Mass-spectrometric exploration of proteome structure and function

Recent advances in proteomic-based diagnostics of cystic fibrosis

INTRODUCTION

Cystic fibrosis (CF) is a genetic disease characterized by thick and sticky mucus accumulation, which may harm numerous internal organs. Various variables such as gene modifiers, environmental factors, age of diagnosis, and CF transmembrane conductance regulator (CFTR) gene mutations influence phenotypic disease diversity. Biomarkers that are based on genomic information may not accurately represent the underlying mechanism of the disease as well as its lethal complications. Therefore, recent advancements in mass spectrometry (MS)-based proteomics may provide deep insights into CF mechanisms and cellular functions by examining alterations in the protein expression patterns from various samples of individuals with CF.

AREAS COVERED

We present current developments in MS-based proteomics, its application, and findings in CF. In addition, the future roles of proteomics in finding diagnostic and prognostic novel biomarkers.

EXPERT OPINION

Despite significant advances in MS-based proteomics, extensive research in a large cohort for identifying and validating diagnostic, prognostic, predictive, and therapeutic biomarkers for CF disease is highly needed.

Positional proteomics: is the technology ready to study clinical cohorts?

INTRODUCTION

Positional proteomics provides proteome-wide information on protein termini and their modifications, uniquely enabling unambiguous identification of site-specific, limited proteolysis. Such proteolytic cleavage irreversibly modifies protein sequences resulting in new proteoforms with distinct protease-generated neo-N and C-termini and altered localization and activity. Misregulated proteolysis is implicated in a wide variety of human diseases. Protein termini, therefore, constitute a huge, largely unexplored source of specific analytes that provides a deep view into the functional proteome and a treasure trove for biomarkers.

AREAS COVERED

We briefly review principal approaches to define protein termini and discuss recent advances in method development. We further highlight the potential of positional proteomics to identify and trace specific proteoforms, with a focus on proteolytic processes altered in disease. Lastly, we discuss current challenges and potential for applying positional proteomics in biomarker and pre-clinical research.

EXPERT OPINION

Recent developments in positional proteomics have provided significant advances in sensitivity and throughput. In-depth analysis of proteolytic processes in clinical cohorts thus appears feasible in the near future. We argue that this will provide insights into the functional state of the proteome and offer new opportunities to utilize proteolytic processes altered or targeted in disease as specific diagnostic, prognostic and companion biomarkers.

Accurate Label-Free Quantification by directLFQ to Compare Unlimited Numbers of Proteomes

Recent advances in mass spectrometry-based proteomics enable the acquisition of increasingly large datasets within relatively short times, which exposes bottlenecks in the bioinformatics pipeline. Although peptide identification is already scalable, most label-free quantification (LFQ) algorithms scale quadratic or cubic with the sample numbers, which may even preclude the analysis of large-scale data. Here we introduce directLFQ, a ratio-based approach for sample normalization and the calculation of protein intensities. It estimates quantities via aligning samples and ion traces by shifting them on top of each other in logarithmic space. Importantly, directLFQ scales linearly with the number of samples, allowing analyses of large studies to finish in minutes instead of days or months. We quantify 10,000 proteomes in 10 min and 100,000 proteomes in less than 2 h, a 1000-fold faster than some implementations of the popular LFQ algorithm MaxLFQ. In-depth characterization of directLFQ reveals excellent normalization properties and benchmark results, comparing favorably to MaxLFQ for both data-dependent acquisition and data-independent acquisition. In addition, directLFQ provides normalized peptide intensity estimates for peptide-level comparisons. It is an important part of an overall quantitative proteomic pipeline that also needs to include high sensitive statistical analysis leading to proteoform resolution. Available as an open-source Python package and a graphical user interface with a one-click installer, it can be used in the AlphaPept ecosystem as well as downstream of most common computational proteomics pipelines.

Mapping early serum proteome signatures of liver regeneration in living donor liver transplant cases

The liver is the only solid organ capable of regenerating itself to regain 100% of its mass and function after liver injury and/or partial hepatectomy (PH). This exceptional property represents a therapeutic opportunity for severe liver disease patients. However, liver regeneration (LR) might fail due to poorly understood causes. Here, we have investigated the regulation of liver proteome and phosphoproteome at a short time after PH (9 h), to depict a detailed mechanistic background of the early LR phase. Furthermore, we analyzed the dynamic changes of the serum proteome and metabolome of healthy living donor liver transplant (LDLT) donors at different time points after surgery. The molecular profiles from both analyses were then correlated. Insulin and FXR-FGF15/19 signaling were stimulated in mouse liver after PH, leading to the activation of the main intermediary kinases (AKT and ERK). Besides, inhibition of the hippo pathway led to an increased expression of its target genes and of one of its intermediary proteins (14-3-3 protein), contributing to cell proliferation. In association with these processes, metabolic reprogramming coupled to enhanced mitochondrial activity cope for the energy and biosynthetic requirements of LR. In human serum of LDLT donors, we identified 56 proteins and 13 metabolites statistically differential which recapitulate some of the main cellular processes orchestrating LR in its early phase. These results provide mechanisms and protein mediators of LR that might prove useful for the follow-up of the regenerative process in the liver after PH as well as preventing the occurrence of complications associated with liver resection.

Targeted cross-linker delivery for the in situ mapping of protein conformations and interactions in mitochondria

Current methods for intracellular protein analysis mostly require the separation of specific organelles or changes to the intracellular environment. However, the functions of proteins are determined by their native microenvironment as they usually form complexes with ions, nucleic acids, and other proteins. Here, we show a method for in situ cross-linking and analysis of mitochondrial proteins in living cells. By using the poly(lactic-co-glycolic acid) (PLGA) nanoparticles functionalized with dimethyldioctadecylammonium bromide (DDAB) to deliver protein cross-linkers into mitochondria, we subsequently analyze the cross-linked proteins using mass spectrometry. With this method, we identify a total of 74 pairs of protein-protein interactions that do not exist in the STRING database. Interestingly, our data on mitochondrial respiratory chain proteins ( ~ 94%) are also consistent with the experimental or predicted structural analysis of these proteins. Thus, we provide a promising technology platform for in situ defining protein analysis in cellular organelles under their native microenvironment.

Optimized Suspension Trapping Method for Phosphoproteomics Sample Preparation

A successful mass spectrometry-based phosphoproteomics analysis relies on effective sample preparation strategies. Suspension trapping (S-Trap) is a novel, rapid, and universal method of sample preparation that is increasingly applied in bottom-up proteomics studies. However, the performance of the S-Trap protocol for phosphoproteomics studies is unclear. In the existing S-Trap protocol, the addition of phosphoric acid (PA) and methanol buffer creates a fine protein suspension to capture proteins on a filter and is a critical step for subsequent protein digestion. Herein, we demonstrate that this addition of PA is detrimental to downstream phosphopeptide enrichment, rendering the standard S-Trap protocol suboptimal for phosphoproteomics. In this study, the performance of the S-Trap digestion for proteomics and phosphoproteomics is systematically evaluated in large-scale and small-scale samples. The results of this comparative analysis show that an optimized S-Trap approach, where trifluoroacetic acid is substituted for PA, is a simple and effective method to prepare samples for phosphoproteomics. Our optimized S-Trap protocol is applied to extracellular vesicles to demonstrate superior sample preparation workflow for low-abundance, membrane-rich samples.

