Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: opportunities and potential limitations

Proteomic insights into paediatric cancer: Unravelling molecular signatures and therapeutic opportunities

Survival rates in some paediatric cancers have improved greatly over recent decades, in part due to the identification of diagnostic, prognostic and predictive molecular signatures, and the development of risk-directed therapies. However, other paediatric cancers have proved difficult to treat, and there is an urgent need to identify novel biomarkers that reveal therapeutic opportunities. The proteome is the total set of expressed proteins present in a cell or tissue at a point in time, and is vastly more dynamic than the genome. Proteomics holds significant promise for cancer research, as proteins are ultimately responsible for cellular phenotype and are the target of most anticancer drugs. Here, we review the discoveries, opportunities and challenges of proteomic analyses in paediatric cancer, with a focus on mass spectrometry (MS)-based approaches. Accelerating incorporation of proteomics into paediatric precision medicine has the potential to improve survival and quality of life for children with cancer.

Proteomic Analysis Identifies GSN as a Noninvasive Circulating Serum Biomarker for Predicting Early Recurrence of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is susceptible to early recurrence, but it lacks effective predictive biomarkers. In this study, we retrospectively selected 179 individuals as a discovery cohort (126 HCC patients and 53 liver cirrhosis (LC) patients) for screening candidate serum biomarkers of early recurrence based on data independent acquisition-mass spectrometry strategy. And then, the candidate biomarkers were validated in an additional independent cohort with 192 individuals (142 HCC patients and 50 LC patients) using parallel reaction monitoring targeted quantitative techniques (PXD047852). Eventually, we validated that gelsolin (GSN) concentrations were significantly lower in HCC than in LC (p < 0.0001), patients with low GSN concentrations had a poor prognosis (p < 0.0001), and GSN concentrations were significantly lower in early recurrence HCC than in late recurrence HCC (p < 0.0001). These trends were also observed in alpha-fetoprotein (AFP)-negative HCC patients. The area under the curve of machine-learning-based predictive model (GSN and microvascular invasion) for predicting early recurrence risk reached 0.803 (95% confidence interval (CI): 0.786-0.820) and maintained the same efficacy in AFP-negative patients. In conclusion, GSN is a novel serum biomarker for early recurrence of HCC. The model could provide timely warning to HCC patients at high risk of recurrence.

A systematic assessment of matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) application for rapid identification of pathogenic microbes that affect food crops: delivered and future deliverables

MALDI-TOF MS has decades of experience in the detection and identification of microbial pathogens. This has now become a valuable analytical tool when it comes to the identification and detection of clinical microbial pathogens. This review gives a brief synopsis of what has been achieved using MALDI-TOF MS in clinical microbiology. The major focus, however, is on summarizing and highlighting the effectiveness of MALDI-TOF MS as a novel tool for rapid identification of food crop microbial pathogens. The methods used and the sample preparation methodologies reported thus far have been highlighted and the challenges and gaps and recommendations for fine tuning the technique have been put forth. In an era where anything close to the health and welfare of humanity has been considered as the top priority, this review pitches on one such relevant research topics.

Proteomic Portrait of Human Lymphoma Reveals Protein Molecular Fingerprint of Disease Specific Subtypes and Progression

An altered proteome in lymph nodes often suggests abnormal signaling pathways that may be associated with diverse lymphatic disorders. Current clinical biomarkers for histological classification of lymphomas have encountered many discrepancies, particularly for borderline cases. Therefore, we launched a comprehensive proteomic study aimed to establish a proteomic landscape of patients with various lymphatic disorders and identify proteomic variations associated with different disease subgroups. In this study, 109 fresh-frozen lymph node tissues from patients with various lymphatic disorders (with a focus on Non-Hodgkin's Lymphoma) were analyzed by data-independent acquisition mass spectrometry. A quantitative proteomic landscape was comprehensively characterized, leading to the identification of featured protein profiles for each subgroup. Potential correlations between clinical outcomes and expression profiles of signature proteins were also probed. Two representative signature proteins, phospholipid-binding proteins Annexin A6 (ANXA6) and Phospholipase C Gamma 2 (PLCG2), were successfully validated via immunohistochemistry. We also evaluated the capability of acquired proteomic signatures to segregate multiple lymphatic abnormalities and identified several core signature proteins, such as Sialic Acid Binding Ig Like Lectin 1 (SIGLEC1) and GTPase of immunity-associated protein 5 (GIMAP5). In summary, the established lympho-specific data resource provides a comprehensive map of protein expression in lymph nodes during multiple disease states, thus extending the existing human tissue proteome atlas. Our findings will be of great value in exploring protein expression and regulation underlying lymphatic malignancies, while also providing novel protein candidates to classify various lymphomas for more precise medical practice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00075-w.

Nano-DESI Mass Spectrometry Imaging of Proteoforms in Biological Tissues with High Spatial Resolution

Mass spectrometry imaging (MSI) is a powerful tool for label-free mapping of the spatial distribution of proteins in biological tissues. We have previously demonstrated imaging of individual proteoforms in biological tissues using nanospray desorption electrospray ionization (nano-DESI), an ambient liquid extraction-based MSI technique. Nano-DESI MSI generates multiply charged protein ions, which is advantageous for their identification using top-down proteomics analysis. In this study, we demonstrate proteoform mapping in biological tissues with a spatial resolution down to 7 μm using nano-DESI MSI. A substantial decrease in protein signals observed in high-spatial-resolution MSI makes these experiments challenging. We have enhanced the sensitivity of nano-DESI MSI experiments by optimizing the design of the capillary-based probe and the thickness of the tissue section. In addition, we demonstrate that oversampling may be used to further improve spatial resolution at little or no expense to sensitivity. These developments represent a new step in MSI-based spatial proteomics, which complements targeted imaging modalities widely used for studying biological systems.

Mass Spectrometry-Based Untargeted Approaches to Reveal Diagnostic Signatures of Male Infertility in Seminal Plasma: A New Laboratory Perspective for the Clinical Management of Infertility?

