MaltSci 麦伴科研

Appearance

Popular Reports / proteomics

This report is written by MaltSci based on the latest literature and research findings

How does quantitative proteomics measure protein abundance? ​

Abstract ​

Quantitative proteomics has emerged as a pivotal analytical technique in the biomedical field, enabling researchers to measure protein abundance in various biological samples. This advancement has significant implications for understanding cellular functions, elucidating disease mechanisms, and identifying potential therapeutic targets. Proteins, being the primary executors of cellular functions, are involved in virtually every biological process. Thus, quantifying protein levels provides critical insights into the complex interplay of biological systems, facilitating the identification of biomarkers for diseases, the exploration of drug mechanisms, and the assessment of protein interactions and modifications. The significance of quantitative proteomics is underscored by its rapid evolution over the past few decades, driven largely by technological advancements in mass spectrometry (MS) and bioinformatics. Current methodologies in quantitative proteomics can be broadly categorized into label-free quantification and isotope labeling techniques. Despite the progress made in quantitative proteomics, several challenges remain, particularly in data interpretation and reproducibility. This review delves into the various techniques employed in quantitative proteomics, examining their principles, applications, and associated challenges. The applications of quantitative proteomics in biomarker discovery, drug development, and understanding disease mechanisms are addressed, highlighting the challenges faced in the field. Finally, future directions in quantitative proteomics, including emerging technologies and their potential integration with other omics approaches, are considered, emphasizing the implications for personalized medicine.

Outline ​

This report will discuss the following questions.

  • 1 Introduction
  • 2 Overview of Quantitative Proteomics
    • 2.1 Definition and Importance
    • 2.2 Historical Context and Technological Advancements
  • 3 Techniques for Measuring Protein Abundance
    • 3.1 Label-Free Quantification
    • 3.2 Stable Isotope Labeling Techniques
    • 3.3 Tandem Mass Tags (TMT) and Isobaric Tags for Relative and Absolute Quantitation (iTRAQ)
  • 4 Applications of Quantitative Proteomics
    • 4.1 Biomarker Discovery
    • 4.2 Drug Development and Mechanism of Action Studies
    • 4.3 Disease Mechanisms and Pathway Analysis
  • 5 Challenges and Limitations
    • 5.1 Data Interpretation and Reproducibility
    • 5.2 Technical Limitations and Sample Complexity
    • 5.3 Bioinformatics and Data Management
  • 6 Future Directions in Quantitative Proteomics
    • 6.1 Emerging Technologies
    • 6.2 Integration with Other Omics Approaches
    • 6.3 Potential for Personalized Medicine
  • 7 Summary

1 Introduction ​

Quantitative proteomics has emerged as a pivotal analytical technique in the biomedical field, enabling researchers to measure protein abundance in various biological samples. This advancement has significant implications for understanding cellular functions, elucidating disease mechanisms, and identifying potential therapeutic targets. Proteins, being the primary executors of cellular functions, are involved in virtually every biological process. Thus, quantifying protein levels provides critical insights into the complex interplay of biological systems, facilitating the identification of biomarkers for diseases, the exploration of drug mechanisms, and the assessment of protein interactions and modifications [1][2].

The significance of quantitative proteomics is underscored by its rapid evolution over the past few decades, driven largely by technological advancements in mass spectrometry (MS) and bioinformatics. These innovations have markedly improved the sensitivity, accuracy, and throughput of protein analyses, allowing for the comprehensive profiling of proteomes across diverse biological contexts [1][3]. As a result, quantitative proteomics has become an indispensable tool in various research domains, including cancer biology, neurobiology, and systems biology, among others [4][5].

Current methodologies in quantitative proteomics can be broadly categorized into label-free quantification and isotope labeling techniques. Label-free approaches, such as spectral counting and intensity-based methods, rely on measuring the abundance of proteins based on their mass spectral peak intensities [6][7]. In contrast, isotope labeling techniques, including stable isotope labeling by amino acids in cell culture (SILAC) and tandem mass tags (TMT), provide a more accurate quantification by allowing the comparison of labeled peptides from different samples [8][9]. Each of these techniques presents unique advantages and challenges, particularly concerning sensitivity, accuracy, and the complexity of biological samples [7][10].

Despite the progress made in quantitative proteomics, several challenges remain, particularly in data interpretation and reproducibility. The inherent complexity of proteomic datasets, compounded by variations in sample preparation and analysis methods, poses significant hurdles for researchers [1][7]. Furthermore, the development of robust bioinformatics tools is crucial for effectively managing and interpreting the vast amounts of data generated [10][11].

In this review, we will delve into the various techniques employed in quantitative proteomics, examining their principles, applications, and the challenges associated with each method. We will begin with an overview of quantitative proteomics, discussing its definition, importance, historical context, and technological advancements. Subsequently, we will explore the primary techniques for measuring protein abundance, including label-free quantification, stable isotope labeling, and TMT/iTRAQ methods. The applications of quantitative proteomics in biomarker discovery, drug development, and understanding disease mechanisms will also be addressed. Additionally, we will highlight the challenges and limitations faced in the field, particularly regarding data interpretation and technical complexities. Finally, we will consider future directions in quantitative proteomics, including emerging technologies and their potential integration with other omics approaches, as well as the implications for personalized medicine.

By providing a comprehensive overview of how quantitative proteomics measures protein abundance, this report aims to serve as a valuable resource for researchers and practitioners in the field, fostering a deeper understanding of the complexities of proteomic analysis and its applications in biomedical research.

2 Overview of Quantitative Proteomics ​

2.1 Definition and Importance ​

Quantitative proteomics is a critical area of study that focuses on measuring protein abundance within biological samples. This field employs various methodologies to obtain quantitative data, which is essential for understanding biological processes, disease mechanisms, and the overall functioning of cells.

The measurement of protein abundance in quantitative proteomics can be categorized into several approaches, each with distinct methodologies and advantages. One prevalent technique involves the use of stable isotope labeling, such as (18)O labeling, which serves as a "universal" reference sample. In this method, a pooled (18)O-labeled reference sample is spiked into each unlabeled biological sample. The relative abundances of peptides or proteins are then quantified based on the ratios of (16)O to (18)O isotopic peptide pairs, enabling a comprehensive quantitative analysis across large sample sets. This dual-quantitation approach, which combines labeled and label-free methods, demonstrates improved precision and allows for the detection of subtle abundance changes in complex biological systems [2].

Another method employed in quantitative proteomics is label-free quantitation, which relies on measuring mass spectral peak intensities. For instance, in a study focusing on raw pork meat proteins, proteins were separated via one-dimensional gel electrophoresis, followed by trypsin digestion. The identification and quantification of these proteins were performed using nanoliquid chromatography coupled with a mass spectrometer, demonstrating that relative quantitation can be achieved based on the correlation of peak intensities with protein abundances [6].

