Urine proteomics for profiling of human disease using high accuracy mass spectrometry

Knowledge of the biologically relevant components of human tissues has enabled the invention of numerous clinically useful diagnostic tests, as well as non-invasive ways of monitoring disease and its response to treatment. Recent use of advanced MS-based proteomics revealed that the composition of human urine is more complex than anticipated. Here, we extend the current characterization of the human urinary proteome by extensively fractionating urine using ultra-centrifugation, gel electrophoresis, ion exchange and reverse-phase chromatography, effectively reducing mixture complexity while minimizing loss of material. By using high-accuracy mass measurements of the linear ion trap-Orbitrap mass spectrometer and LC-MS/MS of peptides generated from such extensively fractionated specimens, we identified 2362 proteins in routinely collected individual urine specimens, including more than 1000 proteins not described in previous studies. Many of these are biomedically significant molecules, including glomerularly filtered cytokines and shed cell surface molecules, as well as renally and urogenitally produced transporters and structural proteins. Annotation of the identified proteome reveals distinct patterns of enrichment, consistent with previously described specific physiologic mechanisms, including 336 proteins that appear to be expressed by a variety of distal organs and glomerularly filtered from serum. Comparison of the proteomes identified from 12 individual specimens revealed a subset of generally invariant proteins, as well as individually variable ones, suggesting that our approach may be used to study individual differences in age, physiologic state and clinical condition. Consistent with this, annotation of the identified proteome by using machine learning and text mining exposed possible associations with 27 common and more than 500 rare human diseases, establishing a widely useful resource for the study of human pathophysiology and biomarker discovery.     

Analysis of the human testis proteome by mass spectrometry and bioinformatics

The testis is a unique organ responsible for sperm production and androgen secretion in men. To analyze the human testis proteome on a large scale, 1-D SDS-PAGE and RP-LC-MS/MS were applied and 1430 proteins in the human testis were identified. Both the false-positive rate of peptides and protein identification confidence scores were calculated in the present study. Subsequent bioinformatics analysis of the human testis proteome revealed 39 testis-specific proteins which may be important for testis functions. And a large family of proteins were identified possibly involved in alternative splicing, which may also be involved in testis-specific splicing events and explain why splicing is so prevalent in the testis. Compared with the studies on brain proteome, researches on the testis proteome is still very limited. Studies of these proteins will give a better understanding on the function of the testis. Moreover, this large-scale identification of testis proteins in humans could serve as a reference for future studies on the mechanisms underlying male infertility, searching for potential contraceptive targets, and developing new treatments for testis cancer.     

An evaluation of multidimensional fingerprinting in the context of clinical proteomics

Multidimensional fingerprinting (MDF) utilizes measurable peptide characteristics to identify proteins. In this study, 3‐D fingerprinting, namely, parent protein molecular weight, peptide mass, and peptide retention time on RPLC, is used to identify 331 differentially expressed proteins between normal and human colon cancer plasma membrane samples. A false discovery rate (FDR) procedure is introduced to evaluate the performance of MDF on the colon cancer dataset. This evaluation establishes a false protein identification rate below 15% for this dataset. Western blot analysis is performed to validate the differential expression of the MDF‐identified protein VDAC1 on the original tissue samples. The limits of MDF are further assessed by a simulation study where key parameters such as database size, query size, and mass accuracy are varied. The results of this simulation study demonstrate that fingerprinting with three dimensions yields low FDR values even for large queries on the complete human proteome without the need for prior peptide sequencing by tandem mass spectrometry. Specifically, when mass accuracy is 10 ppm or lower, full human proteome searches can achieve FDR values of 10% or less.     

Clinical application of tear proteomics: Present and future prospects

Human tear fluid is charactered with very small volume and complex protein constitutes with a very large orders of magnitude. The tear proteome analysis provides a unique dataset (i.e., specific protein markers or protein patterns) that may be correlated to more effective diagnosis, prognosis, and response to therapy. Compared to less than 100 tear proteins obtained by the traditional methods, more than 400 proteins have been found in human tear fluid by current proteomic technologies. Many proteomics techniques, such as 2-DE, MALDI-TOF-MS, LC-MS, SELDI-TOF-MS, protein arrays, have been used to perform tear proteome analysis in healthy and/or disease subjects. The clinical application of tear proteomics needs suitable tear collection methods, standard tear handling procedures, and more sensitive and reliable proteomic technologies.     

