The development of multiple reaction monitoring assays for liver-derived plasma proteins

There is wide interpatient variability in toxicity to chemotherapeutic drugs and a lack of routine clinical tests for prospectively identifying patients at risk of developing toxicity from chemotherapy. An empirically driven MS strategy has been developed to monitor liver-derived plasma proteins as potential biomarkers of early toxicity. Multiple reaction monitoring (MRM) has been used to assess 46 candidate peptides from 18 liver-derived proteins. Following an iterative process of assay design, optimisation and assessment we selected 29 MRM assays (median CV 4.6%, range 1.2-11.6%) and monitored changes in levels of plasma proteins from a small number of colorectal cancer (CRC) patients undergoing chemotherapy. We demonstrated MRM assay robustness, and show that patients undergo minor elevation in plasma proteins when profiled on Day 3 of the chemotherapeutic regime. The MRM assays were in general agreement with 2-D DIGE-based quantitation from the same patient samples. The data supports the application of MRM-based methods as facile, highly reproducible, medium-throughput techniques that warrant expanded investigation for clinical utility in identifying patients at risk of developing chemotoxicity.     

CE-MS-based proteomics in biomarker discovery and clinical application

CE-MS is applied in clinical proteomics for both the identification of biomarkers of disease and assessment of biomarkers in clinical diagnosis. The analysis is reproducible, fast, and requires only small sample volumes. However, successful CE-MS analysis depends on several critical steps that can be consolidated as follows: (i) proper sample preparation and fractionation, (ii) application of suitable capillary coating and appropriate CE-MS interfaces, to ensure the reproducibility and stability of the analysis, and (iii) an optimized clinical and statistical study design to increase the chances for obtaining clinically relevant results. In this review, we cover all these aspects, and present several examples of the application of CE-MS in clinical proteomics.     

Identification of circulating endorepellin LG3 fragment: Potential use as a serological biomarker for breast cancer

Comparative proteome analysis was performed on the cultured media of human nontumor and malignant breast cell lines, Hs578Bst and Hs578T, respectively, in search of a serological biomarker(s) for breast cancer. Proteins in the conditioned media were separated by 2‐D PAGE and then visualized by silver‐staining. Eight proteins changed differentially by more than two‐fold were identified by MALDI‐TOF/TOF MS. Among the proteins identified, the terminal laminin‐like globular (LG3) domain of endorepellin, which was recently reported as an antiangiogenesis factor, was decreased in the cancer cell line. We confirmed the bone morphogenic protein‐1 (BMP‐1) mediated cleavage site on the N‐terminus of endorepellin LG3 fragment. This finding suggests that the LG3 fragment is specifically released by a BMP‐1 driven limited proteolytic process. The protein was also detected in plasma by Western blot analysis and selected reaction monitoring (SRM). The plasma level of the endorepellin LG3 fragment was significantly lower in breast cancer patients compared to healthy donors (p = 0.017; n = 12). The LG3 protein concentration in the control plasma was measured at approximately 3.7 pmol/mL compared to 1.8 pmol/mL in plasma from the cancer patients. We suggest that these results support the potential use of the endorepellin LG3 fragment as a new serological biomarker for breast cancer.     

Using data-independent,high-resolution mass spectrometry in protein biomarker research: Perspectives and clinical applications

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)—one of the data-independent strategies that have gained wide application of late.     

Recent advances in targeted proteomics for clinical applications

MS-based approaches using targeted methods have been widely adopted by the proteomics community to study clinical questions such as the evaluation of biomarkers. At present, the most widely used targeted MS method is the SRM technique typically performed on a triple quadrupole instrument. However, the high analytical demands for performing clinical studies in combination with the extreme complexity of the samples involved are a serious challenge. The segmentation of the biomarker evaluation workflow has only partially alleviated these issues by differently balancing the analytical requirements and throughput at different stages of the process. The recent introduction of targeted high-resolution and accurate-mass analyses on fast sequencing mass spectrometers operated in parallel reaction monitoring (PRM) mode offers new avenues to conduct clinical studies and thus overcome some of the limitations of the triple quadrupole instrument. This article discusses the attributes and specificities of the PRM technique, in terms of experimental design, execution, and data analysis, and the implications for biomarker evaluation. The benefits of PRM on data quality and the impact on the consistency of results are highlighted and the definitive progress on the overall output of clinical studies, including high throughput, is discussed.     