In addition to these methods, protein inference plays a significant role in quantification. In mass spectrometry-based shotgun proteomics, researchers often face the challenge of inferring protein identities from raw data before estimating their abundances. By treating the protein inference problem as a special case of protein quantification, where the presence of a protein is indicated by a non-zero abundance value, it is possible to enhance the accuracy of protein quantification [12].

Moreover, advanced statistical approaches have been developed to improve the reliability of protein abundance estimates. For instance, methods that include shared peptides in the quantification process have been shown to significantly enhance the accuracy of abundance estimates. This hierarchical modeling approach considers biological and technical errors, thereby providing a more robust framework for analyzing large datasets [13].

Finally, large-scale quantitative proteomics studies often utilize sophisticated computational tools to manage and analyze the vast amounts of data generated. Techniques such as space-partitioning data structures and graph-theoretic algorithms facilitate the collection of relative protein abundance data across numerous experimental conditions [3].

In summary, quantitative proteomics employs a variety of techniques, including stable isotope labeling, label-free quantitation, and advanced statistical methods, to measure protein abundance accurately. These methodologies are crucial for advancing our understanding of biological systems and have significant implications for fields such as biomarker discovery and therapeutic development.

2.2 Historical Context and Technological Advancements ​

Quantitative proteomics is a critical field that focuses on measuring protein abundance within biological samples, enabling researchers to understand complex biological systems and the roles of proteins in various conditions. This field has evolved significantly over the years, driven by advancements in mass spectrometry and computational techniques.

Historically, quantitative proteomics began with the need to compare protein levels across different biological states, such as healthy versus diseased tissues. Early methodologies primarily relied on two-dimensional gel electrophoresis, which allowed for the separation of proteins based on their isoelectric point and molecular weight. However, this approach had limitations in terms of sensitivity and dynamic range, leading to the exploration of mass spectrometry (MS) as a more robust alternative.

With the advent of mass spectrometry, particularly techniques like liquid chromatography-tandem mass spectrometry (LC-MS/MS), quantitative proteomics gained momentum. These techniques enable the identification and quantification of proteins in complex mixtures by measuring the mass-to-charge ratio of peptide ions. One of the primary strategies employed in quantitative proteomics is the use of stable isotope labeling, which includes methods such as isotope-coded affinity tag (ICAT) and tandem mass tags (TMT). These techniques involve tagging peptides with isotopes that differ in mass, allowing for relative quantification based on the intensity of the detected ions [8].

Label-free quantification methods have also emerged as a significant advancement in the field. These methods measure protein abundance based on the intensity of mass spectral peaks, correlating them with protein concentrations. Recent studies have optimized label-free methodologies to enhance their reliability and speed, making them suitable for large-scale proteomic analyses [6]. For instance, the relative quantitation of proteins extracted from raw pork meat was successfully performed using one-dimensional gel electrophoresis followed by LC-MS, demonstrating the feasibility of label-free approaches [6].

Another crucial aspect of quantitative proteomics is the statistical treatment of the data. Various computational frameworks have been developed to improve the accuracy of protein quantification. For example, the SCAMPI model incorporates shared peptide information to refine protein abundance estimations, addressing the inherent challenges posed by peptide assignment ambiguity [5]. Additionally, methodologies that combine graph-theoretic algorithms with data structures have been introduced to handle large proteomics datasets, allowing for comprehensive protein abundance assessments across numerous experimental conditions [3].

Furthermore, the field has seen the development of targeted proteomics approaches, such as multiple reaction monitoring (MRM), which provide deep and accurate quantification of specific proteins. The in vitro proteome-assisted MRM (iMPAQT) platform exemplifies this trend, enabling genome-wide protein quantification by utilizing a large library of recombinant proteins [9].

In conclusion, quantitative proteomics has evolved from traditional gel-based methods to sophisticated mass spectrometry techniques that incorporate both labeling and label-free strategies. The integration of advanced statistical methods and targeted proteomics has further enhanced the precision and scope of protein abundance measurements, making it a powerful tool for biological research. As the field continues to advance, it promises to yield deeper insights into the dynamics of proteomes across various biological contexts.

3 Techniques for Measuring Protein Abundance ​

3.1 Label-Free Quantification ​

Quantitative proteomics measures protein abundance through various techniques, one of which is label-free quantification (LFQ). LFQ approaches have gained prominence due to their robustness and ease of application across different experimental workflows without the need for additional costs associated with labeling. LFQ methods can be broadly categorized into two main strategies: peak-abundance-based approaches and spectral counting (SpC).

Peak-abundance-based methods, such as MaxLFQ, operate by analyzing the intensity of mass spectrometric signals corresponding to peptide ions. This method requires precursor peak alignment, which can be computationally intensive and may not be applicable to low-resolution data. In contrast, spectral counting methods, like SpC, quantify protein abundance by simply counting the number of peptide identifications linked to a specific protein. This technique does not suffer from the same limitations as peak-abundance methods, making it particularly advantageous for large-scale discovery proteomics[14].

In a study evaluating spectral counts from multidimensional proteomic datasets, researchers demonstrated that these counts exhibit a mean-dispersion relationship that can be modeled using edgeR, allowing for effective determination of differential protein expression[14]. Furthermore, the use of a stable isotope-labeled "universal" reference sample, as described in a 2009 study, allows for quantitative comparisons across multiple biological samples. This approach utilizes (16)O/(18)O isotopic peptide pair abundance ratios to quantify protein abundances, thereby facilitating dual quantitation via both labeling and label-free methods[2].

The effectiveness of LFQ techniques can be further enhanced by employing various strategies for transforming relative abundance measurements into absolute quantification. For instance, a study highlighted the utility of different label-free quantification techniques to estimate absolute protein abundance in yeast, comparing several methods based on spectral counting and extracted-ion chromatograms (XIC). This study found that certain spectral counting methods yielded optimal results in terms of accuracy and reproducibility[7].

Moreover, the scalability of LFQ methods makes them particularly suited for high-throughput applications. For example, advancements in data analysis algorithms enable the processing of large datasets, which is crucial for untargeted proteomics studies. These algorithms can manage extensive data volumes and perform thorough statistical analyses to ensure reliable quantification results[15].

In summary, label-free quantification in proteomics employs various strategies, primarily peak-abundance-based approaches and spectral counting, to measure protein abundance. These methods offer flexibility, scalability, and the ability to accurately quantify proteins across diverse biological samples, making them integral to contemporary proteomic research.

3.2 Stable Isotope Labeling Techniques ​

Quantitative proteomics employs various methodologies to measure protein abundance, with stable isotope labeling techniques being among the most prominent. These techniques utilize stable isotopes to introduce a mass difference between peptides derived from different samples, facilitating accurate quantification during mass spectrometry analysis.

