Accurate Protein Quantification Research Articles

GwAAP: A genome-wide amino acid coding-decoding quantitative proteomics system.

Mass spectrometry-based proteomic technology has greatly improved and has been widely applied in various biological science fields. However, proteome-wide accurate quantification of proteins in signaling pathways remains challenging. Here, we report a genome-wide amino acid coding-decoding quantitative proteomic (GwAAP) system to facilitate precise proteome quantification. For each protein, a unique code peptide was assigned and incorporated into the N-terminus of the targeted protein and used for identification and quantification. As a proof of principle, we systematically tagged 40 yeast proteins with codes and employed mass spectrometry to decode. We successfully recovered all 40 code peptides with a large and consistent quantitative dynamic range (CV slope <10%, R2>0.8). We further verified the alteration of the glucose and galactose metabolism pathways in yeast under different carbon source conditions. The GwAAP system could potentially provide a strategy to achieve absolute quantification of the entire yeast proteome without bias.

Prediction of Protein Concentration in Pea (Pisum sativum L.) Using Near-Infrared Spectroscopy (NIRS) Systems.

Breeding for increased protein concentration is a priority in field peas. Having a quick, accurate, and non-destructive protein quantification method is critical for screening breeding materials, which the near-infrared spectroscopy (NIRS) system can provide. Partial least square regression (PLSR) models to predict protein concentration were developed and compared for DA7250 and FT9700 NIRS systems. The reference protein data were accurate and exhibited a wider range of variation (15.3-29.8%). Spectral pre-treatments had no clear advantage over analyses based on raw spectral data. Due to the large number of samples used in this study, prediction accuracies remained similar across calibration sizes. The final PLSR models for the DA7250 and FT9700 systems required 10 and 13 latent variables, respectively, and performed well and were comparable (R2 = 0.72, RMSE = 1.22, and bias = 0.003 for DA7250; R2 = 0.79, RMSE = 1.23, and bias = 0.055 for FT9700). Considering three groupings for protein concentration (Low: &lt;20%, Medium: ≥20%, but ≤25%, and High: &gt;25%), none of the entries changed from low to high or vice versa between the observed and predicted values for the DA7250 system. Only a single entry moved from a low category in the observed data to a high category in the predicted data for the FT9700 system in the calibration set. Although the FT9700 system outperformed the DA7250 system by a small margin, both systems had the potential to predict protein concentration in pea seeds for breeding purposes. Wavelengths between 950 nm and 1650 nm accounted for most of the variation in pea protein concentration.

Data-independent acquisition proteomics methods for analyzing post-translational modifications.

Protein post-translational modifications (PTMs) increase the functional diversity of the cellular proteome. Accurate and high throughput identification and quantification of protein PTMs is a key task in proteomics research. Recent advancements in data-independent acquisition (DIA) mass spectrometry (MS) technology have achieved deep coverage and accurate quantification of proteins and PTMs. This review provides an overview of DIA data processing methods that cover three aspects of PTMs analysis, that is, detection of PTMs, site localization, and characterization of complex modification moieties, such as glycosylation. In addition, a survey of deep learning methods that boost DIA-based PTMs analysis is presented, including in silico spectral library generation, as well as feature scoring and error rate control. The limitations and future directions of DIA methods for PTMs analysis are also discussed. Novel data analysis methods will take advantage of advanced MS instrumentation techniques to empower DIA MS for in-depth and accurate PTMs measurements.

Surrogate peptide selection and internal standardization for accurate quantification of endogenous proteins.

Relative quantification techniques have dominated the field of proteomics. However, biomarker discovery, mathematical model development and studies on transporter-mediated drug disposition still need absolute quantification of proteins. The quality of data of trace-level protein quantification is solely dependent on the specific selection of surrogate peptides. Selection of surrogate peptides has a major impact on the accuracy of the method. In this article, the advanced approaches for selection of surrogate peptides, which can provide absolute quantification of the proteinsare discussed. In addition, internal standardization, which accounts for variations in the quantitation processto achieve absolute protein quantification is discussed.

Hydrogel-backed nanopores in proteomic studies

Accurate identification and quantification of proteins in a solution using nanopores is technologically challenging in part because of the large fraction of missed translocation events due to short event times and limitations of conventional current amplifiers. Previously, we have shown that a nanopore interfaced with PEG(1000)-DMA hydrogel significantly enhances protein residence time inside the nanopore, reducing the number of missed events. (Acharya, ACS Sensors 2020) Following up on this work, we also have demonstrated the sensitivity of the hydrogel-backed nanopores to measure unlabeled proteins as small as 5.5 kD in size and 10 fM in concentration.

