MStern Blotting-High Throughput Polyvinylidene Fluoride (PVDF) Membrane-Based Proteomic Sample Preparation for 96-Well Plates

Technical Evaluation of Plasma Proteomics Technologies

Plasma proteomics technologies are rapidly evolving and of critical importance to the field of biomedical research. Here, we report a technical evaluation of six notable plasma proteomics technologies─unenriched (Neat), acid depletion, PreOmics ENRICHplus, Mag-Net, Seer Proteograph XT, and Olink Explore HT. The methods were compared on proteomic depth, reproducibility, linearity, tolerance to lipid interference, and limit of detection/quantification. In total, we performed 618 LC-MS/MS experiments and 93 Olink Explore HT assays. The Seer method achieved the greatest proteomic depth (∼4500 proteins detected), while Olink detected ∼2600 proteins. Other MS-based methods ranged from ∼500-2200 proteins detected. In our analysis, Neat, Mag-Net, Seer, and Olink had good reproducibility, while PreOmics and Acid had higher variability (>20% median coefficient of variation). All MS methods showed good linearity with spiked-in C-reactive protein (CRP); CRP was surprisingly not in the Olink assay. None of the methods were affected by lipid interference. Seer produced the highest number of quantifiable proteins with a measurable LOD (4407) and LOQ (2696). Olink had the next highest number of quantifiable proteins, with 2002 having an LOD and 1883 having an LOQ. Finally, we tested the applicability of these methods for detecting differences between healthy and cancer groups in a nonsmall cell lung cancer (NSCLC) cohort. All six methods detected differentially abundant proteins between the cancer and healthy samples but disagreed on which proteins were significant, highlighting the contrast between each method.

Recent implementations of data-independent acquisition for cancer biomarker discovery in biological fluids

INTRODUCTION

Cancer is the second-leading cause of death worldwide and accurate biomarkers for early detection and disease monitoring are needed to improve outcomes. Biological fluids, such as blood and urine, are ideal samples for biomarker measurements as they can be routinely collected with relatively minimally invasive methods. However, proteomics analysis of fluids has been a challenge due to the high dynamic range of its protein content. Advances in data-independent acquisition (DIA) mass spectrometry-based proteomics can address some of the technical challenges in the analysis of biofluids, thus enabling the ability for mass spectrometry to propel large-scale biomarker discovery.

AREAS COVERED

We reviewed principles of DIA and its recent applications in cancer biomarker discovery using biofluids. We summarized DIA proteomics studies using biological fluids in the context of cancer research over the past decade, and provided a comprehensive overview of the benefits and challenges of DIA-MS.

EXPERT OPINION

Various studies showed the potential of DIA-MS in identifying putative cancer biomarkers in a high-throughput manner. However, the lack of proper study design and standardization of methods across platforms still needs to be addressed to fully utilize the benefits of DIA-MS to accelerate the biomarker discovery and verification processes.

Microparticle-assisted protein capture method facilitates proteomic and glycoproteomic analysis of urine samples

Serum tests have become a partial alternative to renal biopsy for diagnosing primary membranous nephropathy (pMN). However, urine tests, due to their non-invasive nature and ability to more accurately reflect glomerular diseases, hold great promise for the detection of pMN. However, the low protein concentration and the time-consuming sample preparation procedure of urine samples challenges the proteomic and glycoproteomic analysis to find urine-derived signatures associated with pMN. In this study, we presented a microparticle-assisted protein capture (MAPC) method to efficiently prepare urine samples. It was found that proteins and N-linked intact glycopeptides can be sensitively identified from urine samples of pMN patients and healthy controls by using this method. For comparison, proteins and N-linked intact glycopeptides from serum were also analyzed. Interestingly, discrepancy was found in the changing trends for proteins/intact glycopeptides between serum and urine. Moreover, urine derived proteins, N-linked intact glycopeptides, and glycans exhibited more pronounced changes between pMN and healthy control compared to serum sample. Notably, urine IgG4 not only up-regulated in pMN at global peptide level, its corresponding site-specific glycans were specifically identified in pMN urine with significantly up-regulations, suggested the potential of using glycosylated urine IgG4 for the non-invasive diagnosis of pMN.

Cerebrospinal fluid protein biomarkers are associated with response to multiagent intraventricular chemotherapy in patients with CNS lymphoma

Background

Central nervous system lymphoma (CNSL), is a rare subtype of non-Hodgkin lymphoma, primarily affecting the brain and spinal cord. Most therapeutic systemic agents have limited penetration of the blood-brain and blood-cerebrospinal fluid (CSF) barrier, with the latter potentially promoting a treatment "sanctuary" for cancer cells. Evaluation of occult disease, particularly in the CSF, is challenging. In limited clinical experience, the addition of multiagent intraventricular chemotherapy (MAIVC), delivered through intracranially implanted CSF reservoirs, to systemic therapy has demonstrated encouraging outcomes, enhancing both progression-free survival and overall survival. However, given the potential morbidity associated with MAIVC, identification of minimally invasive biomarkers for guiding patient selection and management is necessary. Leveraging the longitudinal, large volume of CSF, the objective of this study was to identify CSF-based proteomic biomarkers that can serve as reliable indicators of CSF clearance in response to MAIVC and CNSL treatment outcome.

Methods

One hundred fifteen CSF samples from 59 CNSL patients receiving MAIVC were profiled using a high-throughput protocol coupled with mass-spectrometry that only requires 30 μL of CSF.

Results

More than 2000 unique proteins were detected using shotgun proteomics. Cerebrospinal fluid proteomics revealed key proteins (SGCE, LCP1, AGRN, OLFML3, and HRSP12) distinguishing early from never responders to MAIVC, with area under the receiver operating characteristic (AUROC) 0.86 (95% CI: 0.696-1). By integrating tumor volume from brain MRI scans with proteomic data, we identified potential intraventricular tumor burden markers for CNSL management, in particular LCP1.

Conclusions

The study identified CSF-based proteomic biomarkers, particularly LCP1, that can classify MAIVC response and indicate tumor burden in CNSL patients.

Comparative proteomic analysis of the changes in mare milk associated with different lactation stages and management systems

Mare milk has traditionally been attributed a number of health promoting properties. However, knowledge on its composition and functionality remains scarce, with particularly limited studies on mare milk proteomics. This study deeply characterized mare milk proteome accounting for both caseins and proteins in the whey fraction, also addressing the impact of lactation stage and different management systems. Milk samples from Basque Mountain Horse breed mares belonging to three different farms and three lactation stages were analysed after in-gel and in-solution digestion using nLC-MS/MS. Among the 469 proteins identified, the content of alpha-1 antitrypsin was significantly higher in pasture-based compared to other systems. Moreover, lactation stage significantly affected the content of beta-lactoglobulin II, immunoglobulin-like domain-containing protein, interferon alpha-inducible protein 27, lactotransferrin, polypeptide N-acetylgalactosaminyltransferase, and transforming acidic coiled-coil containing protein 2. This study contributes to the deep characterization of mare milk proteome and provides new insights into the effect of different production factors.

