In mass spectrometry-based proteomics, quantitation accuracy, sample throughput, and the long-term stability and reproducibility of the workflow are crucial for obtaining reliable results that advance biological insights. Accurately quantifying abundances of proteins of interest in complex samples is a prerequisite for developing suitable predictive models and testing them against experimental data sets. Statistical significance is improved by increasing the sample set and ensuring reproducible results from run to run.
There are multiple workflows currently available for quantitative proteomics. In general, they can be split into isotopic labeling or label-free methods. There are tradeoffs for each workflow, and proteomics researchers need to choose the one that best fits the question at hand. Here we focus on the label-free methods.
In principle, two mass spectrometry acquisition modes are commonly used for label free quantification experiments: data-dependent (DDA) and data-independent (DIA) analysis. In recent years the latter approach has gained in popularity and overtaken DDA as the method of choice for proteomics researchers. The following sections addresses the reasons and discusses the criteria that are needed for a good quantitative proteomics workflow.
What are challenges with traditional data-dependent analysis?
Traditional DDA approaches have been widely used in label-free quantitation (LFQ) experiments that are aimed to understand global protein expression and modifications within biological samples. However, these methods suffer vastly from run-to-run inconsistencies, caused by the method-inherent intensity-based stochastic triggering of precursor ions. They can also lead to under sampling, especially when analyzing low-abundant proteins.
Why is data-independent acquisition a superior choice over DDA?
Results from DDA methods are often inconsistent and difficult to replicate, requiring larger sample sets to achieve comparable statistical significance in measuring differential abundances. Consequently, DDA label-free quantification (LFQ) studies can become both time-consuming and costly for your lab.
In contrast, a DIA method can address missing value concerns by equally cycling through defined m/z windows along the survey scan range. The resulting spectral complexity of the mixed precursor fragmentation and mixed product ions is often addressed by employing large spectral libraries for data processing. However, recent developments in data analysis software (e.g., using machine-learning approaches for in silico prediction of high-quality spectral libraries), have made library-free approaches a valid time- and cost-effective alternative.
What performance factors produce a statistically significant proteome study?
Large-scale clinical studies are the roadmap for application in the real world. Obtaining meaningful insights into biological systems, e.g. health vs disease states, is the main objective, and the patient samples are especially precious. Thus, a rugged and high-performing workflow is key for these applications.
To achieve high levels of performance, they must meet high criteria, including sensitivity, mass resolution, mass accuracy, quantitative accuracy, precision, reproducibility, and robustness. In many cases, these performance factors are influenced by counteracting method parameters, and each must be balanced in an optimized setup, including a well-developed DIA method, to fulfill these criteria.
Why is it important to run large numbers of samples?
The need for analyzing large numbers of samples, especially in clinical and biomarker discovery studies, ensures reproducible quantitative analyses. In addition, large cohort studies require a robust workflow setup, including separation technologies, columns, and mass spectrometers, that can run stably over long periods of time. DIA workflows are ideal for such experiments because they are amenable for short run times ensuring large number of samples can be analyzed. While meeting the criteria mentioned ABOVE.
How do you ensure confidence in quantitative results?
Aside from robustness and reproducibility, reliable identification and quantification is imperative for impactful proteomics research. Confidence in quantitative results is driven by ensuring accurate and precise measurements, but also by careful data analysis and rigid validation methods, such as strict control of false discovery rates. Systems that deliver both highly accurate mass as well as high resolution are those that result in the statistical sensitivities necessary for proteome study. These two factors are the key to reliable identifications and the precise detection and resolution of ion species within complex DIA scans.