Absolute quantification of proteins using element mass spectrometry and generic standards

Elemental mass spectrometry is a powerful analytical technique widely established in inorganic analysis. However, despite its quantitative capabilities, it is not yet fully integrated or considered in Life Sciences fields like proteomics. Whereas it is true that ICP-MS has suffered from several instrumental and analytical limitations that have hindered its applicability in protein analysis, significant developments during the last decades have turned ICP-MS into an interesting and, in our opinion, a powerful tool to consider for accurate protein quantification without recourse to specific protein standards. Herein we will try to discuss how these traditional limitations in ICP-MS have been overcome, what further improvements are yet necessary (some of which are shared with MS-based proteomics platforms) and enlighten some of the already existing and potential applications of ICP-MS in absolute quantitative proteomics. SignificanceICP-MS has the potential to become a complementary tool to help molecular mass spectrometry cope with existing limitations, especially those related to standardization and accuracy, in the absolute proteomics field. It can provide absolute quantification of diverse proteoforms using a single generic compound containing sulfur and/or another target element (e.g., phosphorous). Moreover, its applications in quantitative proteomics are no longer limited to protein standards certification or quantification of simple or purified mixtures. Interestingly, absolute quantification of proteins using ICP-MS is favored when carried out at the intact level, making it very compatible with top-down proteomics approaches. Recent instrumental and methodological advances enable synergic combination of ICP-MS with stablished LC-MS proteomics methodologies, setting the basis for its implementation in quantitative proteomics workflows.

A large-scale assay library for targeted protein quantification in renal cell carcinoma tissues.

Renal cell carcinoma (RCC) represents 2.2% of all cancer incidences; however, prognostic or predictive RCC biomarkers at protein level are largely missing. To support proteomics research of localized and metastatic RCC, we introduce a new library of targeted mass spectrometry assays for accurate protein quantification in malignant and normal kidney tissue. Aliquots of 86 initially localized RCC, 75 metastatic RCC and 17 adjacent non-cancerous fresh frozen tissue lysates were trypsin digested, pooled, and fractionated using hydrophilic chromatography. The fractions were analyzed using LC-MS/MS on QExactive HF-X mass spectrometer in data-dependent acquisition (DDA) mode. A resulting spectral library contains 77,817 peptides representing 7960 protein groups (FDR=1%). Further, we confirm applicability of this library on four RCC datasets measured in data-independent acquisition (DIA) mode, demonstrating a specific quantification of a substantially increased part of RCC proteome, depending on LC-MS/MS instrumentation. Impact of sample specificity of the library on the results of targeted DIA data extraction was demonstrated by parallel analyses of two datasets by two pan human libraries. The new RCC specific library has potential to contribute to better understanding the RCC development at molecular level, leading to new diagnostic and therapeutic targets.

Magnetic-Nanowaxberry-Based Simultaneous Detection of Exosome and Exosomal Proteins for the Intelligent Diagnosis of Cancer.

Exosome concentration and exosomal proteins are regarded as promising cancer biomarkers. Herein, a waxberry-like magnetic bead (magnetic-nanowaxberry) which has huge surface area and strong affinity was synthesized to couple with aptamer for exosome capture and recovery. Subsequently, we developed a fluorescent assay for the sensitive, accurate, and simultaneous quantification of exosome and cancer-related exosomal proteins [epidermal growth factor receptor (EGFR) and epithelial cell adhesion molecule (EpCAM)] by using triple-colored probes to recognize EGFR and EpCAM or spontaneously anchor to the lipid bilayer. In this design, the interference of soluble proteins can be avoided due to the dual recognition strategy. Moreover, the lipid-based quantification of exosome concentration can improve the accuracy. Besides, the simultaneous detection mode can save samples and simplify the operation steps. Consequently, the assay shows high sensitivity (the limits of detection are down to 0.96 pg/mL for EGFR, 0.19 pg/mL for EpCAM, and 2.4 × 104 particles/μL for exosome), high specificity, and satisfactory accuracy. More importantly, this technique is successfully used to analyze exosomes in plasma to distinguish cancer patients from healthy individuals. To improve the diagnostic efficacy, the deep learning was used to exploit the potential pattern hidden in data obtained by the proposed method. Also, the accuracy for the intelligent diagnosis of cancer can achieve 96.0%. This study provides a new avenue for developing new biosensors for exosome analysis and intelligent disease diagnosis.

