparison of Database Searching Programs for the Analysis of Single-Cell Proteomics Data.

Single-cell proteomics is emerging as an important subfield in the proteomics and mass spectrometry communities, with potential to reshape our understanding of cell development, cell differentiation, disease diagnosis, and the development of new therapies. Compared with significant advancements in the "hardware" that is used in single-cell proteomics, there has been little work comparing the effects of using different "software" packages to analyze single-cell proteomics datasets. To this end, seven popular proteomics programs were compared here, applying them to search three single-cell proteomics datasets generated by three different platforms. The results suggest that MSGF+, MSFragger, and Proteome Discoverer are generally more efficient in maximizing protein identifications, that MaxQuant is better suited for the identification of low-abundance proteins, that MSFragger is superior in elucidating peptide modifications, and that Mascot and X!Tandem are better for analyzing long peptides. Furthermore, an experiment with different loading amounts was carried out to investigate changes in identification results and to explore areas in which single-cell proteomics data analysis may be improved in the future. We propose that this comparative study may provide insight for experts and beginners alike operating in the emerging subfield of single-cell proteomics.

Integrated proteomic and phosphoproteomic data-independent acquisition data evaluate the personalized drug responses of primary and metastatic tumors in colorectal cancer.

Mass spectrometry (MS)-based proteomics and phosphoproteomics are powerful methods to study the biological mechanisms, diagnostic biomarkers, prognostic analysis, and drug therapy of tumors. Data-independent acquisition (DIA) mode is considered to perform better than data-dependent acquisition (DDA) mode in terms of quantitative reproducibility, specificity, accuracy, and identification of low-abundance proteins. Mini patient derived xenograft (MiniPDX) model is an effective model to assess the response to antineoplastic drugs in vivo and is helpful for the precise treatment of cancer patients. Kinases are favorable spots for tumor-targeted drugs, and their functional completion relies on signaling pathways through phosphorylating downstream substrates. Kinase-phosphorylation networks or edge interactions are considered more credible and permanent for characterizing complex diseases. Here, we provide a workflow for personalized drug response assessment in primary and metastatic colorectal cancer (CRC) tumors using DIA proteomic data, DIA phosphoproteomic data, and MiniPDX models. Three kinase inhibitors, afatinib, gefitinib, and regorafenib, are tested pharmacologically. The process mainly includes the following steps: clinical tissue collection, sample preparation, hybrid spectral libraries establishment, MS data acquisition, kinase-substrate network construction, in vivo drug test, and elastic regression modeling. Our protocol gives a more direct data basis for individual drug responses, and will improve the selection of treatment strategies for patients without the druggable mutation.

Early Cancer Biomarker Discovery Using DIA-MS Proteomic Analysis of EVs from Peripheral Blood.

One of the cornerstones of effective cancer treatment is early diagnosis. In this context, the identification of proteins that can serve as cancer biomarkers in bodily fluids ("liquid biopsies") has gained attention over the last decade. Plasma and serum fractions of blood are the most commonly investigated sources of potential cancer liquid biopsy biomarkers. However, the high complexity and dynamic range typical of these fluids hinders the sensitivity of protein detection by the most commonly used mass spectrometry technology (data-dependent acquisition mass spectrometry (DDA-MS)). Recently, data-independent acquisition mass spectrometry (DIA-MS) techniques have overcome the limitations of DDA-MS, increasing sensitivity and proteome coverage. In addition to DIA-MS, isolating extracellular vesicles (EVs) can help to increase the depth of serum/plasma proteome coverage by improving the identification of low-abundance proteins which are a potential treasure trove of diagnostic molecules. EVs, the nano-sized membrane-enclosed vesicles present in most bodily fluids, contain proteins which may serve as potential biomarkers for various cancers. Here, we describe a detailed protocol that combines DIA-MS and EV methodologies for discovering and validating early cancer biomarkers using blood serum. The pipeline includes size exclusion chromatography methods to isolate serum-derived extracellular vesicles and subsequent EV sample preparation for liquid chromatography and mass spectrometry analysis. Procedures for spectral library generation by DDA-MS incorporate methods for off-line peptide separation by microflow HPLC with automated fraction concatenation. Analysis of the samples by DIA-MS includes recommended protocols for data processing and statistical methods. This pipeline will provide a guide to discovering and validating EV-associated proteins that can serve as sensitive and specific biomarkers for early cancer detection and other diseases.

