scholarly journals Comparison of a Label-Free Quantitative Proteomic Method Based on Peptide Ion Current Area to the Isotope Coded Affinity Tag Method

2008 ◽  
Vol 6 ◽  
pp. CIN.S385 ◽  
Author(s):  
Soyoung Ryu ◽  
Byron Gallis ◽  
Young Ah Goo ◽  
Scott A. Shaffer ◽  
Dragan Radulovic ◽  
...  
Keyword(s):  
Direct Measurement ◽  
Expression Profiling ◽  
Mean Squared Error ◽  
Clinical Analysis ◽  
Ion Current ◽  
Label Free ◽  
Affinity Tag ◽  
Stable Isotope Dilution ◽  
Large Numbers ◽  
Current Area

Recently, several research groups have published methods for the determination of proteomic expression profiling by mass spectrometry without the use of exogenously added stable isotopes or stable isotope dilution theory. These so-called label-free, methods have the advantage of allowing data on each sample to be acquired independently from all other samples to which they can later be compared in silico for the purpose of measuring changes in protein expression between various biological states. We developed label free software based on direct measurement of peptide ion current area (PICA) and compared it to two other methods, a simpler label free method known as spectral counting and the isotope coded affinity tag (ICAT) method. Data analysis by these methods of a standard mixture containing proteins of known, but varying, concentrations showed that they performed similarly with a mean squared error of 0.09. Additionally, complex bacterial protein mixtures spiked with known concentrations of standard proteins were analyzed using the PICA label-free method. These results indicated that the PICA method detected all levels of standard spiked proteins at the 90% confidence level in this complex biological sample. This finding confirms that label-free methods, based on direct measurement of the area under a single ion current trace, performed as well as the standard ICAT method. Given the fact that the label-free methods provide ease in experimental design well beyond pair-wise comparison, label-free methods such as our PICA method are well suited for proteomic expression profiling of large numbers of samples as is needed in clinical analysis.

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>

scholarly journals Multiplexed microRNA expression profiling by combined asymmetric PCR and label-free detection using silicon photonic sensor arrays

2018 ◽  
Vol 10 (14) ◽  
pp. 1618-1623 ◽  
Author(s):  
Richard M. Graybill ◽  
Maria C. Cardenosa-Rubio ◽  
Hongwei Yang ◽  
Mark D. Johnson ◽  
Ryan C. Bailey
Keyword(s):  
Polymerase Chain Reaction ◽  
Expression Profiling ◽  
Sensor Arrays ◽  
Label Free ◽  
Asymmetric Pcr ◽  
Chain Reaction ◽  
Photonic Sensor ◽  
Silicon Photonic ◽  
Label Free Detection ◽  
Polymerase Chain

Analysis methods based upon the quantitative, real-time polymerase chain reaction are extremely powerful; however, they face intrinsic limitations in terms of target multiplexing.

scholarly journals Missing Value Imputation using XGboost for Label-Free Mass Spectrometry-Based Proteomics Data

2021 ◽  
Author(s):  
Jian Song ◽  
Changbin Yu
Keyword(s):  
Mass Spectrometry ◽  
High Performance ◽  
Missing Values ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Pearson Correlation ◽  
Data Matrix ◽  
Label Free ◽  
Proteomics Data ◽  
Benchmark Datasets

AbstractThe label-free mass spectrometry-based proteomics data inevitably suffer from the problem of missing values. The existence of missing values prevents the downstream analyses which need a complete data matrix. Our motivation is to introduce the state-of-art machine learning algorithm XGboost to realize a method of imputation which can improve the accuracy of imputation. But in practical, XGboost has many parameters need to be tuned to deliver on its potential high performance. Although cross validation may find the best parameters, it is much time-consuming. Alternatively, we empirically determined the parameters to two kinds of base learners of XGboost. To explore the robustness and performance of XGboost based imputation with predetermined parameters, we conducted tests on three benchmark datasets. As a comparative, six common imputation methods were also experimented in terms of normalized root mean squared error and Pearson correlation coefficient. The comparative experimental results indicated that the XGboost based imputation method using the linear base learner is competitive to or out-performs its competitors, including the random forest based imputation, by achieving smaller imputation errors and better structure preservation under the empirical parameters for the three benchmark datasets.

