This review highlights the growing impact of StageTips (Stop and Go Extraction Tips), a pipette tip-based LC column in MS-based proteomics. By packing standard pipette tips with reversed-phase, ion-exchange, or metal oxide materials, StageTips enable efficient peptide desalting, fractionation, selective enrichment, and in-tip reactions with minimal sample loss. Recent improvements, including new resin designs and integrated workflows, have further expanded the applications to phosphoproteomics, protein terminomics, and single-cell proteomics. With their simplicity, high reproducibility, and low cost, StageTips offer a versatile platform that can be seamlessly integrated into automated pipelines, increasing the throughput and the depth of proteome analysis. As materials and protocols continue to evolve, StageTips will continue to develop as an essential keystone for robust sample preparation in next-generation proteomics research.

Glaucoma is a group of neurodegenerative eye diseases characterized by progressive damage to the optic nerve which is typically asymptomatic until irreversible vision loss has occurred. Early screening is essential for timely treatment to prevent visual impairment. However, existing detection methods struggle to achieve a balance between accuracy, time efficiency, and portability. The lateral flow assay (LFA) is a well-established immunoanalytical tool for point-of-care (POC) analysis. Here, we have developed a unique dual-testing, quantum nanobeads-based fluorescence LFA, allowing for the simultaneous and quantitative detection of two glaucoma biomarkers: tumor necrosis factor-α (TNF-α) and brain-derived neurotrophic factor (BDNF). By coating quantum dots on the surface of a SiO2 core, the fluorescent intensity of the quantum nanobeads was enhanced enabling an accurate, efficient, and high-throughput bioanalytical performance, with low detection limits of 3.39 pg mL-1 for TNF-α and 4.13 pg mL-1 for BDNF. The LFA also demonstrated superior selectivity, reproducibility, and stability to the standard enzyme-linked immunosorbent assay (ELISA). Using a 3D-printed readout box, the analysis of the LFA requires only a readily accessible smartphone and image processing software, making it an ideal POC detection tool. This ultrasensitive, economical, and user-friendly LFA demonstrates significant potential as an alternative for glaucoma screening.

Antibodies are indispensable in laboratory and clinical applications due to their high specificity and affinity for protein antigens. However, selecting the right protein fragments as immunogens for antibody production remains challenging. Leveraging the Human Protein Atlas, this study systematically evaluates immunogen properties aiming to identify key factors that influence their suitability. Antibodies were classified as successful or unsuccessful based on standardized validation experiments, and the structural and functional properties of their immunogens were analyzed. Results indicated that longer immunogens often resulted in more successful but less specific antibodies. Shorter immunogens (50 residues or fewer) with disordered or unfolded regions at the N- or C-terminus and long coil stretches were more likely to generate successful antibodies. Conversely, immunogens with high beta sheet content, transmembrane regions, or disulfide bridges were associated with poorer antibody performance. Post-translational modification sites within immunogens appeared to mark beneficial regions for antibody generation. To support antibody selection, a novel R package, immunogenViewer, was developed, enabling researchers to easily apply these insights when immunogen sequences are disclosed. By providing a deeper understanding of immunogen suitability, this study promotes the development of more effective antibodies, ultimately addressing issues of reproducibility and reliability in antibody-based research. The findings are highly relevant to the research community, as end users often lack control over the immunogen selection process in antibody production. The R package is freely available as part of Bioconductor: https://bioconductor.org/packages/release/bioc/html/immunogenViewer.html.

Biomarkers are useful clinical tools but only a handful of them are used routinely for patient care. Despite intense efforts to discover new, clinically useful biomarkers, very few new circulating biomarkers were implemented in clinical practice in the last 40 years. This is mainly due to rather poor clinical performance. Here, our goal was to validate the performance of a group of newly discovered circulating biomarkers for glioma by comparing our data with data from a paper recently published in Science Advances.

We analyzed our own sets of clinical samples (gliomas (n=30), meningiomas (n=20)) and a different analytical assay (Proximity Extension Assay, OLINK Proteomics) to compare the results of Shen and colleagues.

Despite the sophistication of the utilized discovery method by the original investigators, we found that the newly proposed biomarkers for glioma (the best one presumably being SERPINA6) did not perform as originally claimed.

Scientific irreproducibility has been extensively discussed in the literature. A large proportion of newly discovered candidate biomarkers likely represent "false discovery" and significantly contribute to the propagation of irreproducible results between investigators. One of the best ways to assess the value of any new biomarker is by independent and extensive validation. Based on our previous classification of irreproducible results, we believe that this new work likely represents another example of biomarker false discovery.

Proteomic evaluation of plasma samples could accelerate the identification of novel Alzheimer's disease (AD) biomarkers. We evaluated the novel NUcleic acid Linked Immuno-Sandwich Assay (NULISA) proteomic method in an ethnically diverse cohort.

Plasma biomarkers were measured with NULISA in the Human Connectome Project, a predominantly preclinical biracial community cohort in southwestern Pennsylvania. Selected biomarkers were additionally measured using Simoa and Quest immunoassays and compared.

