Q&A of Label-Free Quantification Proteomics
Q1: What are the key features of Label-Free Quantification (LFQ)?
A1: 1. No chemical or isotopic labeling required
Quantitation is derived directly from MS signal intensity, avoiding the extra steps and cost introduced by labels.
2. Slightly lower accuracy than label-based methods
LFQ, lacking internal or external labeling-based correction, typically shows slightly lower precision than methods such as SILAC or TMT.
3. High throughput and flexibility
It is not limited by the number of label channels, allowing more samples to be analyzed in parallel and making it suitable for large-scale studies.
4. High requirement for experimental consistency
Results of LFQ are sensitive to sample preparation, LC stability, and instrument performance, requiring strict quality control and normalization.
5. Replicates matter
Use at least three technical and three biological replicates to increase statistical reliability and reduce the impact of experimental variability on quantitative results.
Q2: What are the common strategies of Label-Free Quantification?
A2: LFQ typically uses two strategies:
1. Area under the curve (AUC)
Extract chromatographic peaks at the MS1 level and integrate the peak area to reflect relative peptide abundance of different samples. This approach provides higher precision and works best when signals are stable and peaks are well shaped.
2. Spectral counting
Count the number of MS/MS spectra acquired and confidently assigned to the target protein to estimate relative abundance. The method is simple, but it is less sensitive for proteins at low abundance.
Q3: In LFQ, which acquisition mode is preferable, DDA or DIA?
A3: In Label-Free Quantification, DIA is generally preferred for the reasons below.
1. DDA (Data-Dependent Acquisition)
DDA selects precursors for fragmentation based on real-time intensity. It is effective for exploratory identification but often misses low-abundance peptides, and missing values across samples are common.
2. DIA (Data-Independent Acquisition)
DIA fragments all precursors within predefined m/z windows. It provides stronger reproducibility and cross-run consistency with a lower missing value rate, which is well suited for large-scale and multi-batch LFQ projects.
Q4: Why can label-free quantitative proteomics compare different sample types?
A4: Label-free quantification measures relative abundance from MS signal intensity or spectral counts and does not rely on chemical or isotopic labels. Protein abundances can be compared across sample types provided that samples are processed with the same preprocessing, LC conditions, MS acquisition parameters, and data-analysis workflow, and that algorithms such as retention-time alignment are used to match peptides across runs.
Q5: How can missing values be addressed in LFQ?
A5: Label-free quantification relies on MS signal intensity for relative quantitation, so variability at any step, including sample preparation, LC stability, and instrument performance, can cause signal loss or non-detection. This appears as missing values in the data matrix.
To reduce the impact of missing values:
1. Optimize sample preparation to minimize protein degradation and peptide loss.
2. Keep LC and MS systems stable to limit peak drift and signal fluctuations.
3. Increase technical and biological replicates to improve coverage and statistical reliability.
4. Use DIA or other comprehensive acquisition modes to lower the miss rate for low-abundance peptides.
5. Impute missing values appropriately during data analysis, for example, by using a low-abundance assumption, and normalize the data to improve the precision of between-sample comparisons.
Q6: How should shared peptides be handled?
A6: Shared peptides are peptide sequences that are identical and map to more than one protein. Common approaches to handling shared peptides include:
1. Exclusion: Use only unique peptides for quantitation to avoid ambiguity introduced by shared peptides.
2. Allocation: Distribute each shared peptide's MS1 intensity to candidate proteins within the same run, using weights proportional to each protein's unique peptide signals (or weights estimated by a probabilistic model), ensuring the peptide contributes only once. This approach can preserve more quantitative information but introduces modeling assumptions.
Choose the method based on your study goal. Discovery analyses often favor allocation to retain more information, while high-precision quantitation typically prefers exclusion.
Q7: How can the significance of label-free quantitative results be determined?
A7: Use statistical tests such as t-tests or ANOVA combined with multiple hypothesis testing correction, for example false discovery rate (FDR) control. It is also recommended to calculate the coefficient of variation (CV) from biological replicates to assess the robustness of the quantitation.
Q8: What are the differences in application scenarios between label-free quantitative proteomics and label-based quantitative proteomics?
A8: 1. Label-free quantitation (LFQ, especially DIA mode)
LFQ is best suited for parallel proteome quantitation studies involving a larger number of sample groups, typically more than ten, and samples with substantial source variation such as different species, tissues, or anatomical sites. It is appropriate for projects aiming to determine protein presence, achieve high-accuracy identification and quantitation, and avoid systematic bias introduced by labeling or grouping. The acquired data can also be reanalyzed for deeper insights.
2. Label-based quantitation (such as TMT, iTRAQ, SILAC)
Label-based quantitation is better suited for experiments comparing a smaller number of sample groups (typically ten or fewer) with high background consistency, such as samples from the same species and tissue type. In these cases, label-based quantitation enables high-precision comparison of multiple samples in a single MS run while minimizing batch effects.
Q9: How can "with or without" differences be determined?
A9: Results from multiple MS runs of the same sample are rarely identical, with an overlap rate of around 70%. For reliable statistical analysis, it is common practice to retain only proteins that are identified in at least two out of three technical replicates and use the average value for quantitation. If a protein shows no quantitation values (or values of zero) in all three replicates of one sample group but appears with non-zero values in at least two replicates of another group, it can be preliminarily classified as having a "with or without" difference. However, such findings should be interpreted with caution. Additional confirmation by methods such as PRM or western blotting is recommended.
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