Using power spectrum analysis to evaluate (18)O-water labeling data acquired from low resolution mass spectrometers.
ABSTRACT: We describe a method for ratio estimations in (18)O-water labeling experiments acquired from low resolution isotopically resolved data. The method is implemented in a software package specifically designed for use in experiments making use of zoom-scan mode data acquisition. Zoom-scan mode data allow commonly used ion trap mass spectrometers to attain isotopic resolution, which makes them amenable to use in labeling schemes such as (18)O-water labeling, but algorithms and software developed for high resolution instruments may not be appropriate for the lower resolution data acquired in zoom-scan mode. The use of power spectrum analysis is proposed as a general approach that may be uniquely suited to these data types. The software implementation uses a power spectrum to remove high-frequency noise and band-filter contributions from coeluting species of differing charge states. From the elemental composition of a peptide sequence, we generate theoretical isotope envelopes of heavy-light peptide pairs in five different ratios; these theoretical envelopes are correlated with the filtered experimental zoom scans. To automate peptide quantification in high-throughput experiments, we have implemented our approach in a computer program, MassXplorer. We demonstrate the application of MassXplorer to two model mixtures of known proteins and to a complex mixture of mouse kidney cortical extract. Comparison with another algorithm for ratio estimations demonstrates the increased precision and automation of MassXplorer.
Project description:We describe a method for assessing the quality of mass spectra and improving reliability of relative ratio estimations from (18)O-water labeling experiments acquired from low resolution mass spectrometers. The mass profiles of heavy and light peptide pairs are often affected by artifacts, including coeluting contaminant species, noise signal, instrumental fluctuations in measuring ion position and abundance levels. Such artifacts distort the profiles, leading to erroneous ratio estimations, thus reducing the reliability of ratio estimations in high throughput quantification experiments. We used support vector machines (SVMs) to filter out mass spectra that deviated significantly from expected theoretical isotope distributions. We built an SVM classifier with a decision function that assigns a score to every mass profile based on such spectral features as mass accuracy, signal-to-noise ratio, and differences between experimental and theoretical isotopic distributions. The classifier was trained using a data set obtained from samples of mouse renal cortex. We then tested it on protein samples (bovine serum albumin) mixed in five different ratios of labeled and unlabeled species. We demonstrated that filtering the data using our SVM classifier results in as much as a 9-fold reduction in the coefficient of variance of peptide ratios, thus significantly improving the reliability of ratio estimations.
Project description:Proteolytic (18)O-labeling has been widely used in quantitative proteomics since it can uniformly label all peptides from different kinds of proteins. There have been multiple algorithms and tools developed over the last few years to analyze high-resolution proteolytic (16)O/(18)O labeled mass spectra. We have developed a software package, O18Quant, which addresses two major issues in the previously developed algorithms. First, O18Quant uses a robust linear model (RLM) for peptide-to-protein ratio estimation. RLM can minimize the effect of outliers instead of iteratively removing them which is a common practice in other approaches. Second, the existing algorithms lack applicable implementation. We address this by implementing O18Quant using C# under Microsoft.net framework and R. O18Quant automatically calculates the peptide/protein relative ratio and provides a friendly graphical user interface (GUI) which allows the user to manually validate the quantification results at scan, peptide, and protein levels. The intuitive GUI of O18Quant can greatly enhance the user's visualization and understanding of the data analysis. O18Quant can be downloaded for free as part of the software suite ProteomicsTools.