On NULISA, phosphorylated tau (p-tau217, p-tau231, and p-tau181), glial fibrillary acidic protein (GFAP), and microtubule-associated protein tau (MAPT-tau) showed the top significant association with amyloid beta (Aβ) positron emission tomography (PET) status, followed by the neuroinflammation markers C-C motif ligand 2 (CCL2), chitotriosidase 1 (CHIT1) and interleukin-8 (CXCL8), and the synaptic marker neurogranin (NRGN). Biomarkers associated with cortical thickness included astrocytic protein chitinase-3-like protein 1 (CHI3L1), cytokine CD40 ligand (CD40LG), brain-derived neurotrophic factor (BDNF), the Aβ-associated metalloprotein TIMP3 (tissue inhibitor of metalloprotein 3), and ficolin 2 (FCN2). Furthermore, moderate to strong between-platform correlations were observed for various assays.

NULISA multiplexing advantage allowed concurrent assessment of established and novel plasma biomarkers of Aβ pathology and neurodegeneration.

Classical Alzheimer's disease (AD) biomarkers measured using the NUcleic acid Linked Immuno-Sandwich Assay (NULISA) with next-generation sequencing readout (NULISAseq) CNS panel showed strong concordance with those measured using established immunoassay methods from Quanterix and Quest, with glial fibrillary acidic protein (GFAP) and neurofilament light (NfL) exhibiting the strongest correlation. NULISAseq proteomic analysis identified several plasma biomarkers strongly associated with AD pathology in a biracial community cohort of older adults. Notably, phosphorylated tau-217 (p-tau217), GFAP, and p-tau231 displayed the strongest association with amyloid beta (Aβ) pathology, whereas brain-derived neurotrophic factor (BDNF) was strongly associated with neurodegeneration. We demonstrate that plasma biomarker levels could be influenced by age, sex, apolipoprotein E (APOE) genotype, and self-identified race. Specifically, GFAP, NfL, and surfactant protein D (SFTPD) showed a strong association with age; CD63 and S100 calcium-binding protein B (S100B) with self-identified race; synaptosomal-associated protein 25 (SNAP25) with APOE genotype; and serum amyloid A1 (SAA1) and superoxide dismutase 1 (SOD1) with significant sex differences.

Stanford Type B Aortic Dissection (TBAD), a critical aortic disease, has exhibited stable mortality rates over the past decade. However, diagnostic approaches for TBAD during routine health check-ups are currently lacking. This study focused on developing a model to improve the diagnosis in a population.

Serum biomarkers were investigated in 88 participants using proteomic profiling combined with machine learning. The findings were validated using ELISA in other 80 participants. Subsequently, a diagnostic model for TBAD integrating biomarkers with clinical indicators was developed and assessed using machine learning.

Six differentially expressed proteins (DEPs) were identified through proteomic profiling and machine learning in discovery and derivation cohorts. Five of these (GDF-15, IL6, CD58, LY9, and Siglec-7) were further verified through ELISA validation within the validation cohort. In addition, ten blood-related indicators were selected as clinical indicators. Combining biomarkers and clinical indicators, the machine learning-based models performed well (AUC of the biomarker model = 0.865, AUC of the clinical model = 0.904, and AUC of the combined model = 0.909) using relative quantitation. The performance of the three models was verified (AUC of biomarker model = 0.866, AUC of clinical model = 0.868, and AUC of combined model = 0.886) using absolute quantitation. Crucially, the combined models outperformed individual biomarkers and clinical models, demonstrating superior efficacy.

Using proteomic profiling, we identified serum IL-6, GDF-15, CD58, LY9, and Siglec-7 as TBAD biomarkers. The machine-learning-based diagnostic model exhibited significant potential for TBAD diagnosis using only blood samples within the population.

Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.

Blood-based biomarkers are gaining grounds for the detection of Alzheimer's disease (AD) and related disorders (ADRDs). However, two key obstacles remain: the lack of methods for multi-analyte assessments and the need for biomarkers for related pathophysiological processes like neuroinflammation, vascular, and synaptic dysfunction. A novel proteomic method for pre-selected analytes, based on proximity extension technology, was recently introduced. Referred to as the NULISAseq CNS disease panel, the assay simultaneously measures ~ 120 analytes related to neurodegenerative diseases, including those linked to both core (i.e., tau and amyloid-beta (Aβ)) and non-core AD processes. This study aimed to evaluate the technical and clinical performance of this novel targeted proteomic panel.

The NULISAseq CNS disease panel was applied to 176 plasma samples from 113 individuals in the MYHAT-NI cohort of predominantly cognitively normal participants from an economically underserved region in southwestern Pennsylvania, USA. Classical AD biomarkers, including p-tau181, p-tau217, p-tau231, GFAP, NEFL, Aβ40, and Aβ42, were independently measured using Single Molecule Array (Simoa) and correlations and diagnostic performances compared. Aβ pathology, tau pathology, and neurodegeneration (AT(N) statuses) were evaluated with [11C] PiB PET, [18F]AV-1451 PET, and an MRI-based AD-signature composite cortical thickness index, respectively. Linear mixed models were used to examine cross-sectional and Wilcoxon rank sum tests for longitudinal associations between NULISA and neuroimaging-determined AT(N) biomarkers.

