scholarly journals IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts

2018 ◽  
Vol 115 (21) ◽  
pp. E4767-E4776 ◽  
Author(s):  
Xiaomeng Shen ◽  
Shichen Shen ◽  
Jun Li ◽  
Qiang Hu ◽  
Lei Nie ◽  
...  
Keyword(s):  
Missing Data ◽  
Large Scale ◽  
Label Free ◽  
Robust Solution ◽  
Traumatic Injuries ◽  
Data Independent Acquisition ◽  
Quantitative Accuracy ◽  
Protein Discovery ◽  
Sensitivity Specificity ◽  
Protein Measurement

Reproducible quantification of large biological cohorts is critical for clinical/pharmaceutical proteomics yet remains challenging because most prevalent methods suffer from drastically declined commonly quantified proteins and substantially deteriorated quantitative quality as cohort size expands. MS2-based data-independent acquisition approaches represent tremendous advancements in reproducible protein measurement, but often with limited depth. We developed IonStar, an MS1-based quantitative approach enabling in-depth, high-quality quantification of large cohorts by combining efficient/reproducible experimental procedures with unique data-processing components, such as efficient 3D chromatographic alignment, sensitive and selective direct ion current extraction, and stringent postfeature generation quality control. Compared with several popular label-free methods, IonStar exhibited far lower missing data (0.1%), superior quantitative accuracy/precision [∼5% intragroup coefficient of variation (CV)], the widest protein abundance range, and the highest sensitivity/specificity for identifying protein changes (<5% false altered-protein discovery) in a benchmark sample set (n = 20). We demonstrated the usage of IonStar by a large-scale investigation of traumatic injuries and pharmacological treatments in rat brains (n = 100), quantifying >7,000 unique protein groups (>99.8% without missing data across the 100 samples) with a low false discovery rate (FDR), two or more unique peptides per protein, and high quantitative precision. IonStar represents a reliable and robust solution for precise and reproducible protein measurement in large cohorts.

scholarly journals FLEXIQuant-LF to quantify protein modification extent in label-free proteomics data

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Christoph N Schlaffner ◽  
Konstantin Kahnert ◽  
Jan Muntel ◽  
Ruchi Chauhan ◽  
Bernhard Y Renard ◽  
...  
Keyword(s):  
Protein Modification ◽  
Large Scale ◽  
Software Tool ◽  
Label Free ◽  
Anaphase Promoting Complex ◽  
Proteomics Data ◽  
Single Experiment ◽  
Post Translational Modifications ◽  
Data Independent Acquisition ◽  
Modified Peptides

Improvements in LC-MS/MS methods and technology have enabled the identification of thousands of modified peptides in a single experiment. However, protein regulation by post-translational modifications (PTMs) is not binary, making methods to quantify the modification extent crucial to understanding the role of PTMs. Here, we introduce FLEXIQuant-LF, a software tool for large-scale identification of differentially modified peptides and quantification of their modification extent without knowledge of the types of modifications involved. We developed FLEXIQuant-LF using label-free quantification of unmodified peptides and robust linear regression to quantify the modification extent of peptides. As proof of concept, we applied FLEXIQuant-LF to data-independent-acquisition (DIA) data of the anaphase promoting complex/cyclosome (APC/C) during mitosis. The unbiased FLEXIQuant-LF approach to assess the modification extent in quantitative proteomics data provides a better understanding of the function and regulation of PTMs. The software is available at https://github.com/SteenOmicsLab/FLEXIQuantLF.

scholarly journals Data-independent acquisition method for ubiquitinome analysis reveals regulation of circadian biology

2020 ◽  
Author(s):  
Fynn M. Hansen ◽  
Maria C. Tanzer ◽  
Franziska Brüning ◽  
Isabell Bludau ◽  
Brenda A. Schulman ◽  
...  
Keyword(s):  
Large Scale ◽  
Tnf Alpha ◽  
Circadian Cycle ◽  
Cellular Processes ◽  
Data Independent Acquisition ◽  
Protein Ubiquitination ◽  
Quantitative Accuracy ◽  
Protein Receptors ◽  
Spectral Libraries ◽  
Acquisition Method

