Mass spectrometry and protein analysis

Allosteric aptasensor-initiated target cycling and transcription amplification of light-up RNA aptamer for sensitive detection of protein

The early detection of biomarker proteins in clinical samples is of great significance for the diagnosis of diseases. However, it is still a challenge to detect low-concentration protein. Herein, a label-free aptamer-based amplification assay, termed the ATC-TA system, that allows fluorescence detection of very low numbers of protein without time-consuming washing steps and pre-treatment was developed. The target induces a conformational change in the allosteric aptasensor, triggers the target cycling and transcription amplification, and ultimately converts the input of the target protein into the output of the light-up aptamer (R-Pepper). It exhibits ultrahigh sensitivity with a detection limit of 5.62 fM at 37 ℃ and the accuracy is comparable to conventional ELISA. ATC-TA has potential application for the detection of endogenous PDGF-BB in serum samples to distinguish tumor mice from healthy mice at an early stage. It also successfully detects exogenous SARS-CoV-2 spike proteins in human serum. Therefore, this high-sensitive, universality, easy-to-operate and cost-effective biosensing platform holds great clinical application potential in early clinical diagnosis.

Salivary Biomarkers in Oral Squamous Cell Carcinoma: A Proteomic Overview

BACKGROUND

Oral squamous cell carcinoma (OSCC) is one of the most frequent cancers worldwide. Endoscopic methods may be useful in the evaluation of oral injuries even though the diagnostic gold standard is a biopsy. Targeted screenings could be considered the best way to prevent the occurrence of oral cancer. Aimed to elucidate the potential identification of specific biomarkers of OSCC, the use of saliva is convenient and noninvasive. Many studies reported more than a hundred putative saliva biomarkers for OSCC, and proteogenomic approaches were fundamental to disclosing this issue.

METHODS

Relevant literature published in the last few years was systematically searched on PubMed and we focused on articles about the use and study of salivary biomarkers in the diagnostics of head and neck cancer (n = 110). Thereafter, we performed a selection focusing on diagnosis with salivary proteomics in OSCC (n = 8).

RESULTS

Saliva proteomics can be a source of biomarkers for OSCC. We reviewed literature of biomarker proteins in saliva that could also be evaluated as probable targets for non-invasive screening of oral neoplasm such as cytokines, matrix metalloproteinases, and acute-phase response proteins.

CONCLUSIONS

The measurement of salivary biomarkers is a highly hopeful technique for the diagnosis of OSCC. Proteogenomic approaches could permit an accurate and early diagnosis of OSCC. This review seeks to generate an up-to-date view on translational OSCC issues by raising awareness of researchers, physicians, and surgeons. Renewed clinical studies, which will validate the sensitivity and specificity of salivary biomarkers, are necessary to translate these results into possible strategies for early diagnosis of OSCC, thus improving patient outcomes.

Ion Mobility Mass Spectrometry Development and Applications

Although as an analytical method with high specificity and high sensitivity, mass spectrometry (MS) has a wide range of applications in many fields, it still needs other technologies as the assist and supplement to enhance the scope and capability of analysis. Coupling with ion mobility (IM) can make an enhancement effect in the field of pharmaceutical analysis as a supplementary method. The two-dimensional mass technology improves the confidence of compounds annotations while increasing peak capacity, with the gradual deepening of theoretical research on IM-MS, it has shown unique advantages in the complex analysis conditions. IM-MS owns great potential for improving the depth, range, dimension of in-depth drug research. In this review, the principle, instruments and methods, applications, advantages and limitations of IM-MS are described. Here, we also elaborate on the prospects in structural evaluation, separation, and identification of complex compounds for the drug discovery and development phase and the great advantages of macromolecules and omics.

Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences

A critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N -dimensional datasets; examples of this process include finding peaks in multi-dimensional molecular spectra or emitters in fluorescence microscopy images. Identifying such features involves determining if the overall shape of the data is consistent with an expected shape; however, it is generally unclear how to quantitatively make this determination. In practice, many analysis methods employ subjective, heuristic approaches, which complicates the validation of any ensuing results-especially as the amount and dimensionality of the data increase. Here, we present a probabilistic solution to this problem by using Bayes' rule to calculate the probability that the data have any one of several potential shapes. This probabilistic approach may be used to objectively compare how well different theories describe a dataset, identify changes between datasets and detect features within data using a corollary method called Bayesian Inference-based Template Search; several proof-of-principle examples are provided. Altogether, this mathematical framework serves as an automated 'engine' capable of computationally executing analysis decisions currently made by visual inspection across the sciences.

Systems Biology Approach to Investigate Biomarkers, Boron-10 Carriers, and Mechanisms Useful for Improving Boron Neutron Capture Therapy

Systems biology approach, carried out with high-throughput omics technologies, has become a fundamental aspect of the study of complex diseases like cancer. It can molecularly characterize subjects, physiopathological conditions, and interactions, allowing a precise description, to reach personalized medicine. In particular, proteomics, typically performed with liquid chromatography coupled to mass spectrometry, is a powerful tool for systems biology, giving the possibility to perform diagnosis, patient stratification, and prediction of therapy effects. Boron Neutron Capture Therapy (BNCT) is a selective antitumoral radiotherapy based on a nuclear reaction that occurs when ¹⁰B atoms are irradiated by low-energy thermal neutrons, leading to cell death, thanks to the production of high-energy α particles. Since BNCT is recently becoming an important therapy for the treatment of different types of solid tumors such as gliomas, head and neck cancers, and others, it can take advantage of molecular investigation to improve the understanding of effects and mechanisms and so help its clinical applications. In this context, proteomics can provide a better understanding of mechanisms related to BNCT effect, identify potential biomarkers, and individuate differential responses by specific patients, stratifying responders and nonresponders. Another key aspect of BNCT is the study of new potential Boron-10 carriers to improve the selectivity of Boron delivery to tumors and proteomics can be important in this application, studying the effectiveness of new boron delivery agents, including protein-based carriers, also using computational studies that can investigate new molecules, such as boronated monoclonal antibodies, for improving BNCT.