Deep Bottom-up Proteomics Enabled by the Integration of Liquid-Phase Ion Trap

In bottom-up proteomics, the complexity of the proteome requires advanced peptide separation and/or fractionation methods to acquire an in-depth understanding of protein profiles. Proposed earlier as a solution-phase ion manipulation device, liquid phase ion traps (LPITs) were used in front of mass spectrometers to accumulate target ions for improved detection sensitivity. In this work, an LPIT-reversed phase liquid chromatography-tandem mass spectrometry (LPIT-RPLC-MS/MS) platform was established for deep bottom-up proteomics. LPIT was used here as a robust and effective method for peptide fractionation, which also shows good reproducibility and sensitivity on both qualitative and quantitative levels. LPIT separates peptides based on their effective charges and hydrodynamic radii, which is orthogonal to that of RPLC. With excellent orthogonality, the integration of LPIT with RPLC-MS/MS could effectively increase the number of peptides and proteins being detected. When HeLa cells were analyzed, peptide and protein coverages were increased by ∼89.2% and 50.3%, respectively. With high efficiency and low cost, this LPIT-based peptide fraction method could potentially be used in routine deep bottom-up proteomics.

Correlated Protein Modules Revealing Functional Coordination of Interacting Proteins Are Detected by Single-Cell Proteomics

Single-cell proteomics has attracted a lot of attention in recent years because it offers more functional relevance than single-cell transcriptomics. However, most work to date has focused on cell typing, which has been widely accomplished by single-cell transcriptomics. Here we report the use of single-cell proteomics to measure the correlation between the translational levels of a pair of proteins in a single mammalian cell. In measuring pairwise correlations among ∼1000 proteins in a population of homogeneous K562 cells under a steady-state condition, we observed multiple correlated protein modules (CPMs), each containing a group of highly positively correlated proteins that are functionally interacting and collectively involved in certain biological functions, such as protein synthesis and oxidative phosphorylation. Some CPMs are shared across different cell types while others are cell-type specific. Widely studied in omics analyses, pairwise correlations are often measured by introducing perturbations into bulk samples. However, some correlations of gene or protein expression under the steady-state condition would be masked by perturbation. The single-cell correlations probed in our experiment reflect intrinsic steady-state fluctuations in the absence of perturbation. We note that observed correlations between proteins are experimentally more distinct and functionally more relevant than those between corresponding mRNAs measured in single-cell transcriptomics. By virtue of single-cell proteomics, functional coordination of proteins is manifested through CPMs.

PhosY-secretome profiling combined with kinase-substrate interaction screening defines active c-Src-driven extracellular signaling

c-Src tyrosine kinase is a renowned key intracellular signaling molecule and a potential target for cancer therapy. Secreted c-Src is a recent observation, but how it contributes to extracellular phosphorylation remains elusive. Using a series of domain deletion mutants, we show that the N-proximal region of c-Src is essential for its secretion. The tissue inhibitor of metalloproteinases 2 (TIMP2) is an extracellular substrate of c-Src. Limited proteolysis-coupled mass spectrometry and mutagenesis studies verify that the Src homology 3 (SH3) domain of c-Src and the P31VHP34 motif of TIMP2 are critical for their interaction. Comparative phosphoproteomic analyses identify an enrichment of PxxP motifs in phosY-containing secretomes from c-Src-expressing cells with cancer-promoting roles. Inhibition of extracellular c-Src using custom SH3-targeting antibodies disrupt kinase-substrate complexes and inhibit cancer cell proliferation. These findings point toward an intricate role for c-Src in generating phosphosecretomes, which will likely influence cell-cell communication, particularly in c-Src-overexpressing cancers.

Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIA^XMBD, for direct analysis of DIA data. Dear-DIA^XMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIA^XMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIA^XMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.

An optogenetic-phosphoproteomic study reveals dynamic Akt1 signaling profiles in endothelial cells

The serine/threonine kinase AKT is a central node in cell signaling. While aberrant AKT activation underlies the development of a variety of human diseases, how different patterns of AKT-dependent phosphorylation dictate downstream signaling and phenotypic outcomes remains largely enigmatic. Herein, we perform a systems-level analysis that integrates methodological advances in optogenetics, mass spectrometry-based phosphoproteomics, and bioinformatics to elucidate how different intensity, duration, and pattern of Akt1 stimulation lead to distinct temporal phosphorylation profiles in vascular endothelial cells. Through the analysis of ~35,000 phosphorylation sites across multiple conditions precisely controlled by light stimulation, we identify a series of signaling circuits activated downstream of Akt1 and interrogate how Akt1 signaling integrates with growth factor signaling in endothelial cells. Furthermore, our results categorize kinase substrates that are preferably activated by oscillating, transient, and sustained Akt1 signals. We validate a list of phosphorylation sites that covaried with Akt1 phosphorylation across experimental conditions as potential Akt1 substrates. Our resulting dataset provides a rich resource for future studies on AKT signaling and dynamics.

Quantitative Proteomic Analysis on the Slightly Acidic Electrolyzed Water Triggered Viable but Non-Culturable Listeria monocytogenes

This study undertakes a comprehensive exploration of the impact of slightly acidic electrolyzed water (SAEW) on Listeria monocytogenes, a common foodborne pathogen, with a particular focus on understanding the molecular mechanisms leading to the viable but nonculturable (VBNC) state. Given the widespread application of SAEW as an effective disinfectant in the food industry, uncovering these molecular pathways is crucial for improving food safety measures. We employed tandem mass tags (TMT), labeling proteomic techniques and LC-MS/MS to identify differentially expressed proteins under two doses of SAEW conditions. We indicated 203 differential expressed proteins (DEPs), including 78 up-regulated and 125 down-regulated DEPs. The functional enrichment analysis of these proteins indicated that ribosomes, biosynthesis of secondary metabolites, and aminoacyl-tRNA biosynthesis were enriched functions affected by SAEW. Further, we delved into the role of protein chlorination, a potential consequence of reactive chlorine species generated during the SAEW production process, by identifying 31 chlorinated peptides from 22 proteins, with a dominant sequence motif of Rxxxxx[cY] and functionally enriched in translation. Our findings suggest that SAEW might prompt alterations in the protein translation process and trigger compensatory ribosome biosynthesis. However, an imbalance in the levels of elongation factors and AARSs could hinder recovery, leading to the VBNC state. This research carries substantial implications for food safety and sanitation, as it adds to our understanding of the SAEW-induced VBNC state in L. monocytogenes and offers potential strategies for its control.