Project description:BACKGROUND: Protein quantification is an essential step in many proteomics experiments. A number of labeling approaches have been proposed and adopted in mass spectrometry (MS) based relative quantification. The mTRAQ, one of the stable isotope labeling methods, is amine-specific and available in triplex format, so that the sample throughput could be doubled when compared with duplex reagents. METHODS AND RESULTS: Here we propose a novel data analysis algorithm for peptide quantification in triplex mTRAQ experiments. It improved the accuracy of quantification in two features. First, it identified and separated triplex isotopic clusters of a peptide in each full MS scan. We designed a schematic model of triplex overlapping isotopic clusters, and separated triplex isotopic clusters by solving cubic equations, which are deduced from the schematic model. Second, it automatically determined the elution areas of peptides. Some peptides have similar atomic masses and elution times, so their elution areas can have overlaps. Our algorithm successfully identified the overlaps and found accurate elution areas. We validated our algorithm using standard protein mixture experiments. CONCLUSIONS: We showed that our algorithm was able to accurately quantify peptides in triplex mTRAQ experiments. Its software implementation is compatible with Trans-Proteomic Pipeline (TPP), and thus enables high-throughput analysis of proteomics data.
Project description:We present new algorithms and a software implementation for assigning confidence to peptide sequence assignments obtained through classic accurate mass and retention time (AMT) matching techniques, as well as methods for integrating these assignments with standard proteomics workflows. The algorithms are intended to increase the number of peptides and proteins identified (and, when applicable, quantitated by isotopic labeling) among related proteomics experiments that use high-resolution mass spectrometry instrumentation. The motivations for our extensions include the need to exploit high-resolution data to support highly complex proteomics experiments, especially those involving extensive off-line fractionation, to which recent label-free workflows might not easily generalize.
Project description:Protein homeostasis (proteostasis) is a result of a dynamic equilibrium between protein synthesis and degradation. It is important for healthy cell/organ functioning and is often associated with diseases such as neurodegenerative diseases and non-Alcoholic Fatty Liver disease. Heavy water metabolic labeling, combined with liquid-chromatography and mass spectrometry (LC-MS), is a powerful approach to study proteostasis in vivo in high throughput. Traditionally, intact peptide signals are used to estimate stable isotope incorporation in time-course experiments. The time-course of label incorporation is used to extract protein decay rate constant (DRC). Intact peptide signals, computed from integration in chromatographic time and mass-to-charge ratio (m/z) domains, usually, provide an accurate estimate of label incorporation. However, sample complexity (co-elution), limited dynamic range, and low signal-to-noise ratio (S/N) may adversely interfere with the peptide signals. These artifacts complicate the DRC estimations by distorting peak shape in chromatographic time and m/z domains. Fragment ions, on the other hand, are less prone to these artifacts and are potentially well suited in aiding DRC estimations. Here, we show that the label incorporation encoded into the isotope distributions of fragment ions reflect the isotope enrichment during the metabolic labeling with heavy water. We explore the label incorporation statistics for devising practical approaches for DRC estimations.
Project description:Isobaric labeling techniques coupled with high-resolution mass spectrometry have been widely employed in proteomic workflows requiring relative quantification. For each high-resolution tandem mass spectrum (MS/MS), isobaric labeling techniques can be used not only to quantify the peptide from different samples by reporter ions, but also to identify the peptide it is derived from. Because the ions related to isobaric labeling may act as noise in database searching, the MS/MS spectrum should be preprocessed before peptide or protein identification. In this article, we demonstrate that there are a lot of high-frequency, high-abundance isobaric related ions in the MS/MS spectrum, and removing isobaric related ions combined with deisotoping and deconvolution in MS/MS preprocessing procedures significantly improves the peptide/protein identification sensitivity. The user-friendly software package TurboRaw2MGF (v2.0) has been implemented for converting raw TIC data files to mascot generic format files and can be downloaded for free from https://github.com/shengqh/RCPA.Tools/releases as part of the software suite ProteomicsTools. The data have been deposited to the ProteomeXchange with identifier PXD000994.