Male infertility has been recognized as a global health problem. Semen analysis, although considered the golden standard, may not provide a confident male infertility diagnosis alone. Hence, there is the urgent request for an innovative and reliable platform to detect biomarkers of infertility. The rapid expansion of mass spectrometry (MS) technology in the field of the 'omics' disciplines, has incredibly proved the great potential of MS-based diagnostic tests to revolutionize the future of pathology, microbiology and laboratory medicine. Despite the increasing success in the microbiology area, MS-biomarkers of male infertility currently remain a proteomic challenge. In order to address this issue, this review encompasses proteomics investigations by untargeted approaches with a special focus on experimental designs and strategies (bottom-up and top-down) for seminal fluid proteome profiling. The studies reported here witness the efforts of the scientific community to address these investigations aimed at the discovery of MS-biomarkers of male infertility. Proteomics untargeted approaches, depending on the study design, might provide a great plethora of biomarkers not only for a male infertility diagnosis, but also to address a new MS-biomarkers classification of infertility subtypes. From the early detection to the evaluation of infertility grade, new MS-derived biomarkers might also predict long-term outcomes and clinical management of infertility.

Metabolic-Dysregulation-Based iEESI-MS Reveals Potential Biomarkers Associated with Early-Stage and Progressive Colorectal Cancer

The application of rapid and accurate diagnostic methods can improve colorectal cancer (CRC) survival rates dramatically. Here, we used a non-targeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry (iEESI-MS) to detect metabolite ions associated with the progression of CRC from 172 tissues (45 stage I/II CRC, 41 stage III/IV CRC, and 86 well-matched normal tissues). A support vector machine (SVM) model based on 10 differential metabolite ions for differentiating early-stage CRC from normal tissues was built with a good prediction accuracy of 92.6%. The biomarker panel consisting of lysophosphatidylcholine (LPC) (18:0) has good diagnostic potential in differentiating early-stage CRC from advanced-stage CRC. We showed that the down-regulation of LPC (18:0) in tumor tissues is associated with CRC progression and related to the regulation of the epidermal growth factor receptor. Pathway analysis showed that metabolic pathways in CRC are related to glycerophospholipid metabolism and purine metabolism. In conclusion, we built an SVM model with good performance to distinguish between early-stage CRC and normal groups based on iEESI-MS and found that LPC (18:0) is associated with the progression of CRC.

Discontinuously Dewetting Solvent Arrays: Droplet Formation and Poly-Synchronous Surface Extraction for Mass Spectrometry Imaging Applications

We describe a new liquid tissue stamping method called poly-synchronous surface extraction (PSSE) that utilizes an omniphobic substrate patterned with hydrophilic surface energy traps (SETs), which when wet with a solvent form a dense microdroplet array. When contacted with a tissue sample, each droplet locally extracts analytes from the tissue surface, which subsequentially can be analyzed by matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-IMS) or ambient ionization-MS techniques. Optimization of the patterned surface with six different solvents was carried out to increase the droplet density, height, and reproducibility of volume deposition. Once optimized, sister slices of a strawberry (Fragaria × ananassa) were spatially extracted using the PSSE technique and the chemical distribution of selected compounds was analyzed with both MALDI-IMS and a lower resolution but faster ambient liquid microjunction surface sampling probe (LMJ-SSP) approach. Heat maps for target analytes for the PSSE approach are compared to those produced using traditional MALDI-IMS analysis. The PSSE method aligned well with direct analysis and demonstrated the potential to increase the speed of ambient MS tissue imaging techniques by decreasing the number of steps required for sample preparation.

Recent Advances in Surface Plasmon Resonance Sensors for Sensitive Optical Detection of Pathogens

The advancement of science and technology has led to the recent development of highly sensitive pathogen biosensing techniques. The effective treatment of pathogen infections requires sensing technologies to not only be sensitive but also render results in real-time. This review thus summarises the recent advances in optical surface plasmon resonance (SPR) sensor technology, which possesses the aforementioned advantages. Specifically, this technology allows for the detection of specific pathogens by applying nano-sized materials. This review focuses on various nanomaterials that are used to ensure the performance and high selectivity of SPR sensors. This review will undoubtedly accelerate the development of optical biosensing technology, thus allowing for real-time diagnosis and the timely delivery of appropriate treatments as well as preventing the spread of highly contagious pathogens.

Loggerhead sea turtle Caretta caretta plasma biochemistry and proteome profile modulation during recovery

The aim of the study was to monitor and analyse injured and diseased loggerhead sea turtles (Caretta caretta) plasma proteome profiles and biochemistry parameters during their recovery period in rescue centre within different age and recovery period groups, and determine the potential biomarkers that can be used in diagnostics. The plasma biochemical parameters of total protein and glucose content, accompanied by aspartate aminotransferase (AST) and N-acetyl-cystein-activated creatinine kinase (CK-NAC) are highlighted as valuable and potential biomarkers of turtle's health status and condition. Using high throughput tandem mass tag (TMT)-based proteomic approach we identified 913 plasma proteins, 12 of which shown to be modulated in loggerheads age groups, and identified as a part of (i) platelet degranulation, (ii) neutrophil degranulation, and (iii) innate immune system pathways. The neurofascin (NFASC) is shown to be differentially abundant among all the age groups, and alpha-1-acid glycoprotein 2-like (ORM2) and alpha-1-antitrypsin-like (SERPINA1) proteins were recognized as members of all three above mentioned REACTOME pathways. Furthermore, 29 of plasma proteins were significantly differentially abundant in loggerheads age and recovery period groups. Out of 15 recognized pathways, those proteins were mostly included in three specific REACTOME pathways: (i) post-translational phosphorylation, (ii) regulation of Insulin-like Growth Factor (IGF) transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs), and (iii) platelet degranulation. The alpha-fetoprotein (AFP) was the only protein which showed statistically significant up-regulation patterns in all loggerhead age groups before release from the rescue centre, and the complement component 3 (C3) protein was the only protein modulated in all recovery period groups. Furthermore, C3 protein takes part in 9; and followed up with apolipoprotein A-I (APOA1) in 7; complement component 4 (C4), complement component 5 (C5) and kininogen-1 (KNG1) in 6 REACTOME pathways. Thereby, those proteins are highlighted and recommended as potential biomarkers of turtle's health status. Data are available via ProteomeXchange with identifier PXD029569. Finally, based on our results, we believe that comprehensive omics approach and routine plasma biochemical analysis, accompanied by proteins of acute phase, acid-base status and immune-response indicator analysis may significantly and reliably improve assessment of captive loggerheads rehabilitation and medication. SIGNIFICANCE: Monitoring and comparison of loggerhead sea turtles (C. caretta) blood plasma biochemistry parameters and plasma proteome profiles in relation to the age, and recovery period pointed out significantly differentially abundant proteins, along with certain biochemical parameter contents as potential biomarkers of turtle's fitness, health status and physiology.