Regional vulnerability of brain white matter in vanishing white matter

Vanishing white matter (VWM) is a leukodystrophy that primarily manifests in young children. In this disease, the brain white matter is differentially affected in a predictable pattern with telencephalic brain areas being most severely affected, while others remain allegedly completely spared. Using high-resolution mass spectrometry-based proteomics, we investigated the proteome patterns of the white matter in the severely affected frontal lobe and normal appearing pons in VWM and control cases to identify molecular bases underlying regional vulnerability. By comparing VWM patients to controls, we identified disease-specific proteome patterns. We showed substantial changes in both the VWM frontal and pons white matter at the protein level. Side-by-side comparison of brain region-specific proteome patterns further revealed regional differences. We found that different cell types were affected in the VWM frontal white matter than in the pons. Gene ontology and pathway analyses identified involvement of region specific biological processes, of which pathways involved in cellular respiratory metabolism were overarching features. In the VWM frontal white matter, proteins involved in glycolysis/gluconeogenesis and metabolism of various amino acids were decreased compared to controls. By contrast, in the VWM pons white matter, we found a decrease in proteins involved in oxidative phosphorylation. Taken together, our data show that brain regions are affected in parallel in VWM, but to different degrees. We found region-specific involvement of different cell types and discovered that cellular respiratory metabolism is likely to be differentially affected across white matter regions in VWM. These region-specific changes help explain regional vulnerability to pathology in VWM.

Toward the analysis of functional proteoforms using mass spectrometry-based stability proteomics

Functional proteomics aims to elucidate biological functions, mechanisms, and pathways of proteins and proteoforms at the molecular level to examine complex cellular systems and disease states. A series of stability proteomics methods have been developed to examine protein functionality by measuring the resistance of a protein to chemical or thermal denaturation or proteolysis. These methods can be applied to measure the thermal stability of thousands of proteins in complex biological samples such as cell lysate, intact cells, tissues, and other biological fluids to measure proteome stability. Stability proteomics methods have been popularly applied to observe stability shifts upon ligand binding for drug target identification. More recently, these methods have been applied to characterize the effect of structural changes in proteins such as those caused by post-translational modifications (PTMs) and mutations, which can affect protein structures or interactions and diversify protein functions. Here, we discussed the current application of a suite of stability proteomics methods, including thermal proteome profiling (TPP), stability of proteomics from rates of oxidation (SPROX), and limited proteolysis (LiP) methods, to observe PTM-induced structural changes on protein stability. We also discuss future perspectives highlighting the integration of top-down mass spectrometry and stability proteomics methods to characterize intact proteoform stability and understand the function of variable protein modifications.

Age-Related Changes in Post-Translational Modifications of Proteins from Whole Male and Female Skeletal Elements

One of the key questions in forensic cases relates to some form of age inference, whether this is how old a crime scene is, when in time a particular crime was committed, or how old the victim was at the time of the crime. These age-related estimations are currently achieved through morphological methods with varying degrees of accuracy. As a result, biomolecular approaches are considered of great interest, with the relative abundances of several protein markers already recognized for their potential forensic significance; however, one of the greatest advantages of proteomic investigations over genomics ones is the wide range of post-translational modifications (PTMs) that make for a complex but highly dynamic resource of information. Here, we explore the abundance of several PTMs including the glycosylation, deamidation, and oxidation of several key proteins (collagen, fetuin A, biglycan, serum albumin, fibronectin and osteopontin) as being of potential value to the development of an age estimation tool worthy of further evaluation in forensic contexts. We find that glycosylations lowered into adulthood but deamidation and oxidation increased in the same age range.

LRRC6 regulates biogenesis of motile cilia by aiding FOXJ1 translocation into the nucleus

BACKGROUND

LRRC6 is an assembly factor for dynein arms in the cytoplasm of motile ciliated cells, and when mutated, dynein arm components remained in the cytoplasm. Here, we demonstrate the role of LRRC6 in the active nuclear translocation of FOXJ1, a master regulator for cilia-associated gene transcription.

METHODS

We generated Lrrc6 knockout (KO) mice, and we investigated the role of LRRC6 on ciliopathy development by using proteomic, transcriptomic, and immunofluorescence analysis. Experiments on mouse basal cell organoids confirmed the biological relevance of our findings.

RESULTS

The absence of LRRC6 in multi-ciliated cells hinders the assembly of ODA and IDA components of cilia; in this study, we showed that the overall expression of proteins related to cilia decreased as well. Expression of cilia-related transcripts, specifically ODA and IDA components, dynein axonemal assembly factors, radial spokes, and central apparatus was lower in Lrrc6 KO mice than in wild-type mice. We demonstrated that FOXJ1 was present in the cytoplasm and translocated into the nucleus when LRRC6 was expressed and that this process was blocked by INI-43, an importin α inhibitor.

CONCLUSIONS

Taken together, these results hinted at the LRRC6 transcriptional regulation of cilia-related genes via the nuclear translocation of FOXJ1. Video Abstract.

Application of functional proteomics in understanding RNA virus-mediated infection

Together with the expansion of genome sequencing research, the number of protein sequences whose function is yet unknown is increasing dramatically. The primary goals of functional proteomics, a developing area of study in the realm of proteomic science, are the elucidation of the biological function of unidentified proteins and the molecular description of cellular systems at the molecular level. RNA viruses have emerged as the cause of several human infectious diseases with large morbidity and fatality rates. The introduction of high-throughput sequencing tools and genetic-based screening approaches over the last few decades has enabled researchers to find previously unknown and perplexing elements of RNA virus replication and pathogenesis on a scale never feasible before. Viruses, on the other hand, frequently disrupt cellular proteostasis, macromolecular complex architecture or stoichiometry, and post-translational changes to take over essential host activities. Because of these consequences, structural and global protein and proteoform monitoring is highly necessiated. Mass spectrometry (MS) has the potential to elucidate key details of virus-host interactions and speed up the identification of antiviral targets, giving precise data on the stoichiometry of cellular and viral protein complexes as well as mechanistic insights, has lately emerged as a key part of the RNA virus biology toolbox as a functional proteomics approach. Affinity-based techniques are primarily employed to identify interacting proteins in stable complexes in living organisms. A protein's biological role is strongly suggested by its relationship with other members of a certain protein complex that is involved in a particular process. With a particular emphasis on the most recent advancements in defining host responses and their translational implications to uncover novel tractable antiviral targets, this chapter provides insight on several functional proteomics techniques in RNA virus biology.

Proteomics of immune cells from liver tumors reveals immunotherapy targets

Elucidating the mechanisms by which immune cells become dysfunctional in tumors is critical to developing next-generation immunotherapies. We profiled proteomes of cancer tissue as well as monocyte/macrophages, CD4⁺ and CD8⁺ T cells, and NK cells isolated from tumors, liver, and blood of 48 patients with hepatocellular carcinoma. We found that tumor macrophages induce the sphingosine-1-phospate-degrading enzyme SGPL1, which dampened their inflammatory phenotype and anti-tumor function in vivo. We further discovered that the signaling scaffold protein AFAP1L2, typically only found in activated NK cells, is also upregulated in chronically stimulated CD8⁺ T cells in tumors. Ablation of AFAP1L2 in CD8⁺ T cells increased their viability upon repeated stimulation and enhanced their anti-tumor activity synergistically with PD-L1 blockade in mouse models. Our data reveal new targets for immunotherapy and provide a resource on immune cell proteomes in liver cancer.

