Advancements in clinical approaches, analytical methods, and smart sampling for LC-MS-based protein determination from dried matrix spots.

Determination of proteins from dried matrix spots using MS is an expanding research area. Mainly, the collected dried matrix sample is whole blood from a finger or heal prick, resulting in dried blood spots. However as other matrices such as plasma, serum, urine, and tear fluid also can be collected in this way, the term dried matrix spot is used as an overarching term. In this review, the focus is on advancements in the field made from 2017 up to 2023. In the first part reviews concerning the subject are discussed. After this, advancements made for clinical purposes are highlighted. Both targeted protein analyses, with and without the use of affinity extractions, as well as untargeted, global proteomic approaches are discussed. In the last part, both methodological advancements are being reviewed as well as the possibility to integrate sample preparation steps during the sample handling. The focus, of this so-called smart sampling, is on the incorporation of cell separation, proteolysis, and antibody-based affinity capture.

Microwave-assisted and methanol/acetic acid-free method for rapid staining of proteins in SDS-PAGE gels.

We describe a microwave-assisted, methanol and acetic acid-free, inexpensive method for rapid staining of SDS-PAGE proteins. Only citric acid, benzoic acid, and Coomassie brilliant blue G-250 (CBG) were used. Microwave irradiation reduced the detection duration, and proteins in a clear background were visualized within 30 min of destaining, after 2 min of fixing and 12 min of staining. By using this protocol, comparable band intensities were obtained to the conventional methanol/acetic acid method.

Self-Powered Microfluidics for Point-of-Care Solutions: From Sampling to Detection of Proteins and Nucleic Acids.

Self-powered microfluidics presents a revolutionary approach to address the challenges of healthcare in decentralized and point-of-care settings where limited access to resources and infrastructure prevails or rapid clinical decision-making is critical. These microfluidic systems exploit physical and chemical phenomena, such as capillary forces and surface tension, to manipulate tiny volumes of fluids without the need for external power sources, making them cost-effective and highly portable. Recent technological advancements have demonstrated the ability to preprogram complex multistep liquid operations within the microfluidic circuit of these standalone systems, which enabled the integration of sensitive detection and readout principles. This chapter first addresses how the accessibility to in vitro diagnostics can be improved by shifting toward decentralized approaches like remote microsampling and point-of-care testing. Next, the crucial role of self-powered microfluidic technologies to enable this patient-centric healthcare transition is emphasized using various state-of-the-art examples, with a primary focus on applications related to biofluid collection and the detection of either proteins or nucleic acids. This chapter concludes with a summary of the main findings and our vision of the future perspectives in the field of self-powered microfluidic technologies and their use for in vitro diagnostics applications.

Exploring the fragmentation efficiency of proteins analyzed by MALDI-TOF-TOF tandem mass spectrometry using computational and statistical analyses.

Matrix-assisted laser desorption/ionization time-of-flight-time-of-flight (MALDI-TOF-TOF) tandem mass spectrometry (MS/MS) is a rapid technique for identifying intact proteins from unfractionated mixtures by top-down proteomic analysis. MS/MS allows isolation of specific intact protein ions prior to fragmentation, allowing fragment ion attribution to a specific precursor ion. However, the fragmentation efficiency of mature, intact protein ions by MS/MS post-source decay (PSD) varies widely, and the biochemical and structural factors of the protein that contribute to it are poorly understood. With the advent of protein structure prediction algorithms such as Alphafold2, we have wider access to protein structures for which no crystal structure exists. In this work, we use a statistical approach to explore the properties of bacterial proteins that can affect their gas phase dissociation via PSD. We extract various protein properties from Alphafold2 predictions and analyze their effect on fragmentation efficiency. Our results show that the fragmentation efficiency from cleavage of the polypeptide backbone on the C-terminal side of glutamic acid (E) and asparagine (N) residues were nearly equal. In addition, we found that the rearrangement and cleavage on the C-terminal side of aspartic acid (D) residues that result from the aspartic acid effect (AAE) were higher than for E- and N-residues. From residue interaction network analysis, we identified several local centrality measures and discussed their implications regarding the AAE. We also confirmed the selective cleavage of the backbone at D-proline bonds in proteins and further extend it to N-proline bonds. Finally, we note an enhancement of the AAE mechanism when the residue on the C-terminal side of D-, E- and N-residues is glycine. To the best of our knowledge, this is the first report of this phenomenon. Our study demonstrates the value of using statistical analyses of protein sequences and their predicted structures to better understand the fragmentation of the intact protein ions in the gas phase.