Detection of Circulating Serum Protein Biomarkers of Non-Muscle Invasive Bladder Cancer after Protein Corona-Silver Nanoparticles Analysis by SWATH-MS

Because cystoscopy is expensive and invasive, a new method of detecting non-invasive muscular bladder cancer (NMIBC) is needed. This study aims to identify potential serum protein markers for NMIBC to improve diagnosis and to find treatment approaches that avoid disease progression to a life-threatening phenotype (muscle-invasive bladder cancer, MIBC). Here, silver nanoparticles (AgNPs, 9.73 ± 1.70 nm) as a scavenging device together with sequential window acquisition of all theoretical mass spectra (SWATH-MS) were used to quantitatively analyze the blood serum protein alterations in two NMIBC subtypes, T1 and Ta, and they were compared to normal samples (HC). NMIBC’s analysis of serum samples identified three major groups of proteins, the relative content of which is different from the HC content: proteins implicated in the complement and coagulation cascade pathways and apolipoproteins. In conclusion, many biomarker proteins were identified that merit further examination to validate their useful significance and utility within the clinical management of NMIBC patients.

Automated affinity selection for rapid discovery of peptide binders

In-solution affinity selection (AS) of large synthetic peptide libraries affords identification of binders to protein targets through access to an expanded chemical space. Standard affinity selection methods, however, can be time-consuming, low-throughput, or provide hits that display low selectivity to the target. Here we report an automated bio-layer interferometry (BLI)-assisted affinity selection platform. When coupled with tandem mass spectrometry (MS), this method enables both rapid de novo discovery and affinity maturation of known peptide binders with high selectivity. The BLI-assisted AS-MS technology also features real-time monitoring of the peptide binding during the library selection process, a feature unattainable by current selection approaches. We show the utility of the BLI AS-MS platform toward rapid identification of novel nanomolar (dissociation constant, K_D < 50 nM) non-canonical binders to the leukemia-associated oncogenic protein menin. To our knowledge, this is the first application of BLI to the affinity selection of synthetic peptide libraries. We believe our approach can significantly accelerate the use of synthetic peptidomimetic libraries in drug discovery.

This work reports an automated affinity selection-mass spectrometry (AS-MS) approach amenable to both de novo peptide binder discovery and affinity maturation of known binders in a high-throughput and selective manner.

Recent Advances in Sensing Applications of Molecularly Imprinted Photonic Crystals

Photonic crystals (PhCs) with a brightly colored structure are novel materials and are widely used in chemical and biological sensing. Combining PhCs with molecular imprinting technology (MIT), the molecularly imprinted PhC (MIPC) sensors are fabricated, which can specifically recognize the target molecules. Aside from high sensitivity and selectivity, the MIPC sensors could recognize the naked eye detection because of its optical properties. In this review, an overview of recent advances in sensing applications of MIPC sensors including the responsive mechanisms, application in environmental monitoring, and the application to human health were illustrated. The MIPC sensors all responded to the analytes specifically and also showed high sensitivity in real samples, which provided a method to realize the rapid, convenient, naked eye, and real-time detection. Furthermore, the current limitations and potential future directions of MIPC sensors were also discussed.

Proteome analysis using machine learning approaches and its applications to diseases

With the tremendous developments in the fields of biological and medical technologies, huge amounts of data are generated in the form of genomic data, images in medical databases or as data on protein sequences, and so on. Analyzing this data through different tools sheds light on the particulars of the disease and our body's reactions to it, thus, aiding our understanding of the human health. Most useful of these tools is artificial intelligence and deep learning (DL). The artificially created neural networks in DL algorithms help extract viable data from the datasets, and further, to recognize patters in these complex datasets. Therefore, as a part of machine learning, DL helps us face all the various challenges that come forth during protein prediction, protein identification and their quantification. Proteomics is the study of such proteins, their structures, features, properties and so on. As a form of data science, Proteomics has helped us progress excellently in the field of genomics technologies. One of the major techniques used in proteomics studies is mass spectrometry (MS). However, MS is efficient with analysis of large datasets only with the added help of informatics approaches for data analysis and interpretation; these mainly include machine learning and deep learning algorithms. In this chapter, we will discuss in detail the applications of deep learning and various algorithms of machine learning in proteomics.

Remodeling nanoDESI Platform with Ion Mobility Spectrometry to Expand Protein Coverage in Cancerous Tissue

Nanospray desorption electrospray ionization mass spectrometry is an ambient ionization technique that is capable of mapping proteins in tissue sections. However, high-abundant molecules or isobaric interference in biological samples hampers its broad applications in probing low-abundant proteins. To address this challenge, herein we demonstrated an integrated module that coupled pneumatic-assisted nanospray desorption electrospray ionization mass spectrometry with high-field asymmetric ion mobility spectrometry. Using this module to analyze mouse brain sections, the protein coverage was significantly increased. This improvement allowed the mapping of low-abundant proteins in tissue sections with a 5 μm spatial resolution enabled by computationally assisted fusion with optical microscopic images. Moreover, the module was successfully applied to characterize melanoma in skin tissues based on the enhanced protein profiles. The results suggested that this integrating module will be potentially applied to discover novel proteins in cancers.