Development of an in situ cell-type specific proteome analysis method using antibody-mediated biotinylation

Since proteins are essential molecules exerting cellular functions, decoding proteome changes is the key to understanding the normal physiology and pathogenesis mechanism of various diseases. However, conventional proteomic studies are often conducted on tissue lumps, in which multiple cell types are entangled, presenting challenges in interpreting the biological dynamics among diverse cell types. While recent cell-specific proteome analysis techniques, like BONCAT, TurboID, and APEX, have emerged, their necessity for genetic modifications limits their usage. The alternative, laser capture microdissection (LCM), although it does not require genetic alterations, is labor-intensive, time-consuming, and requires specialized expertise, making it less suitable for large-scale studies. In this study, we develop the method for in situ cell-type specific proteome analysis using antibody-mediated biotinylation (iCAB), in which we combined immunohistochemistry (IHC) with the biotin-tyramide signal amplification approach. Poly-horseradish peroxidase (HRP) conjugated to the secondary antibody will be localized at a target cell type via a primary antibody specific to the target cell type and biotin-tyramide activated by HRP will biotinylate the nearby proteins. Therefore, the iCAB method can be applied to any tissues that can be used for IHC. As a proof-of-concept, we employed iCAB for mouse brain tissue enriching proteins for neuronal cell bodies, astrocytes, and microglia, followed by identifying the enriched proteins using 16-plex TMT-based proteomics. In total, we identified ~8,400 and ~6,200 proteins from enriched and non-enriched samples. Most proteins from the enriched samples showed differential expressions when we compared different cell type data, while there were no differentially expressed proteins from non-enriched samples. The cell type enrichment analysis with the increased proteins in respective cell types using Azimuth showed that neuronal cell bodies, astrocytes, and microglia data exhibited Glutamatergic Neuron, Astrocyte and Microglia/Perivascular Macrophage as the representative cell types, respectively. The proteome data of the enriched proteins showed similar subcellular distribution as non-enriched proteins, indicating that the iCAB-proteome is not biased toward any subcellular compartment. To our best knowledge, this study represents the first implementation of a cell-type-specific proteome analysis method using an antibody-mediated biotinylation approach. This development paves the way for the routine and widespread use of cell-type-specific proteome analysis. Ultimately, this could accelerate our understanding of biological and pathological phenomena.

Structure Relaxation Approximation (SRA) for Elucidation of Protein Structures from Ion Mobility Measurements (II). Protein Complexes

Characterizing structures of protein complexes and their disease-related aberrations is essential to understanding molecular mechanisms of many biological processes. Electrospray ionization coupled with hybrid ion mobility/mass spectrometry (ESI-IM/MS) methods offer sufficient sensitivity, sample throughput, and dynamic range to enable systematic structural characterization of proteomes. However, because ESI-IM/MS characterizes ionized protein systems in the gas phase, it generally remains unclear to what extent the protein ions characterized by IM/MS have retained their solution structures. Here, we discuss the first application of our computational structure relaxation approximation [Bleiholder, C.; et al. J. Phys. Chem. B 2019, 123 (13), 2756-2769] to assign structures of protein complexes in the range from ∼16 to ∼60 kDa from their "native" IM/MS spectra. Our analysis shows that the computed IM/MS spectra agree with the experimental spectra within the errors of the methods. The structure relaxation approximation (SRA) indicates that native backbone contacts appear largely retained in the absence of solvent for the investigated protein complexes and charge states. Native contacts between polypeptide chains of the protein complex appear to be retained to a comparable extent as contacts within a folded polypeptide chain. Our computations also indicate that the hallmark "compaction" often observed for protein systems in native IM/MS measurements appears to be a poor indicator of the extent to which native residue-residue interactions are lost in the absence of solvent. Further, the SRA indicates that structural reorganization of the protein systems in IM/MS measurements appears driven largely by remodeling of the protein surface that increases its hydrophobic content by approximately 10%. For the systems studied here, this remodeling of the protein surface appears to occur mainly by structural reorganization of surface-associated hydrophilic amino acid residues not associated with β-strand secondary structure elements. Properties related to the internal protein structure, as assessed by void volume or packing density, appear unaffected by remodeling of the surface. Taken together, the structural reorganization of the protein surface appears to be generic in nature and to sufficiently stabilize protein structures to render them metastable on the time scale of IM/MS measurements.

Improved Cross-Linking Coverage for Protein Complexes Containing Low Levels of Lysine by Using an Enrichable Photo-Cross-Linker

Chemical cross-linking coupled with mass spectrometry (XL-MS) is an important technique for the structural analysis of protein complexes where the coverage of amino acids and the identification of cross-linked sites are crucial. Photo-cross-linking has multisite reactivity and is valuable for the structural analysis of chemical cross-linking. However, a high degree of heterogeneity results from this multisite reactivity, which results in samples with higher complexity and lower abundance. Additionally, the applicability of photo-cross-linking is limited to purified protein complexes. In this work, we demonstrate a photo-cross-linker, alkynyl-succinimidyl-diazirine (ASD) with the reactive groups of N-hydroxysuccinimide ester and diazirine, as well as the click-enrichable alkyne group. Photo-cross-linkers can provide higher site reactivity for proteins that contain only a small number of lysine residues, thereby complementing the more commonly used lysine-targeting cross-linkers. By systematically analyzing proteins with differing lysine contents and differing flexibilities, we demonstrated clear enhancement in structure elucidation for proteins containing less lysine and with high flexibility. In addition, enrichment approaches of alkynyl-azide click chemistry conjugated with biotin-streptavidin purification (coinciding with parallel orthogonal digestion) improved the identification coverage of cross-links. We show that this photo-cross-linking approach can be used for membrane proteome-wide complex analysis. This method led to the identification of a total of 14066 lysine-X cross-linked site pairs from a total of 2784 proteins. Thus, this cross-linker is a valuable addition to a photo-cross-linking toolkit and improves the identification coverage of XL-MS in functional structure analysis.

Network analysis of plasma proteomes in affective disorders

The conventional differentiation of affective disorders into major depressive disorder (MDD) and bipolar disorder (BD) has insufficient biological evidence. Utilizing multiple proteins quantified in plasma may provide critical insight into these limitations. In this study, the plasma proteomes of 299 patients with MDD or BD (aged 19-65 years old) were quantified using multiple reaction monitoring. Based on 420 protein expression levels, a weighted correlation network analysis was performed. Significant clinical traits with protein modules were determined using correlation analysis. Top hub proteins were determined using intermodular connectivity, and significant functional pathways were identified. Weighted correlation network analysis revealed six protein modules. The eigenprotein of a protein module with 68 proteins, including complement components as hub proteins, was associated with the total Childhood Trauma Questionnaire score (r = -0.15, p = 0.009). Another eigenprotein of a protein module of 100 proteins, including apolipoproteins as hub proteins, was associated with the overeating item of the Symptom Checklist-90-Revised (r = 0.16, p = 0.006). Functional analysis revealed immune responses and lipid metabolism as significant pathways for each module, respectively. No significant protein module was associated with the differentiation between MDD and BD. In conclusion, childhood trauma and overeating symptoms were significantly associated with plasma protein networks and should be considered important endophenotypes in affective disorders.