DIGE Analysis of ProteoMiner™ Fractionated Serum/Plasma Samples.

The discovery of clinically relevant biomarkers using gel-based proteomics has proven extremely challenging, principally because of the large dynamic range of protein abundances in biofluids such as blood and the fact that only a small number of proteins constitute the vast majority of total blood protein mass. Various separation, depletion, enrichment, and quantitative developments coupled with improvements in gel-based protein quantification technologies, specifically fluorescence two-dimensional difference gel electrophoresis (2D-DIGE), have contributed to significant improvements in the detection and identification of lower abundance proteins. One of these enrichment technologies, ProteoMiner, is the focus of this chapter. The ProteoMiner technology utilizes hexapeptide bead library with huge diversity to bind and enrich low-abundance proteins but at the same time suppresses the concentration of high-abundance proteins in subsequent analysis.

Preparing mare milk sample for proteomic analysis using two-dimensional electrophoresis

There is no universal method to prepare physiological fluids for 2-DE proteomic analysis. Furthermore, interspecies differences in milk composition require the formulation of a species-specific sample preparation procedure. The study was carried out on mare's milk which was prepared for 2-DE in four different methods: the first sample (M1) was defatted, sample M2 was defatted and after casein precipitation, sample M3 was sample M2 after reduction of high molecular proteins and sample M4 was desalted sample M3. The milk samples prepared in different methods were separated by 1-DE and 2-DE. The obtained gels were analysed qualitatively and quantitatively. Furthermore, selected protein spots were identified by MALDI-TOF MS. Analysis of 1-DE and 2-DE gel images indicated that the optimal procedure for preparing mare milk samples for 2-DE and identification of proteins by MS is a method based on defatting and precipitation of caseins. The preparation of mare's milk samples by defatting and then precipitation of caseins is enough to obtain an optimal 2-DE separation for identification of proteins of this body fluid. The method of caseins precipitation should be improved in order to reduce the proportion of these high-abundance protein in milk samples which could increase in the identification of low-abundance proteins in mare's milk.

Soybean seed proteomics: Methods for the isolation, detection, and identification of low abundance proteins.

The salt-soluble globulins, glycinins (11S globulin), and β-conglycinins (7S globulin), are the most abundant seed proteins of soybean seeds. Together, these two groups of proteins account for 60-70% of total soybean seed proteins. Proteomic assessment of the less abundant soybean seed proteins using general isolation protocols is challenging due to the overwhelming abundance of storage proteins. Development of a simple, fast, and inexpensive method to remove most storage proteins from a seed extract will significantly enhance the study of the nonabundant proteins within seeds. We have developed two simple methods for the depletion of abundant seed proteins resulting in the enrichment of low abundance proteins from soybean seeds. Here, we provide a detailed procedure for the isolation, separation, identification, and quantification of low abundance seed proteins of soybean.

Selective enrichment of N-terminal proline peptides via hydrazide chemistry for proteomics analysis

A challenge for shotgun proteomics is the identification of low abundance proteins, which is always hampered owing to the extreme complexity of protein digests and highly dynamic concentration range of proteins. To reduce the complexity of the peptide mixture, we developed a novel method to selectively enrich N-terminal proline peptides via hydrazide chemistry. This method consisted of ortho-phthalaldehyde (OPA) blocking of primary amines in peptides, reductive glutaraldehydation of N-terminal proline and solid phase hydrazide chemistry enrichment of aldehyde-modified N-terminal proline peptide. After enrichment, the number of detected peptides containing N-terminal proline increased from 1304 to 4039 and the ratio of N-terminal proline peptides jumped from 4.4% to 93.7%, showing good enrichment specificity towards N-terminal proline peptides. Besides, the ratio of identified peptides to proteins was decreased from 7.8 (29751/3811) to 1.5 (4347/2821), indicating that sample complexity was drastically reduced through this method. As a result, this novel approach for enriching N-terminal proline peptides is effective in identification of low abundance protein owing to the reduction of sample complexity.