Proteomic identification of proteins in the human brain: Towards a more comprehensive understanding of neurodegenerative disease

Proteomics has revealed itself as a powerful tool in the identification and determination of proteins and their biological significance. More recently, several groups have taken advantage of the high-throughput nature of proteomics in order to gain a more in-depth understanding of the human brain. In turn, this information has provided researchers with invaluable insight into the potential pathways and mechanisms involved in the pathogenesis of several neurodegenerative disorders, e.g., Alzheimer and Parkinson disease. Furthermore, these findings likely will improve methods to diagnose disease and monitor disease progression as well as generate novel targets for therapeutic intervention. Despite these advances, comprehensive understanding of the human brain proteome remains challenging, and requires development of improved sample enrichment, better instrumentation, and innovative analytic techniques. In this review, we will focus on the most recent progress related to identification of proteins in the human brain under normal as well as pathological conditions, mainly Alzheimer and Parkinson disease, their potential application in biomarker discovery, and discuss current advances in protein identification aimed at providing a more comprehensive understanding of the brain.     

Proteomics and the lung: Analysis of bronchoalveolar lavage fluid

Our knowledge of the complex bronchoalveolar lavage fluid (BALF) proteome has increased significantly over the last decade; but still, there remain many aspects of the BALF proteome that need characterization. Current proteomic methodologies resolve proteins within limited dynamic ranges: thereby, being limited in their ability to examine important areas of the BALF proteome, such as low molecular weight, low abundance proteins. To ensure proper coverage of these proteins in the BALF proteome, a refined 2-DE standard operation protocol is presented, highlighting important issues in sample collection, sample preparation, and 2-D DIGE analysis. It is hoped that this will help advance the field of BALF proteomics, BALFomics, which has lagged behind similar biofluids such as plasma and serum.     

基于改进U-Net的人脑黑质致密部分割

人脑黑质致密部分割能够为帕金森病的诊断提供一定依据。黑质致密部在人脑核磁共振成像中像素占比低、类间差异小,为提高计算机辅助诊断系统对人脑黑质致密部的分割精度,提出一种基于改进U形神经网络（U-Net）的人脑黑质致密部分割方法。为了提取更多有效的多尺度图像语义特征,结合U-Net的跨连接结构并采用多头注意力机制,同时融合基于Transformer编码器的高维语义编码模块以提取高维语义特征,避免浅层噪声对特征造成的影响。建立多任务模型并设计基于二维高斯核权重掩膜的损失函数,解决神经网络分割模型因多次下采样造成的不连续分割误差问题。构建包括140个帕金森病患者以及48个健康对照者的高精度核磁共振脑成像数据集进行实验,结果表明,相较常用的医疗影像分割方法R2U-Net、HANet等,该方法的多任务分割效果取得明显提升,戴斯相关系数和AUC指标分别达到0.869 1和0.943 9,消融实验结果也验证了改进编码器和改进损失这2个模块的有效性。     

Analysis of membrane microdomain-associated proteins in the insular cortex of post-mortem human brain

Membrane microdomains (MM) are membrane rafts within the cell membrane enriched in cholesterol and glycosphingolipids that have been implicated in the trafficking and sorting of membrane proteins, secretory and endocytotic pathways, and signal transduction. To date, MM have not been characterised in the human brain. We reason that by identifying MM in the normal human cortex, we may better understand the molecular mechanisms of human brain dysfunction. To characterize the protein composition of MM in the human brain, we have carried out a comprehensive proteomic analysis of detergent resistant membranes (DRMs) associated proteins derived from human postmortem insular cortex using 1-DE separation prior to LC coupled to MS/MS or GeLC-MS/MS. Eighty five proteins were identified including 57 unique to human brain cortex DRMs (by comparison with DRM proteins reported in other cell types). High levels of signal transduction, cell adhesion, cell transport and cell trafficking proteins were identified including synaptic proteins such as synapsin II and synaptic vesicle membrane protein, mitochondrial proteins such as ATPase subunits and metabolic enzymes such as malate dehydrogenase. This data will facilitate our understanding of protein expression changes within membranes in candidate brain regions in human brain diseases such as schizophrenia, bipolar disorder and other psychiatric and neurodegenerative disorders.     

Proteomic analysis of human reproductive fluids

Fertilization, fetal development, and delivery depend upon a coordinated series of events in the oocyte, the embryo, and the supporting tissues and fluids. Proteomic techniques which are capable of identifying and characterizing multiple proteins simultaneously have added new dimensions to the field of human reproduction. Application of these high throughput methodologies in pregnancy-related research has begun to provide a novel perspective on the biochemical pathways involved in pregnancy and its related disorders. Most of the existing research on human reproduction and gestation has focused on follicular fluid (FF) and amniotic fluid (AF). Proteome analysis of FF has yielded significant information relevant to oocyte maturation and quality. Studies performed on the protein content of AF cells and supernatant contributed to the comprehension of the underlying pathophysiology, clinical diagnosis of pregnancy-related disorders and identification of relevant disease biomarkers. Although proteome technologies in reproduction research are not as yet widely applied, characterization of the proteome of reproductive fluids can be expected to significantly improve maternal healthcare in the future.     

