Deep Learning Powers Protein Identification from Precursor MS Information.

Clinical proteomics involves the analysis of protein expression of disease proteomes, with the aim of solving a specific clinical problem. Discoveries made from proteomicbased studies contribute to the growing need for innovative medical diagnostics for disease detection. Taking into consideration the global health burden of cardiac disease, clinical proteomics is a valuable tool to improve risk stratification associated with this disease. In cardiovascular medicine, the identification of novel proteins, or biomarkers, that are differentially expressed in cardiac disease proteomes may enable early detection of the disease state, thereby preventing progression to disease end points. This review outlines various proteomic platforms and their technical advancements and relates these to the cardiovascular sciences. The entire protein complement of the cell, or proteome, is dynamic and changes in response to the disease state. 1 Proteomic-based experiments can be used to characterize such alterations in protein expression during disease progression. 2 With combined improvements in mass spectrometry (MS) technology as well as innovative molecular biology screening tools, there has been widespread growth in the characterization of cardiac disease proteomes. In fact, proteomic technology has been an important tool in the analysis of heart failure (HF), 3,4 cardiac hypertrophy, 5,6 and dilated cardiomyopathy.7 Several of the overarching aims of such studies include providing greater understanding of general biological mechanisms as well as identifying unique proteins that are clinically useful in the detection of cardiac disease in the early stages or potentially used as novel therapeutic targets. Clinical proteomics continues to benefit from advancements in technologies that allow for fast and consistent identification of proteins with corresponding increases in the dynamic range of proteins detectable in the disease proteome. MS-related technologies have improved in their ability to detect low-abundance proteins as well as membrane proteins and have benefited from sample preprocessing strategies that decrease the complexity of large-scale analyses. With the emergence of different methodologies for biomarker identification, the following 3 criteria must be taken into careful consideration to determine whether the protein is, in fact,

Contemporary glycomic approaches using ion mobility–mass spectrometry

In high-throughput proteomics the development of computational methods and novel experimental strategies often rely on each other. In certain areas, mass spectrometry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass spectra. Particularly, although there are numerous situations in which a mixture tandem mass spectrum can contain fragment ions from two or more peptides, nearly all database search tools still make the assumption that each tandem mass spectrum comes from one peptide. Common examples include mixture spectra from co-eluting peptides in complex samples, spectra generated from data-independent acquisition methods, and spectra from peptides with complex post-translational modifications. We propose a new database search tool (MixDB) that is able to identify mixture tandem mass spectra from more than one peptide. We show that peptides can be reliably identified with up to 95% accuracy from mixture spectra while considering only a 0.01% of all possible peptide pairs (four orders of magnitude speedup). Comparison with current database search methods indicates that our approach has better or comparable sensitivity and precision at identifying single-peptide spectra while simultaneously being able to identify 38% more peptides from mixture spectra at significantly higher precision.

Mobile applications present new image quality challenges. Automotive vision requires reliable capture of scene detail. Photospace measurements have shown that the extremely wide intrascene dynamic range of traffic scenes necessitates wide-dynamic-range (WDR) technology. Multiple-slope complementary metal-oxide semiconductor (CMOS) technology adaptively extends dynamic range by partially resetting the pixel, resulting in a response curve with piecewise linear slopes of progressively increasing compression. As compression and thus dynamic range increase, a trade-off against detail loss is observed. Incremental signal-to-noise ratio (iSNR) has been proposed in ISO/TC42 standards for determining dynamic range, and this work describes how to adapt these to WDR. Measurements and computer simulations reveal that the observed trade-off between WDR extension and the loss of local detail can be explained by a drop in iSNR at each reset point. If a reset is not timed optimally, then iSNR may drop below the detection limit causing an iSNR hole to appear within the dynamic range. Thus iSNR has extended utility: it not only determines the dynamic range limits but also defines dynamic range as the luminance range where detail detection is reliable. It has become the critical criterion when maximizing dynamic range to maintain the minimum necessary level of detection reliability.