Deep Learning Neural Network Prediction Method Improves Proteome Profiling of Vascular Sap of Grapevines during Pierce's Disease Development.

Plant secretome studies highlight the importance of vascular plant defense proteins against pathogens. Studies on Pierce’s disease of grapevines caused by the xylem-limited bacterium Xylella fastidiosa (Xf) have detected proteins and pathways associated with its pathobiology. Despite the biological importance of the secreted proteins in the extracellular space to plant survival and development, proteome studies are scarce due to methodological challenges. Prosit, a deep learning neural network prediction method is a powerful tool for improving proteome profiling by data-independent acquisition (DIA). We explored the potential of Prosit’s in silico spectral library predictions to improve DIA proteomic analysis of vascular leaf sap from grapevines with Pierce’s disease. The combination of DIA and Prosit-predicted libraries increased the total number of identified grapevine proteins from 145 to 360 and Xf proteins from 18 to 90 compared to gas-phase fractionation (GPF) libraries. The new proteins increased the range of molecular weights, assisted in the identification of more exclusive peptides per protein, and increased identification of low-abundance proteins. These improvements allowed identification of new functional pathways associated with cellular responses to oxidative stress, to be investigated further.

In-Depth Investigation of Low-Abundance Proteins in Matured and Filling Stages Seeds of Glycine max Employing a Combination of Protamine Sulfate Precipitation and TMT-Based Quantitative Proteomic Analysis.

Despite the significant technical advancements in mass spectrometry-based proteomics and bioinformatics resources, dynamic resolution of soybean seed proteome is still limited because of the high abundance of seed storage proteins (SSPs). These SSPs occupy a large proportion of the total seed protein and hinder the identification of low-abundance proteins. Here, we report a TMT-based quantitative proteome analysis of matured and filling stages seeds of high-protein (Saedanbaek) and low-protein (Daewon) soybean cultivars by application of a two-way pre-fractionation both at the levels of proteins (by PS) and peptides (by basic pH reverse phase chromatography). Interestingly, this approach led to the identification of more than 5900 proteins which is the highest number of proteins reported to date from soybean seeds. Comparative protein profiles of Saedanbaek and Daewon led to the identification of 2200 and 924 differential proteins in mature and filling stages seeds, respectively. Functional annotation of the differential proteins revealed enrichment of proteins related to major metabolism including amino acid, major carbohydrate, and lipid metabolism. In parallel, analysis of free amino acids and fatty acids in the filling stages showed higher contents of all the amino acids in the Saedanbaek while the fatty acids contents were found to be higher in the Daewon. Taken together, these results provide new insights into proteome changes during filling stages in soybean seeds. Moreover, results reported here also provide a framework for systemic and large-scale dissection of seed proteome for the seeds rich in SSPs by two-way pre-fractionation combined with TMT-based quantitative proteome analysis.

Nanoparticles: Synthesis, Morphophysiological Effects, and Proteomic Responses of Crop Plants.

Plant cells are frequently challenged with a wide range of adverse environmental conditions that restrict plant growth and limit the productivity of agricultural crops. Rapid development of nanotechnology and unsystematic discharge of metal containing nanoparticles (NPs) into the environment pose a serious threat to the ecological receptors including plants. Engineered nanoparticles are synthesized by physical, chemical, biological, or hybrid methods. In addition, volcanic eruption, mechanical grinding of earthquake-generating faults in Earth’s crust, ocean spray, and ultrafine cosmic dust are the natural source of NPs in the atmosphere. Untying the nature of plant interactions with NPs is fundamental for assessing their uptake and distribution, as well as evaluating phytotoxicity. Modern mass spectrometry-based proteomic techniques allow precise identification of low abundant proteins, protein–protein interactions, and in-depth analyses of cellular signaling networks. The present review highlights current understanding of plant responses to NPs exploiting high-throughput proteomics techniques. Synthesis of NPs, their morphophysiological effects on crops, and applications of proteomic techniques, are discussed in details to comprehend the underlying mechanism of NPs stress acclimation.