Integrated proteomics and phosphoproteomics profiling reveals the cardioprotective mechanism of bioactive compounds derived from Salvia miltiorrhiza Burge

BACKGROUND

Natural products are an important source for discovering novel drugs due to their various pharmacological activities. Salvia miltiorrhiza Burge (Danshen) has been shown to have promising therapeutic potential in the management of heart diseases, making it a candidate for cardiovascular drug discovery. Currently, there is limited quantitative analysis of the phosphorylation levels of Danshen-derived natural products on a proteome-wide, which may bias the study of their mechanisms of action.

PURPOSE

This study aimed to evaluate the global signaling perturbation induced by Danshen-derived bioactive compounds and their potential relationship with myocardial ischemia/reperfusion (IR) injury therapy.

STUDY DESIGN

We employed quantitative proteome and phosphoproteome analysis to identify dysregulated signaling in IR injury hearts from mice. We compared changes induced by Danshen-derived compounds based on IR-associated phospho-events, using an integrative approach that maps relative abundance of proteins and phosphorylation sites.

METHODS

Isobaric chemical tandem mass tags (TMT) labeled multiplexing strategy was used to generate unbiased quantitative proteomics and phosphoproteomics data. Highly accurate and precise TMT quantitation was performed using the Orbitrap Fusion Tribrid Mass Spectrometer with synchronous precursor selection MS3 detection mode. Mass spectrometric raw files were analyzed with MaxQuant (2.0.1.0) and statistical and bioinformatics analysis was conducted with Perseus (1.6.15).

RESULTS

We quantified 3661 proteins and over 11,000 phosphosites in impaired heart tissue of the IR mice model, expanding our knowledge of signaling pathways and other biological processes disrupted in IR injury. Next, 1548 and 5545 differently expressed proteins and phosphosites were identified by quantifying the proteome and phosphoproteome of H9c2 cells treated by five Danshen bioactive compounds respectively. Results revealed the vast differences in abilities of five Danshen-derived bioactive compounds to regulate phosphorylation modifications in cardiomyocytes, with dihydrotanshinone I (DHT) showing potential for protecting against IR injury by modulating the AMPK/mTOR signaling pathway.

CONCLUSIONS

This study provides a new strategy for analyzing drug/natural product-regulated phosphorylation modification levels on a proteome-wide scale, leading to a better understanding of cell signaling pathways and downstream phenotypic responses.

Global quantification of newly synthesized proteins reveals cell type- and inhibitor-specific effects on protein synthesis inhibition

Manipulation of protein synthesis is commonly applied to uncover protein functions and cellular activities. Multiple inhibitors with distinct mechanisms have been widely investigated and employed in bio-related research, but it is extraordinarily challenging to measure and evaluate the synthesis inhibition efficiencies of individual proteins by different inhibitors at the proteome level. Newly synthesized proteins are the immediate and direct products of protein synthesis, and thus their comprehensive quantification provides a unique opportunity to study protein inhibition. Here, we systematically investigate protein inhibition and evaluate different popular inhibitors, i.e. cycloheximide, puromycin, and anisomycin, through global quantification of newly synthesized proteins in several types of human cells (A549, MCF-7, Jurkat, and THP-1 cells). The inhibition efficiencies of protein synthesis are comprehensively measured by integrating azidohomoalanine-based protein labeling, selective enrichment, a boosting approach, and multiplexed proteomics. The same inhibitor results in dramatic variation of the synthesis inhibition efficiencies for different proteins in the same cells, and each inhibitor exhibits unique preferences. Besides cell type- and inhibitor-specific effects, some universal rules are unraveled. For instance, nucleolar and ribosomal proteins have relatively higher inhibition efficiencies in every type of cells treated with each inhibitor. Moreover, proteins intrinsically resistant or sensitive to the inhibition are identified and found to have distinct functions. Systematic investigation of protein synthesis inhibition in several types of human cells by different inhibitors provides valuable information about the inhibition of protein synthesis, advancing our understanding of inhibiting protein synthesis.

Mitochondrial dysfunction rapidly modulates the abundance and thermal stability of cellular proteins

Cellular functionality relies on a well-balanced, but highly dynamic proteome. Dysfunction of mitochondrial protein import leads to the cytosolic accumulation of mitochondrial precursor proteins which compromise cellular proteostasis and trigger a mitoprotein-induced stress response. To dissect the effects of mitochondrial dysfunction on the cellular proteome as a whole, we developed pre-post thermal proteome profiling. This multiplexed time-resolved proteome-wide thermal stability profiling approach with isobaric peptide tags in combination with a pulsed SILAC labelling elucidated dynamic proteostasis changes in several dimensions: In addition to adaptations in protein abundance, we observed rapid modulations of the thermal stability of individual cellular proteins. Different functional groups of proteins showed characteristic response patterns and reacted with group-specific kinetics, allowing the identification of functional modules that are relevant for mitoprotein-induced stress. Thus, our new pre-post thermal proteome profiling approach uncovered a complex response network that orchestrates proteome homeostasis in eukaryotic cells by time-controlled adaptations of the abundance and the conformation of proteins.

LFQ-Based Peptide and Protein Intensity Differential Expression Analysis

Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings.

Comprehensive Profiling of Rapamycin Interacting Proteins with Multiple Mass Spectrometry-Based Omics Techniques

Profiling drug-protein interactions is critical for understanding a drug's mechanism of action and predicting the possible adverse side effects. However, to comprehensively profile drug-protein interactions remains a challenge. To address this issue, we proposed a strategy that integrates multiple mass spectrometry-based omics analysis to provided global drug-protein interactions, including physical interactions and functional interactions, with rapamycin (Rap) as a model. Chemoproteomics profiling reveals 47 Rap binding proteins including the known target protein FKBP12 with high confidence. Gen Ontology enrichment analysis suggested that the Rap binding proteins are implicated in several important cellular processes, such as DNA replication, immunity, autophagy, programmed cell death, aging, transcription modulation, vesicle-mediated transport, membrane organization, and carbohydrate and nucleobase metabolic processes. The phosphoproteomics profiling revealed 255 down-regulated and 150 up-regulated phosphoproteins responding to Rap stimulation; they mainly involve the PI3K-Akt-mTORC1 signaling axis. Untargeted metabolomic profiling revealed 22 down-regulated metabolites and 75 up-regulated metabolites responding to Rap stimulation; they are mainly associated with the synthesis processes of pyrimidine and purine. The integrative multiomics data analysis provides deep insight into the drug-protein interactions and reveals Rap's complicated mechanism of action.