NULISA concurrently measured 116 plasma biomarkers with good technical performance (97.2 ± 13.9% targets gave signals above assay limits of detection), and significant correlation with Simoa assays for the classical biomarkers. Cross-sectionally, p-tau217 was the top hit to identify Aβ pathology, with age, sex, and APOE genotype-adjusted AUC of 0.930 (95%CI: 0.878-0.983). Fourteen markers were significantly decreased in Aβ-PET + participants, including TIMP3, BDNF, MDH1, and several cytokines. Longitudinally, FGF2, IL4, and IL9 exhibited Aβ PET-dependent yearly increases in Aβ-PET + participants. Novel plasma biomarkers with tau PET-dependent longitudinal changes included proteins associated with neuroinflammation, synaptic function, and cerebrovascular integrity, such as CHIT1, CHI3L1, NPTX1, PGF, PDGFRB, and VEGFA; all previously linked to AD but only reliable when measured in cerebrospinal fluid. The autophagosome cargo protein SQSTM1 exhibited significant association with neurodegeneration after adjusting age, sex, and APOE ε4 genotype.

Together, our results demonstrate the feasibility and potential of immunoassay-based multiplexing to provide a comprehensive view of AD-associated proteomic changes, consistent with the recently revised biological and diagnostic framework. Further validation of the identified inflammation, synaptic, and vascular markers will be important for establishing disease state markers in asymptomatic AD.

Extracellular vesicles (EVs) are a heterogeneous collection of particles that play a crucial role in cell-to-cell communication, primarily due to their ability to transport molecules, such as proteins. Thus, profiling EV-associated proteins offers insight into their biological effects. EVs can be isolated from various biological fluids, including donor blood components such as cryoprecipitate and fresh frozen plasma (FFP). In this study, we conducted a proteomic analysis of five single donor units of cryoprecipitate, FFP, and EVs derived from these blood components using a quantitative mass spectrometry approach. EVs were successfully isolated from both cryoprecipitate and FFP based on community guidelines. We identified and quantified approximately 360 proteins across all sample groups. Principal component analysis and heatmaps revealed that both cryoprecipitate and FFP are similar. Similarly, EVs derived from cryoprecipitate and FFP are comparable. However, they differ between the originating fluids and their derived EVs. Using the R-package MS-DAP, differentially expressed proteins (DEPs) were identified. The DEPs for all comparisons, when submitted for gene enrichment analysis, are involved in the complement and coagulation pathways. The protein profile generated from this study will have important clinical implications in increasing our knowledge of the proteins that are associated with EVs derived from blood components.

Plasma biomarker has the potential to be the reliable and propagable approach in the early stage diagnosis of Alzheimer's disease (AD). However, conventional methods appear powerless in the detection of these biomarkers at low concentrations in plasma. Here, we determined plasma biomarker concentrations of patients across the AD spectrum by an improved digital enzyme-linked immunosorbent assay (ELISA) technique. Confirms the predictive and diagnostic value of this method for AD patients and study the relationships between these biomarkers and cognitive status.

Plasma concentrations of amyloid-beta 40 (Aβ40), amyloid-beta 42 (Aβ42) and plasma phosphorylated tau at threonine 181 (p-tau181) were determined in 43 AD patients, 33 mild cognitive impairment (MCI) patients and 40 normal cognition (NC) subjects as healthy controls using the improved digital ELISA technique. In addition, all subjects were required to receive neuropsychological assessments.

Plasma p-tau181 level showed certain discrepancies between NC and MCI (p < 0.05), AD (p < 0.01) groups. The level of plasma Aβ42 (p < 0.05) and Aβ40 (p < 0.01) was significantly different between AD and NC group. The p-tau181 level was able to distinguish AD (AUC = 0.8768) and MCI (AUC = 0.7932) from NC with higher accuracy than Aβ42/Aβ40 ratio (AUC = 0.8343, AUC = 0.6569). Both p-tau181 (CDR: r = 0.388 p < 0.001; MMSE: r = -0.394 p < 0.001) and Aβ42/Aβ40 ratio (CDR: r = -0.413 p < 0.001; MMSE: r = 0.358 p < 0.001) showed stronger positive correlation with clinical dementia rating (CDR) and mini mental state examination (MMSE) scores than Aβ42 (CDR: r = -0.280 p = 0.003; MMSE: r = 0.266 p = 0.005) or Aβ40 (CDR: r = 0.373 p < 0.001; MMSE: r = -0.288 p = 0.002) alone.

Plasma p-tau181 level and Aβ42/Aβ40 ratio showed promising values in diagnosis of AD and MCI. Our results indicate that this improved digital ELISA diagnosis approach can facilitate early recognition and management of AD and pre-AD patients.

Blood-based biomarkers are gaining grounds for Alzheimer's disease (AD) detection. However, two key obstacles need to be addressed: the lack of methods for multi-analyte assessments and the need for markers of neuroinflammation, vascular, and synaptic dysfunction. Here, we evaluated a novel multi-analyte biomarker platform, NULISAseq CNS disease panel, a multiplex NUcleic acid-linked Immuno-Sandwich Assay (NULISA) targeting ~120 analytes, including classical AD biomarkers and key proteins defining various disease hallmarks.

The NULISAseq panel was applied to 176 plasma samples from the MYHAT-NI cohort of cognitively normal participants from an economically underserved region in Western Pennsylvania. Classical AD biomarkers, including p-tau181 p-tau217, p-tau231, GFAP, NEFL, Aβ40, and Aβ42, were also measured using Single Molecule Array (Simoa). Amyloid pathology, tau pathology, and neurodegeneration were evaluated with [11C] PiB PET, [18F]AV-1451 PET, and MRI, respectively. Linear mixed models were used to examine cross-sectional and Wilcoxon rank sum tests for longitudinal associations between NULISA biomarkers and AD pathologies. Spearman correlations were used to compare NULISA and Simoa.