Solid-phase extraction protocol for isolating retinol-d4 and retinol from plasma for parallel processing for epidemiological studies

1993 ◽  
Vol 39 (11) ◽  
pp. 2318-2322 ◽  
Author(s):  
S R Dueker ◽  
J M Lunetta ◽  
A D Jones ◽  
A J Clifford
Keyword(s):  
Parallel Processing ◽  
Solid Phase Extraction ◽  
Solid Phase ◽  
Epidemiological Studies ◽  
Hplc Method ◽  
Technical Skill ◽  
Gas Chromatography Mass Spectrometry ◽  
Phase Extraction ◽  
Stable Isotope Dilution ◽  
Large Numbers

Abstract Solid-phase extraction permits the parallel processing of samples in large numbers. We have applied this technique to the isolation of retinol isotopomers from plasma of humans participating in a study of vitamin A stable isotope dilution. The isotopomers were analyzed by gas chromatography/mass spectrometry. The extraction involves the separation of retinol from its aqueous matrix with a C18 silica-based sorbent followed by removal of lipid contaminants with an aminopropyl silica-based sorbent. Overall recovery of retinol from plasma was 47.2% +/- 1.8%. Purity of the retinol isolated from plasma is comparable with that obtained with a single HPLC method. This method permits the preparation of 32 samples per day by one analyst. Elimination of the need for HPLC permits sample preparation in the field with a minimum of equipment and technical skill.

scholarly journals Label-free high-throughput microRNA expression profiling from total RNA

2011 ◽  
Vol 39 (22) ◽  
pp. e154-e154 ◽  
Author(s):  
Demin Duan ◽  
Ke-xiao Zheng ◽  
Ye Shen ◽  
Rong Cao ◽  
Li Jiang ◽  
...  
Keyword(s):  
High Throughput ◽  
Expression Profiling ◽  
Microrna Expression ◽  
Label Free ◽  
Total Rna

scholarly journals IceR improves proteome coverage and data completeness in global and single-cell proteomics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mathias Kalxdorf ◽  
Torsten Müller ◽  
Oliver Stegle ◽  
Jeroen Krijgsveld
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Missing Values ◽  
Peptide Identification ◽  
Protein Quantification ◽  
Developmental Trajectory ◽  
Ion Current ◽  
Label Free ◽  
Proteomics Data ◽  
Data Completeness

AbstractLabel-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion current extraction Re-quantification), an efficient and user-friendly quantification workflow that combines high identification rates of data-dependent acquisition with low missing value rates similar to data-independent acquisition. Specifically, IceR uses ion current information for a hybrid peptide identification propagation approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. Applied to plasma and single-cell proteomics data, IceR enhanced the number of reliably quantified proteins, improved discriminability between single-cell populations, and allowed reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package.

scholarly journals Fitness benefit plays a vital role in the retention of the Pi-ta susceptible alleles

2020 ◽  
Author(s):  
Jia Liu ◽  
Yue Wang ◽  
Suobing Zhang ◽  
Fan Wu ◽  
Long Wang ◽  
...  
Keyword(s):  
Expression Profiling ◽  
Single Copy ◽  
Vital Role ◽  
Fine Tuning ◽  
Plant Defense Response ◽  
Endogenous Hormones ◽  
R Gene ◽  
R Genes ◽  
Evolutionary Time ◽  
Large Numbers

In plants, large numbers of R genes, which segregate as loci with alternative alleles conferring different resistance to pathogens, have been maintained over a long evolutionary time. In theory, there seem to be no reason for hosts to harbor these susceptible alleles in view of their null contribution to resistance. As such, why should populations support disease-susceptible individuals along with disease-resistant individuals? In rice, a single copy R gene Pi-ta segregates for two expressed clades of alleles, one resistant and the other susceptible. We discovered that knockout of the Pi-ta susceptible alleles induced drastic fitness decline in the absence of pathogens. Gene expression profiling and endogenous hormones quantification showed that the susceptible alleles might serve as an off-switch to the downstream immune signaling, thus contributing to fine-tuning of plant defense response. The fitness benefit of carrying a susceptible Pi-ta allele provides a plausible explanation for their retention in the genome.

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