Biomarker Discovery of Acute Coronary Syndrome Using Proteomic Approach

Acute coronary syndrome (ACS) is a condition in which the coronary artery supplying blood to the heart is infarcted via formation of a plaque and thrombus, resulting in abnormal blood supply and high mortality and morbidity. Therefore, the prompt and efficient diagnosis of ACS and the need for new ACS diagnostic biomarkers are important. In this study, we aimed to identify new ACS diagnostic biomarkers with high sensitivity and specificity using a proteomic approach. A discovery set with samples from 20 patients with ACS and 20 healthy controls was analyzed using mass spectrometry. Among the proteins identified, those showing a significant difference between each group were selected. Functional analysis of these proteins was conducted to confirm their association with functions in the diseased state. To determine ACS diagnostic biomarkers, standard peptides of the selected protein candidates from the discovery set were quantified, and these protein candidates were validated in a validation set consisting of the sera of 50 patients with ACS and 50 healthy controls. We showed that hemopexin, leucine-rich α-2-glycoprotein, and vitronectin levels were upregulated, whereas fibronectin level was downregulated, in patients with ACS. Thus, the use of these biomarkers may increase the accuracy of ACS diagnosis.

Cancer Cell Detection on the Surface of Top-Gated Monolayer Graphene via Raman Spectroscopy

A label-free biosensor is described based on the Raman spectroscopic signatures of monolayer graphene, which are modified in the compartment of cancer cells because of electron-phonon coupling in monolayer graphene. Specifically, the Raman spectra of electrostatically gated monolayer graphene on SiO₂/Si substrates, in the voltage range from 0 to 5 V, were studied in the absence and the presence of cancer cells. Density functional theory simulations afforded a correlation between cancer cells and the observed Raman spectra, through the regulation of the intensities of the G and 2D Raman vibrational modes with applied voltage. The C-H and N-H bonds of phenylalanine enabled the detection of this biosensing activity. Significantly, this detection can be carried out even in the absence of cancer cell-culturing steps.

Characteristic of Oral Squamous Cell Carcinoma Tissues Using Isotope Ratio Mass Spectrometry

Overall prognosis for patients with oral squamous cell carcinomas (OSSC) is still unfavourable. However, there is a hope that a novel diagnostic method may establish better cancer biology characteristics. The aim of this study was to evaluate the isotope ratio of nitrogen and carbon in OSSC as compared to margin and healthy tissue. A total of 18 patients with OSSC were included in the study. Specimens collected covered: four tumour, four margin and two healthy oral mucosa samples. The samples underwent further procedures: lyophilization and isotope ratio mass spectrometry. Measurements of the ratio of stable isotopes of nitrogen ¹⁵N/¹⁴N and carbon ¹³C/¹²C were performed. It is noticeable that the highest average nitrogen concentration was observed in tumour 12 ± 0.4% and the lowest in healthy tissues 8 ± 0.9% (p < 0.00001). The highest average carbon content was observed in healthy tissues 57 ± 2.2% and the lowest in tumour 46 ± 1.3% (p < 0.00001). Moreover, values of ¹⁵N/¹⁴N expressed in delta notation were the highest in healthy tissues 9.84 ± 0.61 and the lowest in tumour 8.92 ± 0.58. Values of ¹³C/¹²C tended to be higher in tumour −22.2 ± 0.89 and the lowest in healthy tissues −23.7 ± 1.2. Tumour tissues differ in isotopic composition from tissues taken from margin and healthy tissues taken from distant oral mucosa.

Application of Multiplex Microfluidic Electrochemical Sensors in Monitoring Hematological Tumor Biomarkers

Rapid yet accurate detection of disease-related biomarkers is key for point of care testing, where there is an increasing demand for multi-index analysis. Here, we present a versatile device for multianalyte quantification based on the microfluidic technique and electrochemical sensor array. The analytes were shunted through inner-built channels to screen-printed electrodes installed at different positions of the chip. These electrodes were modified with different nanomaterials and sensing agents to afford specific responses to the corresponding indicators. To prove the applicability of the platform for multifunction, we chose leukemia as the model disease and determined four relevant markers: methotrexate (MTX), lactate dehydrogenase (LDH), uric acid (UA), and urea. They are indicative as/for the therapeutic drug (MTX), prognosis (LDH), and side effect (UA and urea). The sensing chip exhibited low detection limits of 35 nM, 25 U/L, 450 nM, and 20 μM toward the four analytes, which are much lower than their minimum contents in human serum. Furthermore, practical application of the chip was demonstrated by simultaneous detection of the four analytes in the blood plasma of rabbit. By simply replacing the modification agents, the sensing platform is expected to serve the detection of a wide range of chem/biosubstances in various fields.

Gingival Crevicular Fluid Peptidome Profiling in Healthy and in Periodontal Diseases

Given its intrinsic nature, gingival crevicular fluid (GCF) is an attractive source for the discovery of novel biomarkers of periodontal diseases. GCF contains antimicrobial peptides and small proteins which could play a role in specific immune-inflammatory responses to guarantee healthy gingival status and to prevent periodontal diseases. Presently, several proteomics studies have been performed leading to increased coverage of the GCF proteome, however fewer efforts have been done to explore its natural peptides. To fill such gap, this review provides an overview of the mass spectrometric platforms and experimental designs aimed at GCF peptidome profiling, including our own data and experiences gathered from over several years of matrix-assisted laser desorption ionization/time of flight mass spectrometry (MALDI-TOF MS) based approach in this field. These tools might be useful for capturing snapshots containing diagnostic clinical information on an individual and population scale, which may be used as a specific code not only for the diagnosis of the nature or the stage of the inflammatory process in periodontal disease, but more importantly, for its prognosis, which is still an unmet medical need. As a matter of fact, current peptidomics investigations suffer from a lack of standardized procedures, posing a serious problem for data interpretation. Descriptions of the efforts to address such concerns will be highlighted.