NULISA concurrently measured 116 plasma biomarkers with good technical performance, and good correlation with Simoa measures. Cross-sectionally, p-tau217 was the top hit to identify Aβ pathology, with age, sex, and APOE genotype-adjusted AUC of 0.930 (95%CI: 0.878-0.983). Fourteen markers were significantly decreased in Aβ-PET+ participants, including TIMP3, which regulates brain Aβ production, the neurotrophic factor BDNF, the energy metabolism marker MDH1, and several cytokines. Longitudinally, FGF2, IL4, and IL9 exhibited Aβ PET-dependent yearly increases in Aβ-PET+ participants. Markers with tau PET-dependent longitudinal changes included the microglial activation marker CHIT1, the reactive astrogliosis marker CHI3L1, the synaptic protein NPTX1, and the cerebrovascular markers PGF, PDGFRB, and VEFGA; all previously linked to AD but only reliably measured in cerebrospinal fluid. SQSTM1, the autophagosome cargo protein, exhibited a significant association with neurodegeneration status after adjusting age, sex, and APOE ε4 genotype.

Together, our results demonstrate the feasibility and potential of immunoassay-based multiplexing to provide a comprehensive view of AD-associated proteomic changes. Further validation of the identified inflammation, synaptic, and vascular markers will be important for establishing disease state markers in asymptomatic AD.

Gliomas are aggressive malignant tumors, with poor prognosis. There is an unmet need for the discovery of new, non-invasive biomarkers for differential diagnosis, prognosis, and management of brain tumors. Our objective is to validate four plasma biomarkers - glial fibrillary acidic protein (GFAP), neurofilament light (NEFL), matrix metalloprotease 3 (MMP3) and fatty acid binding protein 4 (FABP4) - and compare them with established brain tumor molecular markers and survival.

Our cohort consisted of patients with benign and malignant brain tumors (GBM = 77, Astrocytomas = 26, Oligodendrogliomas = 23, Secondary tumors = 35, Meningiomas = 70, Schwannomas = 15, Pituitary adenomas = 15, Normal individuals = 30). For measurements, we used ultrasensitive electrochemiluminescence multiplexed immunoassays.

High plasma GFAP concentration was associated with GBM, low GFAP and high FABP4 were associated with meningiomas, and low GFAP and low FABP4 were associated with astrocytomas and oligodendrogliomas. NEFL was associated with progression of disease. Several prognostic genetic alterations were significantly associated with all plasma biomarker levels. We found no independent associations between plasma GFAP, NEFL, FABP4 and MMP3, and overall survival. The candidate biomarkers could not reliably discriminate GBM from primary or secondary CNS lymphomas.

GFAP, NEFL, FABP4 and MMP3 are useful for differential diagnosis and prognosis, and are associated with molecular changes in gliomas.

Achieving long-term disease control using therapeutic immunomodulation is a long-standing concept with a strong tradition in blood malignancies. Besides allogeneic hematopoietic stem cell transplantation that continues to provide potentially curative treatment for otherwise challenging diagnoses, recent years have seen impressive progress in immunotherapies for leukemias and lymphomas with immune checkpoint blockade, bispecific monoclonal antibodies, and CAR T cell therapies. Despite their success, non-response, relapse, and immune toxicities remain frequent, thus prioritizing the elucidation of the underlying mechanisms and identifying predictive biomarkers. The increasing availability of single-cell genomic tools now provides a system's immunology view to resolve the molecular and cellular mechanisms of immunotherapies at unprecedented resolution. Here, we review recent studies that leverage these technological advancements for tracking immune responses, the emergence of immune resistance, and toxicities. As single-cell immune monitoring tools evolve and become more accessible, we expect their wide adoption for routine clinical applications to catalyze more precise therapeutic steering of personal immune responses.

Multiplex immunoassays have been developed to detect multiple proteins simultaneously and are used to search for biomarkers, including those present in major psychiatric disorders. This study aimed to review multiplex immunoassay studies on cerebrospinal fluid (CSF) biomarkers in patients with schizophrenia, bipolar disorder (BD), and major depressive disorder (MDD) and examine future research directions using improved proteomic techniques. According to the results of previous multiplex immunoassay studies, increased CSF IFN-β, IL-8, MCP-2, MMP-2, PAI-1, sICAM-1, and sVCAM-1 and decreased CSF ACE, APP, fibrinogen, and GDNF were observed in patients with schizophrenia, while CSF HGF and S100B were positively correlated with psychotic symptom and CSF IL-11, IL-29/IFN-λ1, and TSLP were negatively correlated. Increased CSF IFN-β and IL-1β and decreased CSF Aβ42, APP, IL-6, and NCAM-1 were observed, while CSF S100B was positively correlated with manic symptom in patients with BD. Increased CSF IL-4, MCP-1, MIP-1β, and MMP-2 were observed in patients with MDD, while CSF HGF and MMP-2 were positively correlated with depressive symptom and CSF IL-15 and MCP-1 were negatively correlated. However, signal cross-talk and cross-reactivity problems have been observed in previous studies using multiplex immunoassay. The proximity extension assay can be used to overcome cross-reactivity and enable ultrasensitive multiplexed detection and quantification of more than 1000 target proteins. However, proteomic studies using proximity extension assay technology in patients with schizophrenia, BD, or MDD are still scarce. Therefore, future high-quality proteomic studies are required to identify CSF biomarkers for larger sets of target proteins in patients with major psychiatric disorders.

Gastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis.

We quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models.

In the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively.

This study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.

Atopic Dermatitis (AD) is the most common inflammatory skin disease with a complex and multifactorial pathogenesis. The use of proteomics in understanding AD has yielded the discovery of novel biomarkers and may further expand therapeutic options.

This review summarizes the most recent proteomic studies and the methodologies used in AD. It describes novel biomarkers that may monitor disease course and therapeutic response. The review also highlights skin and blood biomarkers characterizing different AD phenotypes and differentiates AD from other inflammatory skin disorders. A literature search was conducted by querying Scopus, Google Scholar, Pubmed/Medline, and Clinicaltrials.gov up to June 2023.

The integration of proteomics into research efforts in atopic dermatitis has broadened our understanding of the molecular profile of AD through the discovery of new biomarkers. In addition, proteomics may contribute to the development of targeted treatments ultimately improving personalized medicine. An increasing number of studies are utilizing proteomics to explore this heterogeneous disease.

The integration of cancer biomarkers into oncology has revolutionized cancer treatment, yielding remarkable advancements in cancer therapeutics and the prognosis of cancer patients. The development of personalized medicine represents a turning point and a new paradigm in cancer management, as biomarkers enable oncologists to tailor treatments based on the unique molecular profile of each patient's tumor. In this review, we discuss the scientific milestones of cancer biomarkers and explore future possibilities to improve the management of patients with solid tumors. This progress is primarily attributed to the biological characterization of cancers, advancements in testing methodologies, elucidation of the immune microenvironment, and the ability to profile circulating tumor fractions. Integrating these insights promises to continually advance the precision oncology field, fostering better patient outcomes.

The objective of this study was to compare plasma proteomes of patients with confirmed fracture-related infections (FRIs) matched to noninfected controls using liquid chromatography-mass spectrometry.

This was a prospective case-control study.

The study was conducted at a single, academic, Level 1 trauma center.

Patients meeting confirmatory FRI criteria were matched to controls without infection based on fracture region, age, and time after surgery from June 2019 to January 2022. Tandem mass tag liquid chromatography-mass spectrometry analysis of patient plasma samples was performed.

Protein abundance ratios in plasma for patients with FRI compared with those for matched controls without infection were calculated.

Twenty-seven patients meeting confirmatory FRI criteria were matched to 27 controls. Abundance ratios for more than 1000 proteins were measured in the 54 plasma samples. Seventy-three proteins were found to be increased or decreased in patients with FRI compared with those in matched controls (unadjusted t test P < 0.05). Thirty-two of these proteins were found in all 54 patient samples and underwent subsequent principal component analysis to reduce the dimensionality of the large proteomics dataset. A 3-component principal component analysis accounted for 45.7% of the variation in the dataset and had 88.9% specificity for the diagnosis of FRI. STRING protein-protein interaction network analysis of these 3 PCs revealed activation of the complement and coagulation cascades through the Reactome pathway database (false discovery rates <0.05).

Proteomic analyses of plasma from patients with FRI demonstrate systemic activation of the complement and coagulation cascades. Further investigation along these lines may help to better understand the systemic response to FRI and improve diagnostic strategies using proteomics.

Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

A simple and sensitive homogeneous protein detection system is required for the early detection of biomarkers. Thermo-responsive magnetic particles (TM) have already been developed to achieve easy bound/free separation at the homogeneous protein detection system, but they are still limited owing to the requirement of secondary antibodies and negatively charged polymers, and it is challenging to control the TM aggregation behavior because of the size of the TM. Therefore, at new method to control TM aggregation behavior that is simple, easy, and highly sensitive is required. In this study, we developed a DNA aptamer-based TM assay as a simple protein detection system without additional secondary molecular recognition elements or negatively charged polymer. In the first attempt, a DNA aptamer was modified on the TM surface, and its aggregation behavior was monitored depending on the target molecule concentration. The TM aggregation rate during the heating process decreased depending on the amount of the DNA aptamer and increased depending on the target protein level. This suggests that the DNA aptamer prevented TM aggregation owing to its negative charge and achieved target protein detection owing to the cancellation of repulsion. Capturable aptamers were used in the TM assay to improve the sensitivity and limit of detection. The designed Capture DNA was modified on the TM surface, and the aptamer was captured in the presence of the target protein through a conformational change. Eventually, Capturable aptamer-based TM assay achieved a sub-nanomolar limit of detection and higher sensitivity than that of our initial investigation. Through this study and the ease of the DNA aptamer design, it was shown that the DNA aptamer-modified TM assay enabled the development of a simple and sensitive homogeneous protein detection system.