Project description:BACKGROUND AND PURPOSE:Visualization of structural details of treatment devices during neurointerventional procedures can be challenging. A new true two-resolution imaging X-ray detector system features a 194 µm pixel conventional flat-panel detector (FPD) mode and a 76 µm pixel high-resolution high-definition (Hi-Def) zoom mode in one detector panel. The Hi-Def zoom mode was developed for use in interventional procedures requiring superior image quality over a small field of view (FOV). We report successful use of this imaging system during intracranial aneurysm treatment in 1 patient with a Pipeline-embolization device and 1 patient with a low-profile visualized intramural support (LVIS Blue) device plus adjunctive coiling. METHODS:A guide catheter was advanced from the femoral artery insertion site to the proximity of each lesion using standard FPD mode. Under magnified small FOV Hi-Def imaging mode, an intermediate catheter and microcatheters were guided to the treatment site, and the PED and LVIS Blue plus coils were deployed. Radiation doses were tracked intraprocedurally. RESULTS:Critical details, including structural changes in the PED and LVIS Blue and position and movement of the microcatheter tip within the coil mass, were more readily apparent in Hi-Def mode. Skin-dose mapping indicated that Hi-Def mode limited radiation exposure to the smaller FOV of the treatment area. CONCLUSIONS:Visualization of device structures was much improved in the high-resolution Hi-Def mode, leading to easier, more controlled deployment of stents and coils than conventional FPD mode.
Project description:The advancement of intraoral scanners has allowed for more efficient workflow in the dental clinical setting. However, limited data exist regarding the accuracy of the digital impressions produced with various scanner settings and scanning approaches. The purpose of this in vitro study was to compare the accuracy of digital impressions at the crown preparation margin using different scanning resolutions of a specific intraoral scanner system. An all-ceramic crown preparation of a mandibular first molar was constructed in a typodont, and a scan (n = 3) was created with an industrial-grade laboratory scanner (3Shape D2000) as the control. Digital impressions were obtained with an intraoral scanner (3Shape TRIOS 3) under three settings-high resolution (HR), standard resolution (SR), and combined resolution (SHR). Comparative 3D analysis of scans was performed with Geomagic Control X software to measure the discrepancy between intraoral scans and the control scan along the preparation finish line. The scan time and number of images captured per scan were recorded. Statistical analysis was performed by one-way ANOVA, two-way repeated measures ANOVA, Pearson's correlation, and Dunnett's T3 test (? = 0.05). Significant differences were observed for scan time and for number of images captured among scan resolution settings (? < 0.05). The scan time for the SR group was, on average, 34.2 s less than the SHR group and 46.5 s less than the HR group. For discrepancy on the finish line, no significant differences were observed among scanning resolutions (HR: 31.5 ± 5.5 ?m, SHR: 33.2 ± 3.7 ?m, SR: 33.6 ± 3.1 ?m). Significant differences in discrepancy were observed among tooth surfaces, with the distal surface showing the highest discrepancies. In conclusion, the resolution of the intra-oral scanner is primarily defined by the system hardware and optimized for default scans. A software high-resolution mode that obtains more data over a longer time may not necessarily benefit the scan accuracy, while the tooth preparation and surface parameters do affect the accuracy.
Project description:High-throughput next-generation sequencing technologies pose increasing demands on the efficiency, accuracy and usability of data analysis software. In this article, we present ZOOM Lite, a software for efficient reads mapping and result visualization. With a kernel capable of mapping tens of millions of Illumina or AB SOLiD sequencing reads efficiently and accurately, and an intuitive graphical user interface, ZOOM Lite integrates reads mapping and result visualization into a easy to use pipeline on desktop PC. The software handles both single-end and paired-end reads, and can output both the unique mapping result or the top N mapping results for each read. Additionally, the software takes a variety of input file formats and outputs to several commonly used result formats. The software is freely available at http://bioinfor.com/zoom/lite/.
Project description:We present IncucyteDRC, an R package for the analysis of data from live cell imaging cell proliferation experiments carried out on the Essen Biosciences IncuCyte ZOOM instrument. The package provides a simple workflow for summarising data into a form that can be used to calculate dose response curves and EC50 values for small molecule inhibitors. Data from different cell lines, or cell lines grown under different conditions, can be normalised as to their doubling time. A simple graphical web interface, implemented using shiny, is provided for the benefit of non-R users. The software is potentially useful to any research group studying the impact of small molecule inhibitors on cell proliferation using the IncuCyte ZOOM.