Report on the Barbados Workshop

This paper proposes a burst-mode optical fiber amplifier (OFA) that consists of a combination of an automatic gain controlled praseodymium-doped fiber amplifier (AGC-PDFA), a fast automatic level controlled circuit (ALC), and a PDFA with just forward pumping. The second PDFA is used as a booster to achieve high gain, over 20 dB. The combination of AGC and ALC techniques suppresses optical surge generation against strong burst signal inputs and also prevents overloading of the burst-mode receivers; it provides OFA-based passive optical network (PON) repeaters with wide input dynamic range and solves the near–far problem in upstream transmission in long-reach PONs. We experimentally compare the operating range of the proposed burst-mode PDFA with that of a conventional burst-mode PDFA. Experiments show that the proposed PDFA achieves a much wider operating area than a conventional PDFA; a wide dynamic range in an access span of 17.5 dB, which is equivalent to the input dynamic range of the burst-mode receiver used in 10 gigabit Ethernet PON (10G-EPON), is obtained with an allowable loss margin of 14 dB in a trunk span. A maximum link budget of 55 dB is achievable.

A reversible optical membrane sensor for detection of Hg(II) was developed based on the colorimetric determination of tetrathia-12-crown-4-Hg(II), incorporating chromoionophore I (ETH5294) as a lipophilic H+-selective indicator, and sodium tetra phenyl borate (NaTPB) as a lipophilic anionic site. A decrease in absorption of the membrane at 664 nm and/or increase at 555 nm could be used as a suitable wavelength for quantitative determination of Hg(II). This membrane is capable of determining mercury(II) ions over a wide linear dynamic range 1.0 × 10−9 to 9.5 × 10−5 mol L−1 Hg(II) at pH 7.0 with detection limits of 8.1 × 10−10 mol L−1 Hg(II) and response time of 100 s. The optode reveals a very good selectivity toward Hg(II) ions over a wide variety of other metal ions in solution and the response to Hg(II) is fully reversible. Performance characteristics of the sensor evaluated as good reversibility, wide dynamic range, long life span, long-term response stability, and high reproducibility. The optode was applied to the determination of mercury(II) in water samples. The accuracy of the results was comparable to those obtained by cold vapor atomic absorption spectrometry.

Metal-oxide nano-porous microstructures have shown high electrocatalytic activity toward hydrogen peroxide oxidation. In this paper, we establish the copper oxide (CuO) porous hollow structures via the hydrothermal method using pluronic F-127 as a surfactant. The electrochemical performance involves the oxidation between Cu(I) and Cu(II) at room temperature. For verifying the formation of the desired morphology of CuO porous hollow structures, the scanning electron microscopy, Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray diffraction spectroscopy and H2-temperature-programmed reduction (TPR) were applied. The synthesized samples are hollow microstructures which are formed by CuO nanoparticles. The achieved CuO hollow structures exhibit excellent H2O2 detection properties such as a very low detection limit of 141.1 nM, an extremely wide dynamic detection range of 50 nM–70 mM, an outstanding sensitivity of 13.7 µA µM−1 cm−2 and satisfactory stability in alkaline solution. The good tolerance toward interfering species, the satisfactory behaviour in repeatability and reproducibility and favourable stability verified the promising performance of the proposed sensor. The results of the study on sensing low levels of H2O2 concentrations by using a modified glassy carbon electrode as an optimized electrochemical sensor have been reported. The proposed synthesized nanostructures (CuO porous hollow structures) can be used as sensitive H2O2 biosensors. Essential characterizations and measurements have been performed. The constructed sensor shows acceptable electrocatalytic activity for detecting H2O2. High sensitivity, very low LOD, wide dynamic range, excellent tolerance toward interfering agents, favorable stability and satisfactory reproducibility and repeatability of the sensor have made it suitable for practical applications.

Two-dimensional HPLC coupled with tandem MS (MS/MS) has become a mainstream technique in the shotgun proteomics for large-scale identification of proteins from biological samples. This powerful technology provides speed, sensitivity, and dynamic range which are essential to probe complex peptide mixtures from proteomic samples. Herein we present a pH gradient SCX-RP 2D HPLC-MS/MS method designed to improve the peptide resolution and protein identification from complex proteomic samples. The comparison between the pH gradient SCX-RP 2D HPLC method and traditional salt gradient SCX-RP method was presented. A two-step sample prefractionation method utilizing microwave-assisted tryptic digestion to improve the identification of insoluble proteins was also introduced. This novel 2D HPLC-MS/MS method was applied to the heart proteomic sample of the zebrafish, Danio rerio, to provide comprehensive cardiac proteomic profiling of this important model organism for cardiovascular and environmental toxicology studies.