Establishment of a universal and sensitive plasmonic biosensor platform based on the hybridization chain reaction (HCR) amplification induced by a triple-helix molecular switch

Herein, we established a universal and sensitive plasmonic sensing strategy for biomolecule assays by coupling the hybridization chain reaction (HCR) strategy and a triple-helix molecular switch. Upon the recognition of the target, a single-stranded DNA as a universal trigger (UT) was released from the triple-helix molecular switch (THMS). Thus, the HCR process can be triggered between two hairpins M1 and M2, resulting in the aggregation of gold nanoparticles (AuNPs) via the hybridization between the tail sequence on M1 (or M2) and a DNA-AuNP probe with a dramatic change in the absorbance at 521 nm. More specifically, the strategy, which was conducted by the introduction of target-specific recognition of THMS and universalized by virtue of altering the aptamer or DNA sequence without changing the triple-helix structure, enables simple design for multiple target detection. By taking advantage of THMS, this strategy could enable stable and sensitive detection of a variety of targets including nucleic acids, small molecules and proteins, which may possess great potential for practical applications.

Rapid Prostate Cancer Noninvasive Biomarker Screening Using Segmented Flow Mass Spectrometry-Based Untargeted Metabolomics

Spectrometric methods with rapid biomarker detection capacity through untargeted metabolomics are becoming essential in the clinical cancer research. Liquid chromatography-mass spectrometry (LC-MS) is a rapidly developing metabolomic-based biomarker technique due to its high sensitivity, reproducibility, and separation efficiency. However, its translation to clinical diagnostics is often limited due to long data acquisition times (∼20 min/sample) and laborious sample extraction procedures when employed for large-scale metabolomics studies. Here, we developed a segmented flow approach coupled with high-resolution mass spectrometry (SF-HRMS) for untargeted metabolomics, which has the capability to acquire data in less than 1.5 min/sample with robustness and reproducibility relative to LC-HRMS. The SF-HRMS results demonstrate the capability for screening metabolite-based urinary biomarkers associated with prostate cancer (PCa). The study shows that SF-HRMS-based global metabolomics has the potential to evolve into a rapid biomarker screening tool for clinical research.

Detection of Epithelial Ovarian Cancer using C8Magnetic Bead Separation and MALDI-TOF Plasma Proteome Profiling in Egyptian Females

Background:

Ovarian cancer is the seventh most common cancer in females with the highest mortality rate of all gynecological cancers due to its late discovery and ambiguous symptoms. Thus, there is a need for new promising strategies to diagnose ovarian cancer. We aimed at finding a characteristic plasma proteome pattern that could be used for the detection of epithelial ovarian cancer, in comparison with benign ovarian masses and healthy controls. We also aimed at differentiating between profiling of plasma proteins in early and advanced stages of ovarian cancer and between serous and non-serous histopathological types.

Methods:

The combination of MagSi-proteomics C8 beads, Ultraflextreme MALDI-TOF and ClinPro Tools software was used to compare the plasma protein spectra from 50 patients with epithelial ovarian cancer, 20 patients with benign ovarian masses and 50 age matched healthy females.

Results:

A plasma proteome profile of 21 peaks differentiated patients with epithelial ovarian cancer from healthy controls with a sensitivity of 73 % and a specificity of 82.8% upon external validation, while a 5-peak profile differentiated patients with epithelial ovarian cancer from patients with benign ovarian masses with a sensitivity of 81% and a specificity of 73.7%. A 20 peak profile was generated to discriminate between early and late stages of the disease with 88.3% recognition capability and 70% cross validation.

Conclusion:

MALDI-TOF proteomic profiling represents a promising potential tool for diagnosing epithelial ovarian cancer, discriminating between early and advanced stages and between serous and non- serous types.

Current Biochemical Applications and Future Prospects of Chlorotoxin in Cancer Diagnostics and Therapeutics

Chlorotoxin (CTX) is a minute 4 kDa protein made up of 36 amino acid residues, commonly known for its binding affinity to chloride channels and matrix metalloproteinase-2 (MMP-2) of glioma tumors of the spine and brain. This property and the possibility of conjugating this peptide to nanoparticles have enabled its diverse use in various biotechnological and biomedical applications for cancer treatment, such as in tumor imaging and radiotherapy. Because of the fascinating biological properties CTX possesses, elucidating its mechanism of action may hold promise for the development of new and effective therapeutic drugs, as well as more sensitive and highly specific cancer-screening kits. This article therefore reviews the currently known applications of CTX and suggests diverse ways in which it can be applied for the design of improved drugs and diagnostic tools for cancer.

[Liquid biopsy in tumor diagnostics : Applications, perspectives, and limitations of the "cancer liquidome"]

The detection of tumor-specific genetic alterations in body fluids as an addition to or even replacement for established tissue-based tumor diagnostics is currently a hot topic in academic research and industry. Progress in methods for nucleic acid analyses together with promising results from clinical studies have raised great expectations for cancer screening, diagnosis, prognosis, and therapy monitoring by means of a minimally invasive blood draw. Individual focused assays have already been introduced into routine diagnostics and represent a valuable option in cases where no tissue samples are available. However, before the use of liquid biopsy outside of clinical studies is enforced and more complex markers (like tumor mutational burden) are analyzed, several practical challenges and principal problems have to be addressed. This review focusses on the detection of free-circulating nucleic acids in blood plasma and critically discusses established and future applications as well as challenges and limitations of this new method.

