Low-Abundance Protein Enrichment for Medical Applications: The Involvement of Combinatorial Peptide Library Technique.

The discovery of low- and very low-abundance proteins in medical applications is considered a key success factor in various important domains. To reach this category of proteins, it is essential to adopt procedures consisting of the selective enrichment of species that are present at extremely low concentrations. In the past few years pathways towards this objective have been proposed. In this review, a general landscape of the enrichment technology situation is made first with the presentation and the use of combinatorial peptide libraries. Then, a description of this peculiar technology for the identification of early-stage biomarkers for well-known pathologies with concrete examples is given. In another field of medical applications, the determination of host cell protein traces potentially present in recombinant therapeutic proteins, such as antibodies, is discussed along with their potentially deleterious effects on the health of patients on the one hand, and on the stability of these biodrugs on the other hand. Various additional applications of medical interest are disclosed for biological fluids investigations where the target proteins are present at very low concentrations (e.g., protein allergens).

Protocol for Increasing the Sensitivity of MS-Based Protein Detection in Human Chorionic Villi.

An important step in the proteomic analysis of missing proteins is the use of a wide range of tissues, optimal extraction, and the processing of protein material in order to ensure the highest sensitivity in downstream protein detection. This work describes a purification protocol for identifying low-abundance proteins in human chorionic villi using the proposed "1DE-gel concentration" method. This involves the removal of SDS in a short electrophoresis run in a stacking gel without protein separation. Following the in-gel digestion of the obtained holistic single protein band, we used the peptide mixture for further LC-MS/MS analysis. Statistically significant results were derived from six datasets, containing three treatments, each from two tissue sources (elective or missed abortions). The 1DE-gel concentration increased the coverage of the chorionic villus proteome. Our approach allowed the identification of 15 low-abundance proteins, of which some had not been previously detected via the mass spectrometry of trophoblasts. In the post hoc data analysis, we found a dubious or uncertain protein (PSG7) encoded on human chromosome 19 according to neXtProt. A proteomic sample preparation workflow with the 1DE-gel concentration can be used as a prospective tool for uncovering the low-abundance part of the human proteome.

Combinatorial peptides: A library that continuously probes low-abundance proteins.

After a decade of experimental applications, it is the objective of this review to make a point on combinatorial peptide ligand libraries dedicated to low-abundance proteins from animals to plants and to microorganism proteomics. It is, thus, at the light of the recent technical developments and applications that we will examine the state of the art, its usage within the scientific community, and its openness to unexplored fields. The improvements of the methodology and its implementation in connection with analytical determinations of combinatorial peptide ligand library (CPLL)-treated samples are extensively reviewed and commented upon. Relevant examples covering few critical aspects describe the performance of the technology. Finally, a reflection on the technological future is attempted in particular by involving new concepts adapted to the limited availability of certain biological samples.

FRACPRED-2D-PRM: A Fraction Prediction Algorithm-Assisted 2D Liquid Chromatography-Based Parallel Reaction Monitoring-Mass Spectrometry Approach for Measuring Low-Abundance Proteins in Human Plasma.

Multidimensional fractionation-based enrichment methods improve the sensitivity of proteomic analysis for low-abundance proteins. However, a major limitation of conventional multidimensional proteomics is the extensive labor and instrument time required for analyzing many fractions obtained from the first dimension separation. Here, a fraction prediction algorithm-assisted 2D LC-based parallel reaction monitoring-mass spectrometry (FRACPRED-2D-PRM) approach for measuring low-abundance proteins in human plasma is presented. Plasma digests are separated by the first dimension high-pH RP-LC with data-dependent acquisition (DDA). The FRACPRED algorithm is then usedto predict the retention times of undetectable target peptides according to those of other abundant plasma peptides during the first dimension separation. Fractions predicted to contain target peptides are analyzed by the second dimension low-pH nano RP-LC PRM. The accuracy and robustness of fraction prediction with the FRACPRED algorithm are demonstrated by measuring two low-abundance proteins, aldolase B and carboxylesterase 1, in human plasma. The FRACPRED-2D-PRM proteomics approach demonstrates markedly improved efficiency and sensitivity over conventional 2D-LC proteomics assays. It is expected that this approach will be widely used in the study of low-abundance proteins in plasma and other complex biological samples.