SUMMARYProtein ubiquitination is involved in virtually all cellular processes. Enrichment strategies employing antibodies targeting ubiquitin-derived diGly remnants combined with mass spectrometry (MS) have enabled investigations of ubiquitin signaling at a large scale. However, so far the power of data independent acquisition (DIA) with regards to sensitivity in single run analysis and data completeness have not yet been explored. We developed a sensitive workflow combining diGly antibody-based enrichment and optimized Orbitrap-based DIA with comprehensive spectral libraries together containing more than 90,000 diGly peptides. This approach identified 35,000 diGly peptides in single measurements of proteasome inhibitor-treated cells – double the number and quantitative accuracy of data dependent acquisition. Applied to TNF-alpha signaling, the workflow comprehensively captured known sites while adding many novel ones. A first systems-wide investigation of ubiquitination of the circadian cycle uncovered hundreds of cycling ubiquitination sites and dozens of cycling ubiquitin clusters within individual membrane protein receptors and transporters, highlighting novel connections between metabolism and circadian regulation.

scholarly journals FLEXIQuant-LF: Robust Regression to Quantify Protein Modification Extent in Label-Free Proteomics Data

2020 ◽  
Author(s):  
Konstantin Kahnert ◽  
Christoph N. Schlaffner ◽  
Jan Muntel ◽  
Ruchi Chauhan ◽  
Bernhard Y. Renard ◽  
...  
Keyword(s):  
Protein Modification ◽  
Large Scale ◽  
Robust Regression ◽  
Software Tool ◽  
Label Free ◽  
Anaphase Promoting Complex ◽  
Proteomics Data ◽  
Single Experiment ◽  
Data Independent Acquisition ◽  
Modified Peptides

AbstractImprovements in LC-MS/MS methods and technology have enabled the identification of thousands of modified peptides in a single experiment. However, protein regulation by post-translational modifications (PTMs) is not binary, making methods to quantify the modification extent crucial to understanding the role of PTMs. Here, we introduce FLEXIQuant-LF, a software tool for large-scale identification of differentially modified peptides and quantification of their modification extent without prior knowledge of the type of modification. We developed FLEXIQuant-LF using label-free quantification of unmodified peptides and robust linear regression to quantify the modification extent of peptides. As proof of concept, we applied FLEXIQuant-LF to data-independent-acquisition (DIA) data of the anaphase promoting complex/cyclosome (APC/C) during mitosis. The unbiased FLEXIQuant-LF approach to assess the modification extent in quantitative proteomics data provides a better understanding of the function and regulation of PTMs. The software is available at https://github.com/SteenOmicsLab/FLEXIQuantLF.

scholarly journals Direct data-independent acquisition (direct DIA) enables substantially improved label-free quantitative proteomics in Arabidopsis

2020 ◽  
Author(s):  
Devang Mehta ◽  
Sabine Scandola ◽  
R. Glen Uhrig
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Cost Effective ◽  
Shotgun Proteomics ◽  
Label Free ◽  
Data Independent Acquisition ◽  
Quantitative Accuracy ◽  
Ratio Distortion ◽  
Direct Data ◽  
Critical Resource