Esophageal adenocarcinoma, currently the seventh leading cause of cancer-related death, has been associated with the presence of Barrett metaplasia. The malignant potential of Barrett metaplasia is evidenced by ultimate progression of this condition to invasive adenocarcinoma. We utilized liquid phase separation of proteins with chromatofocusing in the first dimension and nonporous reverse phase HPLC in the second dimension followed by ESI-TOF mass spectrometry to identify proteins differentially expressed in six Barrett metaplasia samples as compared with six esophageal adenocarcinoma samples; all six Barrett samples were obtained from the identical six patients from whom we obtained the esophageal adenocarcinoma tissue. Approximately 300 protein bands were detected by mass mappings, and 38 differentially expressed proteins were identified by microLC-MS/MS. The false positive rates of the peptide identifications were evaluated by reversed database searching. Among the proteins that were identified, Rho GDP dissociation inhibitor 2, alpha-enolase, Lamin A/C, and nucleoside-diphosphate kinase A were demonstrated to be up-regulated in both mRNA and protein expression in esophageal adenocarcinomas relative to Barrett metaplasia. Candidate proteins were examined at the mRNA level using high density oligonucleotide microarrays. The cellular expression patterns were verified in both esophageal adenocarcinomas and in Barrett metaplasia by immunohistochemistry. These differentially expressed proteins may have utility as useful candidate markers of esophageal adenocarcinoma.

Digital detectors in mammography have wide dynamic range in addition to the benefit of decoupled acquisition and display. How wide the dynamic range is and how it compares to film-screen systems in the clinical x-ray exposure domain are unclear. In this work, we compare the effective dynamic ranges of film-screen and flat panel mammography systems, along with the dynamic ranges of their component image receptors in the clinical x-ray exposure domain. An ACR mammography phantom was imaged using variable mAs (exposure) values for both systems. The dynamic range of the contrast-limited film-screen system was defined as that ratio of mAs (exposure) values for a 26 kVp Mo/Mo (HVL=0.34 mm Al) beam that yielded passing phantom scores. The same approach was done for the noise-limited digital system. Data from three independent observers delineated a useful phantom background optical density range of 1.27 to 2.63, which corresponded to a dynamic range of 2.3 +/- 0.53. The digital system had a dynamic range of 9.9 +/- 1.8, which was wider than the film-screen system (p<0.02). The dynamic range of the film-screen system was limited by the dynamic range of the film. The digital detector, on the other hand, had an estimated dynamic range of 42, which was wider than the dynamic range of the digital system in its entirety by a factor of 4. The generator/tube combination was the limiting factor in determining the digital system's dynamic range.

HDL (high-density lipoprotein) has been linked to a variety of cardio-protective functions possibly due to its structural and compositional heterogeneity. Depending on the detection method, close to 100 proteins were identified as belonging to HDL. However, apolipoprotein A1 (APOA1) and apolipoprotein A2 (APOA2), the two most abundant proteins in HDL particles, make up to 90% of the protein content. Therefore, many HDL proteins may be present only in certain HDL subclasses. Little is known about HDL subclasses composition and how protein distribution affects HDL functions. As many of these proteins are present in low abundance, the accurate quantification is a challenge. In this sense, mass spectrometry (MS)-based targeted quantitative proteomics is a powerful approach to accurately differentiate HDL subclasses, based on their distinct protein composition. Targeted MS methodologies have high sensitivity and specificity to quantify concomitantly multiple peptides present in a complex biological sample with wide dynamic range. We therefore quantified the proteome of HDL subclasses of 19 apparently healthy volunteers. HDL particles were separated in HDL2 and HDL3 by density gradient ultracentrifugation and their proteome was quantified employing two targeted methodologies, PRM (Parallel Reaction Monitoring) and DIA (Data independent acquisition). PRM yielded quantitative results for a total of 35 distinct HDL-associated proteins against 32 proteins determined by DIA. The majority of proteins are shared by both subclasses, but their abundance varies considerably. Dense HDL3 is significantly enriched with proteins related to antioxidant activity, such paraoxonase (PON1) and apoliprotein L1 (APOL-1), besides apolipoprotein D (APOD), apoliprotein A-IV (APOA-IV), apolipoprotein H (APOH), apolipoprotein J (APOJ) and phosphatidylcholine-sterol acyltransferase (LCAT). While HDL2 was enriched with APOA1, APOA2, apoliprotein Cs (apoC-I, apoC-II, apoC-III), apoliprotein E (APOE), and serum amyloid A-4 (SAA4). Thus, PRM and DIA are attractive strategies to differentiate HDL subclasses proteome and may contribute to understand how HDL proteome affects its functionality.