Combining protein and RNA quantification to evaluate promoter activity by using dual-color fluorescent reporting systems.

Herein, Broccoli/mCherry and an EGFP/mCherry dual-color fluorescent reporting systems have been established to quantify the promoter activity at transcription and translation levels in eukaryotic cells. Based on those systems, four commonly used promoters (CMV and SV40 of Pol II and U6, H1 of Pol III) were accurately evaluated at both the transcriptional and translational levels by combining accurate protein and RNA quantification. Furthermore, we verified that Pol III promoters can induce proteins expression, and Pol II promoter can be applied to express RNA molecules with defined length by combining a self-cleaving ribozyme and an artificial poly(A) tail. The dual-color fluorescence reporting systems described here could play a significant role in evaluating other gene expression regulators for gene therapy.

Lattice-like 3-dimensional DNA nanostructure-based bioseparation strategy for highly accurate quantification of low-abundance cancer biomarker via mass spectrometry

Disease-associated low-abundance protein biomarkers have attracted growing attention for the diagnosis and treatment of numerous diseases. Traditional immunoassays are commonly adopted for the detection of low-abundance proteins due to fast and sensitive analyses, but the used antibodies are normally lack of satisfactory selectivity that may bring about biased results. Immunoaffinity mass spectrometry approach can remedy the deficiency of antibodies and has emerged as a powerful technique for highly selective and accurate quantification assay. However, due to extremely large dynamic range of the body fluids, efficient bioseparation of low-abundance proteins is a key issue before mass spectrometry (MS) quantification. In this study, a 3-dimensional DNA tetrahedron (DNA TET) nanomaterial, MoS2@Fe3O4@AuNPs@DNA TET@Ab, was meticulously designed and constructed for the enrichment of cancer biomarker heat shock protein 90 alpha (HSP90α) in human plasma. The nanocomposites have combined advantages of quick magnetic response, a large amount of surface antibodies and excellent stability. Furthermore, through the spatial separation by pendant probes on DNA TETs, antibodies are immobilized with appropriate density and well controlled arrangement. The feasibility of this approach was demonstrated by the pleasing quantitative performance for HSP90α with fine repeatability and a dynamic range from 20 ng/g to 480 ng/g. Compared with the extraction efficiency of conventional nanomaterials with random antibody distribution, the concentrations of HSP90α captured by our materials were at least 2 folds higher, which were more close to ELISA data. Through this ingenious assay, highly efficient enrichment by controlled antibodies arrangement on the nanomaterials and accurate quantification of low-abundance proteins by MS were achieved. This promising strategy can be extended to quantify other low-abundance proteins from the biological samples in clinic.

Quantitative Measurements of Protein Volume and Concentration using Hydrogel-Backed Nanopores.

Accurate identification and quantification of proteins in solution using nanopores is technically challenging in part because of the large fraction of missed translocation events due to short event times and limitations of conventional current amplifiers. Previously, we have shown that a nanopore interfaced with a poly(ethylene glycol)-dimethacrylate hydrogel with an average mesh size of 3.1 nm significantly enhances the protein residence time within the pore, reducing the number of missed events. We used hydrogel-backed nanopores to sense unlabeled proteins as small as 5.5 kDa in size and 10 fM in concentration. We show that the frequency of protein translocation events linearly scales with bulk concentration over a wide range of concentrations and that unknown protein concentrations can be determined from an interpolation of the frequency-concentration curve with less than 10% error. Further, we show an iterative method to determine a protein volume accurately from measurement data for proteins with a diameter comparable to a nanopore diameter. Our measurements and analysis also suggest several competing mechanisms for the detection enhancement enabled by the presence of the hydrogel.

Prediction of α-Lactalbumin and β-Lactoglobulin Composition of Aqueous Whey Solutions Using Fourier Transform Mid-Infrared Spectroscopy and Near-Infrared Spectroscopy.

