What Is Label-Free Quantification in Proteomics?
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MS1 intensity-based quantification: Peptide abundances are quantified by extracting chromatographic peak areas at the MS1 level and comparing the intensities of identical peptides across different samples.
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Spectral counting: Protein abundance is estimated based on the number of MS/MS spectra assigned to a given protein in different samples.
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Protein extraction → quantification → in-gel or in-solution digestion
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Optional high-pH peptide fractionation to enhance proteome depth
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High-resolution Orbitrap mass spectrometry platform
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Data-dependent acquisition (DDA) or data-independent acquisition (DIA), both compatible with LFQ
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Peptide and protein identification (FDR < 1%)
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Relative quantification based on MS1 signal intensity
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Differential protein analysis (fold change and p-value)
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GO and KEGG pathway enrichment analysis
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Disease mechanism studies: identification of disease-associated proteins and potential therapeutic targets.
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Drug mechanism investigations: comparison of protein expression profiles between treated and control groups.
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Phenotype association analyses: characterization of expression changes across different cellular or tissue states.
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Clinical cohort studies: analysis of differential expression patterns in large-scale clinical sample sets.
Quantitative analysis is a fundamental approach in proteomics research for elucidating dynamic biological processes, identifying differentially expressed proteins, and investigating disease mechanisms. Compared with labeling-based quantification strategies, such as SILAC, iTRAQ, and TMT, label-free quantification (LFQ) has been increasingly adopted in recent years owing to its operational simplicity, broad applicability, and relatively low cost. In particular, LFQ offers distinct advantages for the analysis of clinical samples, large-scale cohorts, and valuable materials that are difficult or impossible to label. With continuous advances in high-resolution mass spectrometry instrumentation and data analysis algorithms, the accuracy and reproducibility of label-free quantification have been substantially improved.
What Is Label-Free Quantification (LFQ)?
Label-free quantification is a proteomic quantification strategy that relies on mass spectrometry signal intensity at the MS1 level or on spectral counting. Unlike labeling-based approaches such as SILAC and TMT, LFQ does not require the introduction of exogenous labels during sample preparation, thereby enabling greater experimental throughput and broader sample compatibility.
Two commonly used label-free quantification approaches include:
Core Principles of Label-Free Quantification
The fundamental assumption of label-free quantification is that, under identical mass spectrometry acquisition conditions, the MS signal intensity of a given peptide is proportional to its relative abundance in the sample.
The key steps include:
1. Protein digestion: Proteins are enzymatically cleaved into peptides.
2. Liquid chromatography-tandem mass spectrometry (LC-MS/MS): Peptides are separated and detected online.
3. Peptide identification and quantification: Peak intensities or spectral counts are extracted using software tools such as MaxQuant and Proteome Discoverer.
4. Cross-sample alignment and normalization: Accurate comparisons across samples are achieved through retention time alignment, mass error correction, and normalization procedures.
Advantages of Label-Free Quantification
1. Simplified Sample Preparation
LFQ eliminates the need for isotope or chemical labeling, thereby reducing experimental complexity and overall cost.
2. Higher Sample Throughput
The approach can be readily scaled to dozens or even hundreds of samples, making it well suited for large cohort studies.
3. Broad Sample Compatibility
LFQ is applicable to a wide range of sample types, including cells, tissues, and body fluids, and is particularly advantageous for clinical and limited-quantity samples.
4. Continuously Improving Sensitivity
Advances in high-resolution mass spectrometry platforms, such as the Orbitrap Exploris 480, have markedly enhanced the quantitative accuracy and proteome coverage achievable with LFQ.
Challenges and Corresponding Solutions
Despite its advantages, LFQ also presents several challenges:
1. Batch Effects and Signal Drift
Systematic variability may arise from LC-MS analyses performed across different batches.
Solution: MtoZ Biolabs implements rigorous quality control procedures and internal standard calibration, in combination with retention time alignment and normalization algorithms, to improve data consistency.
2. Missing Values and Quantitative Accuracy
Identical peptides may not be detected across all samples, resulting in missing values.
Solution: The Match Between Runs function in MaxQuant, together with data-driven missing value imputation strategies, is applied to preserve quantitative information to the greatest extent possible.
3. Dependence on Instrument Stability
LFQ places relatively high demands on the long-term stability and performance of mass spectrometry platforms.
Solution: MtoZ Biolabs utilizes high-throughput nano-LC-MS systems and conducts daily instrument performance monitoring to ensure analytical consistency and stability.
Detailed LFQ Workflow
The standard workflow for label-free quantitative proteomics services at MtoZ Biolabs includes:
1. Sample Preparation
2. LC-MS/MS Analysis
3. Data Analysis
Typical Applications of Label-Free Quantification
Label-free quantification represents one of the most widely used and versatile quantitative strategies in contemporary proteomics research. Owing to its streamlined workflow, high-throughput capability, and continuously evolving data analysis methodologies, LFQ is increasingly serving as an important link between basic research and clinical translation. For researchers seeking a stable and reliable LFQ-based proteomics platform, consultation with MtoZ Biolabs may facilitate the development of tailored analytical strategies and project designs.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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