Insight of low-abundance proteins in rice leaves under Cd stress using combinatorial peptide ligand library technology.

Low-abundance proteins (LAPs) play a very important role in interaction, regulation, and metabolism of plant biological processes. A combinatorial peptide ligand library (CPLL) can solve the problem of high-abundance proteins (HAPs) masking LAPs and enlarging the dynamic range of protein concentrations perfectly and be considered as one of the most advanced approaches for plant proteomics research. In this paper, a proper CPLL method to rice leaf proteins was established for the first time and 1056 proteins were identified in rice leaf extracts, and 624 (59.1%) LAPs were newly detected after CPLL. Based on this technology, we detected the response of rice to Cd stress and analyzed the differential LAPs and the biological significance of misexpressed proteins before and after Cd stress by bioinformatics analysis. An important contribution has also been made to a better understanding of the complex mechanisms by which rice adapts to Cd stress. Graphical abstract.

Multiparameter Optimization of Two Common Proteomics Quantification Methods for Quantifying Low-Abundance Proteins.

Quantitative proteomics has been extensively applied in the screening of differentially regulated proteins in various research areas for decades, but its sensitivity and accuracy have been a bottleneck for many applications. Every step in the proteomics workflow can potentially affect the quantification of low-abundance proteins, but a systematic evaluation of their effects has not been done yet. In this work, to improve the sensitivity and accuracy of label-free quantification and tandem mass tags (TMT) labeling in quantifying low-abundance proteins, multiparameter optimization was carried out using a complex 2-proteome artificial sample mixture for a series of steps from sample preparation to data analysis, including the desalting of peptides, peptide injection amount for LC-MS/MS, MS1 resolution, the length of LC-MS/MS gradient, AGC targets, ion accumulation time, MS2 resolution, precursor coisolation threshold, data analysis software, statistical calculation methods, and protein fold changes, and the best settings for each parameter were defined. The suitable cutoffs for detecting low-abundance proteins with at least 1.5-fold and 2-fold changes were identified for label-free and TMT methods, respectively. The use of optimized parameters will significantly improve the overall performance of quantitative proteomics in quantifying low-abundance proteins and thus promote its application in other research areas.

Concepts and strategies of soybean seed proteomics using the shotgun proteomics approach.

Introduction: The last decade has yielded significant developments in the field of proteomics, especially in mass spectrometry (MS) and data analysis tools. In particular, a shift from gel-based to MS-based proteomics has been observed, thereby providing a platform with which to construct proteome atlases for all life forms. Nevertheless, the analysis of plant proteomes, especially those of samples that contain high-abundance proteins (HAPs), such as soybean seeds, remains challenging. Areas covered: Here, we review recent progress in soybean seed proteomics and highlight advances in HAPs depletion methods and peptide pre-fractionation, identification, and quantification methods. We also suggest a pipeline for future proteomic analysis, in order to increase the dynamic coverage of the soybean seed proteome. Expert opinion: Because HAPs limit the dynamic resolution of the soybean seed proteome, the depletion of HAPs is a prerequisite of high-throughput proteome analysis, and owing to the use of two-dimensional gel electrophoresis-based proteomic approaches, few soybean seed proteins have been identified or characterized. Recent advances in proteomic technologies, which have significantly increased the proteome coverage of other plants, could be used to overcome the current complexity and limitation of soybean seed proteomics.

Which Low-Abundance Proteins are Present in the Human Milieu of Gamete/Embryo Maternal Interaction?

The improvement of the embryo culture media is of high relevance due to its influence on successful implantation rates, pregnancy, neonatal outcomes, and potential effects in adult life. The ideal conditions for embryo development are those naturally occurring in the female reproductive tract, i.e., the oviductal and uterine fluids. To shed light on the differences between chemical and natural media, we performed the first comparative study of the low abundance proteins in plasma, uterine, and oviductal fluid collected, simultaneously, from healthy and fertile women that underwent a salpingectomy. The rationale for this design derives from the fact that high-abundant proteins in these fluids are usually those coming from blood serum and frequently mask the detection of low abundant proteins with a potentially significant role in specific processes related to the embryo-maternal interaction. The proteomic analysis by 1D-nano LC ESI-MSMS detected several proteins in higher amounts in oviductal fluid when compared to uterine and plasma samples (RL3, GSTA1, EZRI, DPYSL3, GARS, HSP90A). Such oviductal fluid proteins could be a target to improve fertilization rates and early embryo development if used in the culture media. In conclusion, this study presents a high-throughput analysis of female reproductive tract fluids and contributes to the knowledge of oviductal and uterine secretome.

