Abstract
Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of a lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single-shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to date, we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single- and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.
Highlights
Researchers who are interested in analyzing the global expression of protein have more options than ever before, due to a flurry of developments in proteomics technologies over the last 20 years [1]
One of the most powerful forces driving the growth of proteomics as a field has been the increase in liquid chromatography-coupled tandem mass spectrometry (LCMS) hardware performance over time
On the lab is to obtain deeper proteomic depth with no further alteration in workflows, moving up to the newer generation of hardware may be the best solution for that task
Summary
Researchers who are interested in analyzing the global expression of protein have more options than ever before, due to a flurry of developments in proteomics technologies over the last 20 years [1]. Metrics for the performance of different methods do exist, with relative numbers of peptide and protein identifications per unit time being a metric of choice. A challenge in evaluating peptide and protein counts, as an objective metric for overall method performance, is in the number of variables that can be altered in the data processing pipelines that can affect these results. Utilizing a larger potential database to compare shotgun proteomics data to invariably increases the number of peptide identifications [3]. Increasing the search space further, to evaluate an increasing number of biologically likely post-translational modifications, will have a similar effect [4,5,6]
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