Panda-UV Unlocks Deeper Protein Characterization with Internal Fragments in Ultraviolet Photodissociation Mass Spectrometry.

Ultraviolet photodissociation (UVPD) mass spectrometry unlocks insights into the protein structure and sequence through fragmentation patterns. While N- and C-terminal fragments are traditionally relied upon, this work highlights the critical role of internal fragments in achieving near-complete sequencing of protein. Previous limitations of internal fragment utilization, owing to their abundance and potential for random matching, are addressed here with the development of Panda-UV, a novel software tool combining spectral calibration, and Pearson correlation coefficient scoring for confident fragment assignment. Panda-UV showcases its power through comprehensive benchmarks on three model proteins. The inclusion of internal fragments boosts identified fragment numbers by 26% and enhances average protein sequence coverage to a remarkable 93% for intact proteins, unlocking the hidden region of the largest protein carbonic anhydrase II in model proteins. Notably, an average of 65% of internal fragments can be identified in multiple replicates, demonstrating the high confidence of the fragments Panda-UV provided. Finally, the sequence coverages of mAb subunits can be increased up to 86% and the complementary determining regions (CDRs) are nearly completely sequenced in a single experiment. The source codes of Panda-UV are available at https://github.com/PHOENIXcenter/Panda-UV.

Exploring affinity chromatography in proteomics: A comprehensive review.

Over the past decades, the proteomics field has undergone rapid growth. Progress in mass spectrometry and bioinformatics, together with separation methods, has brought many innovative approaches to the study of the molecular biology of the cell. The potential of affinity chromatography was recognized immediately after its first application in proteomics, and since that time, it has become one of the cornerstones of many proteomic protocols. Indeed, this chromatographic technique exploiting the specific binding between two molecules has been employed for numerous purposes, from selective removal of interfering (over)abundant proteins or enrichment of scarce biomarkers in complex biological samples to mapping the post-translational modifications and protein interactions with other proteins, nucleic acids or biologically active small molecules. This review presents a comprehensive survey of this versatile analytical tool in current proteomics. To navigate the reader, the haphazard space of affinity separations is classified according to the experiment's aims and the separated molecule's nature. Different types of available ligands and experimental strategies are discussed in further detail for each of the mentioned procedures.

Turn-off enzyme activity of histidine-rich peptides for the detection of lysozyme.

An assay that integrates histidine-rich peptides (HisRPs) with high-affinity aptamers was developed enabling the specific and sensitive determination of the target lysozyme. The enzyme-like activity of HisRP is inhibited by its interaction with a target recognized by an aptamer. In the presence of the target, lysozyme molecules progressively assemble on the surface of HisRP in a concentration-dependent manner, resulting in the gradual suppression of enzyme-like activity. This inhibition of HisRP's enzyme-like activity can be visually observed through color changes in the reaction product or quantified using UV-visible absorption spectroscopy. Under optimal conditions, the proposed colorimetric assay for lysozyme had a detection limit as low as 1 nM and exhibited excellent selectivity against other nonspecific interferents. Furthermore, subsequent research validated the practical applicability of the developed colorimetric approach to saliva samples, indicating that the assay holds significant potential for the detection of lysozymes in samples derived from humans.

SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks.

The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determination are time-consuming and expensive, whereas computational methods, if accurate, would represent a much more efficient alternative. This article introduces an ab initio protein subcellular localization predictor based on an ensemble of Deep N-to-1 Convolutional Neural Networks. Our predictor is trained and tested on strict redundancy-reduced datasets and achieves 63% accuracy for the diverse number of classes. This predictor is a step towards bridging the gap between a protein sequence and the protein's function. It can potentially provide information about protein-protein interaction to facilitate drug design and processes like vaccine production that are essential to disease prevention.

A Review for Artificial Intelligence Based Protein Subcellular Localization.

Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer's disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcellular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.

Human epididymis protein 4: Analysis of national health and nutrition examination survey data.