Application of quantitative proteomics technologies to the biomarker discovery pipeline for multiple sclerosis

Multiple sclerosis is an inflammatory-mediated demyelinating disorder most prevalent in young Caucasian adults. The various clinical manifestations of the disease present several challenges in the clinic in terms of diagnosis, monitoring disease progression and response to treatment. Advances in MS-based proteomic technologies have revolutionized the field of biomarker research and paved the way for the identification and validation of disease-specific markers. This review focuses on the novel candidates discovered by the application of quantitative proteomics to relevant disease-affected tissues in both the human context and within the animal model of the disease known as experimental autoimmune encephalomyelitis. The role of targeted MS approaches for biomarker validation studies, such as multiple reaction monitoring will also be discussed.     

Using lectins in biomarker research: Addressing the limitations of sensitivity and availability

Carbohydrates have fundamental roles throughout biology, yet they have not been as well studied as proteins and nucleic acids, in part due to limitations in the experimental tools. Improved methods for studying glycans could spur significant advances in the understanding and application of glycobiology. The use of affinity reagents, such as lectins and glycan-binding antibodies, is a valuable complement to methods involving mass spectrometry and chromatography. This article addresses two limitations that have prevented the broader experimental use of glycan-binding proteins: sensitivity and availability. The sensitivity limitation stems from the poor affinity that many glycan-binding proteins have as isolated analytical reagents. To address this problem, I propose making use of multivalent interactions between lectins and glycans, mimicking those frequently found in the biological setting. Recent experiments show that a practical technique for producing lectin multimers can significantly improve detection sensitivity. The second limitation, availability, is the difficulty of finding and obtaining glycan-binding proteins that recognize less common or arbitrarily defined glycan structures. To address this problem, I propose translating the wealth of existing glycan array data into a quantitative, searchable database of the specificities of glycan-binding proteins. Such a resource would allow us to more easily identify proteins with defined specificities and perform detailed comparisons between reagents. Solutions to these two limitations could lead to the more effective use of, and a broader range of, glycan-binding reagents.     

多元化交互式计算机组成原理CAI软件的设计

在分析计算机组成原理课程特点的基础上,提出了一种多元化、交互式CAI软件的设计思想,并给出了系统结构和实现方法,最后介绍了一个典型实例。     

Development of a model-based clinical sepsis biomarker for critically ill patients

Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48 h. Insulin sensitivity (S_I) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time.Hourly model-based insulin sensitivity S_I values were calculated from glycemic control data of 36 patients with sepsis. The hourly S_I is compared to the hourly sepsis score (ss) for these patients (ss = 0-4 for increasing severity). A multivariate clinical biomarker was also developed to maximize the discrimination between different ss groups. Receiver operator characteristic (ROC) curves for severe sepsis (ss ≥ 2) are created for both S_I and the multivariate clinical biomarker.Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% positive predictive value (PPV), and 98.3% negative predictive value (NPV) at an S_I cut-off value of 0.00013 L/mU/min. Multivariate clinical biomarker combining S_I, temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, the multivariate clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsis score shows potential avenues to improve the positive predictive value.     

Capillary zone electrophoresis on-line coupled to mass spectrometry: A perspective application for clinical proteomics

Clinical proteomics, a rapidly growing field, intends to use specific diagnostic proteomic/peptidomic markers for initial diagnosis or prognosis of the progression of various diseases. Analyses of disease-associated markers in defined biological samples can provide valuable molecular diagnostic information for these diseases. This approach relies on sensitive and highly standardized modern analytical techniques. In the recent years, one of these technologies, CZE online coupled to MS (CZE-MS), has been increasingly used for the detection of peptide biomarkers (<20 kDa) in body fluids such as urine. This review presents the most relevant urinary proteomic studies addressing the application of CZE-MS in clinically relevant biomarker research between the years 2006 and 2014.     