Advances, obstacles, and opportunities for machine learning in proteomics

The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.

Heat-Assisted Dual Neutral Spray Ionization for High-Performance Online Desalting in Mass Spectrometric Analysis

In mass spectrometry (MS), nonvolatile salts contaminate the transmission system and cause ion suppression, hampering MS analysis. When MS is combined with liquid chromatography (LC) that uses a salty mobile phase, the problems become more intractable due to long analysis time. Here, a novel heat-assisted dual neutral spray ionization (HADSI) method was developed, which projected sample solution spray and solvent spray onto a heated plate to achieve online desalting and high ionization. The experimental parameters of HADSI were optimized, which indicated that the plate temperature was crucial for ionization and desalination. Eight drug compounds dissolved in various commonly used buffers were directly analyzed using HADSI-MS, even though the concentration of PBS buffer reached 500 mmol/L. The established method showed considerable sensitivity in the positive ion mode with the limits of detection at the level of nmol/L, and good linearity (R² > 0.99) was achieved for all the analyzed compounds. The repeatability and intra- and interday precisions of the method were evaluated, demonstrating the feasibility and reliability of the analysis of salty samples by HADSI-MS. Further, the method was demonstrated to tolerate the long-time analysis of high-salt LC eluates and the device was easy to maintain. Finally, a crude roxithromycin product was separated by LC and then analyzed by HADSI-MS, and seven unknown impurities and nine known impurities were successfully detected. Our results indicated that HADSI-MS may have potential applications in academic and industrial fields.

A protein identification method for proteomics using amino acid composition analysis with IoT-based remote control

In the present study, we developed a protein identification method using low-cost and easy-to-operate amino acid composition analysis. The identification program automatically compares the quantitative result for each amino acid concentration obtained from the amino acid analysis to the amino acid composition data retrieved from the UniProt protein database. We found that the accuracy of protein identification using amino acid composition analysis was comparable to that of mass spectrometry analysis. The method was able to distinguish and identify differences in amino acid substitutions of several residues between proteins with high sequence homology. The identification accuracy of proteins was also improved by correcting the concentrations in the program for Cys, Trp, and Ile residues, which cannot be quantified by general sample preparation for amino acid analysis. Moreover, the amino acid analyzer was remotely controlled in accordance with the growing demand for remote work. The measured amino acid data were automatically uploaded to the IoT portal within a few minutes of each measurement, allowing researchers to download data and analyze them using the identification program anywhere and at any time by connecting to a network. The results indicated that the present method is useful for protein identification.

High-Throughput Intact Protein Analysis for Drug Discovery Using Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry

Mass spectrometry (MS) is the primary analytical tool used to characterize proteins within the biopharmaceutical industry. Electrospray ionization (ESI) coupled to liquid chromatography (LC) is the current gold standard for intact protein analysis. However, inherent speed limitations of LC/MS prevent analysis of large sample numbers (>1000) in a day. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI-MS), an ambient ionization MS technology, has recently been established as a platform for high-throughput small molecule analysis. Here, we report the applications of such a system for the analysis of intact proteins commonly performed within the drug discovery process. A wide molecular weight range of proteins 10-150 kDa was detected on the system with improved tolerance to salts and buffers compared to ESI. With high concentrations and model proteins, a sample rate of up to 22 Hz was obtained. For proteins at low concentrations and in buffers used in commonly employed assays, robust data at a sample rate of 1.5 Hz were achieved, which is ∼22× faster than current technologies used for high-throughput ESI-MS-based protein assays. In addition, two multiplexed plate-based high-throughput sample cleanup methods were coupled to IR-MALDESI-MS to enable analysis of samples containing excessive amounts of salts and buffers without fully compromising productivity. Example experiments, which leverage the speed of the IR-MALDESI-MS system to monitor NISTmAb reduction, protein autophosphorylation, and compound binding kinetics in near real time, are demonstrated.

Identification and functional annotation of long intergenic non-coding RNAs in Brassicaceae

Long intergenic noncoding RNAs (lincRNAs) are a large yet enigmatic class of eukaryotic transcripts that can have critical biological functions. The wealth of RNA-sequencing (RNA-seq) data available for plants provides the opportunity to implement a harmonized identification and annotation effort for lincRNAs that enables cross-species functional and genomic comparisons as well as prioritization of functional candidates. In this study, we processed >24 Tera base pairs of RNA-seq data from >16,000 experiments to identify ∼130,000 lincRNAs in four Brassicaceae: Arabidopsis thaliana, Camelina sativa, Brassica rapa, and Eutrema salsugineum. We used nanopore RNA-seq, transcriptome-wide structural information, peptide data, and epigenomic data to characterize these lincRNAs and identify conserved motifs. We then used comparative genomic and transcriptomic approaches to highlight lincRNAs in our data set with sequence or transcriptional conservation. Finally, we used guilt-by-association analyses to assign putative functions to lincRNAs within our data set. We tested this approach on a subset of lincRNAs associated with germination and seed development, observing germination defects for Arabidopsis lines harboring T-DNA insertions at these loci. LincRNAs with Brassicaceae-conserved putative miRNA binding motifs, small open reading frames, or abiotic-stress modulated expression are a few of the annotations that will guide functional analyses into this cryptic portion of the transcriptome.