Routine measurement of cancer biomarkers is performed for early detection, risk classification, and treatment monitoring, among other applications, and has substantially contributed to better clinical outcomes for patients. However, there remains an unmet need for clinically validated assays of cancer protein biomarkers. Protein tumor markers are of particular interest since proteins carry out the majority of biological processes and thus dynamically reflect changes in cancer pathophysiology. Mass spectrometry-based targeted proteomics is a powerful tool for absolute peptide and protein quantification in biological matrices with numerous advantages that make it attractive for clinical applications in oncology. The use of liquid chromatography-tandem mass spectrometry (LC-MS/MS) based methodologies has allowed laboratories to overcome challenges associated with immunoassays that are more widely used for tumor marker measurements. Yet, clinical implementation of targeted proteomics methodologies has so far been limited to a few cancer markers. This is due to numerous challenges associated with paucity of robust validation studies of new biomarkers and the labor-intensive and operationally complex nature of LC-MS/MS workflows. The purpose of this review is to provide an overview of targeted proteomics applications in cancer, workflows used in targeted proteomics, and requirements for clinical validation and implementation of targeted proteomics assays. We will also discuss advantages and challenges of targeted MS-based proteomics assays for clinical cancer biomarker analysis and highlight some recent developments that will positively contribute to the implementation of this technique into clinical laboratories.

Proteins localized on the surface or within the lumen of tumor-derived extracellular vesicles (EVs) play distinct roles in cancer progression. However, quantifying both populations of proteins within EVs has been hampered due to the limited sensitivity of the existing protein detection methods and inefficient EV isolation techniques. In this study, the eSimoa framework, an innovative approach enabling spatial decoding of EV protein biomarkers with unmatched sensitivity and specificity is presented. Using the luminal eSimoa pipeline, the absolute concentration of luminal RAS or KRASG12D proteins is released and measured, uncovering their prevalence in pancreatic tumor-derived EVs. The pulldown eSimoa pipeline measured absolute protein concentrations from low-abundance EV subpopulations. The eSimoa assays detected EVs in both PBS and plasma samples, confirming their applicability across diverse clinical sample types. Overall, the eSimoa framework offers a valuable tool to (1) detect EVs at concentrations as low as 105 EV mL-1 in plasma, (2) quantify absolute EV protein concentrations as low as fM, and (3) decode the spatial distribution of EV proteins. This study highlights the potential of eSimoa in identifying disease-specific EV protein biomarkers in clinical samples with minimal pre-purification, thereby driving advancements in clinical translation.

Evaluating the levels of the biomarker carbohydrate antigen 19-9 (CA19-9) is crucial in early cancer diagnosis and prognosis assessment. In this study, an antifouling electrochemical immunosensor was developed for the label-free detection of CA19-9, in which bovine serum albumin (BSA) and graphene were cross-linked with the aid of glutaraldehyde to form a 3D conductive porous network on the surface of an electrode. The electrochemical immunosensor was characterized through the use of transmission electron microscopy (TEM), scanning electron microscopy (SEM), atomic force microscope (AFM), UV spectroscopy, and electrochemical methods. The level of CA19-9 was determined through the use of label-free electrochemical impedance spectroscopy (EIS) measurements. The electron transfer at the interface of the electrode was well preserved in human serum samples, demonstrating that this electrochemical immunosensor has excellent antifouling performance. CA19-9 could be detected in a wide range from 13.5 U/mL to 1000 U/mL, with a detection limit of 13.5 U/mL in human serum samples. This immunosensor also exhibited good selectivity and stability. The detection results of this immunosensor were further validated and compared using an enzyme-linked immunosorbent assay (ELISA). All the results confirmed that this immunosensor has a good sensing performance in terms of CA19-9, suggesting its promising application prospects in clinical applications.

The tear film forms a protective barrier between the ocular surface and the external environment. Despite its small volume, recent advancements in preanalytical and analytical procedures have enabled its in-depth analysis using multiple approaches. However, the diversity of tear film collection methods and the lack of standardization in pre-analytical methods represent the main obstacles to reproducible results and comparison among different studies. In this study, we first improved the pre-analytical procedures for the extraction of various molecular entities from Schirmer strips (ScS). Subsequently, our investigation focused on analyzing the molecular variances that might occur between two primary tear collection methods: capillary tube (CT) and ScS. Additionally, we examined different parts of the ScS to underscore these variations, which could serve as crucial factors for developing a standardized, optimized protocol for sample processing. Our results show that the inclusion of surfactants in the extraction process enhanced both the yield of protein extraction and the number of proteins identified in ScS, by effectively lysing the cells and improving the solubility of several intracellular proteins. In addition to proteins, nucleic acids could also be recovered for gene expression analyses, particularly from the bulb region of the ScS which is placed in the cul-de-sac. Despite their diluted nature, extracts from ScS remain a suitable material for retrieving tear proteins such as IL-17A at levels as low as the fg/mL range, thanks to highly sensitive immunoassays. Collection methods can affect measured tear protein levels. Lactoferrin is found in higher percentages in capillary electrophoresis analysis of tears collected using ScS compared to tears collected by CT (39.6 ± 4.8% versus 31 ± 4.4%).

The blood proteome holds great promise for precision medicine but poses substantial challenges due to the low abundance of most plasma proteins and the vast dynamic range of the plasma proteome. Here we address these challenges with NUcleic acid Linked Immuno-Sandwich Assay (NULISA™), which improves the sensitivity of traditional proximity ligation assays by ~10,000-fold to attomolar level, by suppressing assay background via a dual capture and release mechanism built into oligonucleotide-conjugated antibodies. Highly multiplexed quantification of both low- and high-abundance proteins spanning a wide dynamic range is achieved by attenuating signals from abundant targets with unconjugated antibodies and next-generation sequencing of barcoded reporter DNA. A 200-plex NULISA containing 124 cytokines and chemokines and other proteins demonstrates superior sensitivity to a proximity extension assay in detecting biologically important low-abundance biomarkers in patients with autoimmune diseases and COVID-19. Fully automated NULISA makes broad and in-depth proteomic analysis easily accessible for research and diagnostic applications.

