User:OmarKana/sandbox

Statement of purpose & outline for editing

I hope to attempt to bring the article for Quantitative proteomics from a start article to the quality of a level B article. There are issues of organization within the article. There is also a lack of context and implication within the article regarding the purpose and real-world applications of quantitative proteomics. This project hopes to broaden the scope of the article in this way. I also hope to create my own images to make the article more accessible to readers.

- I want to reorganize the article to create a more narrative flow from background to methodologies to applications.

- I want to create one more image for the article

- I want to add more recent research to the article.

Concerns on my part:

-Going into too specific context in applications

-Going beyond the scope of the article

-Summary Writing

Bibliography

Quantitative mass spectrometry in proteomics: a critical review

Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present

Mass spectrometry-based label-free quantitative proteomics

Innovations: Functional and quantitative proteomics using SILAC

A review on mass spectrometry-based quantitative proteomics: targeted and data independent acquisition

Quantitative proteomics: challenges and opportunities in basic and applied research

Quantitative proteomics of model organisms

Quantitative proteomics in lung cancer

Social network architecture of human immune cells unveiled by quantitative proteomics

Rough Draft of Quantitative Genomics Edits

Quantitative Mass Spectrometry.

Quantitative proteomics is an analytical chemistry technique for determining the amount of proteins in a sample. The methods for protein identification are identical to those used in general (i.e. qualitative) proteomics, but include quantification as an additional dimension. Rather than just providing lists of proteins identified in a certain sample, quantitative proteomics yields information about the physiological differences between two biological samples. For example, this approach can be used to compare samples from healthy and diseased patients. Quantitative proteomics is mainly performed by two-dimensional gel electrophoresis (2-DE) or mass spectrometry (MS). However, a recent developed method of Quantitative dot blot (QDB) analysis is able to measure both the absolute and relative quantity of an individual proteins in the sample in high throughput format, thus open a new direction for proteomic research. In contrast to 2-DE, which requires MS for the downstream protein identification, MS technology can identify and quantify the changes.

Quantification Using Two Dimensional Electrophoresis (2-DE)

Two-dimensional gel electrophoresis (2-DE) represents one of the main technologies for quantitative proteomics with advantages and disadvantages. 2-DE provides information about the protein quantity, charge, and mass of the intact protein. It has limitations for the analysis of proteins larger than 150 kDa or smaller than 5kDa and low solubility proteins. Quantitative MS has higher sensitivity but does not provide information about the intact protein.

Classical 2-DE based on post-electrophoretic dye staining has limitations: at least three technical replicates are required to verify the reproducibility. Difference gel electrophoresis (DIGE) uses fluorescence-based labeling of the proteins prior to separation has increased the precision of quantification as well as the sensitivity in the protein detection. Therefore, DIGE represents the current main approach for the 2-DE based study of proteomes.

Quantification Using Mass Spectometry (MS)

Examples of quantitative proteomic workflows. Red represents physiological sample of interest, while blue represents control sample. White boxes represent areas where errors are most likely to occur, and purple boxes represent where the samples have been mixed.

Mass spectrometry (MS) represents one of the main technologies for quantitative proteomics with advantages and disadvantages. Quantitative MS has higher sensitivity but does not provide information about the intact protein.

For quantitative MS, a commonly applied approach is isotope-coded affinity tags (ICAT), which uses two reagents with heavy and light isotopes, respectively, and a biotin affinity tag to modify cysteine containing peptides. This technology has been used to label whole Saccharomyces cerevisiae cells, and, in conjunction with mass spectrometry, helped lay the foundation of quantitative proteomics.

Relative and absolute quantification

Mass spectrometry is not inherently quantitative because of differences in the ionization efficiency and/or detectability of the many peptides in a given sample, which has sparked the development of methods to determine relative and absolute abundance of proteins in samples. The intensity of a peak in a mass spectrum is not a good indicator of the amount of the analyte in the sample, although differences in peak intensity of the same analyte between multiple samples accurately reflect relative differences in its abundance.

Stable Isotope Labeling In Mass Spectrometry

Stable isotope labels

An approach for relative quantification that is more costly and time-consuming, though less sensitive to experimental bias than label-free quantification, entails labeling the samples with stable isotope labels that allow the mass spectrometer to distinguish between identical proteins in separate samples. One type of label, isotopic tags, consist of stable isotopes incorporated into protein crosslinkers that causes a known mass shift of the labeled protein or peptide in the mass spectrum. Differentially labeled samples are combined and analyzed together, and the differences in the peak intensities of the isotope pairs accurately reflect difference in the abundance of the corresponding proteins.