One effective method is the incorporation of stable isotopes, such as (18)O, into a "universal" reference sample. This reference is spiked into each unlabeled biological sample, allowing for the comparison of peptide abundances based on the (16)O/(18)O isotopic ratios. This dual-quantitation approach enables the simultaneous application of label-free quantitation methods, enhancing the precision of abundance measurements. In a study involving plasma samples from severe burn patients, this method successfully identified and quantified 312 plasma proteins with a minimum of two unique peptides per protein, demonstrating significantly better quantitative precision compared to label-free approaches [2].

Another prominent technique is stable isotope dimethyl labeling, which allows for the parallel analysis of multiple samples. This method labels proteolytic peptides at their amino groups using three different isotopomers of formaldehyde, creating a mass difference that facilitates triplex quantification. In a typical shotgun proteomics experiment, over 1300 proteins were identified, with more than 600 proteins quantified using at least four peptides per protein [16].

The application of stable isotope labeling can also be achieved through metabolic incorporation, where isotopes are introduced into proteins during cell culture, known as stable isotope labeling by amino acids in cell culture (SILAC). This method enables precise quantification by allowing direct comparison of protein abundances from different experimental conditions [17].

Moreover, recent advances in stable isotope labeling techniques have improved their applicability and efficiency in quantitative proteomics. For instance, the isotope-coded protein label (ICPL) method provides high-throughput quantitative profiling and is compatible with various separation methods used in proteomics [18].

In summary, stable isotope labeling techniques are central to quantitative proteomics, providing reliable, reproducible, and precise measurements of protein abundance across diverse biological samples. These methodologies continue to evolve, enhancing their effectiveness in addressing complex biological questions and improving our understanding of cellular functions and processes.

3.3 Tandem Mass Tags (TMT) and Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) ​

Quantitative proteomics measures protein abundance primarily through mass spectrometry (MS) techniques that utilize isobaric labeling, specifically Tandem Mass Tags (TMT) and Isobaric Tags for Relative and Absolute Quantitation (iTRAQ). These methods enable the simultaneous quantification of multiple samples by labeling peptides derived from protein digests with isobaric tags, allowing for a comparative analysis of protein abundance across different conditions or treatments.

In the iTRAQ methodology, each peptide in a protein digest is tagged with a unique isobaric label. When these labeled samples are mixed, the tags are indistinguishable in the MS analysis due to their similar mass. Upon fragmentation of the peptides during MS/MS, each tag releases a specific reporter ion whose intensity correlates with the abundance of the corresponding peptide in the original sample. This reporter ion intensity is then used to quantify the relative abundance of the proteins across the different samples analyzed in a single experiment[19].

However, the quantification accuracy of these methods can be affected by various factors, including interference from co-eluting peptides and variability in signal intensities. For instance, studies have shown that the presence of co-selected peptide ions can distort reporter ion ratios, leading to inaccuracies in abundance measurements[20]. To address these challenges, several improvements and methodologies have been proposed. One such approach is the use of high-field asymmetric waveform ion mobility spectrometry (FAIMS), which helps reduce sample complexity and enhances the quantification of proteins by decreasing the number of interfering ions, resulting in a significant increase in quantifiable peptides[20].

Moreover, advanced data analysis techniques have been developed to enhance the reliability of protein quantification. For example, a hierarchical statistical model called WHATraq has been introduced to simultaneously analyze quantitative data at both peptide and protein levels, effectively identifying differentially expressed proteins with greater accuracy compared to traditional methods[21]. This model addresses issues of variance heterogeneity and ratio compression that are common in iTRAQ datasets[22].

Furthermore, studies have indicated that quantification based on the sum of peak intensities of reporter ions may outperform traditional peak area integration methods, providing a more reliable correlation with Western blot quantitation, which is a conventional validation method in proteomics[23].

In summary, quantitative proteomics employs isobaric labeling techniques like TMT and iTRAQ for measuring protein abundance, utilizing mass spectrometry to analyze the intensity of reporter ions released from labeled peptides. Continuous advancements in sample preparation, data analysis, and statistical modeling are essential for improving the accuracy and precision of these quantitative measurements in complex biological samples[24].

4 Applications of Quantitative Proteomics ​

4.1 Biomarker Discovery ​

Quantitative proteomics employs various methodologies to measure protein abundance, facilitating applications such as biomarker discovery. The primary aim is to accurately determine the identity and relative quantities of proteins present in complex biological samples, which is critical for understanding disease mechanisms and identifying potential biomarkers.

Mass spectrometry (MS) is a cornerstone technology in quantitative proteomics, allowing for both relative and absolute quantification of proteins. Early proteomics focused on cataloging proteins, but by 2005, methods evolved to include protein quantitation using "proteotypic" peptides, which serve as surrogates for the parent proteins. This shift enabled the detection of protein expression changes correlated with diseases, often reported as "up-or-down regulation" or "fold-increases" [25].

Two primary strategies for protein quantification are utilized: relative quantitation and absolute quantitation. Relative quantitation methods typically involve non-targeted "shotgun" proteomics, which assesses changes in protein expression across samples. In contrast, absolute quantitation is essential for clinical applications, where protein concentrations must be measured accurately [25].

A notable example of a quantitative proteomics application is the use of liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the clinical quantitation of prostate-specific antigen (PSA). This method enables the quantification of proteins in the low nanogram/milliliter range without requiring immunoenrichment. The study demonstrated that mass spectrometry could quantify PSA in patient sera effectively, showing good correlation with existing enzyme-linked immunosorbent assays (ELISA) [26].

Furthermore, advancements in statistical approaches for protein quantification have emerged. For instance, the SCAMPI framework allows for the computation of protein abundance scores based on quantified peptides, addressing challenges related to peptide assignments and variability in chemical properties. This model enhances the accuracy of protein quantification, particularly in complex samples [5].

Additionally, new algorithms such as "Landmark Matching" and "Peak Matching" have been developed to improve the reproducibility of chromatographic separations and facilitate robust quantification across different experiments. These algorithms help in recognizing identical molecular species across multiple LC-MS experiments, which is crucial for effective biomarker discovery [27].

In summary, quantitative proteomics utilizes mass spectrometry, advanced statistical models, and innovative algorithms to measure protein abundance accurately. These methodologies are vital for the discovery and validation of biomarkers, providing insights into disease mechanisms and aiding in the development of diagnostic and therapeutic strategies.

4.2 Drug Development and Mechanism of Action Studies ​

Quantitative proteomics is a powerful tool used to measure protein abundance, which is critical for understanding various biological processes, including drug development and mechanism of action studies. The measurement of protein abundance is achieved through several sophisticated techniques, primarily involving mass spectrometry and various quantitative methodologies.

One of the fundamental goals of quantitative proteomics is to determine the identity and relative quantity of proteins present in complex samples. This is typically accomplished through mass spectrometry-based methods that allow for the analysis of peptide fragments derived from proteins. A novel approach described by Griffin et al. (2003) utilizes an algorithm for the automated quantification of chromatographically fractionated, isotope-coded affinity-tagged peptides, combined with MALDI quadrupole time-of-flight tandem mass spectrometry for their identification. This method is particularly effective in detecting proteins that show differences in relative abundance across samples[8].