Protein Albumin Manipulation and Electrical Quantification of Molecular Dielectrophoresis Responses for Biomedical Applications

Research relating to dielectrophoresis (DEP) has been progressing rapidly through time as it is a strong and controllable technique for manipulation, separation, preconcentration, and partitioning of protein. Extensive studies have been carried out on protein DEP, especially on Bovine Serum Albumin (BSA). However, these studies involve the usage of dye and fluorescent probes to observe DEP responses as the physical properties of protein albumin molecular structure are translucent. The use of dye and the fluorescent probe could later affect the protein's physiology. In this article, we review three methods of electrical quantification of DEP responses: electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and capacitance measurement for protein BSA DEP manipulation. The correlation of these methods with DEP responses is further discussed. Based on the observations on capacitance measurement, it can be deduced that the electrical quantifying method is reliable for identifying DEP responses. Further, the possibility of manipulating the protein and electrically quantifying DEP responses while retaining the original physiology of the protein and without the usage of dye or fluorescent probe is discussed.

MALDI-TOF mass spectrometry rapid pathogen identification and outcomes of patients with bloodstream infection: A systematic review and meta-analysis

There was inconsistent evidence regarding the use of matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS) for microorganism identification with/without antibiotic stewardship team (AST) and the clinical outcome of patients with bloodstream infections (BSI). In a systematic review and meta‐analysis, we evaluated the effectiveness of rapid microbial identification by MALDI‐TOF MS with and without AST on clinical outcomes. We searched PubMed and EMBASE databases from inception to 1 February 2022 to identify pre–post and parallel comparative studies that evaluated the use of MALDI‐TOF MS for microorganism identification. Pooled effect estimates were derived using the random‐effects model. Twenty‐one studies with 14,515 patients were meta‐analysed. Compared with conventional phenotypic methods, MALDI‐TOF MS was associated with a 23% reduction in mortality (RR = 0.77; 95% CI: 0.66; 0.90; I² = 35.9%; 13 studies); 5.07‐h reduction in time to effective antibiotic therapy (95% CI: −5.83; −4.31; I² = 95.7%); 22.86‐h reduction in time to identify microorganisms (95% CI: −23.99; −21.74; I² = 91.6%); 0.73‐day reduction in hospital stay (95% CI: −1.30; −0.16; I² = 53.1%); and US$4140 saving in direct hospitalization cost (95% CI: $‐8166.75; $‐113.60; I² = 66.1%). No significant heterogeneity sources were found, and no statistical evidence for publication bias was found. Rapid pathogen identification by MALDI‐TOF MS with or without AST was associated with reduced mortality and improved outcomes of BSI, and may be cost‐effective among patients with BSI.

FLASHIda enables intelligent data acquisition for top-down proteomics to boost proteoform identification counts

The detailed analysis and structural characterization of proteoforms by top-down proteomics (TDP) has gained a lot of interest in biomedical research. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complexity of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. Here, we present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In an analysis of E. coli lysate, FLASHIda increases the number of unique proteoform level identifications from 800 to 1500 or generates a near-identical number of identifications in one third of the instrument time when compared to standard DDA mode. Furthermore, FLASHIda enables sensitive mapping of post-translational modifications and detection of chemical adducts. As a software extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.

Data acquisition suitable for top-down proteomics (TDP) has the potential to significantly improve proteoform analysis. Here, the authors present FLASHIda, an intelligent online data acquisition algorithm for TDP that nearly doubles the number of proteoform-level identifications in complex samples.

Phosphorylation of plant virus proteins: Analysis methods and biological functions

Phosphorylation is one of the most extensively investigated post-translational modifications that orchestrate a variety of cellular signal transduction processes. The phosphorylation of virus-encoded proteins plays an important regulatory role in the infection cycle of such viruses in plants. In recent years, molecular mechanisms underlying the phosphorylation of plant viral proteins have been widely studied. Based on recent publications, our study summarizes the phosphorylation analyses of plant viral proteins and categorizes their effects on biological functions according to the viral life cycle. This review provides a theoretical basis for elucidating the molecular mechanisms of viral infection. Furthermore, it deepens our understanding of the biological functions of phosphorylation in the interactions between plants and viruses.

Membranes under the Magnetic Lens: A Dive into the Diverse World of Membrane Protein Structures Using Cryo-EM

Membrane proteins are highly diverse in both structure and function and can, therefore, present different challenges for structure determination. They are biologically important for cells and organisms as gatekeepers for information and molecule transfer across membranes, but each class of membrane proteins can present unique obstacles to structure determination. Historically, many membrane protein structures have been investigated using highly engineered constructs or using larger fusion proteins to improve solubility and/or increase particle size. Other strategies included the deconstruction of the full-length protein to target smaller soluble domains. These manipulations were often required for crystal formation to support X-ray crystallography or to circumvent lower resolution due to high noise and dynamic motions of protein subdomains. However, recent revolutions in membrane protein biochemistry and cryo-electron microscopy now provide an opportunity to solve high resolution structures of both large, >1 megadalton (MDa), and small, <100 kDa (kDa), drug targets in near-native conditions, routinely reaching resolutions around or below 3 Å. This review provides insights into how the recent advances in membrane biology and biochemistry, as well as technical advances in cryo-electron microscopy, help us to solve structures of a large variety of membrane protein groups, from small receptors to large transporters and more complex machineries.

Intramolecular covalent bonds in Gram-positive bacterial surface proteins

Gram-positive bacteria experience considerable mechanical perturbation when adhering to host surfaces during colonization and infection. They have evolved various adhesion proteins that are mechanically robust to ensure strong surface adhesion. Recently, it was discovered that these adhesion proteins contain rare, extra intramolecular covalent bonds that stabilize protein structures and participate in surface bonding. These intramolecular covalent bonds include isopeptides, thioesters, and ester bonds, which often form spontaneously without the need for additional enzymes. With the development of single-molecule force spectroscopy techniques, the detailed mechanical roles of these intramolecular covalent bonds have been revealed. In this review, we summarize the recent advances in this area of research, focusing on the link between the mechanical stability and function of these covalent bonds in Gram-positive bacterial surface proteins. We also highlight the potential impact of these discoveries on the development of novel antibiotics and chemical biology tools.