Distinct serum proteome profiles associated with collagen-induced arthritis and complete Freund's adjuvant-induced inflammation in CD38⁻/⁻ mice: The discriminative power of protein species or proteoforms.

Collagen-type-II-induced arthritis (CIA) is an autoimmune disease, which involves a complex host systemic response including inflammatory and autoimmune reactions. CIA is milder in CD38(-/-) than in wild-type (WT) mice. ProteoMiner-equalized serum samples were subjected to 2D-DiGE and MS-MALDI-TOF/TOF analyses to identify proteins that changed in their relative abundances in CD38(-/-) versus WT mice either with arthritis (CIA(+) ), with no arthritis (CIA(-) ), or with inflammation (complete Freund's adjuvant (CFA)-treated mice). Multivariate analyses revealed that a multiprotein signature (n = 28) was able to discriminate CIA(+) from CIA(-) mice, and WT from CD38(-/-) mice within each condition. Likewise, a distinct multiprotein signature (n = 16) was identified which differentiated CIA(+) CD38(-/-) mice from CIA(+) WT mice, and lastly, a third multiprotein signature (n = 18) indicated that CD38(-/-) and WT mice could be segregated in response to CFA treatment. Further analyses showed that the discriminative power to distinguish these groups was reached at protein species level and not at the protein level. Hence, the need to identify and quantify proteins at protein species level to better correlate proteome changes with disease processes. It is crucial for plasma proteomics at the low-abundance protein species level to apply the ProteoMiner enrichment. All MS data have been deposited in the ProteomeXchange with identifiers PXD001788, PXD001799 and PXD002071 (http://proteomecentral.proteomexchange.org/dataset/PXD001788, http://proteomecentral.proteomexchange.org/dataset/PXD001799 and http://proteomecentral.proteomexchange.org/dataset/PXD002071).

Absolute quantification of DcR3 and GDF15 from human serum by LC-ESI MS.

Biomarkers are widely used in clinical diagnosis, prognosis and therapy monitoring. Here, we developed a protocol for the efficient and selective enrichment of small and low concentrated biomarkers from human serum, involving a 95% effective depletion of high-abundant serum proteins by partial denaturation and enrichment of low-abundant biomarkers by size exclusion chromatography. The recovery of low-abundance biomarkers was above 97%. Using this protocol, we quantified the tumour markers DcR3 and growth/differentiation factor (GDF)15 from 100 µl human serum by isotope dilution mass spectrometry, using (15) N metabolically labelled and concatamerized fingerprint peptides for the both proteins. Analysis of three different fingerprint peptides for each protein by liquid chromatography electrospray ionization mass spectrometry resulted in comparable concentrations in three healthy human serum samples (DcR3: 27.23 ± 2.49 fmol/ml; GDF15: 98.11 ± 0.49 fmol/ml). In contrast, serum levels were significantly elevated in tumour patients for DcR3 (116.94 ± 57.37 fmol/ml) and GDF15 (164.44 ± 79.31 fmol/ml). Obtained data were in good agreement with ELISA and qPCR measurements, as well as with literature data. In summary, our protocol allows the reliable quantification of biomarkers, shows a higher resolution at low biomarker concentrations than antibody-based strategies, and offers the possibility of multiplexing. Our proof-of-principle studies in patient sera encourage the future analysis of the prognostic value of DcR3 and GDF15 for colon cancer patients in larger patient cohorts.

Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages.

BACKGROUND: Surface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer. RESULTS: Serum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set. SIGNIFICANCE: The experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.

Comprehensive and deep profiling of the plasma proteome with protein corona on zeolite NaY.