Development of an efficient, effective, and economical technology for proteome analysis

We present an efficient, effective, and economical approach, named E3technology, for proteomics sample preparation. By immobilizing silica microparticles into the polytetrafluoroethylene matrix, we develop a robust membrane medium, which could serve as a reliable platform to generate proteomics-friendly samples in a rapid and low-cost fashion. We benchmark its performance using different formats and demonstrate them with a variety of sample types of varied complexity, quantity, and volume. Our data suggest that E3technology provides proteome-wide identification and quantitation performance equivalent or superior to many existing methods. We further propose an enhanced single-vessel approach, named E4technology, which performs on-filter in-cell digestion with minimal sample loss and high sensitivity, enabling low-input and low-cell proteomics. Lastly, we utilized the above technologies to investigate RNA-binding proteins and profile the intact bacterial cell proteome.

Prostate cancer reshapes the secreted and extracellular vesicle urinary proteomes

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine

Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.

Stratification of neurogenic bladder risk in spina bifida using the urinary peptidome

Neurogenic bladder poses a major morbidity in children with spina bifida (SB), and videourodynamic studies (VUDS) are used to stratify this risk. This small-scale pilot study utilized current mass-spectrometry-based proteomic approaches to identify peptides or proteins in urine that may differentiate children at high risk of developing renal complications from a neurogenic bladder. Twenty-two urine samples of which nine had high bladder pressure storage that put the upper urinary tract at risk, while 13 with a lower risk for renal compromise were analyzed. More than 1,900 peptides across all 22 samples were quantified, and 115 peptides differed significantly (P < 0.05) between the two groups. Using machine learning approaches five peptides that showed the greatest differences between these two clinical categories were used to build a classifier. We tested this classifier by blind analysis of an additional six urine samples and showed that it correctly assigned the unknown samples in their proper risk category. These promising results indicate that a urinary screening test based on peptides could be performed on a regular basis to stratify the neurogenic bladder into low or high-risk categories. Expanding this work to larger cohorts as well as across a broad spectrum of urodynamics outcomes may provide a useful diagnostic test for neurogenic bladder.NEW & NOTEWORTHY This approach could help risk stratify the neurogenic bladder in patients with spina bifida and could allow us to safely defer on up to 1/3 of urodynamic studies. These pilot data justify a larger trial before this approach becomes a clinical tool.

Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers

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.

Early cellular and molecular signatures correlate with severity of West Nile virus infection

Infection with West Nile virus (WNV) drives a wide range of responses, from asymptomatic to flu-like symptoms/fever or severe cases of encephalitis and death. To identify cellular and molecular signatures distinguishing WNV severity, we employed systems profiling of peripheral blood from asymptomatic and severely ill individuals infected with WNV. We interrogated immune responses longitudinally from acute infection through convalescence employing single-cell protein and transcriptional profiling complemented with matched serum proteomics and metabolomics as well as multi-omics analysis. At the acute time point, we detected both elevation of pro-inflammatory markers in innate immune cell types and reduction of regulatory T cell activity in participants with severe infection, whereas asymptomatic donors had higher expression of genes associated with anti-inflammatory CD16+ monocytes. Therefore, we demonstrated the potential of systems immunology using multiple cell-type and cell-state-specific analyses to identify correlates of infection severity and host cellular activity contributing to an effective anti-viral response.

Advances in proteomics: characterization of the innate immune system after birth and during inflammation

Proteomics is the characterization of the protein composition, the proteome, of a biological sample. It involves the large-scale identification and quantification of proteins, peptides, and post-translational modifications. This review focuses on recent developments in mass spectrometry-based proteomics and provides an overview of available methods for sample preparation to study the innate immune system. Recent advancements in the proteomics workflows, including sample preparation, have significantly improved the sensitivity and proteome coverage of biological samples including the technically difficult blood plasma. Proteomics is often applied in immunology and has been used to characterize the levels of innate immune system components after perturbations such as birth or during chronic inflammatory diseases like rheumatoid arthritis (RA) and inflammatory bowel disease (IBD). In cancers, the tumor microenvironment may generate chronic inflammation and release cytokines to the circulation. In these situations, the innate immune system undergoes profound and long-lasting changes, the large-scale characterization of which may increase our biological understanding and help identify components with translational potential for guiding diagnosis and treatment decisions. With the ongoing technical development, proteomics will likely continue to provide increasing insights into complex biological processes and their implications for health and disease. Integrating proteomics with other omics data and utilizing multi-omics approaches have been demonstrated to give additional valuable insights into biological systems.

Urine-HILIC: Automated Sample Preparation for Bottom-Up Urinary Proteome Profiling in Clinical Proteomics

Urine provides a diverse source of information related to a patient's health status and is ideal for clinical proteomics due to its ease of collection. To date, most methods for the preparation of urine samples lack the throughput required to analyze large clinical cohorts. To this end, we developed a novel workflow, urine-HILIC (uHLC), based on an on-bead protein capture, clean-up, and digestion without the need for bottleneck processing steps such as protein precipitation or centrifugation. The workflow was applied to an acute kidney injury (AKI) pilot study. Urine from clinical samples and a pooled sample was subjected to automated sample preparation in a KingFisher™ Flex magnetic handling station using the novel approach based on MagReSyn® HILIC microspheres. For benchmarking, the pooled sample was also prepared using a published protocol based on an on-membrane (OM) protein capture and digestion workflow. Peptides were analyzed by LCMS in data-independent acquisition (DIA) mode using a Dionex Ultimate 3000 UPLC coupled to a Sciex 5600 mass spectrometer. The data were searched in Spectronaut™ 17. Both workflows showed similar peptide and protein identifications in the pooled sample. The uHLC workflow was easier to set up and complete, having less hands-on time than the OM method, with fewer manual processing steps. Lower peptide and protein coefficient of variation was observed in the uHLC technical replicates. Following statistical analysis, candidate protein markers were filtered, at ≥8.35-fold change in abundance, ≥2 unique peptides and ≤1% false discovery rate, and revealed 121 significant, differentially abundant proteins, some of which have known associations with kidney injury. The pilot data derived using this novel workflow provide information on the urinary proteome of patients with AKI. Further exploration in a larger cohort using this novel high-throughput method is warranted.