Dual roles of drug or its metabolite-protein conjugate: Cutting-edge strategy of drug discovery using shotgun proteomics

Many drugs can bind directly to proteins or be bioactivated by metabolizing enzymes to form reactive metabolites (RMs) that rapidly bind to proteins to form drug-protein conjugates or metabolite-protein conjugates (DMPCs). The close relationship between DMPCs and idiosyncratic adverse drug reactions (IADRs) has been recognized; drug discovery teams tend to avoid covalent interactions in drug discovery projects. Covalent interactions in DMPCs can provide high potency and long action duration and conquer the intractable targets, inspiring drug design, and development. This forms the dual role feature of DMPCs. Understanding the functional implications of DMPCs in IADR control and therapeutic applications requires precise identification of these conjugates from complex biological samples. While classical biochemical methods have contributed significantly to DMPC detection in the past decades, the low abundance and low coverage of DMPCs have become a bottleneck in this field. An emerging transformation toward shotgun proteomics is on the rise. The evolving shotgun proteomics techniques offer improved reproducibility, throughput, specificity, operability, and standardization. Here, we review recent progress in the systematic discovery of DMPCs using shotgun proteomics. Furthermore, the applications of shotgun proteomics supporting drug development, toxicity mechanism investigation, and drug repurposing processes are also reviewed and prospected.

Statistical methods for Mendelian randomization in genome-wide association studies: A review

Genome-wide association studies have yielded thousands of associations for many common diseases and disease-related complex traits. The identified associations made it possible to identify the causal risk factors underlying diseases and investigate the causal relationships among complex traits through Mendelian randomization. Mendelian randomization is a form of instrumental variable analysis that uses SNP associations from genome-wide association studies as instruments to study and uncover causal relationships between complex traits. By leveraging SNP genotypes as instrumental variables, or proxies, for the exposure complex trait, investigators can tease out causal effects from observational data, provided that necessary assumptions are satisfied. We discuss below the development of Mendelian randomization methods in parallel with the growth of genome-wide association studies. We argue that the recent availability of GWAS summary statistics for diverse complex traits has motivated new Mendelian randomization methods with relaxed causality assumptions and that this area continues to offer opportunities for robust biological discoveries.

Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides

The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tandem mass tags (TMT) are chemical peptide labels that are coupled to free amine groups usually after protein digestion to enable the multiplexed analysis of multiple samples in bottom-up mass spectrometry. It is a standard workflow in proteomics ranging from single-cell to high-throughput proteomics. Particularly for TMT, increasing the number of confidently identified spectra is highly desirable as it provides identification and quantification information with every spectrum. Here, we report on the generation of an extensive resource of synthetic TMT-labeled peptides as part of the ProteomeTools project and present the extension of the deep learning model Prosit to accurately predict the retention time and fragment ion intensities of TMT-labeled peptides with high accuracy. Prosit-TMT supports CID and HCD fragmentation and ion trap and Orbitrap mass analyzers in a single model. Reanalysis of published TMT data sets show that this single model extracts substantial additional information. Applying Prosit-TMT, we discovered that the expression of many proteins in human breast milk follows a distinct daily cycle which may prime the newborn for nutritional or environmental cues.

TMT-based proteomics analysis of the cerebral cortex of TauT knockout rats

Background

Taurine serves a variety of nutritional and physiological roles, and it is mostly transported in cells via taurine transporter (TauT). The effect of taurine transporter in cerebral cortex is still unknown. We employed TMT label-based proteomics to find differences in proteins in the cerebral cortex of TauT knockout rats in this investigation. The goal of this research was to see how TauT deletion affected protein alterations in brain tissue and to see if there was a new research area for TauT.

Methods

The cerebral cortex of TauT knockout rats and wild-type control rats were analyzed using TMT-based proteomics, and differentially expressed proteins were analyzed by bioinformatics analysis means such as GO and KEGG, the association between the proteins was found by PPI, and biologically significant and interesting proteins were selected for verification by WB and immunohistochemistry.

Results

There were total of 8275 proteins found, but only 35 differentially expressed proteins were identified (27 up-regulated and 8 down-regulated), and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to predict the biological pathways and functional classification of the proteins. The results show that these differentially expressed proteins are mainly enriched in lysine degradation, cell cycle, chronic myeloid leukemia, and longevity regulating pathways-multiple species, renal cell carcinoma, pathways in cancer, etc. To verify the proteomic data, we analyzed the expression of Annexin6 and Pik3r2 by western blotting and immunofluorescence. The results are consistent with proteomics, which proves the reliability of our proteomics data.

Conclusion

Through TMT-based proteomics, we have a comprehensive understanding of the effect of TauT knockout on the changes of other proteins in the cerebral cortex, providing new evidence for further understanding the function of TauT.

