Cardiovascular proteomics in the era of big data: experimental and computational advances

Abstract

Proteomics plays an increasingly important role in our quest to understand cardiovascular biology. Fueled by analytical and computational advances in the past decade, proteomics applications can now go beyond merely inventorying protein species, and address sophisticated questions on cardiac physiology. The advent of massive mass spectrometry datasets has in turn led to increasing intersection between proteomics and big data science. Here we review new frontiers in technological developments and their applications to cardiovascular medicine. The impact of big data science on cardiovascular proteomics investigations and translation to medicine is highlighted.

Keywords: Cardiovascular medicine; Clinical proteomics; Mass spectrometry; Shotgun proteomics.

Fig. 1

Trends in cardiovascular proteomics. Both (a) the volume of proteomics studies, and (b) the size of proteomics dataset have skyrocketed in the last decade. a The number of cardiovascular proteomics studies has increased approximately 400 % from 2004 to 2014, far outpacing the natural growth of the cardiovascular field, indicating increasingly common adoption of the technologies. b The protein coverage of proteomics experiments in the same time period has experienced considerable growth also, quantified as the numbers of identifiable cardiac proteins in an experiment. The maximum number of cardiac proteins (dashed lines) is based on estimated significantly expressed loci in the mouse heart and does not take into account proteoforms such as resulting from alternative splicing. This increase is driven by parallel advances in hardware instrumentation and computational technology. Coinciding with the notion of “complete proteomics”, proteomics studies can now interrogate more proteins of interest such as chromatin remodeling factors and transcription factors that express at low copy numbers. Effective means to analyze big proteomics dataset are becoming a new frontier of growth in cardiovascular proteomics

Fig. 2

Analytical and computational overview in protein identification. 1 Cardiac samples are processed to extract the proteomes or subproteomes of interest, which may then be proteolyzed to obtain peptide digests. 2 The resulting peptides are desalted and subjected to LC–MS/MS analysis to acquire MS1 and MS2 spectra. 3 The peptide sequences that are present in the MS dataset can be identified using a database search approach, which uses a sequence database (e.g., UniProt) to generate theoretical peptide sequence and predict their fragmentation patterns in silico, then automatically find the best-match theoretical spectra to the experimental spectra for protein identification. Alternatively, the proteins can be identified using a spectral library search. The resulting protein datasets can be further analyzed to extract other biomedically meaningful information (see Fig. 4)

Fig. 3

Common label-free quantification in proteomics studies. Two common label-free quantification approaches in use are based on spectral count (top) and ion intensity (bottom). Top: spectral counting methods leverage the fact that in stochastic shotgun profiling, the frequency of a protein being sampled by the instrument scales with its relative abundance in the sample. The numbers of spectra matched to an identical protein in healthy (green) versus diseased (red) samples can therefore be compared if appropriate normalization and bioinformatics workflows are implemented. Bottom: ion intensity methods integrate the total signal intensity of peptide ion signals in the mass spectrometer to infer protein quantity. Software is now available to automatically identify and quantify peptide ion signals from mass spectra

Fig. 4

Proteomics data mining and functional annotations. Common computation approaches to extract information from massive proteomics datasets include (1) unsupervised cluster analysis, class discovery and visualization; (2) motif analysis and annotation term enrichment; (3) statistical learning methods for disease signature extraction; (4) network analysis; and (5) annotations with other functional information including protein motifs and cardiac disease relevance