Western blotting (immunoblotting): history, theory, uses, protocol and problems

Western blotting (immunoblotting) is a powerful and commonly used technique that is capable of detecting or semiquantifying an individual protein from complex mixtures of proteins extracted from cells or tissues. The history surrounding the origin of western blotting, the theory behind the western blotting technique, a comprehensive protocol and the uses of western blotting are presented. Lesser known and significant problems in the western blotting field and troubleshooting of common problems are highlighted and discussed. This work is a comprehensive primer and guide for new western blotting researchers and those interested in a better understanding of the technique or getting better results.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

Prostate Cancer Reshapes the Secreted and Extracellular Vesicle Urinary Proteomes

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions, and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome, and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

Development of an Efficient, Effective, and Economical Technology for Proteome Analysis

Proteomics experiments have typically high economic and technical barriers to broad biomedical scientists, which not only result in costly supplies and accessories for sample preparation but also the reluctance to adapt new techniques. In the present study, we present an effective and efficient, yet economical technology, which we call E3technology, for proteomics sample preparation. By immobilizing silica microparticles into a polytetrafluoroethylene (PTFE) matrix, we developed a novel medium, which could be used as a robust and reliable proteomics platform to generate LCMS-friendly samples in a rapid and low-cost fashion. Using different formats of E3technology, including E3tip, E3filter, E3cartridge, and E3plate, we explored a variety of sample types in varied complexity, quantity, volume, and size, including bacterial, fungi, mammalian cells, mouse tissue, and human body fluids. We benchmark their performance against several established approaches. Our data suggest that E3technology outperforms many of the currently available techniques in terms of proteome identification and quantitation. It is widely applicable, highly reproducible, readily scalable and automatable, and is user-friendly and stress-free to non-expert proteomics laboratories. It does not require specialized expertise and equipment, and significantly lowers the technical and economical barrier to proteomics experiments. An enhanced version, E4technology, also opens new avenues to sample preparation for low input and/or low-cell proteomics analysis. The presented technologies by our study represent a breakthrough innovation in biomedical science, and we anticipate widespread adoption by the proteomics community.

Choroid plexus-targeted NKCC1 overexpression to treat post-hemorrhagic hydrocephalus

Post-hemorrhagic hydrocephalus (PHH) refers to a life-threatening accumulation of cerebrospinal fluid (CSF) that occurs following intraventricular hemorrhage (IVH). An incomplete understanding of this variably progressive condition has hampered the development of new therapies beyond serial neurosurgical interventions. Here, we show a key role for the bidirectional Na-K-Cl cotransporter, NKCC1, in the choroid plexus (ChP) to mitigate PHH. Mimicking IVH with intraventricular blood led to increased CSF [K+] and triggered cytosolic calcium activity in ChP epithelial cells, which was followed by NKCC1 activation. ChP-targeted adeno-associated viral (AAV)-NKCC1 prevented blood-induced ventriculomegaly and led to persistently increased CSF clearance capacity. These data demonstrate that intraventricular blood triggered a trans-choroidal, NKCC1-dependent CSF clearance mechanism. Inactive, phosphodeficient AAV-NKCC1-NT51 failed to mitigate ventriculomegaly. Excessive CSF [K+] fluctuations correlated with permanent shunting outcome in humans following hemorrhagic stroke, suggesting targeted gene therapy as a potential treatment to mitigate intracranial fluid accumulation following hemorrhage.

Robust High-Throughput Proteomics Identification and Deamidation Quantitation of Extinct Species up to Pleistocene with Ultrahigh-Resolution MALDI-FTICR Mass Spectrometry

Peptide mass fingerprinting (PMF) using MALDI-TOF mass spectrometry allows the identification of bone species based on their type I collagen sequence. In the archaeological or paleontological field, PMF is known as zooarchaeology mass spectrometry (ZooMS) and is widely implemented to find markers for most species, including the extinct ones. In addition to the identification of bone species, ZooMS enables dating estimation by measuring the deamidation value of specific peptides. Herein, we report several enhancements to the classical ZooMS technique, which reduces to 10-fold the required bone sample amount (down to the milligram scale) and achieves robust deamidation value calculation in a high-throughput manner. These improvements rely on a 96-well plate samples preparation, a careful optimization of collagen extraction and digestion to avoid spurious post-translational modification production, and PMF at high resolution using matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance (MALDI-FTICR) analysis. This method was applied to the identification of a hundred bones of herbivores from the Middle Paleolithic site of Caours (Somme, France) well dated from the Eemian Last Interglacial climatic optimum. The method gave reliable species identification to bones already identified by their osteomorphology, as well as to more challenging samples consisting of small or burned bone fragments. Deamidation values of bones originating from the same geological layers have a low standard deviation. The method can be applied to archaeological bone remains and offers a robust capacity to identify traditionally unidentifiable bone fragments, thus increasing the number of identified specimens and providing invaluable information in specific contexts.

Meta-analysis of published cerebrospinal fluid proteomics data identifies and validates metabolic enzyme panel as Alzheimer's disease biomarkers

To develop therapies for Alzheimer's disease, we need accurate in vivo diagnostics. Multiple proteomic studies mapping biomarker candidates in cerebrospinal fluid (CSF) resulted in little overlap. To overcome this shortcoming, we apply the rarely used concept of proteomics meta-analysis to identify an effective biomarker panel. We combine ten independent datasets for biomarker identification: seven datasets from 150 patients/controls for discovery, one dataset with 20 patients/controls for down-selection, and two datasets with 494 patients/controls for validation. The discovery results in 21 biomarker candidates and down-selection in three, to be validated in the two additional large-scale proteomics datasets with 228 diseased and 266 control samples. This resulting 3-protein biomarker panel differentiates Alzheimer's disease (AD) from controls in the two validation cohorts with areas under the receiver operating characteristic curve (AUROCs) of 0.83 and 0.87, respectively. This study highlights the value of systematically re-analyzing previously published proteomics data and the need for more stringent data deposition.

Bottom-Up Proteomics: Advancements in Sample Preparation

Liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based proteomics is a powerful technique for profiling proteomes of cells, tissues, and body fluids. Typical bottom-up proteomic workflows consist of the following three major steps: sample preparation, LC–MS/MS analysis, and data analysis. LC–MS/MS and data analysis techniques have been intensively developed, whereas sample preparation, a laborious process, remains a difficult task and the main challenge in different applications. Sample preparation is a crucial stage that affects the overall efficiency of a proteomic study; however, it is prone to errors and has low reproducibility and throughput. In-solution digestion and filter-aided sample preparation are the typical and widely used methods. In the past decade, novel methods to improve and facilitate the entire sample preparation process or integrate sample preparation and fractionation have been reported to reduce time, increase throughput, and improve reproducibility. In this review, we have outlined the current methods used for sample preparation in proteomics, including on-membrane digestion, bead-based digestion, immobilized enzymatic digestion, and suspension trapping. Additionally, we have summarized and discussed current devices and methods for integrating different steps of sample preparation and peptide fractionation.