Obtaining Complete Human Proteomes

Proteins are the molecular effectors of the information encoded in the genome. Proteomics aims at understanding the molecular functions of proteins in their biological context. In contrast to transcriptomics and genomics, the study of proteomes provides deeper insight into the dynamic regulatory layers encoded at the protein level, such as posttranslational modifications, subcellular localization, cell signaling, and protein-protein interactions. Currently, mass spectrometry (MS)-based proteomics is the technology of choice for studying proteomes at a system-wide scale, contributing to clinical biomarker discovery and fundamental molecular biology. MS technologies are continuously being developed to fulfill the requirements of speed, resolution, and quantitative accuracy, enabling the acquisition of comprehensive proteomes. In this review, we present how MS technology and acquisition methods have evolved to meet the requirements of cutting-edge proteomics research, which is describing the human proteome and its dynamic posttranslational modifications with unprecedented depth. Finally, we provide a perspective on studying proteomes at single-cell resolution. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Utilizing Heterostructured Porous Particles to Improve Traditional Paper Chromatography for Spontaneous Protein Separation

Chromatography is a classical technique for protein separation. However, the chromatography column is filled with tightly packed separation materials and requires an additional pressurizing pump to propel the flow of fluidic samples, largely restraining their applications. Here, we combine heterostructured porous particles with paper strips, realizing spontaneous separation of similarly sized proteins. The interconnected nanofibrous structure and good hydrophility of paper strips enable the spontaneous flow of the liquid sample, and the heterostructured porous particles provide versatile tools for protein separation via electrostatic interaction. The fabricated paper strips are inexpensive, user-friendly, and disposable and exhibit good separation performance. This work may offer a new avenue for fabricating on-site bioseparation tools and purifying various biomacromolecules.

Recent progress of proteomic analysis on spermatogenesis

Testis, the only organ responsible for generating sperm, is by far the organ with the largest variety of proteins and tissue-specific proteins in humans. In testis, spermatogenesis is a multi-step complex process well-accepted that protein and mRNA are decoupled in certain stages of spermatogenesis. With the fast development of mass spectrometry-based proteomics, it is possible to systemically study protein abundances and modifications in testis and sperm to help us understand the molecular mechanisms of spermatogenesis. This review provides an overview of the recent progress of proteomics analysis on spermatogenesis, including protein expression and multiple PTMs, such as phosphorylation, glycosylation, ubiquitylation, and acetylation.

Proteomic Technologies and their Application for Ensuring Meat Quality, Safety and Authenticity

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Proteomic tools were extensively used to understand the relationship between muscle proteome and conversion of muscle to meat, post-mortem proteolysis, meat texture, and variation in meat color. Developments in proteomic tools have also resulted in their application for addressing the safety and authenticity issues including meat species identification, detection of animal byproducts, non-meat ingredients and tissues in meat products, traceability, identification of genetically modified ingredients, chemical residues and other harmful substances. Proteomic tools are also being used in some of the potential areas like understanding the effect of animal transportation, stunning, slaughter stress, halal authentication and issues related to animal welfare. Emerging advances in proteomic and peptidomic technologies and their application in traceability, meat microbiology, safety and authentication are taking a major stride as an interesting and complementary alternative to DNA-based methods currently in use. Future research in meat science need to be linked to emerging metabolomic, lipidomic and other omic technologies for ensuring integrated meat quality and safety management. In this paper, a comprehensive overview of the use of proteomics for the assessment of quality and safety in the meat value chain and their potential application is discussed.

Advances in mass spectrometry-based footprinting of membrane proteins

Structural biology is entering an exciting time where many new high-resolution structures of large complexes and membrane proteins (MPs) are determined regularly. These advances have been driven by over 15 years of technological improvements, first in macromolecular crystallography, and recently in cryo-electron microscopy. Obtaining information about MP higher order structure and interactions is also a frontier, important but challenging owing to their unique properties and the need to choose suitable detergents/lipids for their study. The development of mass spectrometry (MS), both instruments and methodology in the past 10 years, has also advanced it as a complementary method to study MP structure and interactions. In this review, we discuss advances in MS-based footprinting for MPs and highlight recent methodologies that offer new promise for MP study by chemical footprinting and mass spectrometry.

Microalgae Bioactive Carbohydrates as a Novel Sustainable and Eco-Friendly Source of Prebiotics: Emerging Health Functionality and Recent Technologies for Extraction and Detection

There is a global interest in the novel consumption, nutritional trends, and the market of new prebiotic sources and their potential functional impacts. Commercially available nutritional supplements based on microalgae that are approved to be edible by FDA, like Arthrospira platensis (Cyanobacteria) and Chlorella vulgaris (Chlorophyta) become widely attractive. Microalgae are rich in carbohydrates, proteins, and polyunsaturated fatty acids that have high bioactivity. Recently, scientists are studying the microalgae polysaccharides (PS) or their derivatives (as dietary fibers) for their potential action as a novel prebiotic source for functional foods. Besides, the microalgae prebiotic polysaccharides are used for medication due to their antioxidant, anticancer, and antihypertensive bioactivities. This review provides an overview of microalgae prebiotics and other macromolecules' health benefits. The phytochemistry of various species as alternative future sources of novel polysaccharides were mentioned. The application as well as the production constraints and multidisciplinary approaches for evaluating microalgae phytochemistry were discussed. Additionally, the association between this potential of combining techniques like spectroscopic, chromatographic, and electrochemical analyses for microalgae sensation and analysis novelty compared to the chemical methods was emphasized.

Nanomaterial-assisted microfluidics for multiplex assays

Simultaneous detection of different biomarkers from a single specimen in a single test, allowing more rapid, efficient, and low-cost analysis, is of great significance for accurate diagnosis of disease and efficient monitoring of therapy. Recently, developments in microfabrication and nanotechnology have advanced the integration of nanomaterials in microfluidic devices toward multiplex assays of biomarkers, combining both the advantages of microfluidics and the unique properties of nanomaterials. In this review, we focus on the state of the art in multiplexed detection of biomarkers based on nanomaterial-assisted microfluidics. Following an overview of the typical microfluidic analytical techniques and the most commonly used nanomaterials for biochemistry analysis, we highlight in detail the nanomaterial-assisted microfluidic strategies for different biomarkers. These highly integrated platforms with minimum sample consumption, high sensitivity and specificity, low detection limit, enhanced signals, and reduced detection time have been extensively applied in various domains and show great potential in future point-of-care testing and clinical diagnostics.