A qualitative proteome investigation of the sediment portion of human urine: Implications in the biomarker discovery process

Inherent to the biomarker discovery process is a comparative analysis of physiological states. It is therefore critical that the proteome detection protocol does not bias the analysis. With urine, the sediment portion, obtained upon thawing frozen urine, is routinely discarded prior to proteome analysis. However, our results demonstrate that such a practice inadvertently induces bias, having significant implications in the biomarker discovery process. We present the first proteome investigation of human urinary sediments, identifying 60 proteins in this phase by MS. Many sediment proteins were also detected in the urinary supernatant, indicating that several proteins partition between the two phases. This partitioning is dependant on the pH of the sample, as well as the degree of sample agitation. As a consequence of discarding the sediment portion of urine, the concentration of potential candidate biomarkers in the supernatant phase will be altered or, in other instances, may be completely removed from the sample. To minimize this, the pH of all samples should first be normalized, and the samples vigorously vortexed prior to discarding the sediments. For more comprehensive biomarker investigations, we suggest that urinary sediments be analyzed along with the supernatant proteins.     

Dynamic urinary proteomic analysis reveals stable proteins to be potential biomarkers

Human urinary proteome analysis is a convenient and efficient approach for understanding disease processes affecting the kidney and urogenital tract. Many potential biomarkers have been identified in previous differential analyses; however, dynamic variations of the urinary proteome have not been intensively studied, and it is difficult to conclude that potential biomarkers are genuinely associated with disease rather then simply being physiological proteome variations. In this paper, pooled and individual urine samples were used to analyze dynamic variations in the urinary proteome. Five types of pooled samples (first morning void, second morning void, excessive water‐drinking void, random void, and 24 h void) collected in 1 day from six volunteers were used to analyze intra‐day variations. Six pairs of first morning voids collected a week apart were used to study inter‐day, inter‐individual, and inter‐gender variations. The intra‐day, inter‐day, inter‐individual, and inter‐gender variation analyses showed that many proteins were constantly present with relatively stable abundances, and some of these had earlier been reported as potential disease biomarkers. In terms of sensitivity, the main components of the five intra‐day urinary proteomes were similar, and the second morning void is recommended for clinical proteome analysis. The advantages and disadvantages of pooling samples are also discussed. The data presented describe a pool of stable urinary proteins seen under different physiological conditions. Any significant qualitative or quantitative changes in these stable proteins may mean that such proteins could serve as potential urinary biomarkers.     

Proteomics of human cerebrospinal fluid - the good, the bad, and the ugly

The development of MALDI ESI in the late 1980s has revolutionized the biological sciences and facilitated the emergence of a new discipline called proteomics. Application of proteomics to human cerebrospinal fluid (CSF) has greatly hastened the advancement of characterizing the CSF proteome as well as revealing novel protein biomarkers that are diagnostic of various neurological diseases. While impressive progressions have been made in this field, it has become increasingly clear that proteomics results generated by various laboratories are highly variable. The underlying issues are vast, including limitations and complications with heterogeneity of patients/testing subjects, experimental design, sample processing, as well as current proteomics technology. Accordingly, this review not only summarizes the current status of characterization of the human CSF proteome and biomarker discovery for major neurodegenerative disorders, i.e., Alzheimer's disease and Parkinson's disease, but also addresses a few essential caveats involved in several steps of CSF proteomics that may contribute to the variable/contradicting results reported by different laboratories. The potential future directions of CSF proteomics are also discussed with this analysis.     

Shotgun MS proteomic analysis of bronchoalveolar lavage fluid in normal subjects

We provide a review of proteomic techniques used to characterize the bronchoalveolar lavage fluid (BALF) proteome of normal healthy subjects. Bronchoalveolar lavage (BAL) is the most common technique for sampling the components of the alveolar space. The proteomic techniques used to study normal BALF include protein separation by 2DE, whereby proteins were identified by comparison to a reference gel as well as high pressure liquid chromatography (HPLC)-MS/MS, also known as shotgun proteomics. We summarize recent progress using shotgun MS technologies to define the normal BALF proteome. Surprisingly, we find that despite advances in shotgun proteomic technologies over the course of the last 10 years, which have resulted in greater numbers of proteins being identified, the functional landscape of normal BALF proteome was similarly described by all methods examined.     