Longitudinal serum proteomics analyses identify unique and overlapping host response pathways in Lyme disease and West Nile virus infection

Advancement in proteomics methods for interrogating biological samples has helped identify disease biomarkers for early diagnostics and unravel underlying molecular mechanisms of disease. Herein, we examined the serum proteomes of 23 study participants presenting with one of two common arthropod-borne infections: Lyme disease (LD), an extracellular bacterial infection or West Nile virus infection (WNV), an intracellular viral infection. The LC/MS based serum proteomes of samples collected at the time of diagnosis and during convalescence were assessed using a depletion-based high-throughput shotgun proteomics (dHSP) pipeline as well as a non-depleting blotting-based low-throughput platform (MStern). The LC/MS integrated analyses identified host proteome responses in the acute and recovery phases shared by LD and WNV infections, as well as differentially abundant proteins that were unique to each infection. Notably, we also detected proteins that distinguished localized from disseminated LD and asymptomatic from symptomatic WNV infection. The proteins detected in both diseases with the dHSP pipeline identified unique and overlapping proteins detected with the non-depleting MStern platform, supporting the utility of both detection methods. Machine learning confirmed the use of the serum proteome to distinguish the infection from healthy control sera but could not develop discriminatory models between LD and WNV at current sample numbers. Our study is the first to compare the serum proteomes in two arthropod-borne infections and highlights the similarities in host responses even though the pathogens and the vectors themselves are different.

Characterization of the minimal residual disease state reveals distinct evolutionary trajectories of human glioblastoma

Recurrence of solid tumors renders patients vulnerable to advanced, treatment-refractory disease state with mutational and oncogenic landscape distinctive from initial diagnosis. Improving outcomes for recurrent cancers requires a better understanding of cell populations that expand from the post-therapy, minimal residual disease (MRD) state. We profile barcoded tumor stem cell populations through therapy at tumor initiation, MRD, and recurrence in our therapy-adapted, patient-derived xenograft models of glioblastoma (GBM). Tumors show distinct patterns of recurrence in which clonal populations exhibit either a pre-existing fitness advantage or an equipotency fitness acquired through therapy. Characterization of the MRD state by single-cell and bulk RNA sequencing reveals a tumor-intrinsic immunomodulatory signature with prognostic significance at the transcriptomic level and in proteomic analysis of cerebrospinal fluid (CSF) collected from patients with GBM. Our results provide insight into the innate and therapy-driven dynamics of human GBM and the prognostic value of interrogating the MRD state in solid cancers.

Using plasma proteomics to investigate viral infections of the central nervous system including patients with HIV-associated neurocognitive disorders

State-of-the-art liquid chromatography/mass spectrometry (LC/MS)-based proteomic technologies, using microliter amounts of patient plasma, can detect and quantify several hundred plasma proteins in a high throughput fashion, allowing for the discovery of clinically relevant protein biomarkers and insights into the underlying pathobiological processes. Using such an in-house developed high throughput plasma proteomics allowed us to identify and quantify > 400 plasmas proteins in 15 min per sample, i.e., a throughput of 100 samples/day. We demonstrated the clinical applicability of our method in this pilot study by mapping the plasma proteomes from patients infected with human immunodeficiency virus (HIV) or herpes virus, both groups with involvement of the central nervous system (CNS). We found significant disease-specific differences in the plasma proteomes. The most notable difference was a decrease in the levels of several coagulation-associated proteins in HIV vs. herpes virus, among other dysregulated biological pathways providing insight into the differential pathophysiology of HIV compared to herpes virus infection. In a subsequent analysis, we found several plasma proteins associated with immunity and metabolism to differentiate patients with HIV-associated neurocognitive disorders (HAND) compared to cognitively normal people with HIV (PWH), suggesting the presence of plasma-based biomarkers to distinguishing HAND from cognitively normal PWH. Overall, our high-throughput plasma proteomics pipeline enables the identification of distinct proteomic signatures of HIV and herpes virus, which may help illuminate divergent pathophysiology behind virus-associated neurological disorders.

Urine Proteomics Reveals Sex-Specific Response to Total Pancreatectomy With Islet Autotransplantation

OBJECTIVES

Total pancreatectomy with islet autotransplantation (TPIAT) is a surgical option for refractory chronic pancreatitis-related pain. Despite the known clinical implications of TPIAT, the molecular effects remain poorly investigated. We performed the first hypothesis-generating study of the urinary proteome before and after TPIAT.

METHODS

Twenty-two patients eligible for TPIAT were prospectively enrolled. Urine samples were collected the week before and 12 to 18 months after TPIAT. The urine samples were prepared for bottom-up label-free quantitative proteomics using the "MStern" protocol.

RESULTS

Using 17 paired samples, we identified 2477 urinary proteins, of which 301 were significantly changed post-TPIAT versus pre-TPIAT. Our quantitative analysis revealed that the molecular response to TPIAT was highly sex-specific, with pronounced sex differences pre-TPIAT but minimal differences afterward. Comparing post-TPIAT versus pre-TPIAT, we found changes in cell-cell adhesion, intracellular vacuoles, and immune response proteins. After surgery, immunoglobulins, complement proteins, and cathepsins were increased, findings that may reflect glomerular damage. Finally, we identified both known and novel markers for immunoglobulin A nephropathy after 1 patient developed the disease 2 years after TPIAT.

CONCLUSIONS

We found distinct changes in the urinary proteomic profile after TPIAT and the response to TPIAT is highly sex-specific.