Evaluation and minimization of nonspecific tryptic cleavages in proteomic sample preparation.

In this study, protein extraction and trypsin digestion conditions were extensively evaluated using the less-complex Escherichia coli lysates to improve the sensitivity of detecting low-abundance nonspecific peptides by liquid chromatography/tandem mass spectrometry. Trypsin digestion buffers and digestion times were proved to have a significant effect on nonspecific cleavages. The triethylammonium bicarbonate buffer induces significantly lower nonspecific cleavages than the other two buffers, but a freshly prepared urea solution does not induce more than sodium dodecyl sulfate. Because prolonged trypsin digestion resulted in a considerable number of nonspecific cleavages, an optimized 2-h protocol was developed with 45.2% less semispecific tryptic peptides but 18.5% more unmodified peptides identified than the commonly used 16-h protocol. The significant decrease in nonspecific cleavages and artificial modifications improves the accuracy of protein quantification and the identification of low-abundance proteins, and it is especially useful for studying protein posttranslational modifications. For trypsin digestion, the proposed 2-h protocol can potentially be a replacement for the traditional 16-h protocol.

Detection of Plant Low-Abundance Proteins by Means of Combinatorial Peptide Ligand Library Methods.

The detection and identification of low-abundance proteins from plant tissues is still a major challenge. Among the reasons are the low protein content, the presence of few very high-abundance proteins, and the presence of massive amounts of other biochemical compounds. In the last decade numerous technologies have been devised to resolve the situation, in particular with methods based on solid-phase combinatorial peptide ligand libraries. This methodology, allowing for an enhancement of low-abundance proteins, has been extensively applied with the advantage of deciphering the proteome composition of various plant organs. This general methodology is here described extensively along with a number of possible variations. Specific guidelines are suggested to cover peculiar situations or to comply with other associated analytical methods.

A novel approach for protein identification from complex cell proteome using modified peptide mass fingerprinting algorithm.

A unique peptide based search algorithm for identification of protein mixture using PMF is proposed. The proposed search algorithm utilizes binary search and heapsort programs to generate frequency chart depicting the unique peptides corresponding to all proteins in a proteome. The use of binary search program significantly reduces the time for frequency chart preparation to ∼2 s for a proteome comprising ∼23000 proteins. The algorithm was applied to a three-protein mixture identification, host cell protein (HCP) analysis, and a simulation-generated data set. It was found that the algorithm could identify at least one unique peptide of a protein even in the presence of fourfold higher concentration of another protein. In addition, two HCPs that are known to be difficult to remove were missed by MS/MS approach and were exclusively identified using the presented algorithm. Thus, the proposed algorithm when used along with standard proteomic approaches present avenues for enhanced protein identification efficiency, particularly for applications such as HCP analysis in biopharmaceutical research, where identification of low-abundance proteins are generally not achieved due to dynamic range limitations between the target product and HCPs.

Progress in farm animal proteomics: The contribution of combinatorial peptide ligand libraries

The present review covers proteomics discoveries in farm animals using combinatorial peptide ligand libraries (CPLLs). These libraries enhance the identification of low-abundance proteins by compressing the dynamic range of the protein concentration. This technology can be applied in multiple biological fluids, such as plasma or serum, follicular and cerebrospinal fluids, urine, saliva, tears seminal fluid, milk whey and even lymph. The discovery of low-abundance proteins contributes to the continuous monitoring for animal pathologies, which allows early treatment and disease prevention. Additionally, evidencing rare proteins in animal products used for human consumption can help in assessing their nutritional properties and their positive or negative effects on human health. SignificanceMany investigations are made on the identification of proteins of farm animals. Protein concentration changes under abnormal stressful conditions as a sign of a first physiological reaction. Many of these protein changes are of low-abundance and their detection is dependent on enrichment technologies. These changes are potentially correlated with the type of stressor and may determine actions to preserve the animal welfare.