MoS2 nanopore identifies single amino acids with sub-1 Dalton resolution

The sequencing of single protein molecules using nanopores is faced with a huge challenge due to the lack of resolution needed to resolve single amino acids. Here we report the direct experimental identification of single amino acids in nanopores. With atomically engineered regions of sensitivity comparable to the size of single amino acids, MoS₂ nanopores provide a sub-1 Dalton resolution for discriminating the chemical group difference of single amino acids, including recognizing the amino acid isomers. This ultra-confined nanopore system is further used to detect the phosphorylation of individual amino acids, demonstrating its capability for reading post-translational modifications. Our study suggests that a sub-nanometer engineered pore has the potential to be applied in future chemical recognition and de novo protein sequencing at the single-molecule level.

Orchestration of Mitochondrial Function and Remodeling by Post-Translational Modifications Provide Insight into Mechanisms of Viral Infection

The regulation of mitochondria structure and function is at the core of numerous viral infections. Acting in support of the host or of virus replication, mitochondria regulation facilitates control of energy metabolism, apoptosis, and immune signaling. Accumulating studies have pointed to post-translational modification (PTM) of mitochondrial proteins as a critical component of such regulatory mechanisms. Mitochondrial PTMs have been implicated in the pathology of several diseases and emerging evidence is starting to highlight essential roles in the context of viral infections. Here, we provide an overview of the growing arsenal of PTMs decorating mitochondrial proteins and their possible contribution to the infection-induced modulation of bioenergetics, apoptosis, and immune responses. We further consider links between PTM changes and mitochondrial structure remodeling, as well as the enzymatic and non-enzymatic mechanisms underlying mitochondrial PTM regulation. Finally, we highlight some of the methods, including mass spectrometry-based analyses, available for the identification, prioritization, and mechanistic interrogation of PTMs.

Functional and Clinical Proteomic Exploration of Pancreatic Cancer

Pancreatic cancer, most cases being pancreatic ductal adenocarcinoma (PDAC), is one of the most lethal cancers with a median survival time of less than 6 months. Therapeutic options are very limited for PDAC patients, and surgery is still the most effective treatment, making improvements in early diagnosis critical. One typical characteristic of PDAC is the desmoplastic reaction of its stroma microenvironment, which actively interacts with cancer cells to orchestrate key components in tumorigenesis, metastasis, and chemoresistance. Global exploration of cancer-stroma crosstalk is essential to decipher PDAC biology and design intervention strategies. Over the past decade, the dramatic improvement of proteomics technologies has enabled profiling of proteins, post-translational modifications (PTMs), and their protein complexes at unprecedented sensitivity and dimensionality. Here, starting with our current understanding of PDAC characteristics, including precursor lesions, progression models, tumor microenvironment, and therapeutic advancements, we describe how proteomics contributes to the functional and clinical exploration of PDAC, providing insights into PDAC carcinogenesis, progression, and chemoresistance. We summarize recent achievements enabled by proteomics to systematically investigate PTMs-mediated intracellular signaling in PDAC, cancer-stroma interactions, and potential therapeutic targets revealed by these functional studies. We also highlight proteomic profiling of clinical tissue and plasma samples to discover and verify useful biomarkers that can aid early detection and molecular classification of patients. In addition, we introduce spatial proteomic technology and its applications in PDAC for deconvolving tumor heterogeneity. Finally, we discuss future prospects of applying new proteomic technologies in comprehensively understanding PDAC heterogeneity and intercellular signaling networks. Importantly, we expect advances in clinical functional proteomics for exploring mechanisms of cancer biology directly by high-sensitivity functional proteomic approaches starting from clinical samples.

Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective

Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990, and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

Functional proteomics based on protein microarray technology for biomedical research

This chapter traces a route through Proteomics from its origins to the present day. The different proteomics applications are discussed with a focus on microarray technology. Analytical microarrays, functional microarrays and reverse phase microarrays and their different applications are discussed. Several studies are mentioned where the great versatility of this approach is shown. Finally, the advantages and future challenges of microarray technology are outlined.

Performance benchmarking microplate-immunoassays for quantifying target-specific cysteine oxidation reveals their potential for understanding redox-regulation and oxidative stress

The antibody-linked oxi-state assay (ALISA) for quantifying target-specific cysteine oxidation can benefit specialist and non-specialist users. Specialists can benefit from time-efficient analysis and high-throughput target and/or sample n-plex capacities. The simple and accessible "off-the-shelf" nature of ALISA brings the benefits of oxidative damage assays to non-specialists studying redox-regulation. Until performance benchmarking establishes confidence in the "unseen" microplate results, ALISA is unlikely to be widely adopted. Here, we implemented pre-set pass/fail criteria to benchmark ALISA by evaluating immunoassay performance in diverse contexts. ELISA-mode ALISA assays were accurate, reliable, and sensitive. For example, the average inter-assay CV for detecting 20%- and 40%-oxidised PRDX2 or GAPDH standards was 4.6% (range: 3.6-7.4%). ALISA displayed target-specificity. Immunodepleting the target decreased the signal by ∼75%. Single-antibody formatted ALISA failed to quantify the matrix-facing alpha subunit of the mitochondrial ATP synthase. However, RedoxiFluor quantified the alpha subunit displaying exceptional performance in the single-antibody format. ALISA discovered that (1) monocyte-to-macrophage differentiation amplified PRDX2-oxidation in THP-1 cells and (2) exercise increased GAPDH-specific oxidation in human erythrocytes. The "unseen" microplate data were "seen-to-be-believed" via orthogonal visually displayed immunoassays like the dimer method. Finally, we established target (n = 3) and sample (n = 100) n-plex capacities in ∼4 h with 50-70 min hands-on time. Our work showcases the potential of ALISA to advance our understanding of redox-regulation and oxidative stress.

Establishing Quality Control Procedures for Large-Scale Plasma Proteomics Analyses

Proteomics research has been transformed due to high-throughput liquid chromatography (LC-MS/MS) tandem mass spectrometry instruments combined with highly sophisticated automated sample preparation and multiplexing workflows. However, scaling proteomics experiments to large sample cohorts (hundreds to thousands) requires thoughtful quality control (QC) protocols. Robust QC protocols can help with reproducibility, quantitative accuracy, and provide opportunities for more decisive troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of N = 335 patient samples using tandem mass tag (TMT_pro) 16-plex batches. Over the course of a 10-month data acquisition period for this cohort we collected 271 pooled QC LC-MS/MS result files obtained from MS/MS analysis of a patient-derived pooled plasma sample, representative of the entire cohort population. This sample was tagged with TMT_zero or TMT_pro reagents and used to inform the daily performance of the LC-MS/MS instruments and to allow within and across sample batch normalization. Analytical variability of a number of instrumental and data analysis metrics including protein and peptide identifications, peptide spectral matches (PSMs), number of obtained MS/MS spectra, average peptide abundance, percent of peptides with a Δ m/z between ±0.003 Da, percent of MS/MS spectra obtained at the maximum injection time, and the retention time of selected tracking peptides were evaluated to help inform the design of a robust LC-MS/MS QC workflow for use in future cohort studies. This study also led to general tips for using selected metrics to inform real-time troubleshooting of LC-MS/MS performance issues with daily QC checks.