Absolute proteomic quantification using isotopic peptides entails spiking known concentrations of synthetic, heavy isotopologues of target peptides into an experimental sample and then performing LC-MS/MS. As with relative quantification using isotopic labels, peptides of equal chemistry co-elute and are analyzed by MS simultaneously. Unlike relative quantification, though, the abundance of the target peptide in the experimental sample is compared to that of the heavy peptide and back-calculated to the initial concentration of the standard using a pre-determined standard curve to yield the absolute quantification of the target peptide.

Relative quantification methods include isotope-coded affinity tags (ICAT), isobaric labeling (tandem mass tags (TMT) and isobaric tags for relative and absolute quantification (iTRAQ)), label-free quantification Metal-coded tags (MeCAT), N-terminal labelling, stable isotope labeling with amino acids in cell culture (SILAC), and Terminal amine isotopic labeling of substrates (TAILS). A mathematically rigorous approach that integrates peptide intensities and peptide-measurement agreement into confidence intervals for protein ratios has emerged.

Absolute quantification is performed using selected reaction monitoring (SRM).

MeCAT can be used in combination with element mass spectrometry ICP-MS allowing first-time absolute quantification of the metal bound by MeCAT reagent to a protein or biomolecule. Thus it is possible to determine the absolute amount of protein down to attomol range using external calibration by metal standard solution. It is compatible to protein separation by 2D electrophoresis and chromatography in multiplex experiments. Protein identification and relative quantification can be performed by MALDI-MS/MS and ESI-MS/MS.

Mass spectrometers have a limited capacity to detect low-abundance peptides in samples with a high dynamic range. The limited duty cycle of mass spectrometers also restricts the collision rate, resulting in an undersampling Sample preparation protocols represent sources of experimental bias.

Label-free quantification in Mass Spectrometry

One approach for relative quantification is to separately analyze samples by MS and compare the spectra to determine peptide abundance in one sample relative to another, as in label-free strategies. It is generally accepted, that while label-free quantification is the least accurate of the quantification paradigms, it is also inexpensive and reliable when put under heavy statistical validation. There are two different methods of quantification in label-free quantitative proteomics: AUC (area under the curve) and spectral counting.

Methods of Label-Free Quantification

AUC is a method by which for a given peptide spectrum in an LC-MS run, the area under the spectral peak is calculated. AUC peak measurements are linearly proportional to the concentration of protein in a given analyte mixture. Quantification is achieved with through ion counts, the measurement of the amount of an ion at a specific retention time. Discretion is required for the standardization of the raw data. High-resolution spectrometer can alleviated problems that arise when trying to make data reproducible, however much of the work regarding normalizing data can be done through software such as OpenMS, and MassView.

Spectral Counting involves counting the spectra of an identified protein and then standardizing using some form of normalization. Typically this is done with an abundant peptide mass selection (MS) that is then fragmented and then MS/MS spectra are counted. Multiple samplings of the protein peak is required for accurate estimation of the protein abundance because of the complex physiochemical nature of peptides. Thus, optimization for MS/MS experiments is a constant concern. One alternative to get around this problems is use a data independent technique that cycles between high and low collision energies. Thus a large survey of all possible precursor and product ions is collected. This is limited, however, by the mass spectrometry software's ability to recognize and match peptide patterns of associations between the precursor and product ions.

Quantification Using Spectrophotometry

Alternatively, the concentration of a certain protein in a sample may be determined using spectrophotometric procedures. The concentration of a protein can be determined by measuring the OD at 280 nm on a spectrophotometer, which can be used with a standard curve assay to quantify the presence of Tryptophan, Tyrosine, and Phenylalanine. However, this method is not the most accurate because the composition of proteins can vary greatly and this method would not be able to quantify proteins that do not contain the aforementioned amino acids. This method is also inaccurate due to the possibility of nucleic acid contamination. Other more accurate spectrophotometric procedures for protein quantification include the Biuret, Lowry, BCA, and Bradford methods.

Applications

Single cell proteomics

Work flow of the Quantification of the physiological differences in α and β cells in mice using computer prediction (A) and SILAC isotope-label quantification(B). (C) is the candidate list of kinases that indicate physiological differences in α and β cells.

Traditionally mass-spec proteomics has been applied to bulk samples composed of millions of cells. Yet such population average measurements are blind to the differences between single cells in heterogeneous samples, i.e., human tissues or cancer. Measuring such single cell heterogeneity has motivate efforts to develop Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS), a method that can quantify over a thousand proteins in single mammalian cells.^[1][2]

See also

• Protein mass spectrometry
• Quantitative dot blot