AbstractThe last decade has seen significant advances in the application of quantitative mass spectrometry-based proteomics technologies to tackle important questions in plant biology. This has included the use of both labelled and label-free quantitative liquid-chromatography mass spectrometry (LC-MS) strategies in model1,2 and non-model plants3. While chemical labelling-based workflows (e.g. iTRAQ and TMT) are generally considered to possess high quantitative accuracy, they nonetheless suffer from ratio distortion and sample interference issues4,5, while being less cost-effective and offering less throughput than label-free approaches. Consequently, label free quantification (LFQ) has been widely used in comparative quantitative experiments profiling the native6 and post-translationally modified (PTM-ome)7,8 proteomes of plants. However, LFQ shotgun proteomics studies in plants have so far, almost universally, used data-dependent acquisition (DDA) for tandem MS (MS/MS) analysis. Here, we systematically compare and benchmark a state-of-the-art DDA LFQ workflow for plants against a new direct data-independent acquisition (direct DIA) method9. Our study demonstrates several advantages of direct DIA and establishes it as the method of choice for quantitative proteomics on plant tissue. We also applied direct DIA to perform a quantitative proteomic comparison of dark and light grown Arabidopsis cell cultures, providing a critical resource for future plant interactome studies using this well-established biochemistry platform.

scholarly journals Predicting youth diabetes risk using NHANES data and machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nita Vangeepuram ◽  
Bian Liu ◽  
Po-hsiang Chiu ◽  
Linhua Wang ◽  
Gaurav Pandey
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Guideline ◽  
Diabetes Risk ◽  
Screening Tools ◽  
Clinical Screening ◽  
Predictive Values ◽  
Screening Guideline ◽  
Demographic Subgroups ◽  
Sensitivity Specificity

AbstractPrediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with preDM/DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the clinical guideline and biomarker criteria using established evaluation measures (sensitivity, specificity, positive/negative predictive value, F-measure for the positive/negative preDM/DM classes, and Kappa). We also compared the performance of the guideline to those of machine learning (ML) based preDM/DM classifiers derived from the NHANES dataset. Approximately 29% of the 2858 youth in our study population had preDM/DM based on biomarker criteria. The clinical guideline had a sensitivity of 43.1% and specificity of 67.6%, positive/negative predictive values of 35.2%/74.5%, positive/negative F-measures of 38.8%/70.9%, and Kappa of 0.1 (95%CI: 0.06–0.14). The performance of the guideline varied across demographic subgroups. Some ML-based classifiers performed comparably to or better than the screening guideline, especially in identifying preDM/DM youth (p = 5.23 × 10−5).We demonstrated that a recommended pediatric clinical screening guideline did not perform well in identifying preDM/DM status among youth. Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.

scholarly journals Kernel weighted least square approach for imputing missing values of metabolomics data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nishith Kumar ◽  
Md. Aminul Hoque ◽  
Masahiro Sugimoto
Keyword(s):  
Missing Data ◽  
Large Scale ◽  
Missing Values ◽  
Kernel Weight ◽  
Least Square ◽  
Data Matrix ◽  
Data Imputation ◽  
Metabolomics Data ◽  
Missing Value ◽  
Missing Data Imputation

AbstractMass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA.

scholarly journals Label-free fluorescence predictions from large-scale correlative light and electron microscopy data

2021 ◽  
Vol 27 (S1) ◽  
pp. 94-95
Author(s):  
Ryan Lane ◽  
Luuk Balkenende ◽  
Simon van Staalduine ◽  
Anouk Wolters ◽  
Ben Giepmans ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Large Scale ◽  
Light And Electron Microscopy ◽  
Label Free ◽  
Electron Microscopy Data ◽  
Microscopy Data

Exploring Imputation Techniques for Missing Data in Transportation Management Systems

2003 ◽  
Vol 1836 (1) ◽  
pp. 132-142 ◽  
Author(s):  
Brian L. Smith ◽  
William T. Scherer ◽  
James H. Conklin
Keyword(s):  
Missing Data ◽  
Urban Areas ◽  
Large Scale ◽  
Management Systems ◽  
Data Sets ◽  
Transportation Industry ◽  
Transportation Management ◽  
Reduced Data ◽  
Natural Characteristics

