Reproducible quantitative proteotype data matrices for systems biology

Abstract

Historically, many mass spectrometry-based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.

FIGURE 1:

The proteotype data matrix as often found in proteomics experiments. (a) The data matrix contains quantitative values for different analytes (peptides or proteins) measured across multiple samples. One major goal in proteomics is to achieve high throughput (high number of quantified analytes) consistently quantified across many samples (experimental conditions, perturbations, or patient samples). (b) Sample-centric workflows (such as discovery proteomics or shotgun proteomics) place heavy emphasis on a high number of identifications in a single sample, which is achieved by data-dependent acquisition. However, the resulting data matrices often contain missing values due to undersampling issues, and in large studies, not all analytes can be quantified in every single sample. (c) In analyte-centric workflows (such as SRM and other low-throughput targeted proteomics techniques), the major focus is on achieving highly consistent quantification across many samples. The resulting data matrices are often devoid of missing values but only cover a few, carefully selected analytes.