Heart disease remains a leading cause of death in North America and worldwide. Despite advances in therapies, the chronic nature of cardiovascular diseases ultimately results in frequent hospitalizations and steady rates of mortality. Systems biology approaches have provided a new frontier toward unraveling the underlying mechanisms of cell, tissue, and organ dysfunction in disease. Mapping the complex networks of molecular functions across the genome, transcriptome, proteome, and metabolome has enormous potential to advance our understanding of cardiovascular disease, discover new disease biomarkers, and develop novel therapies. Computational workflows to interpret these data-intensive analyses as well as integration between different levels of interrogation remain important challenges in the advancement and application of systems biology-based analyses in cardiovascular research. This review will focus on summarizing the recent developments in network biology-level profiling in the heart, with particular emphasis on modeling of human heart failure. We will provide new perspectives on integration between different levels of large "omics" datasets, including integration of gene regulatory networks, protein-protein interactions, signaling networks, and metabolic networks in the heart.

The search for reliable protein biomarker candidates is critical for early disease detection and treatment. However, current immunoassay technologies are failing to meet increasing demands for sensitivity and multiplexing. Here, the authors have created a highly sensitive protein microarray using the principle of single-molecule counting for signal amplification, capable of simultaneously detecting a panel of cancer biomarkers at sub-pg/mL levels. To enable this amplification strategy, the authors introduce a novel method of protein patterning using photolithography to subdivide addressable arrays of capture antibody spots into hundreds of thousands of individual microwells. This allows for the total sensor area to be miniaturized, increasing the total possible multiplex capacity. With the immunoassay realized on a standard 75x25 mm form factor glass substrate, sample volume consumption is minimized to <10 μL, making the technology highly efficient and cost-effective. Additionally, the authors demonstrate the power of their technology by measuring six secretory factors related to glioma tumor progression in a cohort of mice. This highly sensitive, sample-sparing multiplex immunoassay paves the way for researchers to track changes in protein profiles over time, leading to earlier disease detection and discovery of more effective treatment using animal models.

Blood serum is arguably the most analyzed biofluid for disease prediction and diagnosis. Herein, we benchmarked five different serum abundant protein depletion (SAPD) kits with regard to the identification of disease-specific biomarkers in human serum using bottom-up proteomics. As expected, the IgG removal efficiency among the SAPD kits is highly variable, ranging from 70% to 93%. A pairwise comparison of database search results showed a 10%-19% variation in protein identification among the kits. Immunocapturing-based SAPD kits against IgG and albumin outperformed the others in the removal of these two abundant proteins. Conversely, non-antibody-based methods (i.e., kits using ion exchange resins) and kits leveraging a multi-antibody approach were proven to be less efficient in depleting IgG/albumin from samples but led to the highest number of identified peptides. Notably, our results indicate that different cancer biomarkers could be enriched up to 10% depending on the utilized SAPD kit compared with the undepleted sample. Additionally, functional analysis of the bottom-up proteomic results revealed that different SAPD kits enrich distinct disease- and pathway-specific protein sets. Overall, our study emphasizes that a careful selection of the appropriate commercial SAPD kit is crucial for the analysis of disease biomarkers in serum by shotgun proteomics.

Cerebrospinal fluid (CSF) is an essential matrix for the discovery of neurological disease biomarkers. However, the high dynamic range of protein concentrations in CSF hinders the detection of the least abundant protein biomarkers by untargeted mass spectrometry. It is thus beneficial to gain a deeper understanding of the secretion processes within the brain. Here, we aim to explore if and how the secretion of brain proteins to the CSF can be predicted. By combining a curated CSF proteome and the brain elevated proteome of the Human Protein Atlas, brain proteins were classified as CSF or non-CSF secreted. A machine learning model was trained on a range of sequence-based features to differentiate between CSF and non-CSF groups and effectively predict the brain origin of proteins. The classification model achieves an area under the curve of 0.89 if using high confidence CSF proteins. The most important prediction features include the subcellular localization, signal peptides, and transmembrane regions. The classifier generalized well to the larger brain detected proteome and is able to correctly predict novel CSF proteins identified by affinity proteomics. In addition to elucidating the underlying mechanisms of protein secretion, the trained classification model can support biomarker candidate selection.

Syndromic CLN3-Batten is a fatal, pediatric, neurodegenerative disease caused by variants in CLN3, which encodes the endolysosomal transmembrane CLN3 protein. No approved treatment for CLN3 is currently available. The protracted and asynchronous disease presentation complicates the evaluation of potential therapies using clinical disease progression parameters. Biomarkers as surrogates to measure the progression and effect of potential therapeutics are needed. We performed proteomic discovery studies using cerebrospinal fluid (CSF) samples from 28 CLN3-affected and 32 age-similar non-CLN3 individuals. Proximal extension assay (PEA) of 1467 proteins and untargeted data-dependent mass spectrometry [MS; MassIVE FTP server (ftp://MSV000090147@massive.ucsd.edu)] were used to generate orthogonal lists of protein marker candidates. At an adjusted p-value of <0.1 and threshold CLN3/non-CLN3 fold-change ratio of 1.5, PEA identified 54 and MS identified 233 candidate biomarkers. Some of these (NEFL, CHIT1) have been previously linked with other neurologic conditions. Others (CLPS, FAM217B, QRICH2, KRT16, ZNF333) appear to be novel. Both methods identified 25 candidate biomarkers, including CHIT1, NELL1, and ISLR2 which had absolute fold-change ratios >2. NELL1 and ISLR2 regulate axonal development in neurons and are intriguing new candidates for further investigation in CLN3. In addition to identifying candidate proteins for CLN3 research, this study provides a comparison of two large-scale proteomic discovery methods in CSF.