Sample Fractionation Techniques for CSF Peptide Spectral Library Generation.

Data-independent acquisition (DIA) is becoming more prominent as a method for comprehensive proteomic analysis of clinical samples due to its ability to acquire essentially all fragment ion spectra in a single LC-ESI-MS/MS experiment. Since the direct correlation between a precursor and its fragment ions is lost when acquiring all ions in a defined m/z range, one data analysis strategy is using so-called peptide spectral libraries. These are usually generated by measuring similar biological samples in data-dependent (DDA) mode. The peptide spectral library content is a major limitation for the successful identification from DIA data. This is because a fragment ion spectrum from the sample can only be matched, and thus identified, when it is present in the peptide spectral library. In order to enhance peptide spectral library size, the sample for generating the peptide spectral library can be subjected to extended separation strategies prior to DDA. These strategies are of special relevance for biological samples containing a few very high-abundant proteins, such as CSF, as they enlarge the identification of low-abundant proteins. In instances of CSF separation, suitable methods include the 1D SDS-PAGE of proteins and high-pH reversed-phase peptide fractionation. Both methods are based on different protein/peptide characteristics, are complementary with one another, and are inexpensive and easy to establish. Ideally, DDA spectra from samples generated with both methods combine to achieve a comprehensive spectral library.

Redefining Tissue Crosstalk via Shotgun Proteomic Analyses of Plasma Extracellular Vesicles.

Protein signaling between tissues, or tissue cross-talk is becoming recognized as a fundamental biological process that is incompletely understood. Shotgun proteomic analyses of tissues and plasma to explore this concept are regularly challenged by high dynamic range of protein abundance, which limits the identification of lower abundance proteins. In this viewpoint article, it is highlighted how a focus on proteins contained within extracellular vesicles (EVs) not only partially addresses this issue, but can also reveal an underappreciated complexity of the circulating proteome in various physiological and pathological contexts. Furthermore, how quantitative proteomics can inform EV mediated crosstalk is highlighted and the importance of high coverage, sensitive proteomic analyses of EVs to identify both the optimal methods to isolate EV subtypes of interest and proteins that characterize them is stressed.

Probing the Sensitivity of the Orbitrap Lumos Mass Spectrometer Using a Standard Reference Protein in a Complex Background.

The use of mass spectrometry as a tool to detect proteins of biological interest has become a cornerstone of proteomics. The popularity of mass spectrometry-based methods has increased along with instrument improvements in detection and speed. The Orbitrap Fusion Lumos mass spectrometer has recently been shown to have better fragmentation and detection than its predecessors. Here, we determined the sensitivity of the Lumos using the NIST monoclonal antibody reference material at various concentrations to detect its peptides in a background of S. cerevisiae whole cell lysate, which was kept at a constant concentration. The data collected by data-dependent acquisition showed that the spiked protein could be detected at 10 pg by an average of 4 peptides in 250 ng of whole cell lysate when the instrument was operated by detecting the peptide masses in the Orbitrap and the fragment masses in the ion trap (FTIT mode). In contrast, when the peptides and fragments were both detected in the Orbitrap on either the Lumos or Q-Exactive Plus (FTFT mode), the lowest concentration of NIST monoclonal antibody detected was 50 pg. The Lumos can detect a single protein at a level 2500 times lower than the whole cell background and the combination of detecting ions in the Orbitrap and ion trap can improve the identification of low abundance proteins. Furthermore, the total number of proteins identified from decreasing starting amounts of whole cell extracts was determined. The Lumos, when operated in FTIT mode, was able to identify twice as many proteins compared to the Q-Exactive+ at 5 ng of whole cell lysate. Similar numbers of proteins were identified on both platforms at higher concentrations of starting material. Therefore, the Lumos mass spectrometer is especially useful for detecting proteins of low abundance in complex backgrounds or samples that have limited starting material.