Dynamic range compression with ProteoMiner™: principles and examples

Isolation of extracellular fluids reveals novel secreted bioactive proteins from muscle and fat tissues

Proteins are secreted from cells to send information to neighboring cells or distant tissues. Because of the highly integrated nature of energy balance systems, there has been particular interest in myokines and adipokines. These are challenging to study through proteomics because serum or plasma contains highly abundant proteins that limit the detection of proteins with lower abundance. We show here that extracellular fluid (EF) from muscle and fat tissues of mice shows a different protein composition than either serum or tissues. Mass spectrometry analyses of EFs from mice with physiological perturbations, like exercise or cold exposure, allowed the quantification of many potentially novel myokines and adipokines. Using this approach, we identify prosaposin as a secreted product of muscle and fat. Prosaposin expression stimulates thermogenic gene expression and induces mitochondrial respiration in primary fat cells. These studies together illustrate the utility of EF isolation as a discovery tool for adipokines and myokines.

DIGE Analysis of ProteoMiner™ Fractionated Serum/Plasma Samples

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

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Advancements in automation for plasma proteomics sample preparation

Automation is necessary to increase sample processing throughput for large-scale clinical analyses. Replacement of manual pipettes with robotic liquid handler systems is especially helpful in processing blood-based samples, such as plasma and serum. These samples are very heterogenous, and protein expression can vary greatly from sample-to-sample, even for healthy controls. Detection of true biological changes requires that variation from sample preparation steps and downstream analytical detection methods, such as mass spectrometry, remains low. In this mini-review, we discuss plasma proteomics protocols and the benefits of automation towards enabling detection of low abundant proteins and providing low sample error and increased sample throughput. This discussion includes considerations for automation of major sample depletion and/or enrichment strategies for plasma toward mass spectrometry detection.

Domont G, C. S. Nogueira F, Marko-Varga G, Sanchez A. Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database

Plasma analysis by mass spectrometry-based proteomics remains a challenge due to its large dynamic range of 10 orders in magnitude. We created a methodology for protein identification known as Wise MS Transfer (WiMT). Melanoma plasma samples from biobank archives were directly analyzed using simple sample preparation. WiMT is based on MS1 features between several MS runs together with custom protein databases for ID generation. This entails a multi-level dynamic protein database with different immunodepletion strategies by applying single-shot proteomics. The highest number of melanoma plasma proteins from undepleted and unfractionated plasma was reported, mapping >1200 proteins from >10,000 protein sequences with confirmed significance scoring. Of these, more than 660 proteins were annotated by WiMT from the resulting ~5800 protein sequences. We could verify 4000 proteins by MS1t analysis from HeLA extracts. The WiMT platform provided an output in which 12 previously well-known candidate markers were identified. We also identified low-abundant proteins with functions related to (i) cell signaling, (ii) immune system regulators, and (iii) proteins regulating folding, sorting, and degradation, as well as (iv) vesicular transport proteins. WiMT holds the potential for use in large-scale screening studies with simple sample preparation, and can lead to the discovery of novel proteins with key melanoma disease functions.

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

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

Plasma/serum proteomics: depletion strategies for reducing high-abundance proteins for biomarker discovery

Plasma and serum are widely used for proteomics-based biomarker discovery. However, analysis of these biofluids is highly challenging due to the complexity and wide dynamic range of their proteomes. Notably, highly abundant proteins tend to obscure the detection of potential biomarkers that are usually of lower concentrations. Among the strategies to resolve this problem are: depletion of high-abundance proteins, enrichment of low abundant proteins of interest and prefractionation. In this review, we focus on current and emerging depletion techniques used to enhance the detection and identification of the less abundant proteins in plasma and serum. We discuss the applications and contributions of these methods to proteomics analysis of plasma and serum alongside their limitations and future perspectives.