Developing proteomics-based biomarkers for colorectal neoplasms for clinical practice: Opportunities and challenges

Colorectal cancer (CRC) arises from the normal colon epithelium through the accumulation of genetic mutations and epigenetic alterations that are associated with progression along the histological adenoma-adenocarcinoma sequence. Elucidating the molecular alterations underlying disease progression will not only provide insight into the behavior of the tumors, but also could lead to the discovery of useful biomarkers for diagnosis, monitoring treatment responsiveness, or predicting disease outcomes. In the past a few years, there have been several evaluating differentially expressed protein biomarkers by employing proteomics technologies coupled with mass spectrometry. In the current review, we will briefly summarize the results from selected recent studies using tissue or serum samples from CRC patients in the past 5 years and discuss the opportunities and challenges in translating these findings from the research setting to clinical practice.     

Glycoproteomic strategies: From discovery to clinical application of cancer carbohydrate biomarkers

Carbohydrate antigens are the most frequently and traditionally used biomarkers for cancer, such as CA19–9, CA125, DUPAN-II, AFP-L3, and many others. The diagnostic potential of them was simply based on the cancer-specific alterations of glycan structures on particular glycoproteins in serum/plasma. In spite of the facts that glycosylation disorders are feasible for cancer biomarkers and glycomic analysis technologies to explore them have been rapidly developed, it remains difficult to sensitively screen glycan structure changes on cancer-associated glycoproteins from clinical specimens. Moreover, a lot of additional issues should be appropriately addressed for the clinical application of newly identified glycosylation biomarkers, including analytical throughput, quantitative confirmation of structural changes, and biological explanation for the alterations. In the last decade, significant improvement of mass spectrometric techniques is being made in the aspects of both hardware spec and preanalytical purification procedures for glycoprotein analysis. Here we review potential approaches to perform comprehensive analysis of glycoproteomic biomarker screening from serum/plasma and to realize high-throughput validation of site-specific oligosaccharide variations. The power and problems of mass spectrometric applications on the clinical use of carbohydrate biomarkers are also discussed in this review.     

Comparison of proteomic biomarker panels in urine and serum for ovarian cancer diagnosis

Purpose : The purposes of this study were to confirm previously found candidate epithelial ovarian cancer biomarkers in urine and to compare a paired serum biomarker panel and a urine biomarker panel from the same study cohort with regard to the receiver operating characteristic curve (ROC) area under the ROC curve (AUC) values. Experimental design : Four significant urine biomarkers were confirmed among 130 pelvic mass patients in the present study. The four biomarkers form a potential urine biomarker panel. From the same study cohort, the potential urine biomarker panel was compared to a serum biomarker panel, consisting of seven proteins/peptides, OvaRI. Results : Multivariate analysis of the urine panel demonstrated a significant differentiation (p<0.0001) between epithelial ovarian cancer patients and patients with benign ovarian pelvic masses. The ROC AUC of the urine panel was 0.84 and the ROC AUC of OvaRI was 0.83. Combining the urine panel with OvaRI demonstrated a significant contribution from both, for urine peaks, OR=2.12 and for OvaRI, OR=1.39; the ROC AUC of this model was 0.88. Conclusions and clinical relevance : We demonstrated that both urine and serum can be used individually or in combination to potentially aid in ovarian cancer diagnostics. Urine proteomic profiling could provide biomarkers for the non‐invasive test required in clinical practice.     