Plasma Proteomic Analysis Distinguishes Severity Outcomes of Human Ebola Virus Disease

Ebola virus (EBV) disease (EVD) is a highly virulent systemic disease characterized by an aggressive systemic inflammatory response and impaired vascular and coagulation systems, often leading to uncontrolled hemorrhaging and death. In this study, the proteomes of 38 sequential plasma samples from 12 confirmed EVD patients were analyzed. Of these 12 cases, 9 patients received treatment with interferon beta 1a (IFN-β-1a), 8 survived EVD, and 4 died; 2 of these 4 fatalities had received IFN-β-1a. Our analytical strategy combined three platforms targeting different plasma subproteomes: a liquid chromatography-mass spectrometry (LC-MS)-based analysis of the classical plasma proteome, a protocol that combines the depletion of abundant plasma proteins and LC-MS to detect less abundant plasma proteins, and an antibody-based cytokine/chemokine multiplex assay. These complementary platforms provided comprehensive data on 1,000 host and viral proteins. Examination of the early plasma proteomes revealed protein signatures that differentiated between fatalities and survivors. Moreover, IFN-β-1a treatment was associated with a distinct protein signature. Next, we examined those proteins whose abundances reflected viral load measurements and the disease course: resolution or progression. Our data identified a prognostic 4-protein biomarker panel (histone H1-5, moesin, kininogen 1, and ribosomal protein L35 [RPL35]) that predicted EVD outcomes more accurately than the onset viral load. IMPORTANCE As evidenced by the 2013-2016 outbreak in West Africa, Ebola virus (EBV) disease (EVD) poses a major global health threat. In this study, we characterized the plasma proteomes of 12 individuals infected with EBV, using two different LC-MS-based proteomics platforms and an antibody-based multiplexed cytokine/chemokine assay. Clear differences were observed in the host proteome between individuals who survived and those who died, at both early and late stages of the disease. From our analysis, we derived a 4-protein prognostic biomarker panel that may help direct care. Given the ease of implementation, a panel of these 4 proteins or subsets thereof has the potential to be widely applied in an emergency setting in resource-limited regions.

96DRA-urine: A high throughput sample preparation method for urinary proteome analysis

Mass spectrometry (MS)-based urinary proteomics is increasingly used for clinical research. A critical step in urinary proteomic analysis comprises the implementation of a reliable sample preparation method with high yields of peptides and proteins. In this study, we developed a urinary sample preparation method, DRA-Urine (Direct reduction/alkylation in urine), which urinary proteins were directly reduced/alkylated in urine, and then precipitated by acetone, washed and digestion on an ultrafilter unit. The qualitative and quantitative comparison of different urinary sample preparation methods (in-solution methods and ultrafilter-assisted methods) showed that DRA-Urine could achieve better results. Adapting DRA-Urine protocol to a 96-well format, namely 96DRA-Urine, shortened the time for buffer change and improved sample preparation throughput. The results showed that 96DRA-Urine displayed similar proteomic performance to DRA-Urine. Finally, the 96DRA-Urine method was used in a label-free, small pilot biomarker discovery analysis for differential urinary proteome analysis of bladder cancer urine. The results showed that urinary proteins could differentiate bladder cancer (BCa) patients from healthy controls and distinguish high-grade BCa from low-grade BCa with area under the curve (AUC) values of 0.972 and 0.847, respectively. Consequently, the 96DRA-Urine method might be a high-throughput method for preparing body fluid samples used in clinical research but needs to be further verified.

Differential Brain and Cerebrospinal Fluid Proteomic Responses to Acute Prenatal Endotoxin Exposure

Chorioamnionitis (CA) is a risk factor for preterm birth and is associated with neurodevelopmental delay and cognitive disorders. Prenatal inflammation-induced brain injury may resolve during the immediate postnatal period when rapid brain remodeling occurs. Cerebrospinal fluid (CSF) collected at birth may be a critical source of predictive biomarkers. Using pigs as a model of preterm infants exposed to CA, we hypothesized that prenatal lipopolysaccharide (LPS) exposure induces proteome changes in the CSF and brain at birth and postnatally. Fetal piglets (103 days gestation of full-term at 117 days) were administered intra-amniotic (IA) lipopolysaccharide (LPS) 3 days before preterm delivery by caesarian section. CSF and brain tissue were collected on postnatal Days 1 and 5 (P1 and P5). CSF and hippocampal proteins were profiled by LC-MS-based quantitative proteomics. Neuroinflammatory responses in the cerebral cortex, periventricular white matter and hippocampus were evaluated by immunohistochemistry, and gene expression was evaluated by qPCR. Pigs exposed to LPS in utero showed changes in CSF protein levels at birth but not at P5. Complement protein C3, hemopexin, vasoactive intestinal peptide, carboxypeptidase N subunit 2, ITIH1, and plasminogen expression were upregulated in the CSF, while proteins associated with axon growth and synaptic functions (FGFR1, BASP1, HSPD1, UBER2N, and RCN2), adhesion (talin1), and neuronal survival (Atox1) were downregulated. Microglia, but not astrocytes, were activated by LPS at P5 in the hippocampus but not in other brain regions. At this time, marginal increases in complement protein C3, LBP, HIF1a, Basp1, Minpp1, and FGFR1 transcription indicated hippocampal proinflammatory responses. In conclusion, few days exposure to endotoxin prenatally induce proteome changes in the CSF and brain at birth, but most changes resolve a few days later. The developing hippocampus has high neuronal plasticity in response to perinatal inflammation. Changes in CSF protein expression at birth may predict later structural brain damage in preterm infants exposed to variable types and durations of CA-related inflammation in utero.

Leveraging the CSF proteome toward minimally-invasive diagnostics surveillance of brain malignancies

Background

Diagnosis and prognostication of intra-axial brain tumors hinges on invasive brain sampling, which carries risk of morbidity. Minimally-invasive sampling of proximal fluids, also known as liquid biopsy, can mitigate this risk. Our objective was to identify diagnostic and prognostic cerebrospinal fluid (CSF) proteomic signatures in glioblastoma (GBM), brain metastases (BM), and primary central nervous system lymphoma (CNSL).

Methods

CSF samples were retrospectively retrieved from the Penn State Neuroscience Biorepository and profiled using shotgun proteomics. Proteomic signatures were identified using machine learning classifiers and survival analyses.

Results

Using 30 µL CSF volumes, we recovered 755 unique proteins across 73 samples. Proteomic-based classifiers identified malignancy with area under the receiver operating characteristic (AUROC) of 0.94 and distinguished between tumor entities with AUROC ≥0.95. More clinically relevant triplex classifiers, comprised of just three proteins, distinguished between tumor entities with AUROC of 0.75-0.89. Novel biomarkers were identified, including GAP43, TFF3 and CACNA2D2, and characterized using single cell RNA sequencing. Survival analyses validated previously implicated prognostic signatures, including blood-brain barrier disruption.

Conclusions

Reliable classification of intra-axial malignancies using low CSF volumes is feasible, allowing for longitudinal tumor surveillance.