Quantitative proteomics also employs techniques such as liquid chromatography-tandem mass spectrometry (LC-MS/MS), which has become a standard in the field. Ahire et al. (2022) highlight the importance of characterizing drug-metabolizing enzymes and transporters to understand interindividual variability in drug disposition. Their review discusses the principles of quantitative proteomics, including data acquisition approaches and sample preparation techniques, which are essential for ensuring rigor and reproducibility in protein quantification[28].

Furthermore, quantitative proteomics allows for the integration of proteomic data into physiologically based pharmacokinetic modeling. This integration is crucial for predicting drug absorption, distribution, metabolism, and excretion (ADME) and is discussed in the context of model-informed drug development by El-Khateeb et al. (2019). They emphasize that these models utilize physiological parameters, including protein expression levels, to extrapolate in vitro measurements to in vivo consequences, thereby enhancing our understanding of drug action and efficacy[29].

In addition to mass spectrometry techniques, statistical frameworks are also essential for accurate protein quantification. Gerster et al. (2014) present a novel framework that computes protein abundance scores based on quantified peptides, addressing the complexities associated with inferring protein concentrations from peptide data. This approach improves quantitation by including information from shared peptides, which is particularly valuable in organisms with homologous sequences[5].

Quantitative proteomics is not only instrumental in drug development but also plays a significant role in elucidating mechanisms of action. By providing insights into the dynamic behavior of proteins and their interactions within biological systems, quantitative proteomics can reveal how drugs exert their effects at the molecular level. This understanding is vital for optimizing therapeutic strategies and developing personalized medicine approaches.

In summary, quantitative proteomics employs advanced mass spectrometry techniques and robust statistical methodologies to measure protein abundance accurately. Its applications in drug development and mechanism of action studies are vast, enabling researchers to gain critical insights into the pharmacokinetics and pharmacodynamics of therapeutic agents.

4.3 Disease Mechanisms and Pathway Analysis ​

Quantitative proteomics is a critical approach for measuring protein abundance, which provides insights into biological processes, including disease mechanisms and pathway analysis. The methodologies employed in quantitative proteomics can be broadly categorized into label-based and label-free techniques, each with distinct advantages and applications.

Label-based techniques, such as those utilizing stable isotopes, allow for precise quantification of protein abundances. For instance, the use of an (18)O-labeled "universal" reference sample facilitates the quantitative comparison of protein abundances across multiple biological samples. This method involves spiking the labeled reference into each unlabeled sample, enabling the quantification of peptide/protein abundances based on the ratios of (16)O/(18)O isotopic peptide pairs. This dual-quantitation approach combines the strengths of both labeling and label-free methods, enhancing the accuracy and precision of protein quantification, particularly in complex biological systems such as plasma from severe burn patients (Qian et al., 2009) [2].

On the other hand, label-free techniques have gained prominence due to their ability to analyze a larger number of proteins without the need for labeling, which can be time-consuming and expensive. Recent studies have optimized label-free methodologies, such as measuring mass spectral peak intensities for relative quantitation of proteins. For example, in the analysis of raw pork meat proteins, a label-free approach demonstrated reliability and accuracy in quantifying changes in protein abundance, making it a practical choice for food chemistry applications (Gallego et al., 2015) [6].

Furthermore, advancements in computational techniques have enhanced the ability to process and analyze quantitative proteomics data. For example, the introduction of algorithms that utilize space-partitioning data structures allows for the analysis of large proteomics datasets across hundreds of experimental conditions, facilitating the extraction of relative protein abundance data (Khan et al., 2009) [3]. Additionally, statistical models like SCAMPI have been developed to compute protein abundance scores based on quantified peptides, improving the accuracy of protein quantification in complex mixtures (Gerster et al., 2014) [5].

The applications of quantitative proteomics extend to elucidating disease mechanisms and pathway analysis. By quantitatively profiling proteins involved in specific biological pathways, researchers can identify key regulatory proteins and biomarkers associated with diseases. For instance, label-free proteomics has been employed to analyze the core proteomes of murine tissues, allowing for the identification of organ-specific proteomic signatures that can inform our understanding of various diseases (Cutillas & Vanhaesebroeck, 2007) [30].

In summary, quantitative proteomics employs a variety of methodologies to measure protein abundance accurately, with applications that significantly contribute to our understanding of disease mechanisms and biological pathways. The combination of label-based and label-free techniques, along with advancements in computational analysis, enhances the capability of researchers to explore complex biological systems and uncover insights that can lead to novel therapeutic strategies.

5 Challenges and Limitations ​

5.1 Data Interpretation and Reproducibility ​

Quantitative proteomics measures protein abundance through various methodologies, primarily utilizing mass spectrometry (MS) coupled with sophisticated bioinformatics tools. The aim is to provide accurate and reliable quantification of proteins in complex biological samples, which is essential for understanding cellular processes and disease mechanisms.

The primary techniques employed in quantitative proteomics include stable isotope labeling, label-free quantitation, and tandem mass tagging (TMT). Stable isotope labeling involves incorporating isotopically labeled amino acids into proteins, allowing for the differentiation of experimental and control samples during mass spectrometric analysis. Label-free quantitation, on the other hand, measures the intensity of mass spectral peaks, which correlate with protein abundance, without the need for labeling [31]. TMT methods facilitate the simultaneous analysis of multiple samples, enhancing throughput and statistical robustness [32].

Despite advancements, quantitative proteomics faces several challenges and limitations. One significant challenge is the accurate quantification of low-abundance proteins, which can be particularly difficult due to the high dynamic range of protein concentrations in biological samples. For instance, while methodologies like bottom-up proteomics are advancing, they often struggle to reliably quantify lower abundance proteins, which limits the precision of experimental outcomes [32]. Additionally, the inherent variability in mass spectrometry measurements, such as day-to-day fluctuations, complicates quantitative analyses and can lead to inconsistencies [33].

Data interpretation in quantitative proteomics is complex due to the need to infer protein concentrations from peptide data. Mass spectrometry typically quantifies peptides rather than proteins directly, which necessitates statistical models to estimate protein abundance based on the peptide intensities [5]. The challenge lies in accurately attributing peptide signals to their corresponding proteins, especially in cases where multiple proteins share identical peptides. Recent approaches, such as SCAMPI, aim to improve protein quantification by incorporating information from shared peptides, thereby enhancing the reliability of abundance estimates [5].