Characterization and quantification of individual whey proteins are of crucial importance to many industrial dairy processes. Labor intensive wet-chemical methods are still being used for this purpose, but a rapid quantification method for individual whey proteins is highly desired. This work investigate the utility of Fourier transform mid-infrared spectroscopy and Fourier transform near-infrared spectroscopy for rapid quantification of the two main whey proteins (β-lactoglobulin and α-lactalbumin) in complex aqueous whey solutions simulating production process streams. MIR and NIR spectra obtained on whey samples with known and varying amounts of the proteins of interest and are used to develop partial least squares prediction models. Selection of informative wavelength regions allowed for prediction of β-lactoglobulin and α-lactalbumin concentrations with very high precision and accuracy (root mean square error of cross-validation, or RMSECV, of 0.6% and R2 of 0.99 for NIR), demonstrating the potential of being implemented for rapid in-line monitoring of protein composition in industrial whey streams. Two-dimensional MIR-NIR correlation spectroscopy is used to identify the most informative parts of the NIR spectra in relation to protein secondary structure. In addition multivariate curve resolution is applied to the MIR data to resolve mixture spectra and to elucidate the spectral ranges that were most useful in distinguishing between the two whey proteins. The study concludes that NIR spectroscopy has potential for accurate in-line protein quantification and overall secondary protein structure quantification which open new possibilities for in-line industrial applications.

Benchmarking Quantitative Performance in Label-Free Proteomics.

Previous benchmarking studies have demonstrated the importance of instrument acquisition methodology and statistical analysis on quantitative performance in label-free proteomics. However, the effects of these parameters in combination with replicate number and false discovery rate (FDR) corrections are not known. Using a benchmarking standard, we systematically evaluated the combined impact of acquisition methodology, replicate number, statistical approach, and FDR corrections. These analyses reveal a complex interaction between these parameters that greatly impacts the quantitative fidelity of protein- and peptide-level quantification. At a high replicate number (n = 8), both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methodologies yield accurate protein quantification across statistical approaches. However, at a low replicate number (n = 4), only DIA in combination with linear models for microarrays (LIMMA) and reproducibility-optimized test statistic (ROTS) produced a high level of quantitative fidelity. Quantitative accuracy at low replicates is also greatly impacted by FDR corrections, with Benjamini–Hochberg and Storey corrections yielding variable true positive rates for DDA workflows. For peptide quantification, replicate number and acquisition methodology are even more critical. A higher number of replicates in combination with DIA and LIMMA produce high quantitative fidelity, while DDA performs poorly regardless of replicate number or statistical approach. These results underscore the importance of pairing instrument acquisition methodology with the appropriate replicate number and statistical approach for optimal quantification performance.

IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs

Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate superior performance of IonQuant with FDR-controlled MBR compared with MaxQuant (19–38 times faster; 6–18% more proteins quantified and with comparable or better accuracy). We further illustrate the performance of IonQuant and highlight the need for FDR-controlled MBR, in two single-cell proteomics experiments, including one acquired with the help of high-field asymmetric ion mobility spectrometry separation. Fully integrated in the FragPipe computational environment, IonQuant with FDR-controlled MBR enables fast and accurate peptide and protein quantification in label-free proteomics experiments.

Dilemmas With Absolute Quantification of Pharmacologically Relevant Proteins Using Mass Spectrometry

Determination of abundances of proteins involved in uptake, distribution, metabolism and excretion of xenobiotics is a prerequisite to understand and predict elimination mechanisms in tissue. Mass spectrometry promises simple and accurate measurements of individual proteins in complex mixtures using isotopically labeled peptide standards. However, comparisons of measurements performed in different laboratories have shown considerable discrepancies in the data generated. Even when very similar approaches are compared, the results differ significantly. An alternative method of measuring protein titers is global proteomics. Depending on sample type, this allows quantification of hundreds to thousands of proteins in a single analysis. It enables system-wide insights by providing protein copy numbers and cell sizes. Regardless of differences, the workflows of both the labeled standard-based and the proteomic approach share several steps. Each can be critical. Selection of optimal techniques is the prerequisite for accurate and reproducible protein quantification.

Quantitative nascent proteome profiling by dual-pulse labelling with O-propargyl-puromycin and stable isotope-labelled amino acids.

Monitoring translational regulation in response to environmental signals is crucial for understanding cellular proteostasis. However, only limited approaches are currently available for quantifying acute changes in protein synthesis induced by stimuli. Recently, a clickable puromycin analogue, O-propargyl-puromycin (OPP), was developed and applied to label the C-termini of nascent polypeptide chains (NPCs). Following affinity purification via a click reaction, OPP allows for a proteomic analysis of NPCs. Despite its advantage, the affinity purification of NPCs using magnetic beads or resins inherently suffers from significant non-specific protein binding, which hinders accurate quantification of the nascent proteins. To address this issue, we employed dual-pulse labelling of NPCs with both OPP and stable isotope-labelled amino acids to distinguish bona fide NPCs from non-specific proteins, thereby enabling the accurate quantitative profiling of NPCs. We applied this method to dissecting translation responses upon transcriptional inhibition and quantified ∼3,000 nascent proteins. We found that the translation of a subset of ribosomal proteins (e.g. RPSA, RPLP0) as well as signalling proteins (e.g. BCAR3, EFNA1, DUSP1) was significantly repressed by transcription inhibition. Together, the present method provides an accurate and broadly applicable nascent proteome profiling for many biological applications at the level of translation.