Systematic optimization of targeted and multiplexed MS-based screening workflows for protein biomarkers

Background: The capability of targeted MS-based methods to simultaneously measure multiple analytes with high selectivity and sensitivity greatly facilitates the discovery and quantitation of novel biomarkers. However, the complexity of biological samples is a major bottleneck that requires extensive sample preparation. Results: This paper reports a generic workflow to optimize surrogate peptide-based protein biomarker screening for seven human proteins in a multiplexed manner without the need for any specific affinity reagents. Each step of the sample processing and LC-MS methods is systematically assessed and optimized for better analytical performance. Conclusion: The established method is used for the screening of multiple myeloma patient samples to determine which proteins could be robustly measured and serve as potential biomarkers of the disease.

Cataloguing the proteome: Current developments in single-molecule protein sequencing

The cellular proteome is complex and dynamic, with proteins playing a critical role in cell-level biological processes that contribute to homeostasis, stimuli response, and disease pathology, among others. As such, protein analysis and characterization are of extreme importance in both research and clinical settings. In the last few decades, most proteomics analysis has relied on mass spectrometry, affinity reagents, or some combination thereof. However, these techniques are limited by their requirements for large sample amounts, low resolution, and insufficient dynamic range, making them largely insufficient for the characterization of proteins in low-abundance or single-cell proteomic analysis. Despite unique technical challenges, several single-molecule protein sequencing (SMPS) technologies have been proposed in recent years to address these issues. In this review, we outline several approaches to SMPS technologies and discuss their advantages, limitations, and potential contributions toward an accurate, sensitive, and high-throughput platform.

Label-Free Protein Analysis by Pyro-Electrohydrodynamic Jet Printing of Gold Nanoparticles

The pyro-electrohydrodynamic jet (p-jet) printing technology has been used for the fabrication of confined assemblies of gold nanoparticles with a round shape and a diameter ranging between 100 and 200 μm. The surface-enhanced Raman spectroscopy (SERS) performance of the p-jet substrate was evaluated by using Rhodamine 6G (R6G) as a reference. The results demonstrate that this kind of SERS substrate exhibits strong plasmonic effects and a significant reproducibility of the signal with a coefficient of variation below 15%. We tested the signal behavior also in case of the bovine serum albumin (BSA) as a model analyte, to demonstrate the affinity with biomolecules. Strong SERS activity was measured also for BSA across the whole spot area. The spectral patterns collected in different locations of the sensing area were highly reproducible. This observation was substantiated by multivariate analysis of the imaging datasets and opens the route towards a potential application of this kind of SERS substrate in biosensing.

Protein Lipidation Types: Current Strategies for Enrichment and Characterization

Post-translational modifications regulate diverse activities of a colossal number of proteins. For example, various types of lipids can be covalently linked to proteins enzymatically or non-enzymatically. Protein lipidation is perhaps not as extensively studied as protein phosphorylation, ubiquitination, or glycosylation although it is no less significant than these modifications. Evidence suggests that proteins can be attached by at least seven types of lipids, including fatty acids, lipoic acids, isoprenoids, sterols, phospholipids, glycosylphosphatidylinositol anchors, and lipid-derived electrophiles. In this review, we summarize types of protein lipidation and methods used for their detection, with an emphasis on the conjugation of proteins with polyunsaturated fatty acids (PUFAs). We discuss possible reasons for the scarcity of reports on PUFA-modified proteins, limitations in current methodology, and potential approaches in detecting PUFA modifications.

Laser Ablation Synthesis in Solution and Nebulization of Silver-109 Nanoparticles for Mass Spectrometry and Mass Spectrometry Imaging

Preparation of monoisotopic silver-109 nanoparticles (¹⁰⁹AgNPs) by pulsed fiber laser (PFL) ablation synthesis in solution (LASiS) with the use of a 2D galvoscanner (2D GS) is described. The procedure of covering of custom-made stainless-steel MALDI targets containing studied objects via nebulization is also presented. Examples of application of the new method (PFL-2D GS LASiS and nebulization) in mass spectrometry (MS) analyses and MS imaging (MSI) are shown. These include tests with a nonionic nucleoside and saccharide, ionic amino acids, and also a low-molecular-weight polymer. Fingerprint MS imaging is shown as an example of a fast and simple MSI procedure.

Affinity Selection from Synthetic Peptide Libraries Enabled by De Novo MS/MS Sequencing

Recently, de novo MS/MS peptide sequencing has enabled the application of affinity selections to synthetic peptide mixtures that approach the diversity of phage libraries (> 108 random peptides). In conjunction with ‘split-mix’ solid phase synthesis to access equimolar peptide mixtures, this approach provides a straightforward means to examine synthetic peptide libraries of considerably higher diversity than has been feasible historically. Here, we offer a critical perspective on this work, report emerging data, and highlight opportunities for further methods refinement. With continued development, ‘affinity selection–mass spectrometry’ may become a complimentary approach to phage display, in vitro selection, and DNA-encoded libraries for the discovery of synthetic ligands that modulate protein function.

SEQUENCE SLIDER: integration of structural and genetic data to characterize isoforms from natural sources

Proteins isolated from natural sources can be composed of a mixture of isoforms with similar physicochemical properties that coexist in the final steps of purification. Yet, even where unverified, the assumed sequence is enforced throughout the structural studies. Herein, we propose a novel perspective to address the usually neglected sequence heterogeneity of natural products by integrating biophysical, genetic and structural data in our program SEQUENCE SLIDER. The aim is to assess the evidence supporting chemical composition in structure determination. Locally, we interrogate the experimental map to establish which side chains are supported by the structural data, and the genetic information relating sequence conservation is integrated into this statistic. Hence, we build a constrained peptide database, containing most probable sequences to interpret mass spectrometry data (MS). In parallel, we perform MS de novo sequencing with genomic-based algorithms to detect point mutations. We calibrated SLIDER with Gallus gallus lysozyme, whose sequence is unequivocally established and numerous natural isoforms are reported. We used SLIDER to characterize a metalloproteinase and a phospholipase A2-like protein from the venom of Bothrops moojeni and a crotoxin from Crotalus durissus collilineatus. This integrated approach offers a more realistic structural descriptor to characterize macromolecules isolated from natural sources.

