Common proteomic technologies, applications, and their limitations.

Technology	Application	Strengths	Limitations
2DE	Protein separation	Relative quantitative	Poor separation of acidic, basis, hydrophobic and low abundant proteins.
	Quantitative expression profiling	PTM information.
DIGE		Relative quantitative
	Protein separation	PTM information	Proteins without lysine cannot be labelled Requires special equipment for visualization and fluorophores are very expensive
	Quantitative expression profiling	High sensitivity
		Reduction of intergel variability
ICAT	Chemical isotope labelling for quantitative proteomics	Sensitive and reproducible	Proteins without cysteine residues and acidic proteins are not detected
		Detect peptides with low expression levels.
SILAC	Direct isotope labelling of cells	Degree of labelling is very high	SILAC labelling of tissue samples is not possible
	Differential expression pattern	Quantitation is straightforward
iTRAQ	Isobaric tagging of peptides	Multiplex several samples	Increases sample complexity
		Relative quantification High-throughput	Require fractionation of peptides before MS.
MUDPIT	Identification of protein-protein interactions	High separation	Not quantitative
		Large protein complexes identification	Difficulty in analysing the huge data set
	Deconvolve complex sets of proteins		Difficult to identify isoforms
Protein array	Quantitate specific proteins used in diagnostics (biomarkers or antibody detection) and discovery research	High-throughput	Limited protein production
		Highly sensitive	Poor expression methods
		Low sample consumption	Availability of the antibodies
			Accessing very large numbers of affinity reagents.
Mass spectrometry	Primary tool for protein identification and characterization	High sensitivity and specificity. High-throughput. Qualitative and quantitative	No individual method to identify all proteins. Not sensitive enough to identify minor or weak spots. MALDI and ESI do not favour identification of hydrophobic peptides and basic peptides
		PTM information
Bioinformatics	Analysis of qualitative and quantitative proteomic data	Functional analysis, data mining, and knowledge discovery from mass spectrometric data	No integrated pipeline for processing and analysis of complex data. Search engines do not yield identical results

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