Quantification of Insoluble Protein Aggregation in Caenorhabditis elegans during Aging with a Novel Data-Independent Acquisition Workflow.

We and others have shown that the aging process results in a proteome-wide accumulation of insoluble proteins. Knocking down genes encoding the insoluble proteins over 40% of the time results in an extension of the lifespan in C. elegans, suggesting that many of these proteins are key determinants of the aging process. Isolation and quantitative identification of these insoluble proteins are crucial to understand key biological processes that occur during aging. Here, we present a modified and improved protocol that details how to extract and isolate the SDS-insoluble proteins (insolublome) from C. elegans more efficiently to streamline mass spectrometric workflows via a novel label-free quantitative proteomics analysis. This improved protocol utilizes a highly efficient sonicator for worm lysis that greatly increases efficiency for protein extraction and allows us to use significantly less starting material (approximately 3,000 worms) than in previous protocols (typically using at least 40,000 worms). Subsequent quantitative proteomic analysis of the insolublome was performed using data-dependent acquisition (DDA) for protein discovery and identification and data-independent acquisition (DIA) for comprehensive and more accurate protein quantification. Bioinformatic analysis of quantified proteins provides potential candidates that can be easily followed up with other molecular methods in C. elegans. With this workflow, we routinely identify more than 1000 proteins and quantify more than 500 proteins. This new protocol enables efficient compound screening with C. elegans. Here, we validated and applied this improved protocol to wild-type C. elegans N2-Bristol strain and confirmed that aged day-10 N2 worms showed greater accumulation of the insolublome than day-2 young worms.

Developing a Robust Assay to Monitor and Quantify Key Players of Metabolic Pathways during Adipogenesis by Targeted Proteomics.

Targeted data acquisition using nano liquid chromatrography (nano-LC) coupled mass spectrometry is an emerging approach when there is a need to quantify proteins with high accuracy, sensitivity, and reproducibility. Nevertheless, creating assays meeting all those criteria still remains a laborious task, especially when investigating low abundant proteins and small concentration changes. In this work a targeted data acquisition workflow is developed reducing time and effort to target and investigate key players of metabolic pathways during the process of adipocyte differentiation. This leads to accurate and sensitive quantification of proteins involved in the synthesis of fatty acids, glycerolipids, glycerophospholipids, sphingolipids, the production of energy and reduction equivalents. Additionally low abundant signaling molecules part of the peroxisome proliferator-activated receptor gamma (PPARγ) and insulin signaling pathway with ≈400 for the insulin receptor substrate and 1100 copies per cell for PPARγ are determined.

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.

A Cole‐Cole Dielectric Relaxation Analysis of Albumin and γ‐Globulins for Protein Quantification by Electrical Impedance Spectroscopy

AbstractOptimal serum protein concentrations are vital for normal body functioning. Affordable while accurate protein quantification methods with minimum processing requirements are needed for diagnosis of related diseases. The standard automated chemistry analyzer is limited by high installation and maintenance costs. This study proposes the use of electrical impedimetric spectroscopy (EIS) as an alternative to current methods. Its practical applicability was tested using albumin and γ‐globulin or their miscellanea in three different media; water, serum and tissue‐mimicking phantoms at 25 °C. Impedance measurements were taken between frequency f=0.10 MHz to 300 MHz by an impedance analyzer. A Cole‐Cole analysis was used to elucidate the stepwise variations in the dielectric parameters of the protein medium so as to obtain empirical dielectric parameter‐protein concentration relationships and their correlation coefficients R2. From the results, linear relationships between parameters and protein concentrations with high correlation coefficients over R2=0.90 were observed. Resistance to charge transfer Rct and characteristic frequency fc were significantly altered by changing protein concentrations as compared to bulk solution resistance Rs, relaxation time constant τ and shape factor α. The relationships developed would aid in monitoring changes in body fluid protein concentrations by EIS.