The role of clinical glyco(proteo)mics in precision medicine

Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity and half-life. The increasingly mature toolbox with glycomic- and glycoproteomic strategies is applied for the development of biopharmaceuticals and discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end it is noted that the identification of biomarkers has received more attention than corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes but developments to accurately quantify these have so far been limited. In this perspective a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address quantification of a multimarker panel will be discussed.

High sensitivity top-down proteomics captures single muscle cell heterogeneity in large proteoforms

Single-cell proteomics has emerged as a powerful method to characterize cellular phenotypic heterogeneity and the cell-specific functional networks underlying biological processes. However, significant challenges remain in single-cell proteomics for the analysis of proteoforms arising from genetic mutations, alternative splicing, and post-translational modifications. Herein, we have developed a highly sensitive functionally integrated top-down proteomics method for the comprehensive analysis of proteoforms from single cells. We applied this method to single muscle fibers (SMFs) to resolve their heterogeneous functional and proteomic properties at the single-cell level. Notably, we have detected single-cell heterogeneity in large proteoforms (>200 kDa) from the SMFs. Using SMFs obtained from three functionally distinct muscles, we found fiber-to-fiber heterogeneity among the sarcomeric proteoforms which can be related to the functional heterogeneity. Importantly, we detected multiple isoforms of myosin heavy chain (~223 kDa), a motor protein that drives muscle contraction, with high reproducibility to enable the classification of individual fiber types. This study reveals single muscle cell heterogeneity in large proteoforms and establishes a direct relationship between sarcomeric proteoforms and muscle fiber types, highlighting the potential of top-down proteomics for uncovering the molecular underpinnings of cell-to-cell variation in complex systems.

The role of eIF4F-driven mRNA translation in regulating the tumour microenvironment

Cells can rapidly adjust their proteomes in dynamic environments by regulating mRNA translation. There is mounting evidence that dysregulation of mRNA translation supports the survival and adaptation of cancer cells, which has stimulated clinical interest in targeting elements of the translation machinery and, in particular, components of the eukaryotic initiation factor 4F (eIF4F) complex such as eIF4E. However, the effect of targeting mRNA translation on infiltrating immune cells and stromal cells in the tumour microenvironment (TME) has, until recently, remained unexplored. In this Perspective article, we discuss how eIF4F-sensitive mRNA translation controls the phenotypes of key non-transformed cells in the TME, with an emphasis on the underlying therapeutic implications of targeting eIF4F in cancer. As eIF4F-targeting agents are in clinical trials, we propose that a broader understanding of their effect on gene expression in the TME will reveal unappreciated therapeutic vulnerabilities that could be used to improve the efficacy of existing cancer therapies.

Spatial Proteomics for the Molecular Characterization of Breast Cancer

Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease's heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to identify early diagnostic biomarkers and therapeutic targets, and to understand protein expression levels and modifications. The subcellular localization of proteins is a key factor in their physiological function, making the study of subcellular localization a major challenge in cell biology. Achieving high resolution at the cellular and subcellular level is essential for obtaining an accurate spatial distribution of proteins, which in turn can enable the application of proteomics in clinical research. In this review, we present a comparison of current methods of spatial proteomics in BC, including untargeted and targeted strategies. Untargeted strategies enable the detection and analysis of proteins and peptides without a predetermined molecular focus, whereas targeted strategies allow the investigation of a predefined set of proteins or peptides of interest, overcoming the limitations associated with the stochastic nature of untargeted proteomics. By directly comparing these methods, we aim to provide insights into their strengths and limitations and their potential applications in BC research.

The use of matrix-assisted laser desorption/ionization mass spectrometry in enzyme activity assays and its position in the context of other available methods

Activity assays are indispensable for studying biochemical properties of enzymes. The purposes of measuring activity are wide ranging from a simple detection of the presence of an enzyme to kinetic experiments evaluating the substrate specificity, reaction mechanisms, and susceptibility to inhibitors. Common activity assay methods include spectroscopy, electrochemical sensors, or liquid chromatography coupled with various detection techniques. This review focuses on the use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) as a growing and modern alternative, which offers high speed of analysis, sensitivity, versatility, possibility of automation, and cost-effectiveness. It may reveal reaction intermediates, side products or measure more enzymes at once. The addition of an internal standard or calculating the ratios of the substrate and product peak intensities and areas overcome the inherent inhomogeneous distribution of analyte and matrix in the sample spot, which otherwise results in a poor reproducibility. Examples of the application of MALDI-TOF MS for assaying hydrolases (including peptidases and β-lactamases for antibiotic resistance tests) and other enzymes are provided. Concluding remarks summarize advantages and challenges coming from the present experience, and draw future perspectives such as a screening of large libraries of chemical compounds for their substrate or inhibitory properties towards enzymes.

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

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

A basic phosphoproteomic-DIA workflow integrating precise quantification of phosphosites in systems biology

Phosphorylation is one of the most important post-translational modifications (PTMs) of proteins, governing critical protein functions. Most human proteins have been shown to undergo phosphorylation, and phosphoproteomic studies have been widely applied due to recent advancements in high-resolution mass spectrometry technology. Although the experimental workflow for phosphoproteomics has been well-established, it would be useful to optimize and summarize a detailed, feasible protocol that combines phosphoproteomics and data-independent acquisition (DIA), along with follow-up data analysis procedures due to the recent instrumental and bioinformatic advances in measuring and understanding tens of thousands of site-specific phosphorylation events in a single experiment. Here, we describe an optimized Phos-DIA protocol, from sample preparation to bioinformatic analysis, along with practical considerations and experimental configurations for each step. The protocol is designed to be robust and applicable for both small-scale phosphoproteomic analysis and large-scale quantification of hundreds of samples for studies in systems biology and systems medicine.

Physiological Benefits of Dietary Lysophospholipid Supplementation in a Marine Fish Model: Deep Analyses of Modes of Action

Given the hydrophilic structure of lysophospholipids (LPLs), their dietary inclusion translates into a better emulsifying capacity of the dietary components. The present study aimed to understand the mechanisms underlying the growth-promoting effect of LPL supplementation by undertaking deep analyses of the proximal intestine and liver interactomes. The Atlantic salmon (Salmo salar) was selected as the main aquaculture species model. The animals were divided into two groups: one was fed a control diet (C-diet) and the other a feed (LPL-diet) supplemented with an LPL-based digestive enhancer (0.1% AQUALYSO^®, Adisseo). The LPL-diet had a positive effect on the fish by increasing the final weight by 5% and reducing total serum lipids, mainly due to a decrease in the plasma phospholipid (p < 0.05). In the intestine, the upregulated interactome suggests a more robust digestive capacity, improving vesicle-trafficking-related proteins, complex sugar hydrolysis, and lipid metabolism. In the liver, the LPL-diet promotes better nutrients, increasing several metabolic pathways. The downregulation of the responses to stress and stimuli could be related to a reduced proinflammatory state. This study on the benefits and modes of action of dietary LPLs opens a new window into fish nutrition and could be extended to other productive species.

Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization

The discovery of functional protein complex and the interrogation of the complex structure-function relationship (SFR) play crucial roles in the understanding and intervention of biological processes. Affinity purification-mass spectrometry (AP-MS) has been proved as a powerful tool in the discovery of protein complexes. However, validation of these novel protein complexes as well as elucidation of their molecular interaction mechanisms are still challenging. Recently, native top-down MS (nTDMS) is rapidly developed for the structural analysis of protein complexes. In this review, we discuss the integration of AP-MS and nTDMS in the discovery and structural characterization of functional protein complexes. Further, we think the emerging artificial intelligence (AI)-based protein structure prediction is highly complementary to nTDMS and can promote each other. We expect the hybridization of integrated structural MS with AI prediction to be a powerful workflow in the discovery and SFR investigation of functional protein complexes.

Mass Spectrometry-Based Proteomic Profiling of a Silvaner White Wine

The comprehensive identification of the proteome content from a white wine (cv. Silvaner) is described here for the first time. The wine protein composition isolated from a representative wine sample (250 L) was identified via mass spectrometry (MS)-based proteomics following in-solution and in-gel digestion methods after being submitted to size exclusion chromatographic (SEC) fractionation to gain a comprehensive insight into proteins that survive the vinification processes. In total, we identified 154 characterized (with described functional information) or so far uncharacterized proteins, mainly from Vitis vinifera L. and Saccharomyces cerevisiae. With the complementarity of the two-step purification, the digestion techniques and the high-resolution (HR)-MS analyses provided a high-score identification of proteins from low to high abundance. These proteins can be valuable for future authentication of wines by tracing proteins derived from a specific cultivar or winemaking process. The proteomics approach presented herein may also be generally helpful to understand which proteins are important for the organoleptic properties and stability of wines.

CDK6 activity in a recurring convergent kinase network motif

In humans, more than 500 kinases phosphorylate ~15% of all proteins in an emerging phosphorylation network. Convergent local interaction motifs, in which ≥two kinases phosphorylate the same substrate, underlie feedback loops and signal amplification events but have not been systematically analyzed. Here, we first report a network-wide computational analysis of convergent kinase-substrate relationships (cKSRs). In experimentally validated phosphorylation sites, we find that cKSRs are common and involve >80% of all human kinases and >24% of all substrates. We show that cKSRs occur over a wide range of stoichiometries, in many instances harnessing co-expressed kinases from family subgroups. We then experimentally demonstrate for the prototypical convergent CDK4/6 kinase pair how multiple inputs phosphorylate the tumor suppressor retinoblastoma protein (RB) and thereby hamper in situ analysis of the individual kinases. We hypothesize that overexpression of one kinase combined with a CDK4/6 inhibitor can dissect convergence. In breast cancer cells expressing high levels of CDK4, we confirm this hypothesis and develop a high-throughput compatible assay that quantifies genetically modified CDK6 variants and inhibitors. Collectively, our work reveals the occurrence, topology, and experimental dissection of convergent interactions toward a deeper understanding of kinase networks and functions.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Challenges and Opportunities for Single-cell Computational Proteomics

Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it is necessary to realize that current algorithms being employed on single-cell data were designed for bulk data and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.

Ion Mobility Mass Spectrometry (IM-MS) for Structural Biology: Insights Gained by Measuring Mass, Charge, and Collision Cross Section

The investigation of macromolecular biomolecules with ion mobility mass spectrometry (IM-MS) techniques has provided substantial insights into the field of structural biology over the past two decades. An IM-MS workflow applied to a given target analyte provides mass, charge, and conformation, and all three of these can be used to discern structural information. While mass and charge are determined in mass spectrometry (MS), it is the addition of ion mobility that enables the separation of isomeric and isobaric ions and the direct elucidation of conformation, which has reaped huge benefits for structural biology. In this review, where we focus on the analysis of proteins and their complexes, we outline the typical features of an IM-MS experiment from the preparation of samples, the creation of ions, and their separation in different mobility and mass spectrometers. We describe the interpretation of ion mobility data in terms of protein conformation and how the data can be compared with data from other sources with the use of computational tools. The benefit of coupling mobility analysis to activation via collisions with gas or surfaces or photons photoactivation is detailed with reference to recent examples. And finally, we focus on insights afforded by IM-MS experiments when applied to the study of conformationally dynamic and intrinsically disordered proteins.

Proteomic Analysis of Huntington's Disease Medium Spiny Neurons Identifies Alterations in Lipid Droplets

Huntington's disease (HD) is a neurodegenerative disease caused by a CAG repeat expansion in the Huntingtin (HTT) gene. The resulting polyglutamine (polyQ) tract alters the function of the HTT protein. Although HTT is expressed in different tissues, the medium spiny projection neurons (MSNs) in the striatum are particularly vulnerable in HD. Thus, we sought to define the proteome of human HD patient-derived MSNs. We differentiated HD72 induced pluripotent stem cells and isogenic controls into MSNs and carried out quantitative proteomic analysis. Using data-dependent acquisitions with FAIMS for label-free quantification on the Orbitrap Lumos mass spectrometer, we identified 6,323 proteins with at least two unique peptides. Of these, 901 proteins were altered significantly more in the HD72-MSNs than in isogenic controls. Functional enrichment analysis of upregulated proteins demonstrated extracellular matrix and DNA signaling (DNA replication pathway, double-strand break repair, G1/S transition) with the highest significance. Conversely, processes associated with the downregulated proteins included neurogenesis-axogenesis, the brain-derived neurotrophic factor-signaling pathway, Ephrin-A: EphA pathway, regulation of synaptic plasticity, triglyceride homeostasis cholesterol, plasmid lipoprotein particle immune response, interferon-γ signaling, immune system major histocompatibility complex, lipid metabolism and cellular response to stimulus. Moreover, proteins involved in the formation and maintenance of axons, dendrites, and synapses (e.g., Septin protein members) were dysregulated in HD72-MSNs. Importantly, lipid metabolism pathways were altered, and using quantitative image, we found analysis that lipid droplets accumulated in the HD72-MSN, suggesting a deficit in the turnover of lipids possibly through lipophagy. Our proteomics analysis of HD72-MSNs identified relevant pathways that are altered in MSNs and confirm current and new therapeutic targets for HD.

Proteomic Approaches and Potential Applications in Autosomal Dominant Polycystic Kidney Disease and Fabry Disease

Although rare, hereditary diseases, such as autosomal dominant polycystic kidney disease (ADPKD) and Fabry disease (FD) may significantly progress towards severe nephropathy. It is crucial to characterize it accurately, predict the course of the illness and estimate treatment effectiveness. A huge effort has been undertaken to find reliable biomarkers that might be useful for an early prevention of the disease progression and/or any invasive diagnostic procedures. The study of proteomics, or the small peptide composition of a sample, is a field of study under continuous development. Over the past years, several strategies have been created to study and define the proteome of samples from widely varying origins. However, urinary proteomics has become essential for discovering novel biomarkers in kidney disease. Here, the extracellular vesicles in human urine that contain cell-specific marker proteins from every segment of the nephron, offer a source of potentially valuable urinary biomarkers, and may play an essential role in kidney development and kidney disease. This review summarizes the relevant literature investigating the proteomic approaches and potential applications in the regular studies of ADPKD and FD.