Systems neuroimmunology: a review of multiomics methodologies to characterize neuroimmunological interactions in spinal and cranial diseases

Neuroimmunology plays a critical role in our understanding of the pathophysiological processes that underlie a variety of diseases treated by neurosurgeons, including degenerative disc disease (DDD), glioblastoma (GBM), aneurysmal subarachnoid hemorrhage (aSAH), and others. Compared with traditional methods in neuroimmunology, which study one pathway or gene at a time, emerging multiomics methodologies allow for holistic interrogation of multiple immune-signaling pathways to test hypotheses and the effects of therapeutics at a systems level. In this review, the authors summarize key concepts for gathering and analyzing multiomics data so that neurosurgeons can contribute to the emerging field of systems neuroimmunology. Additionally, they describe 3 use cases, based on original research published by their group and others, that utilize transcriptomic, metabolomic, and proteomic analyses to study immune-signaling pathways in DDD, aSAH, and GBM. Through these use cases, techniques for performing machine learning and network-based analyses to generate new clinical insights from multiomics data are shared. The authors hope that neurosurgeons might use this review as a summary of common tools and principles in systems immunology to better engage in creating the immunotherapies of tomorrow.

Label-Free Physical Techniques and Methodologies for Proteins Detection in Microfluidic Biosensor Structures

Proteins in biological fluids (blood, urine, cerebrospinal fluid) are important biomarkers of various pathological conditions. Protein biomarkers detection and quantification have been proven to be an indispensable diagnostic tool in clinical practice. There is a growing tendency towards using portable diagnostic biosensor devices for point-of-care (POC) analysis based on microfluidic technology as an alternative to conventional laboratory protein assays. In contrast to universally accepted analytical methods involving protein labeling, label-free approaches often allow the development of biosensors with minimal requirements for sample preparation by omitting expensive labelling reagents. The aim of the present work is to review the variety of physical label-free techniques of protein detection and characterization which are suitable for application in micro-fluidic structures and analyze the technological and material aspects of label-free biosensors that implement these methods. The most widely used optical and impedance spectroscopy techniques: absorption, fluorescence, surface plasmon resonance, Raman scattering, and interferometry, as well as new trends in photonics are reviewed. The challenges of materials selection, surfaces tailoring in microfluidic structures, and enhancement of the sensitivity and miniaturization of biosensor systems are discussed. The review provides an overview for current advances and future trends in microfluidics integrated technologies for label-free protein biomarkers detection and discusses existing challenges and a way towards novel solutions.

Proteomics based markers of clinical pain severity in juvenile idiopathic arthritis

INTRODUCTION

Juvenile idiopathic arthritis (JIA) is a cluster of autoimmune rheumatic diseases occurring in children 16 years of age or less. While it is well-known that pain may be experienced during inflammatory and non-inflammatory states, much remains ambiguous regarding the molecular mechanisms that may drive JIA pain. Thus, in this pilot study, we explored the variability of the serum proteomes in relation to pain severity in a cohort of JIA patients.

METHODS

Serum samples from 15 JIA patients (male and female, 12.7 ± 2.8 years of age) were assessed using liquid chromatography/mass spectrometry (LC/MS). Correlation analyses were performed to determine the relationships among protein levels and self-reported clinical pain severity. Additionally, how the expression of pain-associated proteins related to markers of inflammation (Erythrocyte Sedimentation Rate (ESR)) or morphological properties of the central nervous system (subcortical volume and cortical thickness) implicated in JIA were also evaluated.

RESULTS

306 proteins were identified in the JIA cohort of which 14 were significantly (p < 0.05) associated with clinical pain severity. Functional properties of the identified pain-associated proteins included but were not limited to humoral immunity (IGLV3.9), inflammatory response (PRG4) and angiogenesis (ANG). Associations among pain-associated proteins and ESR (IGHV3.9, PRG4, CST3, VWF, ALB), as well as caudate nucleus volume (BTD, AGT, IGHV3.74) and insular cortex thickness (BTD, LGALS3BP) were also observed.

CONCLUSIONS

The current proteomic findings suggest both inflammatory- and non-inflammatory mediated mechanisms as potential factors associated with JIA pain. Validation of these preliminary observations using larger patient cohorts and a longitudinal study design may further point to novel serologic markers of pain in JIA.

Computer-aided design of reversible hybridization chain reaction (CAD-HCR) enables multiplexed single-cell spatial proteomics imaging

In situ spatial proteomics analysis of a single cell has not been achieved yet, mainly because of insufficient throughput and sensitivity of current techniques. Recent progress on immuno-nucleic acid amplification technology presents tremendous opportunities to address this issue. Here, we report an innovative hybridization chain reaction (HCR) technique that involves computer-aided design (CAD) and reversible assembly. CAD enables highly multiplexed HCR with a sequence database that can work in parallel, while reversible assembly enables the switching of HCR between a working state and a resting state. Thus, CAD-HCR has been successfully adopted for single-cell spatial proteomics analysis. The fluorescence signal of CAD-HCR is comparable with conventional immunofluorescence, and it is positively correlated with the abundance of target proteins, which is beneficial for the visualization of proteins. The method developed here expands the toolbox of single-cell analysis and proteomics studies, as well as the performance and application of HCR.