Reproducibility is another critical aspect of quantitative proteomics. The quality of data analysis tools significantly impacts the reproducibility of results. Variability in sample preparation, instrument calibration, and data processing can introduce errors that affect quantification accuracy [34]. Furthermore, the lack of mature data standards for quantitative proteomics means that different laboratories may adopt varying protocols and analysis strategies, complicating the comparison of results across studies [34]. To address these issues, ongoing research is focused on developing standardized protocols and bioinformatics tools that can handle the increasing complexity and volume of proteomics data [35].

In conclusion, while quantitative proteomics has made significant strides in measuring protein abundance, it continues to face challenges related to the quantification of low-abundance proteins, data interpretation complexities, and reproducibility concerns. The development of robust methodologies and standardization efforts will be crucial in advancing the field and ensuring reliable and comparable results across different studies.

5.2 Technical Limitations and Sample Complexity ​

Quantitative proteomics measures protein abundance through various methodologies, primarily utilizing mass spectrometry (MS) techniques. The process involves the identification and quantification of proteins within complex biological samples, which can be influenced by numerous factors, including sample complexity and the inherent limitations of the analytical techniques employed.

One of the core methodologies in quantitative proteomics is the use of stable isotope labeling, which allows for accurate quantification of protein abundance. However, this technique is often limited by the number of proteins that can be reliably quantified. Label-free quantification techniques have emerged as a prominent alternative, providing a means to assess protein abundance without the constraints of labeling, yet these methods also face challenges in accurately quantifying low-abundance proteins due to their inherent variability in measurements [7].

The complexity of biological samples poses significant challenges in quantitative proteomics. The presence of numerous proteins, each with varying abundances, can complicate the detection and quantification processes. For instance, lower abundance proteins are often difficult to quantify accurately, limiting the precision of experiments. As highlighted in studies, while bottom-up proteomics methods are advancing towards comprehensive coverage of complex proteomes, they still struggle with reliably quantifying lower abundance proteins [32]. This limitation is exacerbated by the requirement for highly sensitive and selective analytical techniques, such as liquid chromatography coupled with mass spectrometry (LC-MS), which are critical for effective sample preparation and analysis [36].

Technical limitations also arise from the data processing and computational challenges associated with mass spectrometry. The vast amount of data generated during proteomic experiments necessitates robust computational approaches to manage and analyze this information effectively. Advanced algorithms are required to handle the data's complexity and to mitigate issues such as false positives, which can arise during protein identification and quantification [37]. Moreover, the development of bioinformatics tools is crucial to enhance the accuracy and reproducibility of quantitative measurements [34].

In summary, while quantitative proteomics has made significant strides in measuring protein abundance, it is hindered by technical limitations related to sample complexity, the variability of protein quantification methods, and the computational demands of data analysis. Continuous advancements in methodologies, including enhanced multiplexing techniques and improved sample preparation workflows, are essential to address these challenges and to improve the robustness and accuracy of quantitative proteomic analyses [38].

5.3 Bioinformatics and Data Management ​

Quantitative proteomics is a critical area of research that aims to measure protein abundance accurately and comprehensively across various biological contexts. The methodologies employed in quantitative proteomics primarily involve mass spectrometry (MS) and various labeling techniques, which are designed to provide insights into the dynamics of protein expression under different experimental conditions.

The measurement of protein abundance typically utilizes techniques such as stable isotope labeling, where isotopes are used to differentiate between control and experimental samples within a single experiment. This approach allows for the relative quantification of proteins by comparing the intensities of the corresponding peptide ions in mass spectrometry. However, quantification is fraught with challenges, particularly regarding the accuracy and reproducibility of measurements. The variability in day-to-day mass spectrometry measurements poses significant obstacles, necessitating the use of stable isotope coding to enhance the reliability of quantitative data [33].

In the context of data management, the computational analysis of proteomics data has become increasingly complex due to the sheer volume of data generated by modern tandem mass spectrometers. The latest instruments can acquire tens of thousands of fragmentation spectra in just a few hours, leading to a significant burden on the computational frameworks that manage this data [37]. To address these challenges, new computational approaches are being developed to analyze large datasets, assess false positives, and improve the reliability of quantitative measurements [37].

One of the primary limitations in quantitative proteomics is the difficulty in quantifying lower abundance proteins, which can lead to incomplete or biased proteomic coverage. Studies have shown that while bottom-up proteomics methods are improving in terms of coverage, they often struggle with reliably quantifying proteins that are present in low abundance [32]. Moreover, different quantification methods, such as tandem mass tagging (TMT) and label-free quantitation (LFQ), have been evaluated for their efficacy in detecting differential protein abundance, with TMT showing higher precision and fewer missing values in measurements [32].

Additionally, the processing of quantitative data is crucial, as the methods used to analyze it can significantly affect the quality of the results. Bioinformatic tools must be capable of handling various issues, such as identifying labeled and unlabeled peptides, constructing representative ion chromatograms, and calculating peptide and protein ratios [34]. Despite advancements, there is still no consensus on the optimal methods for calculating protein abundances, highlighting the ongoing challenges within the field [34].

Overall, while quantitative proteomics provides powerful tools for measuring protein abundance, it is accompanied by substantial challenges related to data accuracy, computational management, and methodological limitations. Continued innovation in both experimental techniques and bioinformatics is essential for overcoming these hurdles and enhancing the reliability of proteomic analyses.

6 Future Directions in Quantitative Proteomics ​

6.1 Emerging Technologies ​

Quantitative proteomics measures protein abundance through various methodologies that have evolved significantly over the years, integrating advanced mass spectrometry techniques and computational approaches. The goal is to accurately determine the identity and relative quantity of proteins present in complex biological samples.

One prevalent method involves the use of stable isotope labeling, such as the incorporation of (18)O-labeled "universal" reference samples. This approach allows for quantitative comparisons across multiple samples by spiking the labeled reference into each unlabeled biological sample. The peptide/protein abundances are quantified based on the (16)O/(18)O isotopic peptide pair abundance ratios, providing a robust means of measuring protein levels. This dual-quantitation method enhances the precision of the results, as demonstrated in studies involving plasma samples from severe burn patients, where 312 proteins were identified and quantified with high confidence[2].

In addition to isotope labeling, label-free quantification techniques have gained prominence due to their ability to analyze thousands of proteins without the need for labeling. Recent studies have optimized label-free methodologies for relative quantitation, particularly in complex biological matrices like raw pork meat, by employing mass spectral peak intensities that correlate with protein abundances[6]. These techniques, however, face challenges in accurately quantifying lower abundance proteins and require further refinement to enhance their utility for absolute quantification[32].

Emerging technologies also focus on improving the scalability and accuracy of quantitative proteomics. For instance, targeted proteomics platforms, such as the in vitro proteome-assisted multiple reaction monitoring (iMPAQT), enable absolute quantification on a genome-wide scale by utilizing large libraries of human recombinant proteins. This method facilitates rapid measurement of predefined protein sets, significantly advancing the field's ability to delineate metabolic landscapes in various biological contexts[9].