Quantification of heart fatty acid binding protein as a biomarker for drug-induced cardiac and musculoskeletal necroses

Heart fatty acid binding protein (Fabp3) is a cytosolic protein expressed primarily in heart, and to a lesser extent in skeletal muscle, brain, and kidney. During myocardial injury, the Fabp3 level in serum is elevated rapidly, making it an ideal early marker for myocardial infarction. In this study, an MS‐based selected reaction monitoring method (LC‐SRM) was developed for quantifying Fabp3 in rat serum. Fabp3 was enriched first through an immobilized antibody, and the protein was digested on beads directly. A marker peptide of Fabp3 was quantified using LC‐SRM with a stable isotope‐labeled peptide standard. For six quality control samples with Fabp3 ranging from 0.256 to 25 ng, the average recovery following the procedure was about 73%, and the precision (%CV) between replicates was less than 7%. The Fabp3 concentrations in rat serum peaked 1 h after isoproterenol treatment, and returned to baseline levels 24 h after the dose. Elevated Fabp3 levels were also detected in rats administered with a PPAR α/δ agonist, which has shown to cause skeletal muscle necrosis. Fabp3 can be used as a biomarker for both cardiac and skeletal necroses. The cross‐validation of the LC‐SRM method with an existing ELISA method is described.     

Application of immunoproteomics to leptospirosis: towards clinical diagnostics and vaccine discovery

Each of the currently available methods for serodiagnosis of leptospirosis, including the microscopic agglutination test (MAT), has its own drawback(s) when used in clinical practice. A new diagnostic test is therefore required for an earlier and more accurate diagnosis of leptospirosis. We applied immunoproteomics to define potential immunogens from five serovars of Leptospira reference strains. A leptospiral whole cell lysate from each serovar was used as the antigen to react with IgG and IgM in the sera from four patients with a positive MAT. Sera from four non-leptospirosis patients with a negative MAT were pooled and used as the negative control. 2-D Western blot analysis showed that the degree of immunoreactivity corresponded with the MAT titers. No immunoreactive spots were detected when the pooled control sera were used. A total of 24 protein spots immunoreacted with IgM and/or IgG from patients with leptospirosis. These immunoreactive proteins were identified by MALDI-TOF MS and were classified into five groups, including flagellar proteins, chaperones/heat shock proteins, transport proteins, metabolic enzymes, and hypothetical proteins. More immunoreactive spots were detected with anti-human IgG in the sera of all patients and with all the serovars of leptospires used. Some of the identified proteins immunoreacted only with IgG, whereas the others were detectable with both IgM and IgG. Among the immunoreactive proteins identified, FlaB proteins (flagellin and flagellar core protein) have been shown to have a potential role in clinical diagnostics and vaccine development. These data underscore the significant impact of immunoproteomics in clinical applications.     

基于泛终端边缘计算的智能图像电力监控系统网络安全防护方式的采用和研究

图像识别、边缘计算和AI技术的不断发展为电力监控方式方法提供了更便捷的手段和更多的迭代数据资料,使电力监控系统更便捷、更高效地迈向数字化发展。但日益增多的设备、计算方式的改变和更大的数据量带来了更多的网络安全漏洞节点和网络安全风险。本文就基于泛终端边缘计算的智能图像电力监控系统网络安全防护方式的采取做了具体的探讨,意图采用更贴近实际的防护手段,为电力智能图像监控系统提供安全稳定的运行保障。     

TimeLapseAnalyzer: multi-target analysis for live-cell imaging and time-lapse microscopy

The direct observation of cells over time using time-lapse microscopy can provide deep insights into many important biological processes. Reliable analyses of motility, proliferation, invasive potential or mortality of cells are essential to many studies involving live cell imaging and can aid in biomarker discovery and diagnostic decisions. Given the vast amount of image- and time-series data produced by modern microscopes, automated analysis is a key feature to capitalize the potential of time-lapse imaging devices. To provide fast and reproducible analyses of multiple aspects of cell behaviour, we developed TimeLapseAnalyzer. Apart from general purpose image enhancements and segmentation procedures, this extensible, self-contained, modular cross-platform package provides dedicated modalities for fast and reliable analysis of multi-target cell tracking, scratch wound healing analysis, cell counting and tube formation analysis in high throughput screening of live-cell experiments. TimeLapseAnalyzer is freely available (MATLAB, Open Source) at http://www.informatik.uni-ulm.de/ni/mitarbeiter/HKestler/tla.