BACKGROUND: Human epididymis protein 4 (HE4) is a tumor marker overexpressed in ovarian cancer and is commonly utilized to aid with diagnosis of an adnexal mass. HE4 levels vary based on pregnancy, age, menopausal status, and tobacco use. OBJECTIVE(S): The objective of this study was to evaluate population-based data to examine factors that affect HE4 among adult women in the United States and stratify levels of HE4 by demographic and gynecologic factors. STUDY DESIGN: A retrospective analysis was conducted using data from 2,480 women aged 20 + who participated in the National Health and Nutrition Examination Survey (2001-2002). From these cross-sectional data, serum HE4 and cotinine, a marker of tobacco exposure, were combined with demographic and interview data. Estimated glomerular filtration rates (eGFR) were based on serum creatinine, age, sex, and race. Other variables of interest included menopausal status, pregnancy, and various gynecologic factors. Summary HE4 data are provided as geometric means with associated 95 % confidence intervals. RESULTS: HE4 levels were independently associated with age, renal function, and nicotine use, all p < 0.001. Pre-menopausal women with a history of endometriosis were found to have elevated HE4 levels compared to those without, p < 0.01; however, we found no such difference among post-menopausal women. Adjusting for age, no differences in HE4 were found based on race/ethnicity, p = 0.29. HE4 levels showed statistically significant associations with income level; however, these were small and clinically irrelevant. CONCLUSION: This study provides evaluation of HE4 levels among a data set representative of 98.5 million non-institutionalized women in the United States and gives insight into extraneous factors that may influence these levels.

Development of Optical Differential Sensing Based on Nanomaterials for Biological Analysis.

The discrimination and recognition of biological targets, such as proteins, cells, and bacteria, are of utmost importance in various fields of biological research and production. These include areas like biological medicine, clinical diagnosis, and microbiology analysis. In order to efficiently and cost-effectively identify a specific target from a wide range of possibilities, researchers have developed a technique called differential sensing. Unlike traditional "lock-and-key" sensors that rely on specific interactions between receptors and analytes, differential sensing makes use of cross-reactive receptors. These sensors offer less specificity but can cross-react with a wide range of analytes to produce a large amount of data. Many pattern recognition strategies have been developed and have shown promising results in identifying complex analytes. To create advanced sensor arrays for higher analysis efficiency and larger recognizing range, various nanomaterials have been utilized as sensing probes. These nanomaterials possess distinct molecular affinities, optical/electrical properties, and biological compatibility, and are conveniently functionalized. In this review, our focus is on recently reported optical sensor arrays that utilize nanomaterials to discriminate bioanalytes, including proteins, cells, and bacteria.

Deciphering biomolecular complexities: the indispensable role of surface-enhanced Raman spectroscopy in modern bioanalytical research.

Surface-enhanced Raman spectroscopy (SERS) has emerged as an indispensable analytical tool in biomolecular research, providing unmatched sensitivity critical for the elucidation of biomolecular structures. This review presents a thorough examination of SERS, outlining its fundamental principles, cataloging its varied applications within the biomolecular sphere, and contemplating its future developmental trajectories. We begin with a detailed analysis of SERS's mechanistic principles, emphasizing both the phenomena of surface enhancement and the complexities inherent in Raman scattering spectroscopy. Subsequently, we delve into the pivotal role of SERS in the structural analysis of diverse biomolecules, including proteins, nucleic acids, lipids, carbohydrates, and biochromes. The remarkable capabilities of SERS extend beyond mere detection, offering profound insights into biomolecular configurations and interactions, thereby enriching our comprehension of intricate biological processes. This review also sheds light on the application of SERS in real-time monitoring of various bio-relevant compounds, from enzymes and coenzymes to metal ion-chelate complexes and cellular organelles, thereby providing a holistic view and empowering researchers to unravel the complexities of biological systems. We also address the current challenges faced by SERS, such as enhancing sensitivity and resolution, developing stable and reproducible substrates, and conducting thorough analyses in complex biological matrices. Nonetheless, the continual advancements in nanotechnology and spectroscopy solidify the standing of SERS as a formidable force in biomolecular research. In conclusion, the versatility and robustness of SERS not only deepen our understanding of biomolecular intricacies but also pave the way for significant developments in medical research, therapeutic innovation, and diagnostic approaches.

Dimethyl sulfoxide as a gas phase charge-reducing agent for the determination of PEGylated proteins' intact mass.