Development and Provisional Validation of a Multiplex LC-MRM-MS Test for Timely Kidney Injury Detection in Urine

Kidney injury is a complication frequently encountered in hospitalized patients. Early detection of kidney injury prior to loss of renal function is an unmet clinical need that should be targeted by a protein-based biomarker panel. In this study, we aim to quantitate urinary kidney injury biomarkers at the picomolar to nanomolar level by liquid chromatography coupled to tandem mass spectrometry in multiple reaction monitoring mode (LC-MRM-MS). Proteins were immunocaptured from urinary samples, denatured, reduced, alkylated, and digested into peptides before LC-MRM-MS analysis. Stable-isotope-labeled peptides functioned as internal standards, and biomarker concentrations were attained by an external calibration strategy. The method was evaluated for selectivity, carryover, matrix effects, linearity, and imprecision. The LC-MRM-MS method enabled the quantitation of KIM-1, NGAL, TIMP2, IGFBP7, CXCL9, nephrin, and SLC22A2 and the detection of TGF-β1, cubilin, and uromodulin. Two to three peptides were included per protein, and three transitions were monitored per peptide for analytical selectivity. The analytical carryover was <1%, and minimal urine matrix effects were observed by combining immunocapture and targeted LC-MRM-MS analysis. The average total CV of all quantifier peptides was 26%. The linear measurement range was determined per measurand and found to be 0.05–30 nmol/L. The targeted MS-based method enables the multiplex quantitation of low-abundance urinary kidney injury biomarkers for future clinical evaluation.

Sample Preparation and Diagnostic Methods for a Variety of Settings: A Comprehensive Review

Sample preparation is an essential step for nearly every type of biochemical analysis in use today. Among the most important of these analyses is the diagnosis of diseases, since their treatment may rely greatly on time and, in the case of infectious diseases, containing their spread within a population to prevent outbreaks. To address this, many different methods have been developed for use in the wide variety of settings for which they are needed. In this work, we have reviewed the literature and report on a broad range of methods that have been developed in recent years and their applications to point-of-care (POC), high-throughput screening, and low-resource and traditional clinical settings for diagnosis, including some of those that were developed in response to the coronavirus disease 2019 (COVID-19) pandemic. In addition to covering alternative approaches and improvements to traditional sample preparation techniques such as extractions and separations, techniques that have been developed with focuses on integration with smart devices, laboratory automation, and biosensors are also discussed.

High-throughput proteomics and AI for cancer biomarker discovery

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.

A Universal and High-Throughput Proteomics Sample Preparation Platform

Major advances have been made to improve the sensitivity of mass analyzers, spectral quality, and speed of data processing enabling more comprehensive proteome discovery and quantitation. While focus has recently begun shifting toward robust proteomics sample preparation efforts, a high-throughput proteomics sample preparation is still lacking. We report the development of a highly automated universal 384-well plate sample preparation platform with high reproducibility and adaptability for extraction of proteins from cells within a culture plate. Digestion efficiency was excellent in comparison to a commercial digest peptide standard with minimal sample loss while improving sample preparation throughput by 20- to 40-fold (the entire process from plated cells to clean peptides is complete in ∼300 min). Analysis of six human cell types, including two primary cell samples, identified and quantified ∼4,000 proteins for each sample in a single high-performance liquid chromatography (HPLC)-tandem mass spectrometry injection with only 100-10K cells, thus demonstrating universality of the platform. The selected protein was further quantified using a developed HPLC-multiple reaction monitoring method for HeLa digests with two heavy labeled internal standard peptides spiked in. Excellent linearity was achieved across different cell numbers indicating a potential for target protein quantitation in clinical research.

Sample Preparation for High-Throughput Urine Proteomics Using 96-Well Polyvinylidene Fluoride (PVDF) Membranes

Proteomics analysis of urine samples allows for studying the impact of system perturbation. However, meaningful proteomics-based biomarker discovery projects often require the analysis of large patient cohorts with hundreds of samples to describe the biological variability. Thus, robust high-throughput sample processing methods are a prerequisite for clinical proteomics pipelines that minimize experimental bias due to individual sample processing methods. Herein we describe a high-throughput method for parallel 96-well plate-based processing of urine samples for subsequent LC/MS-based proteomic analyses. Protein digestion and subsequent sample processing steps are efficiently performed in 96-well polyvinylidene fluoride (PVDF) membrane plate allowing for the use of vacuum manifolds for rapid liquid transfer, and multichannel pipettes and/or liquid handing robots. In this chapter we make available a detailed step-by-step protocol for our 'MStern blotting' sample processing strategy applied to patient urine samples followed by mass spectrometry-based proteomics analysis. Subsequently, we provide an example application using minimal volume of urine samples (e.g. 150 μL) collected from children pre and post thoracotomy to identify the predominant sites of protein catabolism and aid in the design of therapies to ameliorate protein catabolism and breakdown during critical illness. Furthermore, we demonstrate how the systemic state is reflected in the urine as an easily obtainable, stable, and safe biofluid.

Urinary proteome profiling for stratifying patients with familial Parkinson's disease

The prevalence of Parkinson's disease (PD) is increasing but the development of novel treatment strategies and therapeutics altering the course of the disease would benefit from specific, sensitive, and non-invasive biomarkers to detect PD early. Here, we describe a scalable and sensitive mass spectrometry (MS)-based proteomic workflow for urinary proteome profiling. Our workflow enabled the reproducible quantification of more than 2,000 proteins in more than 200 urine samples using minimal volumes from two independent patient cohorts. The urinary proteome was significantly different between PD patients and healthy controls, as well as between LRRK2 G2019S carriers and non-carriers in both cohorts. Interestingly, our data revealed lysosomal dysregulation in individuals with the LRRK2 G2019S mutation. When combined with machine learning, the urinary proteome data alone were sufficient to classify mutation status and disease manifestation in mutation carriers remarkably well, identifying VGF, ENPEP, and other PD-associated proteins as the most discriminating features. Taken together, our results validate urinary proteomics as a valuable strategy for biomarker discovery and patient stratification in PD.

Lab-on-a-Filter Techniques for Economical, Effective, and Flexible Proteome Analysis

Effective and reliable protease digestion of biological samples is critical to the success in bottom-up proteomics analysis. Various filter-based approaches using different types of membranes have been developed in the past several years and largely implemented in sample preparations for modern proteomics. However, these approaches rely heavily on commercial filter products, which are not only costly but also limited in membrane options. Here, we present a plug-and-play device for filter assembly and protease digestion. The device can accommodate a variety of membrane types, can be packed in-house with minimal difficulty, and is extremely cost-effective and reliable. Our protocol offers a versatile platform for general proteome analyses and clinical mass spectrometry.