Computational advancements play a crucial role in quantitative proteomics as well. Techniques such as spectral counting and extracted-ion chromatogram (XIC) analysis are employed to derive quantitative information from mass spectrometry data. Recent studies have highlighted the importance of including shared peptides in estimating protein abundances, demonstrating that a simultaneous analysis of all quantified peptides leads to more reliable abundance estimates compared to traditional methods[13].

As the field progresses, integrating high-throughput proteomics data with other omics data, such as transcriptomics, is becoming increasingly important for a comprehensive understanding of biological systems. The development of databases like ProteomicsDB facilitates the exploration of large quantitative datasets, allowing researchers to analyze protein expression across various conditions and tissues[10].

In summary, quantitative proteomics employs a combination of isotope labeling, label-free methodologies, targeted proteomics, and advanced computational techniques to measure protein abundance accurately. Future directions will likely focus on enhancing the precision of these methods, increasing the number of quantifiable proteins, and integrating multi-omics approaches to deepen our understanding of cellular functions and dynamics.

6.2 Integration with Other Omics Approaches ​

Quantitative proteomics is a sophisticated field focused on measuring protein abundance across various biological samples. The methods employed for this purpose can be broadly categorized into label-based and label-free techniques.

Label-based approaches, such as those utilizing stable isotopes, involve the incorporation of isotopically labeled amino acids or peptides into proteins. One notable method described by Qian et al. (2009) employs an (18)O-labeled "universal" reference sample. This strategy allows for quantitative comparisons by spiking the labeled reference into unlabeled biological samples, enabling the quantification of peptide/protein abundances based on the isotopic abundance ratios of (16)O/(18)O. This method was demonstrated to be effective in a study involving 18 plasma samples from severe burn patients, where a total of 312 plasma proteins were confidently identified and quantified, showing significantly better quantitative precision compared to label-free approaches [2].

Label-free quantification, on the other hand, has gained traction due to its ability to analyze complex biological samples without the need for labeling. Gallego et al. (2015) optimized a label-free methodology for the relative quantitation of proteins extracted from raw pork meat, employing one-dimensional gel electrophoresis followed by nanoliquid chromatography coupled to mass spectrometry. This approach relied on measuring mass spectral peak intensities, which correlate with protein abundances, showcasing its robustness and reliability [6].

In addition to these methodologies, protein inference and quantification are critical aspects of mass spectrometry-based proteomics. He et al. (2016) discussed the integration of protein inference as a specialized protein quantification problem, proposing that the presence of proteins can be inferred based on their abundance values. Their study illustrated the feasibility of employing quantification methods to effectively resolve protein inference issues [12].

Future directions in quantitative proteomics include the integration of these techniques with other omics approaches, such as genomics and transcriptomics. This integration aims to provide a comprehensive understanding of biological systems by correlating protein abundance with gene expression and metabolic pathways. For instance, the use of advanced computational frameworks and statistical methods can enhance the interpretation of proteomics data in the context of other omics data, allowing for a more holistic view of cellular functions and regulatory mechanisms [4].

Furthermore, advancements in mass spectrometry technology, such as data-independent acquisition (DIA) and targeted proteomics assays, are set to improve the accuracy and throughput of protein quantification. The development of resources like ProteomicsDB facilitates the exploration of large-scale quantitative proteomics data, enabling researchers to integrate and visualize proteomic information alongside transcriptomic and metabolomic datasets [10].

In conclusion, quantitative proteomics employs a variety of sophisticated methods to measure protein abundance, with ongoing developments aimed at enhancing accuracy and integrating these techniques with other omics approaches to enrich our understanding of biological systems.

6.3 Potential for Personalized Medicine ​

Quantitative proteomics measures protein abundance through various advanced methodologies that allow for the identification and quantification of proteins in complex biological samples. The goal is to determine the identity and relative quantity of each protein present in multiple samples, which is essential for understanding disease mechanisms and developing personalized medicine approaches.

One prominent method involves the use of mass spectrometry, which is capable of quantifying the relative abundance of proteins by analyzing the intensity of mass spectral peaks corresponding to peptides. For instance, in a study by Gallego et al. (2015), a label-free quantitative proteomic methodology was optimized for the relative quantitation of proteins extracted from raw pork meat, demonstrating that mass spectral peak intensities correlate well with protein abundances[6]. Additionally, quantitative proteomics can utilize isotope-coded affinity tags and automated quantification algorithms to enhance accuracy and efficiency in protein quantification, as described by Griffin et al. (2003)[8].

Moreover, advancements in computational techniques have significantly improved the analysis of quantitative proteomics data. For example, Khan et al. (2009) introduced algorithmic advances that allow for the collection of relative protein abundance data across hundreds of experimental conditions, facilitating a more comprehensive understanding of protein dynamics in various biological contexts[3].

The potential for personalized medicine is immense, as quantitative proteomics can inform clinical diagnostics, treatment stratification, and drug discovery. It provides insights into the dynamic behavior of proteins, which are critical for understanding disease pathology and therapeutic responses. Correa Rojo et al. (2021) emphasized that quantitative proteomics complements genetic information by offering direct biological insights into physiological patterns, thereby enhancing the stratification of chronic and complex diseases[39].

In summary, quantitative proteomics employs sophisticated techniques such as mass spectrometry and computational algorithms to accurately measure protein abundance, which is vital for advancing personalized medicine by tailoring treatments based on individual proteomic profiles. The integration of quantitative proteomics data with genomic information holds promise for further improving the precision of disease diagnosis and treatment.

7 Conclusion ​

Quantitative proteomics has established itself as an essential tool in the biomedical research landscape, enabling the precise measurement of protein abundance across various biological contexts. The primary findings from this review highlight the diverse methodologies employed in quantitative proteomics, including stable isotope labeling, label-free quantification, and advanced statistical approaches. Each technique presents unique advantages and challenges, particularly concerning sensitivity, accuracy, and data interpretation. The field has made significant strides, particularly with advancements in mass spectrometry and computational analysis, which have enhanced the ability to manage and interpret complex proteomic datasets. However, challenges remain, especially in accurately quantifying low-abundance proteins and ensuring reproducibility across studies. Future research directions should focus on integrating quantitative proteomics with other omics approaches, advancing bioinformatics tools for data management, and exploring the potential for personalized medicine. By addressing these challenges and leveraging emerging technologies, quantitative proteomics can continue to provide valuable insights into cellular functions, disease mechanisms, and therapeutic strategies, ultimately contributing to improved healthcare outcomes.