Determination of PEGylated proteins' intact mass by mass spectrometry is challenging due to the molecules' large size, excessive charges, and instrument limitations. Previous efforts have been reported. However, signal variability, ion coalescence, and a generally low degree of robustness have been observed. In this work, we have explored the capabilities of post-column infusion of dimethyl sulfoxide (DMSO) following reversed-phase liquid chromatography-mass spectrometry (RP-LCMS) to determine PEG-filgrastim' intact mass, and to characterize its PEG moiety. The method was optimized around reproducibility (six preparations, and three injection replicates) with an in-house prepared PEG-filgrastim standard. The method showed a mass accuracy of ≤1.2 Da. The average molecular weight (MWEO=483) was 40 147.9 Da. The number average molecular weight (Mn) and the weight average molecular weight (Mw) were observed to be 40 101.1 and 40 113.9 Da, respectively, both with an RSD of 0.03%. The molecular weight distribution of ethylene oxide (EO), the polydispersity index (PDI), was 1.0003 for all preparations with a minimum and maximum number of EO units of 448 ± 2 and 516 ± 2, respectively. The method was finally applied to commercially available Neulasta® lots where the Mn and Mw were 39 995.8 and 40 008.8 Da, respectively, both with an RSD of 0.1%. The minimum and maximum EO units across the lots were observed to be 444.5 ± 1.5 and 514 ± 3, respectively. The PDI for all Neulasta® lots was 1.0003. This study provides an insightful characterization of Neulasta® and describes a robust LC-MS methodology for the characterization of the PEGylated proteins.

Perspective: The complex relationship between charge, mobility, and gas-phase protein structure.

Ion mobility spectrometry coupled to mass spectrometry (IMS/MS) is a widely used tool for biomolecular separations and structural elucidation. The application of IMS/MS has resulted in exciting developments in structural proteomics and genomics. This perspective gives a brief background of the field, addresses some of the important issues in making structural measurements, and introduces complementary techniques.

Biochemical changes in lipid and protein metabolism caused by mannose-Raman spectroscopy studies.

Biochemical analysis of human normal bronchial cells (BEpiC) and human cancer lung cells (A549) has been performed by using Raman spectroscopy and Raman imaging. Our approach provides a biochemical compositional mapping of the main cell components: nucleus, mitochondria, lipid droplets, endoplasmic reticulum, cytoplasm and cell membrane. We proved that Raman spectroscopy and Raman imaging can distinguish successfully BEpiC and A549 cells. In this study, we have focused on the role of mannose in cancer development. It has been shown that changes in the concentration of mannose can regulate some metabolic processes in cells. Presented results suggest lipids and proteins can be considered as Raman biomarkers during lung cancer progression. Analysis obtained for bands 1444 cm-1, and 2854 cm-1 characteristic for lipids and derivatives proved that the addition of mannose reduced levels of these compounds. Results obtained for protein compounds based on bands 858 cm-1, 1004 cm-1 and 1584 cm-1 proved that the addition of mannose increases the values of protein in BEpiC cells and blocks protein glycolisation in A549 cells. Noticing Raman spectral changes in BEpiC and A549 cells supplemented with mannose can help to understand the mechanism of sugar metabolism during cancer development and could play in the future an important role in clinical treatment.

Human epididymal protein 4 and its combined detection show good diagnostic value in lung cancer: A retrospective study.

OBJECTIVES: This study aimed to assess the diagnostic value of human epididymal protein 4 (HE4), a potential novel biomarker for lung cancer, and its combined detection with five other conventional biomarkers in lung cancer diagnosis and subtyping. METHODS: In this retrospective study, 115 lung cancer patients, 50 patients with benign pulmonary disease, and 50 healthy controls were included. Serum HE4, progastrin-releasing peptide (ProGRP), squamous cell carcinoma (SCC) antigen, cytokeratin-19 fragment (CYFRA21-1), neuron-specific enolase (NSE), and carcinoembryonic antigen (CEA) were analyzed using the electrochemiluminescence immunoassay and chemiluminescence immunoassay. The receiver operating characteristic curve was performed to analyze the diagnostic efficacy of individual biomarkers in identifying both lung cancer and its histologic subtypes. RESULTS: All six biomarkers showed significantly elevated levels in the lung cancer group compared to both benign pulmonary disease and control groups (P < 0.05). Among the biomarkers evaluated, HE4 exhibited the highest diagnostic performance for lung cancer, lung adenocarcinoma, and lung squamous cell carcinoma with area under the curve (AUC) values of 0.921, 0.891, and 0.937, respectively. ProGRP was the optimal biomarker for small cell lung cancer with an AUC of 0.973. The combination of all six biomarkers yielded the largest AUCs in the diagnosis of lung cancer subtypes (0.937 for lung adenocarcinoma, 0.998 for lung squamous cell carcinoma, and 0.985 for small cell lung cancer). Furthermore, specific combinations, such as HE4 + CEA, HE4 + SCC, and ProGRP + HE4 + NSE, showed strong diagnostic performance in lung cancer. CONCLUSIONS: HE4 and its combined detection held substantial clinical significance in the diagnosis of lung cancer and its histologic subtyping, especially for lung adenocarcinoma and lung squamous cell carcinoma.