Prostate Cancer Biomarker Development: National Cancer Institute's Early Detection Research Network Prostate Cancer Collaborative Group Review

Prostate cancer remains the most common non-skin cancer and second leading cause of death among men in the United States. Although progress has been made in diagnosis and risk assessment, many clinical questions remain regarding early identification of prostate cancer and management. The early detection of aggressive disease continues to provide high curative rates if diagnosed in a localized state. Unfortunately, prostate cancer displays significant heterogeneity within the prostate organ and between individual patients making detection and treatment strategies complex. Although prostate cancer is common among men, the majority will not die from prostate cancer, introducing the issue of overtreatment as a major concern in clinical management of the disease. The focus of the future is to identify those at highest risk for aggressive prostate cancer and to develop prevention and screening strategies, as well as discerning the difference in malignant potential of diagnosed tumors. The Prostate Cancer Research Group of the National Cancer Institute's Early Detection Research Network has contributed to the progress in addressing these concerns. This summary is an overview of the activities of the group.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Targeted Mass Spectrometry of a Clinically Relevant PSA Variant from Post-DRE Urines for Quantitation and Genotype Determination

PURPOSE

The rs17632542 single nucleotide polymorphism (SNP) results in lower serum prostate specific antigen (PSA) levels which may further mitigate against its clinical utility as a prostate cancer biomarker. Post-digital rectal exam (post-DRE) urine is a minimally invasive fluid that is currently utilized in prostate cancer diagnosis. To detect and quantitate the variant protein in urine.

EXPERIMENTAL DESIGN

Fifty-three post-DRE urines from rs17632542 genotyped individuals processed and analyzed by liquid chromatography/mass spectrometry (LC-MS) in a double-blinded randomized study. The ability to distinguish between homozygous wild-type, heterozygous, or homozygous variant is examined before unblinding.

RESULTS

Stable-isotope labeled peptides are used in the detection and quantitation of three peptides of interest in each sample using parallel reaction monitoring (PRM). Using these data, groupings are predicted using hierarchical clustering in R. Accuracy of the predictions show 100% concordance across the 53 samples, including individuals homozygous and heterozygous for the SNP.

CONCLUSIONS AND CLINICAL RELEVANCE

The study demonstrates that MS based peptide variant quantitation in urine could be useful in determining patient genotype expression. This assay provides a tool to evaluate the utility of PSA variant (rs17632542) in parallel with current and forthcoming urine biomarker panels.

Molecular Dynamics Simulation-assisted Ionic Liquid Screening for Deep Coverage Proteome Analysis

In-depth coverage of proteomic analysis could enhance our understanding to the mechanism of the protein functions. Unfortunately, many highly hydrophobic proteins and low-abundance proteins, which play critical roles in signaling networks, are easily lost during sample preparation, mainly attributed to the fact that very few extractants can simultaneously satisfy the requirements on strong solubilizing ability to membrane proteins and good enzyme compatibility. Thus, it is urgent to screen out ideal extractant from the huge compound libraries in a fast and effective way. Herein, by investigating the interior mechanism of extractants on the membrane proteins solubilization and trypsin compatibility, a molecular dynamics simulation system was established as complement to the experimental procedure to narrow down the scope of candidates for proteomics analysis. The simulation data shows that the van der Waals interaction between cation group of ionic liquid and membrane protein is the dominant factor in determining protein solubilization. In combination with the experimental data, 1-dodecyl-3-methylimidazolium chloride (C12Im-Cl) is on the shortlist for the suitable candidates from comprehensive aspects. Inspired by the advantages of C12Im-Cl, an ionic liquid-based filter-aided sample preparation (i-FASP) method was developed. Using this strategy, over 3,300 proteins were confidently identified from 103 HeLa cells (∼100 ng proteins) in a single run, an improvement of 53% over the conventional FASP method. Then the i-FASP method was further successfully applied to the label-free relative quantitation of human liver cancer and para-carcinoma tissues with obviously improved accuracy, reproducibility and coverage than the commonly used urea-based FASP method. The above results demonstrated that the i-FASP method could be performed as a versatile tool for the in-depth coverage proteomic analysis of biological samples.

Prenatal Endotoxin Exposure Induces Fetal and Neonatal Renal Inflammation via Innate and Th1 Immune Activation in Preterm Pigs

Chorioamnionitis (CA) predisposes to preterm birth and affects the fetal mucosal surfaces (i.e., gut, lungs, and skin) via intra-amniotic (IA) inflammation, thereby accentuating the proinflammatory status in newborn preterm infants. It is not known if CA may affect more distant organs, such as the kidneys, before and after preterm birth. Using preterm pigs as a model for preterm infants, we investigated the impact of CA on fetal and neonatal renal status and underlying mechanisms. Fetal pigs received an IA dose of lipopolysaccharide (LPS), were delivered preterm by cesarean section 3 days later (90% gestation), and compared with controls (CON) at birth and at postnatal day 5. Plasma proteome and inflammatory targets in kidney tissues were evaluated. IA LPS-exposed pigs showed inflammation of fetal membranes, higher fetal plasma creatinine, and neonatal urinary microalbumin levels, indicating renal dysfunction. At birth, plasma proteomics revealed LPS effects on proteins associated with renal inflammation (up-regulated LRG1, down-regulated ICA, and ACE). Kidney tissues of LPS pigs at birth also showed increased levels of kidney injury markers (LRG1, KIM1, NGLA, HIF1A, and CASP3), elevated molecular traits related to innate immune activation (infiltrated MPO⁺ cells, complement molecules, oxidative stress, TLR2, TLR4, S100A9, LTF, and LYZ), and Th1 responses (CD3⁺ cells, ratios of IFNG/IL4, and TBET/GATA3). Unlike in plasma, innate and adaptive immune responses in kidney tissues of LPS pigs persisted to postnatal day 5. We conclude that prenatal endotoxin exposure induces fetal and postnatal renal inflammation in preterm pigs with both innate and adaptive immune activation, partly explaining the potential increased risks of kidney injury in preterm infants born with CA.

Formaldehyde Derivatization: An Unexpected Side Reaction During Filter-Aided Sample Preparation

The filter-aided sample preparation (FASP) method has been commonly used for proteomic sample preparation due to its high efficiency in removing impurities. Herein, we report an overlooked +12 Da side modification during FASP method using Microcon spin filters. We confirmed that the side modification is caused by formaldehyde released from the spin filter and found that the side modification leads to 10.5% and 9.5% loss in proteome-level peptide and protein identification, respectively. We evaluated different pretreatment procedures to reduce the side reaction. Furthermore, on the basis of the evaluation results of different brands of spin filters, we recommend Nanosep spin filters for different proteomic studies, especially for amine-labeling proteomic studies. Our results would benefit researchers employing the spin filters to improve their results and also help spin filter manufacturers to improve the product quality. Data are available via ProteomeXchange with identifier PXD018737.