To unravel the temporal features of the peripheral tissue injury induced persistent nociceptive discharge, single wide dynamic range (WDR) unit activity was recorded extracellularly in lumbar dorsal horn of anesthetized rats and interspike interval (ISI) series were obtained. Subcutaneous (s.c.) bee venom (BV) injection induced persistent discharge of spinal WDR neurons and has been well established to be a good model in evaluation of tissue injury induced pain. By applying a more novel approach, i.e., the unstable periodic orbit (UPO) identification method, we detected a family of significant separate UPOs (period-1, 2 and 3 orbits) within the ISI series of BV-induced nociceptive discharge, but not spontaneous background activity of spinal WDR neuron. Furthermore, temporally dynamic changes of UPOs at lower period-1, 2 and 3 for 4 successive time segments within 1 h time course of WDR unit firing showed temporally dynamic changes, i.e., new orbits with longer ISIs emerged and those with shorter ISIs vanished with time change. By using this method we suggest that BV-induced nociceptive discharge of spinal WDR neuron be a kind of deterministic activity and various UPOs may play some role in temporal coding of sensory information.

Cerebrospinal fluid (CSF) is produced by the chorioid plexus in the ventricles. It surrounds the brain and bone marrow, and reflects several different disorders of the central nervous system (CNS). Proteomics has been used to analyze CSF in order to discover disease-associated proteins and to elucidate the basic molecular mechanisms that either cause, or result from, CNS disorders. However, some disease-associated proteins are of low-abundance and are difficult to detect. A low total-protein concentration, a high amount of albumin and immunoglobins, and a wide dynamic range (several orders of magnitude) of protein concentration cause several difficulties in the identification of low-abundance CSF proteins. In this study, advantage was taken of the range of different hydrophobic properties of CSF proteins, and a reversed-phase solid-phase extraction (SPE) cartridge was used to prefractionate human lumbar CSF proteins into three separate fractions prior to two-dimensional gel electrophoresis resolution of the proteome. A portion of the high-abundance CSF proteins were removed from two (eluted with 35% and 50% acetonitrile) of the three fractions. Some trace CSF proteins were preferentially enriched in the two fractions, and many proteins were detected in the two-dimensional (2-D) gels of the two fractions. Among the novel proteins identified, sixty-two protein spots that represent forty-two proteins were characterized. Most of the proteins have not been annotated in any previous 2-D map of human CSF, and several have been implicated in CNS diseases. The prefractionation of CSF proteins with SPE, followed by proteomics analysis, provides a new method to explore low-abundance, disease-specific CSF proteins.

This paper presents an investigation and a correction technique for fixed pattern noise in wide dynamic range image sensor with on the focal plane Reinhard tone mapping. The Human eye is capable of capturing images over a wide dynamic range of illumination; however, typical CMOS image sensors have limited ability to capture dynamic range available in nature. Even when wide dynamic range images are captured, they need to be displayed on conventional media with limited ability to display WDR scenes. Hence, the image has algorithmically transformed by mathematical operators called tone mapping operators to fit the limited dynamic range. In this paper, we report our research on a pixel, which captures a scene with inbuilt tone mapping operator, particularly, the Reinhard photographic mapping operator, which is a monotonically increasing function. However, the inherent Fixed pattern noise(FPN) limits the performance of image sensors. This is mainly due to the variations between the responses of individual pixels within an array of pixels. A simple procedure has been adapted to reduce FPN in which parametric response of the pixel is used, with FPN modeled as variations in the individual parameters. The parameters of each individual pixel are measured, recorded and then used to correct their response.