New Strategies and Challenges in Lung Proteomics and Metabolomics. An Official American Thoracic Society Workshop Report

Abstract

This document presents the proceedings from the workshop entitled, "New Strategies and Challenges in Lung Proteomics and Metabolomics" held February 4th-5th, 2016, in Denver, Colorado. It was sponsored by the National Heart Lung Blood Institute, the American Thoracic Society, the Colorado Biological Mass Spectrometry Society, and National Jewish Health. The goal of this workshop was to convene, for the first time, relevant experts in lung proteomics and metabolomics to discuss and overcome specific challenges in these fields that are unique to the lung. The main objectives of this workshop were to identify, review, and/or understand: (1) emerging technologies in metabolomics and proteomics as applied to the study of the lung; (2) the unique composition and challenges of lung-specific biological specimens for metabolomic and proteomic analysis; (3) the diverse informatics approaches and databases unique to metabolomics and proteomics, with special emphasis on the lung; (4) integrative platforms across genetic and genomic databases that can be applied to lung-related metabolomic and proteomic studies; and (5) the clinical applications of proteomics and metabolomics. The major findings and conclusions of this workshop are summarized at the end of the report, and outline the progress and challenges that face these rapidly advancing fields.

Keywords: biomarkers; lung diseases; mass spectrometry; nuclear magnetic resonance; systems biology.

Figure 1.

Proteomics and metabolomics are members of systems biology science that includes genomics, epigenomics, and transcriptomics. Metabolomics is particularly reflective of gene and protein activity. DNA structure: https://commons.wikimedia.org/wiki/File:A-DNA,_B-DNA_and_Z-DNA.png. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled GNU Free Documentation License. Protein structure: Structure of the C3 protein. Emw (https://commons.wikimedia.org/wiki/File:Protein_C3_PDB_1c3d.png), “Protein C3 PDB 1c3d,” https://creativecommons.org/licenses/by-sa/3.0/legalcode. Images of metabolites are publically available from: http://www.hmdb.ca/ with citation of: Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, et al., HMDB 3.0 — The Human Metabolome Database in 2013. Nucleic Acids Res. 2013. Jan 1;41(D1):D801-7. 23161693.

Figure 2.

Temporal increase in the number of lung proteomics and metabolomics publications in the PubMed database. Squares represent proteomic publications; triangles represent metabolomic publications.

Figure 3.

The most commonly used analytical platforms for metabolomics are: (A) proton (¹H) nuclear magnetic resonance (NMR); (B) gas chromatography (GC)–mass spectroscopy (MS); and (C) liquid chromatography (LC)-MS. (A) NMR is ideal for the detection of polar compounds like amino acids and for smaller molecular weight (≤100 Da) metabolites that LC-MS can miss. NMR is routinely quantitative when an internal standard, such as 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS), is added to the sample. (B) GC-MS most often requires the derivatization of volatile compounds that are separated by a gas carrier phase and elute based on retention time in the column. After ionization, compounds are detected by MS. The graphic printout shows a typical serum readout of abundance versus time (top) and abundance versus mass/charge ratio (bottom). (C) For LC-MS metabolomics, molecules are ionized, typically by electrospray ionization, and the resulting positive and negative ions are detected by MS. This results in a mass-to-charge ratio (m/z) versus relative peak intensity graphical representation of the data. More details about the advantages and disadvantage of each approach can be found in Table 2. By K. Murray (Kkmurray) (Own work) [GFDL (http://www.gnu.org/copyleft/fdl.html), CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0/) or CC BY-SA 2.5-2.0-1.0 (http://creativecommons.org/licenses/by-sa/2.5-2.0-1.0)], via Wikimedia Commons.

Figure 4.

Summary of two common proteomic data acquisition methods. In a typical unbiased proteomic analysis, tryptic peptides are separated using liquid chromatography and introduced into the mass spectrometer using electrospray ionization. The sum intensity of detected peptides is often visualized as total ion current over time, as in A. (B) In a typical cycle of a data-dependent analysis (DDA), a “full scan” of all precursor (MS1) ions present is performed followed by (C) tandem mass spectrometry (MS/MS) analysis of the topN (e.g., top3; starred peaks) most abundant ions. The MS/MS spectra are used for database searching to identify the corresponding peptides. (D) Finally, identified peptides are quantified based on the area under the curve (AUC) of the MS1 intensity. (E) In a data-independent analysis (DIA), all ions within a selected mass range are subjected to MS/MS fragmentation. (F) Quantitation is performed by AUC of the fragment ions (MS2) that belong to a particular peptide. m/z = mass-to-charge ratio; MS1 = mass spectrometry analyzer 1; MS2 = mass spectrometry analyzer 2; TIC = total ion chromatogram; XIC = extracted ion chromatogram.

Figure 5.

Stable isotope labeling with amino acids in cell culture (SILAC) technology uses cells grown in isotopically labeled amino acids to synthesize the “heavy” forms of proteins that can be mixed with their “light” counterparts before trypsinization and/or peptide/protein fractionation and identification by tandem mass spectroscopy. m/z = mass-to-charge ratio; MS/MS = tandem mass spectrometry.