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

Recent advances in robotic protein sample preparation for clinical analysis and other biomedical applications

Discovery of new protein biomarker candidates has become a major research goal in the areas of clinical chemistry, analytical chemistry, and biomedicine. These important species constitute the molecular target when it comes to diagnosis, prognosis, and further monitoring of disease. However, their analysis requires powerful, selective and high-throughput sample preparation and product (analyte) characterisation approaches. In general, manual sample processing is tedious, complex and time-consuming, especially when large numbers of samples have to be processed (e.g., in clinical studies). Automation via microtiter-plate platforms involving robotics has brought improvements in high-throughput performance while comparable or even better precisions and repeatability (intra-day, inter-day) were achieved. At the same time, waste production and exposure of laboratory personnel to hazards were reduced. In comprehensive protein analysis workflows (e.g., liquid chromatography-tandem mass spectrometry analysis), sample preparation is an unavoidable step. This review surveys the recent achievements in automation of bottom-up and top-down protein and/or proteomics approaches. Emphasis is put on high-end multi-well plate robotic platforms developed for clinical analysis and other biomedical applications. The literature from 2013 to date has been covered.

The Proteome of Tetrasphaera elongata is adapted to Changing Conditions in Wastewater Treatment Plants

The activated sludge in wastewater treatment plants (WWTP) designed for enhanced biological phosphorus removal (EBPR) experiences periodically changing nutrient and oxygen availability. Tetrasphaera is the most abundant genus in Danish WWTP and represents up to 20–30% of the activated sludge community based on 16S rRNA amplicon sequencing and quantitative fluorescence in situ hybridization analyses, although the genus is in low abundance in the influent wastewater. Here we investigated how Tetrasphaera can successfully out-compete most other microorganisms in such highly dynamic ecosystems. To achieve this, we analyzed the physiological adaptations of the WWTP isolate T. elongata str. LP2 during an aerobic to anoxic shift by label-free quantitative proteomics and NMR-metabolomics. Escherichia coli was used as reference organism as it shares several metabolic capabilities and is regularly introduced to wastewater treatment plants without succeeding there. When compared to E. coli, only minor changes in the proteome of T. elongata were observed after the switch to anoxic conditions. This indicates that metabolic pathways for anaerobic energy harvest were already expressed during the aerobic growth. This allows continuous growth of Tetrasphaera immediately after the switch to anoxic conditions. Metabolomics furthermore revealed that the substrates provided were exploited far more efficiently by Tetrasphaera than by E. coli. These results suggest that T. elongata prospers in the dynamic WWTP environment due to adaptation to the changing environmental conditions.

Self-Assembled STrap for Global Proteomics and Salivary Biomarker Discovery

Clinical biomarkers identified by shotgun proteomics require proteins in body fluids or tissues to be enzymatically digested before being separated and sequenced by liquid chromatography-tandem mass spectrometry. How well peptide signals can be resolved and detected is largely dependent on the quality of sample preparation. Conventional approaches such as in-gel, in-solution, and filter-based digestion, despite their extensive implementation by the community, become less appealing due to their unsatisfying protein/peptide recovery rate, lengthy sample processing, and/or lowcost-effectiveness. Suspension trapping has recently been demonstrated as an ultrafast approach for proteomic analysis. Here, for the first time, we extend its application to human salivary proteome analyses. In particular, we present a simple self-assembled glass fiber filter device which can be packed with minimal difficulty, is extremely cost-effective, and maintains the same performance as commercial filters. As a proof-of-principle, we analyzed the whole saliva from 8 healthy individuals as well as a cohort of 10 subjects of oral squamous cell carcinoma (OSCC) patients and non-OSCC subjects. Label-free quantification revealed surprisingly low interindividual variability and several known markers. Our study provides the first evidence of an easy-to-use and low-cost device for clinical proteomics as well as for general proteomic sample preparation.

Dynamic molecular changes during the first week of human life follow a robust developmental trajectory

Systems biology can unravel complex biology but has not been extensively applied to human newborns, a group highly vulnerable to a wide range of diseases. We optimized methods to extract transcriptomic, proteomic, metabolomic, cytokine/chemokine, and single cell immune phenotyping data from <1 ml of blood, a volume readily obtained from newborns. Indexing to baseline and applying innovative integrative computational methods reveals dramatic changes along a remarkably stable developmental trajectory over the first week of life. This is most evident in changes of interferon and complement pathways, as well as neutrophil-associated signaling. Validated across two independent cohorts of newborns from West Africa and Australasia, a robust and common trajectory emerges, suggesting a purposeful rather than random developmental path. Systems biology and innovative data integration can provide fresh insights into the molecular ontogeny of the first week of life, a dynamic developmental phase that is key for health and disease.

Enhancing the Production of the Fungal Pigment Aurofusarin in Fusarium graminearum

There is an increasing demand for products from natural sources, which includes a growing market for naturally-produced colorants. Filamentous fungi produce a vast number of chemically diverse pigments and are therefore explored as an easily accessible source. In this study we examine the positive regulatory effect of the transcription factor AurR1 on the aurofusarin gene cluster in Fusarium graminearum. Proteomic analyses showed that overexpression of AurR1 resulted in a significant increase of five of the eleven proteins belonging to the aurofusarin biosynthetic pathway. Further, the production of aurofusarin was increased more than threefold in the overexpression mutant compared to the wild type, reaching levels of 270 mg/L. In addition to biosynthesis of aurofusarin, several yet undescribed putative naphthoquinone/anthraquinone analogue compounds were observed in the overexpression mutant. Our results suggest that it is possible to enhance the aurofusarin production through genetic engineering.

S-Trap, an Ultrafast Sample-Preparation Approach for Shotgun Proteomics

The success of shotgun proteomic analysis depends largely on how samples are prepared. Current approaches (such as those that are gel-, solution-, or filter-based), although being extensively employed in the field, are time-consuming and less effective with respect to the repetitive sample processing, recovery, and overall yield. As an alternative, the suspension trapping (S-Trap) filter has been commercially available very recently in the format of a single or 96-well filter plate. In contrast to the conventional filter-aided sample preparation (FASP) approach, which utilizes a molecular weight cut-off (MWCO) membrane as the filter and requires hours of processing before digestion-ready proteins can be obtained, the S-Trap employs a three-dimensional porous material as filter media and traps particulate protein suspensions with the subsequent depletion of interfering substances and in-filter digestion. Due to the large (submicron) pore size, each centrifugation cycle of the S-Trap filter only takes 1 min, which significantly reduces the total processing time from approximately 3 h by FASP to less than 15 min, suggesting an ultrafast sample-preparation approach for shotgun proteomics. Here, we comprehensively evaluate the performance of the individual S-Trap filter and 96-well filter plate in the context of global protein identification and quantitation using whole-cell lysate and clinically relevant sputum samples.