Many states have implemented large-scale transportation management systems to improve mobility in urban areas. These systems are highly prone to missing and erroneous data, which results in drastically reduced data sets for analysis and real-time operations. Imputation is the practice of filling in missing data with estimated values. Currently, the transportation industry generally does not use imputation as a means for handling missing data. Other disciplines have recognized the importance of addressing missing data and, as a result, methods and software for imputing missing data are becoming widely available. The feasibility and applicability of imputing missing traffic data are addressed, and a preliminary analysis of several heuristic and statistical imputation techniques is performed. Preliminary results produced excellent performance in the case study and indicate that the statistical techniques are more accurate while maintaining the natural characteristics of the data.

scholarly journals Widespread multi-targeted therapy resistance via drug-induced secretome fucosylation

2021 ◽  
Author(s):  
Mark Borris D. Aldonza ◽  
Junghwa Cha ◽  
Insung Yong ◽  
Jayoung Ku ◽  
Dabin Lee ◽  
...  
Keyword(s):  
Targeted Therapies ◽  
Large Scale ◽  
Kinase Inhibitors ◽  
Tumor Regression ◽  
Therapy Resistance ◽  
Critical Component ◽  
Label Free ◽  
Drug Induced ◽  
Pan Cancer ◽  
Core Fucosylation

AbstractCancer secretome is a reservoir for aberrant glycosylation. How therapies alter this post-translational cancer hallmark and the consequences thereof remain elusive. Here we show that an elevated secretome fucosylation is a pan-cancer signature of both response and resistance to multiple targeted therapies. Large-scale pharmacogenomics revealed that fucosylation genes display widespread association with resistance to these therapies. In both cancer cell cultures and patients, targeted kinase inhibitors distinctively induced core fucosylation of secreted proteins less than 60 kDa. Label-free proteomics of N-glycoproteomes revealed that fucosylation of the antioxidant PON1 is a critical component of the therapy-induced secretome. Core fucosylation in the Golgi impacts PON1 stability and folding prior to secretion, promoting a more degradation-resistant PON1. Non-specific and PON1-specific secretome de-N-glycosylation both limited the expansion of resistant clones in a tumor regression model. Our findings demonstrate that core fucosylation is a common modification indirectly induced by targeted therapies that paradoxically promotes resistance.

scholarly journals Cellular effects of gliotoxin: Evaluation of a proteomic, isotope-based method to detect reactive cysteines

2021 ◽  
Author(s):  
◽  
Sven Sondhauss
Keyword(s):  
Large Scale ◽  
Thiol Group ◽  
Disulfide Bridge ◽  
Reversed Phase ◽  
Cellular Damage ◽  
Full Potential ◽  
Label Free ◽  
Cysteine Modification ◽  
Affinity Tag ◽  
Cell Vitality

<p>Cysteinyl residues in proteins are important for many cellular processes and unregulated modification of the cysteine thiol group can have negative effects on cell vitality and viability. In this thesis, the potential for use of the isotope coded affinity tag (ICAT) method for detection of cysteine modification has been investigated. ICAT reagents label free cysteine thiols. The aim of this study was to use HL-60 cells treated with gliotoxin, a fungal metabolite with a reactive disulfide bridge, as a system to evaluate the performance of ICAT for identification of cysteine modification in a whole cell proteome. Gliotoxin has antimicrobial, antitumor, immunosuppressive and cytotoxic properties that have been related to cysteine modification in proteins. Cellular assays including viability using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, cell cycle analysis, and measurement of reactive oxygen species using dichlorofluorescin diacetate were used to establish conditions for measuring the effects of gliotoxin on HL-60 cells prior to large-scale cellular damage. Cells exposed to gliotoxin and control cells were then labeled with ICAT reagents and analysed by offline reversed phase liquid chromatography followed by matrix-assisted laser desorption/ionization tandem mass spectrometry. The pilot results identified tubulin, glyceraldehyde-3-phosphate dehydrogenase and peptidyl-prolyl cis-trans isomerase as putative targets of gliotoxin. Additionally, this study showed that ICAT can be used to detect modified cysteines from a highly complex sample, but further optimization is needed to unlock the full potential for detection of cysteine modification in complex samples.</p>

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