Proteomic characterization of plasma is critical for the development of novel pharmacodynamic biomarkers. However, the vast dynamic range renders the profiling of proteomes extremely challenging. Here, we synthesized zeolite NaY and developed a simple and rapid method to achieve comprehensive and deep profiling of the plasma proteome using the plasma protein corona formed on zeolite NaY. Specifically, zeolite NaY and plasma were co-incubated to form plasma protein corona on zeolite NaY (NaY-PPC), followed by conventional protein identification using liquid chromatography-tandem mass spectrometry. NaY was able to significantly enhance the detection of low-abundance plasma proteins, minimizing the "masking" effect caused by high-abundance proteins. The relative abundance of middle- and low-abundance proteins increased substantially from 2.54% to 54.41%, and the top 20 high-abundance proteins decreased from 83.63% to 25.77%. Notably, our method can quantify approximately 4000 plasma proteins with sensitivity up to pg/mL, compared to only about 600 proteins identified from untreated plasma samples. A pilot study based on plasma samples from 30 lung adenocarcinoma patients and 15 healthy subjects demonstrated that our method could successfully distinguish between healthy and disease states. In summary, this work provides an advantageous tool for the exploration of plasma proteomics and its translational applications.

Comparative proteome profiling of susceptible and resistant rice cultivars identified an arginase involved in rice defense against Xanthomonas oryzae pv. oryzae.

Xanthomonas oryzae pv. oryzae (Xoo), the causative agent of bacterial blight, is one of the major threats to rice productivity. Yet, the molecular mechanism of rice-Xoo interaction is elusive. Here, we report comparative proteome profiles of Xoo susceptible (Dongjin) and resistant (Hwayeong) cultivars of rice in response to two-time points (3 and 6 days) of Xoo infection. Low-abundance proteins were enriched using a protamine sulfate (PS) precipitation method and isolated proteins were quantified by a label-free quantitative analysis, leading to the identification of 3846 proteins. Of these, 1128 proteins were significantly changed between mock and Xoo infected plants of Dongjin and Hwayeong cultivars. Based on the abundance pattern and functions of the identified proteins, a total of 23 candidate proteins were shortlisted that potentially participate in plant defense against Xoo in the resistant cultivar. Of these candidate proteins, a mitochondrial arginase-1 showed Hwayeong specific abundance and was significantly accumulated following Xoo inoculation. Overexpression of arginase 1 (OsArg 1) in susceptible rice cultivar (Dongjin) resulted in enhanced tolerance against Xoo as compared to the wild-type. In addition, expression analysis of defense-related genes encoding PR1, glucanase I, and chitinase II by qRT-PCR showed their enhanced expression in the overexpression lines as compared to wild-type. Taken together, our results uncover the proteome changes in the rice cultivars and highlight the functions of OsARG1 in plant defense against Xoo.

Three-dimensional feature matching improves coverage for single-cell proteomics based on ion mobility filtering.

Single-cell proteomics (scProteomics) promises to advance our understanding of cell functions within complex biological systems. However, a major challenge of current methods is their inability to identify and provide accurate quantitative information for low-abundance proteins. Herein, we describe an ion-mobility-enhanced mass spectrometry acquisition and peptide identification method, transferring identification based on FAIMS filtering (TIFF), to improve the sensitivity and accuracy of label-free scProteomics. TIFF extends the ion accumulation times for peptide ions by filtering out singly charged ions. The peptide identities are assigned by a three-dimensional MS1 feature matching approach (retention time, accurate mass, and FAIMS compensation voltage). The TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells, with >1,100 proteins consistently identified. As a demonstration, we applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during lipopolysaccharide stimulation and identified time-dependent proteome changes. A record of this paper's transparent peer review process is included in the supplemental information.

Integrated proteomic sample preparation with combination of on-line high-abundance protein depletion, denaturation, reduction, desalting and digestion to achieve high throughput plasma proteome quantification.