References ​

  • [1] Matthias Wilm. Quantitative proteomics in biological research.. Proteomics(IF=3.9). 2009. PMID:19743428. DOI: 10.1002/pmic.200900299.
  • [2] Wei-Jun Qian;Tao Liu;Vladislav A Petyuk;Marina A Gritsenko;Brianne O Petritis;Ashoka D Polpitiya;Amit Kaushal;Wenzhong Xiao;Celeste C Finnerty;Marc G Jeschke;Navdeep Jaitly;Matthew E Monroe;Ronald J Moore;Lyle L Moldawer;Ronald W Davis;Ronald G Tompkins;David N Herndon;David G Camp;Richard D Smith; . Large-scale multiplexed quantitative discovery proteomics enabled by the use of an (18)O-labeled "universal" reference sample.. Journal of proteome research(IF=3.6). 2009. PMID:19053531. DOI: 10.1021/pr800467r.
  • [3] Zia Khan;Joshua S Bloom;Benjamin A Garcia;Mona Singh;Leonid Kruglyak. Protein quantification across hundreds of experimental conditions.. Proceedings of the National Academy of Sciences of the United States of America(IF=9.1). 2009. PMID:19717460. DOI: 10.1073/pnas.0904100106.
  • [4] Wolfram Liebermeister;Elad Noor;Avi Flamholz;Dan Davidi;Jörg Bernhardt;Ron Milo. Visual account of protein investment in cellular functions.. Proceedings of the National Academy of Sciences of the United States of America(IF=9.1). 2014. PMID:24889604. DOI: 10.1073/pnas.1314810111.
  • [5] Sarah Gerster;Taejoon Kwon;Christina Ludwig;Mariette Matondo;Christine Vogel;Edward M Marcotte;Ruedi Aebersold;Peter Bühlmann. Statistical approach to protein quantification.. Molecular & cellular proteomics : MCP(IF=5.5). 2014. PMID:24255132. DOI: 10.1074/mcp.M112.025445.
  • [6] Marta Gallego;Leticia Mora;M Concepción Aristoy;Fidel Toldrá. Optimisation of a simple and reliable label-free methodology for the relative quantitation of raw pork meat proteins.. Food chemistry(IF=9.8). 2015. PMID:25842311. DOI: .
  • [7] Aarón Millán-Oropeza;Mélisande Blein-Nicolas;Véronique Monnet;Michel Zivy;Céline Henry. Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance.. Proteomes(IF=3.6). 2022. PMID:35076627. DOI: 10.3390/proteomes10010002.
  • [8] Timothy J Griffin;Chris M Lock;Xiao-jun Li;Alpesh Patel;Iryna Chervetsova;Hookeun Lee;Michael E Wright;Jeffrey A Ranish;Sharon S Chen;Ruedi Aebersold. Abundance ratio-dependent proteomic analysis by mass spectrometry.. Analytical chemistry(IF=6.7). 2003. PMID:12622378. DOI: 10.1021/ac026127j.
  • [9] Masaki Matsumoto;Fumiko Matsuzaki;Kiyotaka Oshikawa;Naoki Goshima;Masatoshi Mori;Yoshifumi Kawamura;Koji Ogawa;Eriko Fukuda;Hirokazu Nakatsumi;Tohru Natsume;Kazuhiko Fukui;Katsuhisa Horimoto;Takeshi Nagashima;Ryo Funayama;Keiko Nakayama;Keiichi I Nakayama. A large-scale targeted proteomics assay resource based on an in vitro human proteome.. Nature methods(IF=32.1). 2017. PMID:28267743. DOI: 10.1038/nmeth.4116.
  • [10] Tobias Schmidt;Patroklos Samaras;Martin Frejno;Siegfried Gessulat;Maximilian Barnert;Harald Kienegger;Helmut Krcmar;Judith Schlegl;Hans-Christian Ehrlich;Stephan Aiche;Bernhard Kuster;Mathias Wilhelm. ProteomicsDB.. Nucleic acids research(IF=13.1). 2018. PMID:29106664. DOI: 10.1093/nar/gkx1029.
  • [11] Gang Peng;Rashaun Wilson;Yishuo Tang;TuKiet T Lam;Angus C Nairn;Kenneth Williams;Hongyu Zhao. ProteomicsBrowser: MS/proteomics data visualization and investigation.. Bioinformatics (Oxford, England)(IF=5.4). 2019. PMID:30462190. DOI: 10.1093/bioinformatics/bty958.
  • [12] Zengyou He;Ting Huang;Xiaoqing Liu;Peijun Zhu;Ben Teng;Shengchun Deng. Protein inference: A protein quantification perspective.. Computational biology and chemistry(IF=3.1). 2016. PMID:26935399. DOI: 10.1016/j.compbiolchem.2016.02.006.
  • [13] Mélisande Blein-Nicolas;Hao Xu;Dominique de Vienne;Christophe Giraud;Sylvie Huet;Michel Zivy. Including shared peptides for estimating protein abundances: a significant improvement for quantitative proteomics.. Proteomics(IF=3.9). 2012. PMID:22833229. DOI: 10.1002/pmic.201100660.
  • [14] Owen E Branson;Michael A Freitas. Tag-Count Analysis of Large-Scale Proteomic Data.. Journal of proteome research(IF=3.6). 2016. PMID:27797532. DOI: 10.1021/acs.jproteome.6b00554.
  • [15] Sven Nahnsen;Chris Bielow;Knut Reinert;Oliver Kohlbacher. Tools for label-free peptide quantification.. Molecular & cellular proteomics : MCP(IF=5.5). 2013. PMID:23250051. DOI: 10.1074/mcp.R112.025163.
  • [16] Paul J Boersema;Thin Thin Aye;Toon A B van Veen;Albert J R Heck;Shabaz Mohammed. Triplex protein quantification based on stable isotope labeling by peptide dimethylation applied to cell and tissue lysates.. Proteomics(IF=3.9). 2008. PMID:18850632. DOI: 10.1002/pmic.200800297.
  • [17] Shao-En Ong;Matthias Mann. Mass spectrometry-based proteomics turns quantitative.. Nature chemical biology(IF=13.7). 2005. PMID:16408053. DOI: 10.1038/nchembio736.
  • [18] Alexander Schmidt;Josef Kellermann;Friedrich Lottspeich. A novel strategy for quantitative proteomics using isotope-coded protein labels.. Proteomics(IF=3.9). 2005. PMID:15602776. DOI: 10.1002/pmic.200400873.
  • [19] Richard D Unwin;John R Griffiths;Anthony D Whetton. Simultaneous analysis of relative protein expression levels across multiple samples using iTRAQ isobaric tags with 2D nano LC-MS/MS.. Nature protocols(IF=16.0). 2010. PMID:21085123. DOI: 10.1038/nprot.2010.123.
  • [20] Sibylle Pfammatter;Eric Bonneil;Pierre Thibault. Improvement of Quantitative Measurements in Multiplex Proteomics Using High-Field Asymmetric Waveform Spectrometry.. Journal of proteome research(IF=3.6). 2016. PMID:27723353. DOI: 10.1021/acs.jproteome.6b00745.