Single-cell sorting based on secreted products for functionally defined cell therapies

Cell therapies have emerged as a promising new class of "living" therapeutics over the last decade and have been particularly successful for treating hematological malignancies. Increasingly, cellular therapeutics are being developed with the aim of treating almost any disease, from solid tumors and autoimmune disorders to fibrosis, neurodegenerative disorders and even aging itself. However, their therapeutic potential has remained limited due to the fundamental differences in how molecular and cellular therapies function. While the structure of a molecular therapeutic is directly linked to biological function, cells with the same genetic blueprint can have vastly different functional properties (e.g., secretion, proliferation, cell killing, migration). Although there exists a vast array of analytical and preparative separation approaches for molecules, the functional differences among cells are exacerbated by a lack of functional potency-based sorting approaches. In this context, we describe the need for next-generation single-cell profiling microtechnologies that allow the direct evaluation and sorting of single cells based on functional properties, with a focus on secreted molecules, which are critical for the in vivo efficacy of current cell therapies. We first define three critical processes for single-cell secretion-based profiling technology: (1) partitioning individual cells into uniform compartments; (2) accumulating secretions and labeling via reporter molecules; and (3) measuring the signal associated with the reporter and, if sorting, triggering a sorting event based on these reporter signals. We summarize recent academic and commercial technologies for functional single-cell analysis in addition to sorting and industrial applications of these technologies. These approaches fall into three categories: microchamber, microfluidic droplet, and lab-on-a-particle technologies. Finally, we outline a number of unmet needs in terms of the discovery, design and manufacturing of cellular therapeutics and how the next generation of single-cell functional screening technologies could allow the realization of robust cellular therapeutics for all patients.

Identification of Circulating Biomarkers of COVID-19 Using MALDI-TOF Mass Spectrometry

Matrix-assisted laser desorption/ionization source coupled with time-of-flight mass analyzer mass spectrometry (MALDI-TOF MS) is being widely used to obtain proteomic profiles for clinical purposes, as a fast, low-cost, robust, and efficient technique. Here we describe a method for biofluid analysis using MALDI-TOF MS for rapid acquisition of proteomic signatures of COVID-19 infected patients. By using solid-phase extraction, the method allows the analysis of biofluids in less than 15 min.

Ultrasensitive Trace Sample Proteomics Unraveled the Protein Remodeling during Mesenchymal-Amoeboid Transition

Deep mining the proteome of trace biological samples is critical for biomedical applications. However, it remains a challenge due to the loss of analytes caused by current sample preparation procedures. To address this, we recently developed a single-pot and miniaturized in-solution digestion (SMID) method for minute sample handling with three streamlined steps and completed within 3 h. The SMID approach outperformed the traditional workflow in substantially saving time, reducing sample loss, and exhibiting extensive applicability for 10-100 000 cell analysis. This user-friendly and high-sensitivity strategy enables ∼5300 proteins and 53 000 peptides to be confidently identified within 1 h of mass spectrometry (MS) time from a small amount of 1000 HeLa cells. In addition, we accurately and robustly detected proteomes in 10 mouse oocytes with excellent reproducibility. We further adopted SMID for the proteome analysis in cell migration under confinement, which induced cells to undergo a mesenchymal-amoeboid transition (MAT). During the MAT, a systematic quantitative proteome map of 1000 HeLa cells was constructed with seven expression profile clusters, which illustrated the application of SMID and provided a fundamental resource to investigate the mechanism of MAT.

Tandem mass tag (TMT)-based proteomic analysis of Cryptosporidium andersoni oocysts before and after excystation

Background

Cryptosporidium andersoni initiates infection by releasing sporozoites from oocysts through excystation. However, the proteins involved in excystation are unknown. Determining the proteins that participate in the excystation of C. andersoni oocysts will increase our understanding of the excystation process.

Methods

Cryptosporidium andersoni oocysts were collected and purified from the feces of naturally infected adult cows. Tandem mass tags (TMT), coupled with liquid chromatography–tandem mass spectrometry (LC–MS/MS) proteomic analysis, were used to investigate the proteomic expression profiles of C. andersoni oocysts before and after excystation.

Results

Proteomic analysis identified a total of 1586 proteins, of which 17 were differentially expressed proteins (DEPs) upon excystation. These included 10 upregulated and seven downregulated proteins. The 17 proteins had multiple biological functions associated with control of gene expression at the level of transcription and biosynthetic and metabolic processes. Quantitative real-time RT-PCR of eight selected genes validated the proteomic data.

Conclusions

This study provides information on the protein composition of C. andersoni oocysts as well as possible excystation factors. The data may be useful in identifying genes for diagnosis, vaccine development, and immunotherapy for Cryptosporidium.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-05113-6.

Nanoparticles and photochemistry for native-like transmembrane protein footprinting

Mass spectrometry-based footprinting can probe higher order structure of soluble proteins in their native states and serve as a complement to high-resolution approaches. Traditional footprinting approaches, however, are hampered for integral membrane proteins because their transmembrane regions are not accessible to solvent, and they contain hydrophobic residues that are generally unreactive with most chemical reagents. To address this limitation, we bond photocatalytic titanium dioxide (TiO₂) nanoparticles to a lipid bilayer. Upon laser irradiation, the nanoparticles produce local concentrations of radicals that penetrate the lipid layer, which is made permeable by a simultaneous laser-initiated Paternò-Büchi reaction. This approach achieves footprinting for integral membrane proteins in liposomes, helps locate both ligand-binding residues in a transporter and ligand-induced conformational changes, and reveals structural aspects of proteins at the flexible unbound state. Overall, this approach proves effective in intramembrane footprinting and forges a connection between material science and biology.