In this study, we developed an integrated plasma proteome sample preparation system, by which high-abundance proteins from human plasma were first depleted by immunoaffinity column, followed by on-line middle and low-abundance proteins denaturation, reduction, desalting and tryptic digestion. To evaluate the performance of such a system, 20 µL plasma was processed automatically, followed by 1-h gradient liquid chromatography-mass spectrometry analysis (LC-MS). Compared to conventional in-solution protocols, not only the sample preparation time could be shortened from 20 h to 20 min, but also the number of identified proteins were greatly increased by 1.4-2.0 times. Such an integrated system allows us to process 36 human plasma samples per day, with more than 300 proteins and 52 FDA approved disease markers per sample being identified. With combination of such an integrated sample preparation system with label-free single-shot LC-MS/MS, the human plasma proteins could be quantified across more than 6 orders of magnitude of abundance range with high reproducibility (Pearson R = 0.99, n = 9). In addition, the relative quantification of human plasma samples from diabetic retinopathy patients and diabetic patients demonstrated the feasibility of our developed workflow for clinic plasma proteome profiling. All these results demonstrated that our developed integrated plasma proteome sample preparation system would provide a new tool for high throughput biomarker discovery.

Improving identification of low abundance and hydrophobic proteins using fluoroalcohol mediated supramolecular biphasic systems with quaternary ammonium salts.

In this study, a newly discovered Supramolecular Biphasic System (S-BPS) was used in bottom-up proteomics of the Saccharomyces cerevisiae strain of yeast. We took advantage of S-BPS in bottom-up proteomics of this strain of yeast as the protein sample, while the results were compared to routinely used solubilizing reagents, such as urea, and sodium dodecyl sulfate (SDS). With the S-BPS, we identified 3043 proteins as compared to 2653 proteins that were identified in the control system. Interestingly, of the additional 390 proteins characterized by the S-BPS, 300 proteins were low abundance (less than 4000 molecules/cell). Remarkably, the identification of proteins at very low abundance (less than 2000 molecule/cell) was improved by 106%. This suggests that the S-BPS is particularly advantageous for detecting low abundance proteins. Gene Ontology (GO) analysis was conducted to find fractionation pattern of proteins in our two-phase system, and in nearly every gene ontology category, the S-BPS provided greater coverage than the control experiment, i.e., coverage for integral membrane proteins and mitochondrial ribosome proteins are improved by 18% and 58%, respectively. The improvements in proteins coverage for low abundance and membrane proteins can be attributed to the strong solubilizing power of the amphiphile-rich phase of this S-BPS and its capability for concomitant extraction, fractionation, and enrichment of the complex proteomics samples. Each phase has selectivity towards specific yeast protein groups, this selectivity is generally based on pI and hydrophobicity of proteins. Therefore, more hydrophobic proteins and acidic proteins exhibit greater affinities for the amphiphile-rich phase due to the hydrophobic effect and electrostatic interactions.

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.

Low-abundance plant protein enrichment with peptide libraries to enlarge proteome coverage and related applications.

In plant tissues proteins are present in low amounts but in a very large number. To this peculiar situation many complex foreign components render protein extraction and purification very difficult. In the last several years interesting technologies have been described to improve the technical situation to the point that some methodologies allow reaching very low-abundance proteins and minor allergens. Among enrichment methods the one documented in this report is based on combinatorial peptide ligand libraries (CPLLs) that emerged in the last decade by contributing to largely improve the knowledge in plant proteomics. It is the aim of this review to describe how this technology allows detecting low-abundance proteins from various plant tissues and to report the dynamics of the proteome components in response to environmental changes and biotic attacks. Typical documented examples with the description of their scientific interest are reported. The described technical approach and selected applications are considered as one of the most advanced approaches for plant proteomics investigations with possibilities not only to enlarge the knowledge of plant proteomes but also to discover novel allergens as well as plant biomarkers subsequent to stressful situations.

Optimized Detection of Protein-Protein and Protein-DNA Interactions, with Particular Application to Plant Telomeres.

Characterization of protein-protein and protein-DNA interactions is critical to understand mechanisms governing the biology of cells. Here we describe optimized methods and their mutual combinations for this purpose: bimolecular fluorescence complementation (BiFC), co-immunoprecipitation (Co-IP), yeast two-hybrid systems (Y2H), and chromatin immunoprecipitation (ChIP). These improved protocols  detect trimeric complexes in which two proteins of interest interact indirectly via a protein sandwiched between them. They also allow isolation of low-abundance chromatin proteins and confirmation that proteins of interest are associated with specific DNA sequences, for example telomeric tracts. Here we describe these methods and their application to map interactions of several telomere- and telomerase-associated proteins and to purify a sufficient amount of chromatin from Arabidopsis thaliana for further investigations (e.g., next-generation sequencing, hybridization).

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.