Welcome to PEAKS label free quantification tutorial. In this session, we will go over the features and benefits of the software tool for label-free quantification and demonstrate how to perform data analysis.
Workflow in PEAKS LFQ
The PEAKS label-free quantification algorithm is intensity-based. As this diagram shows, the survey scans use peptide feature for quantification. MS2 scans are used for peptide/protein identification. Combining them produces peptide and protein quantification results. Let’s get into more detail for each steps.
LC-MS Heat Map
In shotgun proteomics, proteins are digested into a complex mixture of peptides, which are separated by on-line HPLC. At a given retention time, the fractions of the mixture eluted from the column are sent to a mass spec instrument and their precursor masses and intensities are recorded in a survey scan (the MS1 spectrum).
This figure shows a heat map of the mass spec signals generated by peptides eluting from the column. The map depicts all peptide features detected by the instrument, with the complexity of elution and isotope patterns.
The intensity of a peptide feature is proportional to the abundance and concentration of the peptide in the sample.The abundance ratio of a peptide between two samples can be estimated by the intensity ratio of the peptide feature in two heat maps.
There are several steps to determine the relative abundance of a peptide and protein by label-free quantification. The first step is “feature detection”.
A peptide feature is defined as a group of peaks in a heat map, characterized by eluting pattern in terms of retention time and isotope patterns in terms of mass charge.
The deconvolution of overlapped peptide features and retention alignment between runs are the key factors for the data analysis; for the overlapped peptide feature clusters cannot be avoided even with today’s high resolution instruments and LC separation techniques. PEAKS Label Free quantification successfully deconvolutes overlapped peptide features by using an expectation-maximization algorithm.
RT Alignment and Feature Matching
The second step is Retention time alignment and feature matching.
The retention time of a peptide feature in two LC-MS runs may changes subject to the LC column conditions, and so forth.
To match the same peptide features in different runs, retention time alignment is required. Here are two LC-MS runs, you can see where the retention time changed.
After alignment, the peptide features are matched.
Next step is ratio calculation. The relative abundance ratio is calculated by the area of the extracted ion chromatograms (XICs) in two runs.
In each scan, intensities of isotopic peaks are summed when the XIC is generated.
Here are two XICs of a peptide feature, the red one is from run 1, and blue one from run 2. The abundance ratio can be estimated by the ratio of areas of two XICs.
Next we make a significance assessment.
Technical replicates are used to evaluate the variation of a feature between runs. A quality value is associated with a feature in terms of its intensity, isotope and eluting patterns. The feature quality is defined as 1 log (sigma), where, Sigma is the average variation.
Given the observation of a feature variation in two biological states, a significance value is calculated, which is defined as -10logP, where P is the P-value to observe such variation in the replicate runs.
The last step is peptide feature identification. This is done using the MS/MS spectra associated with the feature. PEAKS label free quantification is seamlessly integrated with PEAKS database search for peptide identification, thus, data analysis of label-free quantification is much easier then switching software or exporting from one format to another.