High-throughput proteomics: a methodological mini-review

Abstract

Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other 'omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery.

Fig. 1. The process of proteomics “from bench to bedside”.

The mass spectrometry (MS)-based methods, single-molecule proteomics (SMP) and single-cell proteomics (SCP) have been widely used to identify and quantify new proteins in the initial discovery stage. Protein pathway array (PPA) is a high-throughput technique to explore the regulation of protein-protein interactions, pathway-pathway interactions, and biological functions to find the position of newly discovered protein in the cell signaling networks. Luminex, Meso-scale Discovery (MSD), Simoa and Olink are effective high-throughput methods for clinical validation after the proteomic markers are verified using tissue microarray (TMA).

Fig. 2. The flow chart of data analysis.

Normalization is the most significant step after acquiring the raw data. Data can be analyzed according to specific study design and available clinical information and it can be based on the raw data after normalization or a result from other analyses. For example, the clustering analysis can be performed on raw data or the proteins that have significant changes after SAM.

Fig. 3. Plotsheet generated by the significance analysis of microarrays: data are presented as a scatter plot of expected (x-axis) vs observed (y-axis) and the solid line indicates the relative difference expression of group.

Red color indicates upgrade and green color indicates downgrade. The data points that exceed a threshold from expected relative differences have significant different.

Fig. 4. Examples of hierarchical clustering analysis (HCA) and grid analysis of time-series expression (GATE).

a The heatmap of a two-way hierarchical clustering analysis was performed by the Multi Experiment Viewer (MeV) (http://mev.tm4.org/). The color in each square represents a numerical value and the bar is on the top. All samples (x-axis) were clustered into three groups, while all protein markers were clustered into four groups. b Protein markers were cluster by multiple data points using GATE. The multiple data points can be divided by time, dosages, or stages of the disease.

Fig. 5. Principal component analysis (PCA).

a The PCA mapping was performed using the Partek Genomics Suite (Partek, St. Louis, MO) (https://www.partek.com/partek-genomics-suite/). Patients with different survival status (red represents dead and blue represents alive) were separated by eight proteins. The first principal component is plotted on the X-axis and captures 34.9% of the variance. The second principal component is plotted on the Y-axis and achieves 15.6 % of the variance. b The scree plot represents the contribution of each principal component in PCA, and each principal component’s contribution decreases sequentially.

Fig. 6. Examples of ingenuity pathway analysis (IPA) and gene-set enrichment analysis (GSEA).

a The signaling networks generated by database-based Ingenuity Pathway Analysis (IPA). The up- and downregulated proteins are represented by molecules in red and green color, respectively. The pathways were labeled outside of the network. b The top canonical pathways that were most significant to the dataset were identified by the IPA. The score assigned to each pathway was presented in –log (p value) using Fisher’s exact test. c The enrichment plot generated by the database-based gene set enrichment analysis (GSEA). The bar in the middle of the figure was labeled in red and blue from left to right, which means risk factor and protective factor separately. The enriched gene set is the IVANOVA_HEMATOPOIESIS_EARLY_PROGENITOR (https://www.gsea-msigdb.org/gsea/msigdb/cards/IVANOVA_HEMATOPOIESIS_EARLY_PROGENITOR), which is a protective factor in this figure.

Fig. 7. Circos plot: Among all eight clinicopathological categories, the gender occupied the most significant proportion of the distribution, suggesting that it is the clinical factor that has the most impact on the signaling network.

Among 20 canonical pathways altered in the disease, the HER-2 and p53 are affected most, suggested that they play essential roles in the pathogenesis of the disease.