Label-free Quantitative Proteomics
Label-free Quantitative Proteomics - Related Videos
11:53
Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling
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Cited by 38 •
2014
Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a rapid, inexpensive strategy for accurate mass spectrometry-based quantitative proteomics. Here we demonstrate a robust method for preparation and analysis of protein mixtures using the ReDi approach that can be applied to nearly any sample type.
11:38
A Quantitative Glycomics and Proteomics Combined Purification Strategy
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Cited by 18 •
2016
A high-throughput protocol was developed for combined proteomics and glycomics purification and LC-MS/MS quantification in plasma. Deamidation analysis of N-linked glycosylation motifs was specific to deglycosylated sites. Accurate quantitation of N-glycans was achieved by coupling filter aided N-glycan separation to the individuality normalization when labeling with glycan hydrazide tags strategy.
08:11
Quantitative Analysis of Chromatin Proteomes in Disease
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Cited by 4 •
2012
Advances in mass spectrometry have allowed the high throughput analysis of protein expression and modification in a host of tissues. Combined with subcellular fractionation and disease models, quantitative mass spectrometry and bioinformatics can reveal new properties in biological systems. The method described herein analyzes chromatin-associated proteins in the setting of heart disease and is readily applicable to other in vivo models of human disease.
11:44
Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures
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Cited by 5 •
2013
Here we describe a robust method for the fractionation of plant plasma membranes into detergent resistant and detergent soluble membranes based on a mixture of unlabeled and in vivo fully 15N labeled Arabidopsis thaliana cell cultures. The procedure is applied for comparative proteomic studies to understand signaling processes.
05:37
Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions
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Cited by 14 •
2020
Here, we present a protocol to profile the interplay between host and pathogen during infection by mass spectrometry-based proteomics. This protocol uses label-free quantification to measure changes in protein abundance of both host (e.g., macrophages) and pathogen (e.g., Cryptococcus neoformans) in a single experiment.
FAQs
Peptide ion intensities and spectral counts provide two signal types for comparing proteins across samples. Ion intensity uses the recorded strength of peptide-related mass spectrometric signals, whereas spectral counts use the number of peptide spectra recorded. Because these measurements come from digested peptides, computational analysis can associate them with protein-level abundance differences.
Computational alignment places measurements from different specimens into a coordinated comparison, while normalization helps make the resulting values suitable for abundance comparisons. These steps matter because the experiment generates peptide-level signals rather than a direct protein inventory. Together, they support interpretation of whether measured differences represent meaningful changes across the analyzed samples.
Enzymatic digestion is important because the mass spectrometry readout is collected from peptides rather than intact protein mixtures in the stated workflow. Liquid chromatography then separates those peptides before tandem mass spectrometry records their signals. This sequence creates organized peptide measurements that computational analysis can align and use when comparing protein abundance between specimens.
Instead of depending on isotopic or chemical tags, it compares measurements obtained from the samples themselves. That choice can simplify sample preparation and remains useful when labeled standards or multiplexed reagents are unavailable. It also supports analysis across many biological specimens, making the approach flexible for discovery-oriented experiments.
A typical analysis begins by enzymatically digesting proteins into peptides. The peptide mixture is separated by liquid chromatography, then examined by tandem mass spectrometry, which records ion intensities or spectral counts. Finally, computational alignment and normalization organize the measurements for cross-sample comparison. Each stage connects sample preparation, measurement, and quantitative interpretation.
It can reveal protein-abundance changes associated with cellular responses, disease-associated protein changes, signaling pathways, and biochemical mechanisms. Researchers can compare specimens and examine the detected protein changes in those biological contexts. This makes the method useful for connecting mass spectrometric measurements with broader questions about cellular regulation and biochemical function.
It can generate comparative protein-abundance measurements across biological specimens without requiring labeled standards or multiplexed reagents. The resulting dataset can support exploratory identification of protein changes and provide a starting point for studying disease-associated alterations, signaling, or biochemical mechanisms. This flexibility is especially relevant when discovery is needed and label-based resources are unavailable.