Hypertensive disorders of pregnancy: Strategy to develop clinical peptide biomarkers for more accurate evaluation of the pathophysiological status of this syndrome

Hypertensive disorders of pregnancy (HDP) is the most common and widely known as serious complication of pregnancy. As this syndrome is a major leading cause of maternal, fetal, and neonatal morbidity/mortality worldwide, many studies have sought to identify candidate molecules as potential disease biomarkers (DBMs) for use in clinical examinations. Accumulating evidence over the past 2 decades that the many proteolytic peptides appear in human humoral fluids, including peripheral blood, in association with an individual's health condition. This review provides the potential utility of peptidomic analysis for monitoring for pathophysiological status in HDP, and presents an overview of current status of peptide quantification technology. Especially, the technical limitations of the methods used for DBM discovery in the blood are discussed.

MALDI-TOF MS-based total serum protein fingerprinting for liver cancer diagnosis

Serum is one of the most commonly used samples in many studies to identify protein biomarkers to diagnose cancer. Although conventional enzyme-linked immunosorbent assay (ELISA) or liquid chromatography-mass spectrometry (LC-MS)-based methods have been applied as clinical tools for diagnosing cancer, there have been troublesome problems, such as inferior multiplexing capabilities, high development costs and long turnaround times, which are inappropriate for high-throughput analytical platforms. Here, we developed a simple and robust cancer diagnostic method using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based total serum protein fingerprinting. First, serum samples were simply diluted with distilled water and subsequently spotted onto a MALDI plate without prior chromatographic purification or separation. The sample preparation method was enough to collect reproducible total serum protein fingerprints and would be highly advantageous for high-throughput assay. Each of the integrated main spectrum profiles (MSPs), which are representative of liver cancer patients (n = 40) or healthy controls (n = 80), was automatically generated by the MALDI Biotyper 3 software. The reliability of the integrated MSPs was successfully evaluated in comparison with a blind test set (n = 31), which consisted of 13 liver cancer patients and 18 healthy controls. Additionally, our partial least squares discriminant analysis (PLS-DA) demonstrated a statistically significant difference in MALDI-TOF MS-based total serum protein fingerprints between liver cancer patients and healthy controls. Taken together, this work suggests that this method may be an effective high-throughput platform technology for various cancer diagnoses and disease evaluations.

Recent Advances on Electrochemical Biosensing Strategies toward Universal Point-of-Care Systems

A number of very recently developed electrochemical biosensing strategies are promoting electrochemical biosensing systems into practical point-of-care applications. The focus of research endeavors has transferred from detection of a specific analyte to the development of general biosensing strategies that can be applied for a single category of analytes, such as nucleic acids, proteins, and cells. In this Minireview, recent cutting-edge research on electrochemical biosensing strategies are described. These developments resolved critical challenges regarding the application of electrochemical biosensors to practical point-of-care systems, such as rapid readout, simple biosensor fabrication method, ultra-high detection sensitivity, direct analysis in a complex biological matrix, and multiplexed target analysis. This Minireview provides general guidelines both for scientists in the biosensing research community and for the biosensor industry on development of point-of-care system, benefiting global healthcare.

Clinical biomarker discovery by SWATH-MS based label-free quantitative proteomics: impact of criteria for identification of differentiators and data normalization method

BACKGROUND

SWATH-MS has emerged as the strategy of choice for biomarker discovery due to the proteome coverage achieved in acquisition and provision to re-interrogate the data. However, in quantitative analysis using SWATH, each sample from the comparison group is run individually in mass spectrometer and the resulting inter-run variation may influence relative quantification and identification of biomarkers. Normalization of data to diminish this variation thereby becomes an essential step in SWATH data processing. In most reported studies, data normalization methods used are those provided in instrument-based data analysis software or those used for microarray data. This study, for the first time provides an experimental evidence for selection of normalization method optimal for biomarker identification.

METHODS

The efficiency of 12 normalization methods to normalize SWATH-MS data was evaluated based on statistical criteria in 'Normalyzer'-a tool which provides comparative evaluation of normalization by different methods. Further, the suitability of normalized data for biomarker discovery was assessed by evaluating the clustering efficiency of differentiators, identified from the normalized data based on p-value, fold change and both, by hierarchical clustering in Genesis software v.1.8.1.

RESULTS

Conventional statistical criteria identified VSN-G as the optimal method for normalization of SWATH data. However, differentiators identified from VSN-G normalized data failed to segregate test and control groups. We thus assessed data normalized by eleven other methods for their ability to yield differentiators which segregate the study groups. Datasets in our study demonstrated that differentiators identified based on p-value from data normalized with Loess-R stratified the study groups optimally.

CONCLUSION

This is the first report of experimentally tested strategy for SWATH-MS data processing with an emphasis on identification of clinically relevant biomarkers. Normalization of SWATH-MS data by Loess-R method and identification of differentiators based on p-value were found to be optimal for biomarker discovery in this study. The study also demonstrates the need to base the choice of normalization method on the application of the data.

A preliminary investigation of circulating extracellular vesicles and biomarker discovery associated with treatment response in head and neck squamous cell carcinoma

BACKGROUND

There is a paucity of plasma-based biomarkers that prospectively segregate the outcome of patients with head and neck squamous-cell carcinoma (HNSCC) treated with chemoradiation therapy (CRT). Plasma extracellular vesicles (EVs) might be an alternative source for discovery of new specific markers present in patients with HNSCC, which could help to re-direct patients to appropriate curative therapies without delay.

METHODS

In order to identify new markers in plasma compartments, Cholerae toxin B chain (CTB) and Annexin V (AV) were used to isolate EVs from pooled plasma samples from patients with locally advanced HNSCC who responded (CR, n = 6) or presented incomplete response (NR, n = 6) to CRT. The crude plasma and EVs cargo were screened by antibody array.

RESULTS

Of the 370 polypeptides detected, 119 proteins were specific to NR patients while 38 were exclusive of the CR subjects. The Gene Set Enrichment Analysis (GSEA) and Search Tool for the Retrieval of Interacting Genes (STRING) database analysis indicated that the content of circulating plasma EVs might have a relevant function for the tumor intercellular communication in the HNSCC patients.