Using differential solubilization and 2-D gel electrophoresis to visualize increased numbers of proteins in the human cortex and caudate nucleus and putamen

The aim of this study was to determine if differential solubilization of human CNS proteins would increase the total number of proteins that could be visualized using 2-D gel electrophoresis. Hence, proteins were solubilized into Tris, CHAPS and SB3-10 before separation across a pH 4-7 IEF gradient and a 12-14% SDS polyacrylamide gel, which could be achieved with a run-to-run variation of 35% in spot intensity. Because Western blot analyses suggested proteins could be in more than one detergent fraction, we completed a conservative analyses of our 2-D gels assuming spots that appeared on multiple gels at the same molecular weight and pI were the same protein. These analyses show that we had visualized over 3000 unique protein spots across three 2-D gels generated from each sample of human frontal cortex and caudate-putamen. This represented, at worst, a significant increase in the number of spots visualized in the acidic protein spectrum compared to what has been reported in other studies of human CNS. This study, therefore, supports the proposal that the analysis of the human CNS proteome using 2-D gel electrophoresis, combined with appropriate sample preparation, can be used to expand the studies on the pathologies of neurological and psychiatric diseases.     

Proteomics in clinical prostate research

The incidence of early prostate cancer (PCa) has increased rapidly in recent years. The majority of newly diagnosed PCa are in early tumor phase. Presently, we do not have adequate biomarkers to assess tumor aggressiveness in individual cases. Consequently, too many patients are given curatively intended treatment. An exploration of the human proteome may provide clinically useful markers. 2-DE has been successfully used for analysis of the protein phenotype using clinical samples. Proteins are separated according to size and charge, gels are compared by image analysis, protein spots of interest are excised, and proteins identified by MS. This method is exploratory and allows protein identification. However, low-abundance proteins are difficult to detect and 2-DE is currently too labor-intensive for routine use. In recent years, nongel based techniques, such as LC-MS, SELDI-MS, and protein arrays have emerged. They require smaller sample sizes and can be more automated than 2-DE. In this review, we describe studies of the protein expression of benign prostatic tissue and PCa, which is likely to serve as the first step in prognostic biomarker discovery. The prostate proteome is still far from a complete mapping which would enhance our understanding of PCa biology.     

Introduction: Knowledge Discovery in High-Throughput Biological Domains

The sequencing of the human genome was a major step in understanding the ways in which we are wired. Although an important milestone, this genetic blueprint provides only a ``parts list"; it does not offer any information about how the human organism is actually working, and it gives little insight into the function or interactions among the approximately thirty thousand constitutive parts that comprise our genome. To date, research in molecular biology had resulted in annotating only a small percentage (around 10%) of the gene set, and even less is known about proteins. Because of the quantity of information being generated, we increasingly rely on computational techniques to provide insight into the genome, proteome, and interactome data. Robotics and computational biology are rapidly changing the way we formulate and test biological hypotheses. Advances in gene expression profiling by microarrays and protein profiling by mass spectrometry have suggested the potential to simultaneously view all genes expressed, all subsequent protein products, and all the interacting partners of each individual protein within a biological system. We can rapidly and accurately measure the relative activity of genes and proteins in normal and diseased tissue. Diverse computational techniques have been applied to solve biological and medical problems over the years. Increasingly, such systems face challenges that arise from the enormous increase in information complexity and volume in these domains. In addition, the pace of evolution of our understanding of underlying principles requires continuous updates to existing databases, as well as systems that support reasoning and knowledge discovery. Performing these changes manually is becoming the bottleneck of the successful application of computer science to biological and medical domains.     

Differential proteomics of human seminal plasma: A potential target for searching male infertility marker proteins

The clinical fertility tests, available in the market, fail to define the exact cause of male infertility in almost half of the cases and point toward a crucial need of developing better ways of infertility investigations. The protein biomarkers may help us toward better understanding of unknown cases of male infertility that, in turn, can guide us to find better therapeutic solutions. Many clinical attempts have been made to identify biomarkers of male infertility in sperm proteome but only few studies have targeted seminal plasma. Human seminal plasma is a rich source of proteins that are essentially required for development of sperm and successful fertilization. This viewpoint article highlights the importance of human seminal plasma proteome in reproductive physiology and suggests that differential proteomics integrated with functional analysis may help us in searching potential biomarkers of male infertility.