  • [21] Cong Zhou;Michael J Walker;Andrew J K Williamson;Andrew Pierce;Carlo Berzuini;Caroline Dive;Anthony D Whetton. A hierarchical statistical modeling approach to analyze proteomic isobaric tag for relative and absolute quantitation data.. Bioinformatics (Oxford, England)(IF=5.4). 2014. PMID:24344193. DOI: 10.1093/bioinformatics/btt722.
  • [22] Natasha A Karp;Wolfgang Huber;Pawel G Sadowski;Philip D Charles;Svenja V Hester;Kathryn S Lilley. Addressing accuracy and precision issues in iTRAQ quantitation.. Molecular & cellular proteomics : MCP(IF=5.5). 2010. PMID:20382981. DOI: 10.1074/mcp.M900628-MCP200.
  • [23] Bastien Burat;Julien Gonzalez;François-Ludovic Sauvage;Hassan Aouad;Hélène Arnion;Emilie Pinault;Pierre Marquet;Marie Essig. Sum of peak intensities outperforms peak area integration in iTRAQ protein expression measurement by LC-MS/MS using a TripleTOF 5600+ platform.. Bioscience reports(IF=4.7). 2019. PMID:31110078. DOI: 10.1042/BSR20190904.
  • [24] Robert Moulder;Santosh D Bhosale;David R Goodlett;Riitta Lahesmaa. Analysis of the plasma proteome using iTRAQ and TMT-based Isobaric labeling.. Mass spectrometry reviews(IF=6.6). 2018. PMID:29120501. DOI: 10.1002/mas.21550.
  • [25] Carol E Parker;Christoph H Borchers. Mass spectrometry based biomarker discovery, verification, and validation--quality assurance and control of protein biomarker assays.. Molecular oncology(IF=4.5). 2014. PMID:24713096. DOI: .
  • [26] Tanguy Fortin;Arnaud Salvador;Jean Philippe Charrier;Cristof Lenz;Xavier Lacoux;Aymeric Morla;Geneviève Choquet-Kastylevsky;Jérôme Lemoine. Clinical quantitation of prostate-specific antigen biomarker in the low nanogram/milliliter range by conventional bore liquid chromatography-tandem mass spectrometry (multiple reaction monitoring) coupling and correlation with ELISA tests.. Molecular & cellular proteomics : MCP(IF=5.5). 2009. PMID:19068476. DOI: 10.1074/mcp.M800238-MCP200.
  • [27] Jacob D Jaffe;D R Mani;Kyriacos C Leptos;George M Church;Michael A Gillette;Steven A Carr. PEPPeR, a platform for experimental proteomic pattern recognition.. Molecular & cellular proteomics : MCP(IF=5.5). 2006. PMID:16857664. DOI: 10.1074/mcp.M600222-MCP200.
  • [28] Deepak Ahire;Laken Kruger;Sheena Sharma;Vijaya Saradhi Mettu;Abdul Basit;Bhagwat Prasad. Quantitative Proteomics in Translational Absorption, Distribution, Metabolism, and Excretion and Precision Medicine.. Pharmacological reviews(IF=17.3). 2022. PMID:35738681. DOI: 10.1124/pharmrev.121.000449.
  • [29] Eman El-Khateeb;Areti-Maria Vasilogianni;Sarah Alrubia;Zubida M Al-Majdoub;Narciso Couto;Martyn Howard;Jill Barber;Amin Rostami-Hodjegan;Brahim Achour. Quantitative mass spectrometry-based proteomics in the era of model-informed drug development: Applications in translational pharmacology and recommendations for best practice.. Pharmacology & therapeutics(IF=12.5). 2019. PMID:31376433. DOI: 10.1016/j.pharmthera.2019.107397.
  • [30] Pedro R Cutillas;Bart Vanhaesebroeck. Quantitative profile of five murine core proteomes using label-free functional proteomics.. Molecular & cellular proteomics : MCP(IF=5.5). 2007. PMID:17565973. DOI: 10.1074/mcp.M700037-MCP200.
  • [31] Jeffrey C Silva;Richard Denny;Craig A Dorschel;Marc Gorenstein;Ignatius J Kass;Guo-Zhong Li;Therese McKenna;Michael J Nold;Keith Richardson;Phillip Young;Scott Geromanos. Quantitative proteomic analysis by accurate mass retention time pairs.. Analytical chemistry(IF=6.7). 2005. PMID:15801753. DOI: 10.1021/ac048455k.
  • [32] Jeremy D O'Connell;Joao A Paulo;Jonathon J O'Brien;Steven P Gygi. Proteome-Wide Evaluation of Two Common Protein Quantification Methods.. Journal of proteome research(IF=3.6). 2018. PMID:29635916. DOI: 10.1021/acs.jproteome.8b00016.
  • [33] Julian P Whitelegge. Mass spectrometry for high throughput quantitative proteomics in plant research: lessons from thylakoid membranes.. Plant physiology and biochemistry : PPB(IF=5.7). 2004. PMID:15707830. DOI: 10.1016/j.plaphy.2004.11.005.
  • [34] King Wai Lau;Andrew R Jones;Neil Swainston;Jennifer A Siepen;Simon J Hubbard. Capture and analysis of quantitative proteomic data.. Proteomics(IF=3.9). 2007. PMID:17640002. DOI: 10.1002/pmic.200700127.
  • [35] Suruchi Aggarwal;Narayan C Talukdar;Amit K Yadav. Advances in Higher Order Multiplexing Techniques in Proteomics.. Journal of proteome research(IF=3.6). 2019. PMID:31074990. DOI: 10.1021/acs.jproteome.9b00228.
  • [36] Surendra Fartade;Tarang Jadav;Niraj Rajput;Pinaki Sengupta. A simplified optimization approach for sample preparation workflow in LC-MS-based quantitative proteomic analysis: Biological samples to peptides.. Archiv der Pharmazie(IF=3.6). 2025. PMID:40038882. DOI: 10.1002/ardp.202400911.
  • [37] Michael J MacCoss. Computational analysis of shotgun proteomics data.. Current opinion in chemical biology(IF=6.1). 2005. PMID:15701459. DOI: 10.1016/j.cbpa.2004.12.010.
  • [38] Bailey L Bowser;Renã A S Robinson. Enhanced Multiplexing Technology for Proteomics.. Annual review of analytical chemistry (Palo Alto, Calif.)(IF=7.5). 2023. PMID:36854207. DOI: 10.1146/annurev-anchem-091622-092353.
  • [39] Alejandro Correa Rojo;Dries Heylen;Jan Aerts;Olivier Thas;Jef Hooyberghs;Gökhan Ertaylan;Dirk Valkenborg. Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review.. Frontiers in physiology(IF=3.4). 2021. PMID:34512391. DOI: 10.3389/fphys.2021.723510.

MaltSci Intelligent Research Services ​

Search for more papers on MaltSci.com ​

Quantitative Proteomics · Protein Abundance · Biomarkers

© 2025 MaltSci