CONCLUSION

This study provides a list of potential markers present in plasma compartments that might contribute to the development of tools for prediction and assessment of CRT response and potentially guide therapeutic decisions in this context.

Retrospective analysis of glycan-related biomarkers based on clinical laboratory data in two medical centers during the past 6 years

Most of clinically used cancer biomarkers are either specific glycan structures or glycoproteins. Although the high serum levels of the cancer biomarkers are also present in certain patients suffering noncancer diseases, systematic measurement and comparison of the serum levels of all cancer biomarkers among cancer and noncancer patients have not been reported. In this study, the serum levels of 17 glucose and glycan-related biomarkers including 10 cancer biomarkers SCCA, CA724, CA50, CA242, CA125, CA199, CA153, AFP, CEA, and PSA were retrospectively investigated based on clinical laboratory data in two medical centers during the past 6 years (2012-2018). The data included a total of 1,477,309 clinical lab test results of 17 biomarkers from healthy controls and patients suffering 64 different types of cancer and noncancer diseases. We found that the median serum levels of CA724, CEA, CA153, SCCA, and CA125 were highest not in cancer patients but in patients suffering gout, lung fibrosis, nephrotic syndrome, uremia, and cirrhosis, respectively. Consistently, the classical ovarian cancer biomarker CA125 had better overall sensitivity and specificity as biomarker for cirrhosis (67% and 92%, respectively) than that for ovarian cancer (41% and 97%, respectively). Furthermore, the information shown as heatmap or waterfall built on the -Log₁₀p values of the 17 glycan-related biomarkers in different clinically defined diseases suggested that all glycan-related biomarkers had cancer-, aging-, and disease-relevant characteristics and cancers were systems disease. The detailed presentation of the data for each of the 17 biomarkers will be deliberated in chapters 6-23 in this book series.

Sensors for Lung Cancer Diagnosis

The positive outcome of lung cancer treatment is strongly related to the earliness of the diagnosis. Thus, there is a strong requirement for technologies that could provide an early detection of cancer. The concept of early diagnosis is immediately extended to large population screening, and then, it is strongly related to non-invasiveness and low cost. Sensor technology takes advantage of the microelectronics revolution, and then, it promises to develop devices sufficiently sensitive to detect lung cancer biomarkers. A number of biosensors for the detection of cancer-related proteins have been demonstrated in recent years. At the same time, the interest is growing towards the analysis of volatile metabolites that could be measured directly from the breath. In this paper, a review of the state-of-the-art of biosensors and volatile compound sensors is presented.

Differences among Unique Nanoparticle Protein Corona Constructs: A Case Study Using Data Analytics and Multi-Variant Visualization to Describe Physicochemical Characteristics

Gold nanoparticles (AuNPs) used in pharmaceutical treatments have been shown to effectively deliver a payload, such as an active pharmaceutical ingredient or image contrast agent, to targeted tissues in need of therapy or diagnostics while minimizing exposure, availability, and accumulation to surrounding biological compartments. Data sets collected in this field of study include some toxico- and pharmacodynamic properties (e.g., distribution and metabolism) but many studies lack information about adsorption of biological molecules or absorption into cells. When nanoparticles are suspended in blood serum, a protein corona cloud forms around its surface. The extent of the applications and implications of this formed cloud are unknown. Some researchers have speculated that the successful use of nanoparticles in pharmaceutical treatments relies on a comprehensive understanding of the protein corona composition. The work presented in this paper uses a suite of data analytics and multi-variant visualization techniques to elucidate particle-to-protein interactions at the molecular level. Through mass spectrometry analyses, corona proteins were identified through large and complex datasets. With such high-output analyses, complex datasets pose a challenge when visualizing and communicating nanoparticle-protein interactions. Thus, the creation of a streamlined visualization method is necessary. A series of user-friendly data informatics techniques were used to demonstrate the data flow of protein corona characteristics. Multi-variant heat maps, pie charts, tables, and three-dimensional regression analyses were used to improve results interpretation, facilitate an iterative data transfer process, and emphasize features of the nanoparticle-protein corona system that might be controllable. Data informatics successfully highlights the differences between protein corona compositions and how they relate to nanoparticle surface charge.

Point-of-care microfluidic devices for pathogen detection

The rapid diagnosis of pathogens is crucial in the early stages of treatment of diseases where the choice of the correct drug can be critical. Although conventional cell culture-based techniques have been widely utilized in clinical applications, newly introduced optical-based, microfluidic chips are becoming attractive. The advantages of the novel methods compared to the conventional techniques comprise more rapid diagnosis, lower consumption of patient sample and valuable reagents, easy application, and high reproducibility in the detection of pathogens. The miniaturized channels used in microfluidic systems simulate interactions between cells and reagents in microchannel structures, and evaluate the interactions between biological moieties to enable diagnosis of microorganisms. The overarching goal of this review is to provide a summary of the development of microfluidic biochips and to comprehensively discuss different applications of microfluidic biochips in the detection of pathogens. New types of microfluidic systems and novel techniques for viral pathogen detection (e.g. HIV, HVB, ZIKV) are covered. Next generation techniques relying on high sensitivity, specificity, lower consumption of precious reagents, suggest that rapid generation of results can be achieved via optical based detection of bacterial cells. The introduction of smartphones to replace microscope based observation has substantially improved cell detection, and allows facile data processing and transfer for presentation purposes.