The blood proteome holds great promise for precision medicine but poses substantial challenges due to the low abundance of most plasma proteins and the vast dynamic range across the proteome. We report a novel proteomic technology - NUcleic acid Linked Immuno-Sandwich Assay (NULISA™) - that incorporates a dual capture and release mechanism to suppress the assay background and improves the sensitivity of the proximity ligation assay by over 10,000-fold to the attomolar level. It utilizes pairs of antibodies conjugated to DNA oligonucleotides that enable immunocomplex purification and generate reporter DNA containing target- and sample-specific barcodes for a next-generation sequencing-based, highly multiplexed readout. A 200-plex NULISA targeting 124 cytokines and chemokines and 80 other immune response-related proteins demonstrated superior sensitivity for detecting low-abundance proteins and high concordance with other immunoassays. The ultrahigh sensitivity allowed the detection of previously difficult-to-detect, but biologically important, low-abundance biomarkers in patients with autoimmune diseases and COVID-19. Fully automated NULISA addresses longstanding challenges in proteomic analysis of liquid biopsies and makes broad and in-depth proteomic analysis accessible to the general research community and future diagnostic applications.

Genotoxic injuries converge on senescence-executive program that promotes production of a senescence-specific secretome (SASP). The study of SASP is particularly intriguing, since through it a senescence process, triggered in a few cells, can spread to many other cells and produce either beneficial or negative consequences for health. We analysed the SASP of quiescent mesenchymal stromal cells (MSCs) following stress induced premature senescence (SIPS) by ionizing radiation exposure. We performed a proteome analysis of SASP content obtained from early and late senescent cells. The bioinformatics studies evidenced that early and late SASPs, besides some common ontologies and signalling pathways, contain specific factors. In spite of these differences, we evidenced that SASPs can block in vitro proliferation of cancer cells and promote senescence/apoptosis. It is possible to imagine that SASP always contains core components that have an anti-tumour activity, the progression from early to late senescence enriches the SASP of factors that may promote SASP tumorigenic activity only by interacting and instructing cells of the immune system. Our results on Caco-2 cancer cells incubated with late SASP in presence of peripheral white blood cells strongly support this hypothesis. We evidenced that quiescent MSCs following SIPS produced SASP that, while progressively changed its composition, preserved the capacity to block cancer growth by inducing senescence and/or apoptosis only in an autonomous manner.

Genotoxic injuries converge on senescence-executive program that promotes production of a senescence-specific secretome (SASP). The study of SASP is particularly intriguing, since through it a senescence process, triggered in a few cells, can spread to many other cells and produce either beneficial or negative consequences for health. We analysed the SASP of quiescent mesenchymal stromal cells (MSCs) following stress induced premature senescence (SIPS) by ionizing radiation exposure. We performed a proteome analysis of SASP content obtained from early and late senescent cells. The bioinformatics studies evidenced that early and late SASPs, besides some common ontologies and signalling pathways, contain specific factors. In spite of these differences, we evidenced that SASPs can block in vitro proliferation of cancer cells and promote senescence/apoptosis. It is possible to imagine that SASP always contains core components that have an anti-tumour activity, the progression from early to late senescence enriches the SASP of factors that may promote SASP tumorigenic activity only by interacting and instructing cells of the immune system. Our results on Caco-2 cancer cells incubated with late SASP in presence of peripheral white blood cells strongly support this hypothesis. We evidenced that quiescent MSCs following SIPS produced SASP that, while progressively changed its composition, preserved the capacity to block cancer growth by inducing senescence and/or apoptosis only in an autonomous manner.

Genotoxic injuries converge on senescence-executive program that promotes production of a senescence-specific secretome (SASP). The study of SASP is particularly intriguing, since through it a senescence process, triggered in a few cells, can spread to many other cells and produce either beneficial or negative consequences for health. We analysed the SASP of quiescent mesenchymal stromal cells (MSCs) following stress induced premature senescence (SIPS) by ionizing radiation exposure. We performed a proteome analysis of SASP content obtained from early and late senescent cells. The bioinformatics studies evidenced that early and late SASPs, besides some common ontologies and signalling pathways, contain specific factors. In spite of these differences, we evidenced that SASPs can block in vitro proliferation of cancer cells and promote senescence/apoptosis. It is possible to imagine that SASP always contains core components that have an anti-tumour activity, the progression from early to late senescence enriches the SASP of factors that may promote SASP tumorigenic activity only by interacting and instructing cells of the immune system. Our results on Caco-2 cancer cells incubated with late SASP in presence of peripheral white blood cells strongly support this hypothesis. We evidenced that quiescent MSCs following SIPS produced SASP that, while progressively changed its composition, preserved the capacity to block cancer growth by inducing senescence and/or apoptosis only in an autonomous manner.