Enriching for Low-Abundance Serum Proteins Using ProteoMiner™ and Protein-Level HPLC

The mammalian immune system acts to protect the body from harmful diseases ranging from cancer to infection. Differentially expressed proteins as a result of such an immune response can shed light on the mechanism of disease or serve as biomarkers. These biomarkers can be used in a diagnostic capacity or as correlates of protection following vaccination. Protein levels in the circulatory system are considered representative of the system as a whole, making serum an ideal matrix for surveilling immune responses. However, serum proteomics using mass spectrometry is extremely challenging due to the complexity of the matrix and the dynamic range of protein concentration. This chapter will describe two orthogonal enrichment strategies that can be used sequentially or in isolation to improve the identification of low-abundance serum proteins by mass spectrometry.

Affinity depletion versus relative protein enrichment: a side-by-side comparison of two major strategies for increasing human cerebrospinal fluid proteome coverage

Cerebrospinal fluid (CSF) is in direct contact with the central nervous system. This makes human CSF an attractive source of potential biomarkers for neurologic diseases. Similarly to blood plasma, proteomic analysis of CSF is complicated by a high dynamic range of individual protein concentrations and by the presence of several highly abundant proteins. To deal with the abundant human CSF proteins, methods developed for blood plasma/serum are routinely used. Multiple affinity removal systems and protein enrichment of less abundant proteins using a combinatorial peptide ligand library are among the most frequent approaches. However, their relative impact on CSF proteome coverage has never been evaluated side-by-side in a single study. Therefore, we explored the effect of CSF depletion using MARS 14 cartridge and ProteoMiner ligand library on the number of CSF proteins identified in subsequent LC–MS/MS analysis. LC–MS/MS analysis of crude (non-treated) CSF provided roughly 500 identified proteins. Depletion of CSF by MARS 14 cartridge increased the number of identifications to nearly 800, while treatment of CSF using ProteoMiner enabled identification of 600 proteins. To explore the potential losses of CSF proteins during the depletion process, we also analyzed the “waste” fractions generated by both methods, i.e., proteins retained by the MARS 14 cartridge, and the molecules present in the flow-through fraction from ProteoMiner. More than 250 proteins were bound to MARS 14 cartridge, 100 of those were not identified in the corresponding depleted CSF. Similarly, analysis of the waste fraction in ProteoMiner workflow provided almost 70 unique proteins not found in the CSF depleted by the ligand library. Both depletion strategies significantly increased the number of identified CSF proteins compared to crude CSF. However, MARS 14 depletion provided a markedly higher number of identified proteins (773) compared to ProteoMiner (611). Further, we showed that CSF proteins are lost due to co-depletion (MARS 14) or exclusion (ProteoMiner) during the depletion process. This suggests that the routinely discarded “waste” fractions contain proteins of potential interest and should be included in CSF biomarker studies.

Proteomic Identification of Plasma Biomarkers in Children and Adolescents with Recurrent Hodgkin Lymphoma

The treatment of paediatric Hodgkin lymphoma (HL) has steadily improved over the years, so that 10- years survival exceed 80%. The purpose of this study was to identify prognostic markers for relapsed HL that might contribute to optimize therapeutic approaches. To this aim we retrospectively analysed differential protein expression profiles obtained from plasma of children/adolescents with HL (age ranging from 10 to 18 years) collected at diagnosis. We examined the protein profiles of 15 HL relapsed (R) patients compared with 14 HL not relapsed (NR) patients treated with the same LH-2004 protocol. Two dimensional difference in gel electrophoresis (2D-DIGE) revealed significant differences (fold change > 1.5; Student's T-test p<0.01) between R and NR patients in 10 proteins: α-1-antitrypsin chain a, apolipoprotein A-IV precursor; inter-α-trypsin inhibitor heavy chain; antithrombin-III; vitronectin; fibrinogen α, β and γ chains, complement C3, and ceruloplasmin. An up-regulation of fibrinogen α (spots 78, 196, 230, 234, 239) and β (spots 98, 291, 296, 300) chains together with a lower level of α-1-antitrypsin (spots 255, 264, 266, 272, 273) were found in R patients, and this difference was validated by immunoblotting. The functional role(s) of these proteins in the coagulation and inflammation associated with paediatric/adolescent HL progression and relapse deserves further investigations.

DIGE Analysis of ProteoMinerTM Fractionated Serum/Plasma Samples

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