Overview and considerations in bottom-up proteomics

Abstract

Proteins are the key biological actors within cells, driving many biological processes integral to both healthy and diseased states. Understanding the depth of complexity represented within the proteome is crucial to our scientific understanding of cellular biology and to provide disease specific insights for clinical applications. Mass spectrometry-based proteomics is the premier method for proteome analysis, with the ability to both identify and quantify proteins. Although proteomics continues to grow as a robust field of bioanalytical chemistry, advances are still necessary to enable a more comprehensive view of the proteome. In this review, we provide a broad overview of mass spectrometry-based proteomics in general, and highlight four developing areas of bottom-up proteomics: (1) protein inference, (2) alternative proteases, (3) sample-specific databases and (4) post-translational modification discovery.

Figure 1:

Sources of proteome complexity. Proteoforms provide a depth of complexity to the proteome which would not be possible if a gene only led to the production of a single protein product. Instead, mutations at the gene-level, variants or alternative splicing at the transcript-level, and post-translational modifications or cleavage events at the protein-level contribute to a still undefined number of proteoforms, which are the functional units of the proteome.

Figure 2:

Experimental workflows for bottom-up and top-down proteomic approaches.

Figure 3:

Comparison of the theoretical and experimental length distribution of tryptic peptides. The length distribution of in silico digested tryptic peptides (grey), as determined by ProteaseGuru, is compared to the length distribution of peptides experimentally identified from MetaMorpheus analysis of the tryptic data Miller et. al. (green). Most experimentally identified peptides are between 7–35 amino acids in length, whereas the theoretical tryptic digest favors the generation of shorter peptides.

Figure 4:

Comparison of short- and long-read sequencing for the reconstruction of transcript isoforms. In short-read RNA-sequencing approaches, RNA fragments are generated from which full-length transcripts must be reconstructed. Depending on the coverage of alternative splice junctions, incorrect transcript inference can be achieved. In this example, based on the fragments identified, a single transcript is reconstructed. Therefore, the two additional transcript isoforms are missed. In long-read RNA-sequencing, full-length transcripts are sequenced, and no reconstruction is required. Therefore, in the provided example, all three transcript isoforms expressed in the sample are identified.