A Guide to Label-Free Quantitative Proteomics: From Experiment to Data Analysis
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Fold Change (FC) > 1.5 or < 0.67
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Adjusted p-value < 0.05 (Benjamini-Hochberg correction)
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Advanced Instrumentation: Equipped with state-of-the-art mass spectrometers including Orbitrap Exploris 480 and Fusion Lumos
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Standardized Workflow: End-to-end processes, from sample preparation to data interpretation, governed by a rigorous quality control framework
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Tailored Analysis: Supports DDA, DIA, and hybrid acquisition schemes to accommodate diverse research objectives
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Comprehensive Bioinformatics: Offers advanced services such as differential protein screening, protein–protein interaction (PPI) network analysis, and biomarker discovery
Label-Free Quantitative Proteomics (LFQ) has emerged as a critical tool in modern biomedical research. Its advantages—eliminating the need for isotopic labeling, simplifying sample preparation, and generating rich datasets—have led to its widespread application in studies of disease mechanisms, drug target identification, and biomarker discovery. By integrating high-resolution mass spectrometry with rigorous data analysis, LFQ enables systematic comparisons of protein expression levels across diverse biological samples. This article provides a comprehensive overview of LFQ workflows, highlighting experimental protocols, key technical considerations, and data analysis strategies to support researchers in the effective design and execution of LFQ-based studies.
MtoZ Biolabs offers integrated LFQ services by combining Orbitrap Exploris high-resolution mass spectrometers with custom-optimized data analysis pipelines, delivering reliable, reproducible, and in-depth proteomic datasets to research clients worldwide.
Overview of Label-Free Quantitative Proteomics
Label-free quantification estimates protein abundance by analyzing either the intensity of peptide signals at the MS1 level or the number of MS/MS spectra. Compared to isotope labeling methods (e.g., SILAC, TMT), LFQ offers greater experimental flexibility, broader sample compatibility, and reduced costs—making it especially suitable for large-scale studies involving clinical cohorts.
Core methodological approaches:
(1) MS1-based quantification: Peptide abundance is determined by measuring the area under the extracted ion chromatogram peaks. Widely used software tools include MaxQuant and Proteome Discoverer.
(2) MS/MS spectral counting: Protein levels are inferred by tallying the number of MS/MS spectra corresponding to specific proteins, which is particularly useful in exploratory or preliminary studies.
Sample Preparation: Foundation for High-Quality Data
1. Sample Types and Preprocessing
LFQ is applicable to a wide range of sample types, including cells, tissues, serum, plasma, and other biological fluids. Each sample type requires specific protocols for effective protein extraction:
(1) Cells and tissues: Mechanical homogenization in combination with lysis buffers containing denaturants, reducing agents, and protease inhibitors is recommended.
(2) Serum and plasma: To enhance the detection of medium- and low-abundance proteins, high-abundance proteins such as albumin and immunoglobulins should be depleted.
2. Protein Extraction and Enzymatic Digestion
Following protein extraction, concentrations should be determined (e.g., using the BCA assay) to ensure equal protein loading across all samples. Proteins are then digested with trypsin at a recommended enzyme-to-substrate ratio (typically 1:50 or 1:100) to produce peptides with consistent quality and representation.
3. Peptide Purification and Fractionation
To reduce sample complexity and improve the detection sensitivity for low-abundance proteins, peptide desalting (e.g., using C18 column purification) followed by high-pH reversed-phase liquid chromatography-based fractionation is strongly recommended.
Mass Spectrometry Detection: The Core of Label-Free Quantitative Proteomics
1. Instrument Selection and Parameter Optimization
MtoZ Biolabs employs a high-resolution, high-sensitivity Orbitrap Exploris 480 mass spectrometer, in combination with a nano-flow liquid chromatography (nanoLC) system, to enable precise detection of low-abundance proteins in complex biological samples.
Representative parameter optimization strategies include:
(1) MS1 resolution: ≥60,000 at m/z 200, improving peak resolution and identification accuracy
(2) Data acquisition mode: Data-Dependent Acquisition (DDA) or Data-Independent Acquisition (DIA)
(3) TopN setting: Adjusted according to sample complexity (e.g., Top20 or Top30) to balance scan speed and proteome coverage
Quality control procedures include:
(1) Incorporation of iRT standard peptides for retention time alignment
(2) Inclusion of QC samples in each analytical batch to enable real-time monitoring of instrument performance
Data Analysis Workflow: From Raw Data to Biological Insight
1. Raw Data Processing
(1) Peak detection and peptide identification are conducted using software platforms such as MaxQuant and Proteome Discoverer
(2) Protein identification and quantification are performed via database searching against a FASTA file, with a 1% false discovery rate (FDR) threshold to ensure data reliability
2. Data Normalization and Missing Value Imputation
(1) Normalization strategies—such as total ion current (TIC) or median normalization—are applied to correct for inter-sample technical variability
(2) Missing values are classified as missing at random (MAR) or missing not at random (MNAR), and imputed using appropriate methods, such as minimal value substitution or the k-nearest neighbor (kNN) algorithm
3. Criteria for Differential Protein Identification
Typical thresholds include:
4. Functional Enrichment and Pathway Analysis
Functional annotation and enrichment analysis of differentially expressed proteins are conducted using resources such as Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to elucidate potential biological mechanisms
Advantages of MtoZ Biolabs Label-Free Quantitative Proteomics Services
Label-Free Quantitative Proteomics (LFQ) is gaining prominence in life sciences and translational medicine due to its high sensitivity, broad dynamic range, and streamlined workflow. Success in LFQ studies hinges on robust experimental design and efficient data processing. MtoZ Biolabs is dedicated to delivering high-quality, full-spectrum proteomics solutions to empower researchers in achieving greater precision and deeper insights in their scientific endeavors. For inquiries regarding LFQ projects, please visit the MtoZ Biolabs website or contact our scientific team.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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