Paper-based sensors: affordable, versatile, and emerging analyte detection platforms.

Paper-based sensors, often referred to as paper-based analytical devices (PADs), stand as a transformative technology in the field of analytical chemistry. They offer an affordable, versatile, and accessible solution for diverse analyte detection. These sensors harness the unique properties of paper substrates to provide a cost-effective and adaptable platform for rapid analyte detection, spanning chemical species, biomolecules, and pathogens. This review highlights the key attributes that make paper-based sensors an attractive choice for analyte detection. PADs demonstrate their versatility by accommodating a wide range of analytes, from ions and gases to proteins, nucleic acids, and more, with customizable designs for specific applications. Their user-friendly operation and minimal infrastructure requirements suit point-of-care diagnostics, environmental monitoring, food safety, and more. This review also explores various fabrication methods such as inkjet printing, wax printing, screen printing, dip coating, and photolithography. Incorporating nanomaterials and biorecognition elements promises even more sophisticated and sensitive applications.

Novel EDTA mediated ethanol protein precipitation method and the application for polysorbate quantification in high protein concentration biopharmaceuticals.

Non-ionic surfactants such as Polysorbate 20/ 80 (PS20/ PS80), are commonly used in protein drug formulations to increase protein stability by protecting against interfacial stress and surface absorption. Polysorbate is susceptible to degradation which can impact product stability, leading to the formation of sub-visible and/or visible particles in the drug product during its shelf-life, affecting patient safety and efficacy. Therefore, it is important to monitor polysorbate concentration in drug product formulations of biotherapeutic drugs. The common method for measuring polysorbate concentration in drug product formulations uses mixed mode ion exchange reversed phase HPLC (MAX) coupled to evaporative light scattering detection (ELSD). However, high protein concentration can adversely impact method performance due to high sample viscosity, gel formation, column clogging, interfering peaks and loss of accuracy. To overcome this, a new method was developed based on EDTA mediated ethanol protein precipitation (EDTA/EtOH). This method was successfully implemented for the analysis of polysorbate in antibody formulations with wide range of protein concentration (10-250 mg/mL).

A Water-Soluble Wavy Coordination Polymer of Cu(II) as a Turn-On Luminescent Probe for Histidine and Histidine-Rich Proteins/Peptides.

Histidine plays an essential role in most biological systems. Changes in the homeostasis of histidine and histidine-rich proteins are connected to several diseases. Herein, we report a water-soluble Cu(II) coordination polymer, labeled CuCP, for the fluorimetric detection of histidine and histidine-rich proteins and peptides. Single-crystal structure determination of CuCP revealed a two-dimensional wavy network structure in which a carboxylate group connects the individual Cu(II) dimer unit in a syn-anti conformation. The weakly luminescent and water-soluble CuCP shows turn-on blue emission in the presence of histidine and histidine-rich peptides and proteins. The polymer can also stain histidine-rich proteins via gel electrophoresis. The limits of quantifications for histidine, glycine-histidine, serine-histidine, human serum albumin (HSA), bovine serum albumin, pepsin, trypsin, and lysozyme were found to be 300, 160, 600, 300, 600, 800, 120, and 290 nM, respectively. Utilizing the fluorescence turn-on property of CuCP, we measured HSA quantitatively in the urine samples. We also validated the present urinary HSA measurement assay with existing analytical techniques. Job's plot, 1H NMR, high-resolution mass spectrometry (HRMS), electron paramagnetic resonance (EPR), fluorescence, and UV-vis studies confirmed the ligand displacement from CuCP in the presence of histidine.