Human coelomic fluid investigation: A MS-based analytical approach to prenatal screening

Coelomic fluid (CF) is the earliest dynamic and complex fluid of the gestational sac. CF contains maternal cells and proteins produced by embryonic cells, tissues and excretions. The biochemical composition of CF is modified throughout the first trimester of pregnancy and its protein profile reflects both physiological/pathological changes affecting the embryo and mother. Identification of variations in the balance of proteins might indicate particular types of pathologies, or ascertain specific genetic disorders. A platform utilizing protein enrichment procedures coupled with shotgun identification and iTRAQ differentiation provided the identification and quantitation of 88 unique embryonic proteins. It is relevant to note that chromosome X protein CXorf23 was found suggesting the embryo sex. Foetal sex was determined by Quantitative Fluorescent Polymerase Chain Reaction (QF-PCR) on coelomic cells, foetal tissues and maternal white blood cells, with a 100% concordance rate between iTRAQ-MS/MS and QF-PCR data. The functional associations among the identified proteins were investigated using STRING database. Open Targets Platform showed as significant the following therapeutic areas: nervous, respiratory, eye and head system disease.

Computational Convolution of SELDI Data for the Diagnosis of Alzheimer's Disease

Alzheimer’s disease is rapidly becoming an endemic for people over the age of 65. A vital path towards reversing this ominous trend is the building of reliable diagnostic devices for definite and early diagnoses in lieu of the longitudinal, usually inconclusive and non-generalize-able methods currently in use. In this article, we present a survey of methods for mining pools of mass spectrometer saliva data in relation to diagnosing Alzheimer’s disease. The computational methods provides new approaches for appropriately gleaning latent information from mass spectra data. They improve traditional machine learning algorithms and are most fit for handling matrix data points including solving problems beyond protein identifications and biomarker discovery.

Lab-on-chip technology for chronic disease diagnosis

Various types of chronic diseases (CD) are the leading causes of disability and death worldwide. While those diseases are chronic in nature, accurate and timely clinical decision making is critically required. Current diagnosis procedures are often lengthy and costly, which present a major bottleneck for effective CD healthcare. Rapid, reliable and low-cost diagnostic tools at point-of-care (PoC) are therefore on high demand. Owing to miniaturization, lab-on-chip (LoC) technology has high potential to enable improved biomedical applications in terms of low-cost, high-throughput, ease-of-operation and analysis. In this direction, research toward developing new LoC-based PoC systems for CD diagnosis is fast growing into an emerging area. Some studies in this area began to incorporate digital and mobile technologies. Here we review the recent developments of this area with the focus on chronic respiratory diseases (CRD), diabetes, and chronic kidney diseases (CKD). We conclude by discussing the challenges, opportunities and future perspectives of this field.

Direct Assessment of Plasma/Serum Sample Quality for Proteomics Biomarker Investigation

Blood proteome analysis for biomarker discovery represents one of the most challenging tasks to be achieved through clinical proteomics due to the sample complexity, such as the extreme heterogeneity of proteins in very dynamic concentrations, and to the observation of proper sampling and storage conditions. Quantitative and qualitative proteomics profiling of plasma and serum could be useful both for the early detection of diseases and for the evaluation of pathological status. Two main sources of variability can affect the precision and accuracy of the quantitative experiments designed for biomarker discovery and validation. These sources are divided into two categories, pre-analytical and analytical, and are often ignored; however, they can contribute to consistent errors and misunderstanding in biomarker research. In this chapter, we review critical pre-analytical and analytical variables that can influence quantitative proteomics. According to guidelines accepted by proteomics community, we propose some recommendations and strategies for a proper proteomics analysis addressed to biomarker studies.

Mass spectrometry and multivariate analysis to classify cervical intraepithelial neoplasia from blood plasma: an untargeted lipidomic study

Cervical cancer is still an important issue of public health since it is the fourth most frequent type of cancer in women worldwide. Much effort has been dedicated to combating this cancer, in particular by the early detection of cervical pre-cancerous lesions. For this purpose, this paper reports the use of mass spectrometry coupled with multivariate analysis as an untargeted lipidomic approach to classifying 76 blood plasma samples into negative for intraepithelial lesion or malignancy (NILM, n = 42) and squamous intraepithelial lesion (SIL, n = 34). The crude lipid extract was directly analyzed with mass spectrometry for untargeted lipidomics, followed by multivariate analysis based on the principal component analysis (PCA) and genetic algorithm (GA) with support vector machines (SVM), linear (LDA) and quadratic (QDA) discriminant analysis. PCA-SVM models outperformed LDA and QDA results, achieving sensitivity and specificity values of 80.0% and 83.3%, respectively. Five types of lipids contributing to the distinction between NILM and SIL classes were identified, including prostaglandins, phospholipids, and sphingolipids for the former condition and Tetranor-PGFM and hydroperoxide lipid for the latter. These findings highlight the potentiality of using mass spectrometry associated with chemometrics to discriminate between healthy women and those suffering from cervical pre-cancerous lesions.

The Immunome of Colon Cancer: Functional In Silico Analysis of Antigenic Proteins Deduced from IgG Microarray Profiling

Characterization of the colon cancer immunome and its autoantibody signature from differentially-reactive antigens (DIRAGs) could provide insights into aberrant cellular mechanisms or enriched networks associated with diseases. The purpose of this study was to characterize the antibody profile of plasma samples from 32 colorectal cancer (CRC) patients and 32 controls using proteins isolated from 15,417 human cDNA expression clones on microarrays. 671 unique DIRAGs were identified and 632 were more highly reactive in CRC samples. Bioinformatics analyses reveal that compared to control samples, the immunoproteomic IgG profiling of CRC samples is mainly associated with cell death, survival, and proliferation pathways, especially proteins involved in EIF2 and mTOR signaling. Ribosomal proteins (e.g., RPL7, RPL22, and RPL27A) and CRC-related genes such as APC, AXIN1, E2F4, MSH2, PMS2, and TP53 were highly enriched. In addition, differential pathways were observed between the CRC and control samples. Furthermore, 103 DIRAGs were reported in the SEREX antigen database, demonstrating our ability to identify known and new reactive antigens. We also found an overlap of 7 antigens with 48 “CRC genes.” These data indicate that immunomics profiling on protein microarrays is able to reveal the complexity of immune responses in cancerous diseases and faithfully reflects the underlying pathology.