Molecular Phenomics in Systems, Synthetic, and Chemical Biology

John McLean previews his plenary lecture at HPLC 2023, where he will describe emerging analytical strategies using liquid chromatography–ion mobility–mass spectrometry (LC–IM–MS) for untargeted molecular phenomics in systems, synthetic, and chemical biology.

One of the most successful big science projects in modern history is the Human Genome Project. Among the many motivations for sequencing the human genome was to better understand what made us human and healthy. The frontiers of our knowledge rapidly expanded and opened new research avenues that continue to grow—such as major advances in genetic engineering and synthetic biology. This knowledge expansion facilitates many big, comprehensive, systems-wide biology projects to better understand the intricate molecular interplay of life—beyond the central dogma of molecular biology—and is largely built upon omics strategies (1).

Omics fields strive to understand the relationships among molecules from a global perspective. This often requires massive molecular cataloging experiments and data organization efforts to interpret these relationships. In many cases, the answers that are sought are tantamount to asking questions such as “who says what, where, when, and to whom?” These efforts initially centered on one or several classes of molecules, which gave rise to individual fields such as genomics, transcriptomics, proteomics, lipidomics, glycomics, and metabolomics, among an astounding array of omics areas of study today. In each of these efforts, experiments focus on cataloging the broad-scale changes in a molecular inventory.

In phenomics, these experiments seek the comprehensive molecular characterization of a phenotype in both space—for example at a cell, tissue, and organismal level—and time—such as healthy vs. disease, lifespan, or in response to exposures, treatments, and lifestyle choices (2). This places enormous demands on measurement technologies, including minimal sample preparation, fast measurements, high concentration dynamic range, low limits of detection, and high selectivity. In general, the omics fields utilize three primary technologies that embody these traits—most typically spectroscopically‑based and array technologies (for example, genomics and transcriptomics), nuclear magnetic resonance (NMR, for example, metabolomics and metabonomics) and mass spectrometry (MS, for example, proteomics, lipidomics, glycomics, metabolomics). The data density resulting from such experiments is enormous and requires computational approaches to organize the millions of potential species present in vanishingly small spatial coordinates (3,4). The interplay between phenomic datasets and bioinformatics forms the nexus of translating phenomics data into actionable information and understanding.

Since the first descriptions of combining MS data with computational approaches for proteomics (5), significant efforts have centred on the ability to expand the analytical capacities of mass spectrometry and computational approaches in omics research. On the data generation side of the equation, chief among these are developments to increase peak capacity for complex biological samples. These enhancements in selectivity are driven by two parallel advances. One is through integrating additional separation dimensions with MS detection to leverage the multiplicative nature of peak capacity (6). The second is through enhancements in resolving power and resolution in each of the individual separation dimensions, including both chromatography and mass spectrometry, to increase peak capacity in each individual dimension. Residing at the intersection of these two is the emergence of gas-phase electrophoretic molecular separations based on ion mobility (IM).

Separation selectivity in time‑dispersive IM is achieved through the electromigration of ions through a neutral gas, the latter providing an attenuating force that is proportional to the number of collisions between the ion and the neutral gas species (7). This in turn is proportional to the molecular ion surface area (measured in units of Ų) and termed the collision cross-section (CCS) (8). Importantly, since separations are performed post-ionization in the gas-phase following ionization, IM is readily interfaced between liquid-phase separations such as chromatography and the mass spectrometer without any increase in total analysis time. In the first proteomic experiments combining liquid chromatography (LC)–IM–MS, this feature was referred to as nesting of spectra, since the LC dimension provided separations in minutes, the IM dimension provided separations in milliseconds, and the MS dimension provided separations in microseconds (9). In this way, there is no tradeoff of increased peak capacity for sample throughput. A second critical aspect of IM for omics studies are the prevailing structures that different classes of biomolecules preferentially adopt. Based on the monomeric units from which different biopolymers are comprised, different classes of molecules occupy specific regions of conformation space, or the correlation of CCS with the mass of the molecule (10). This results from the prevailing gas-phase folding forces of each class of molecule. For example, lipids adopt less dense structures than peptides or carbohydrates. These predictable and highly reproducible correlations of CCS vs. mass have opened new descriptors for performing omics measurements of multiple classes of molecules simultaneously. When combined with retention time, accurate mass, and fragmentation information, CCS provides another measurement to support high accuracy molecular annotation. Like the advances in peak capacity afforded by high resolving power MS, significant recent enhancements in IM resolving power have been a critical area of advancement in IM technologies.

This plenary lecture at HPLC 2023 will describe recent advances in IM–MS-integrated omics measurement strategies in the analyses of complex biological samples of interest in systems, synthetic, and chemical biology. New advances in artificial intelligence (AI) and machine learning based on developments in internet commerce and astronomy will also be described, to approach biological questions from an unbiased and untargeted perspective and to quickly mine these massive datasets. These techniques will be highlighted through selected examples, ranging from the creation of microfluidic human organs-on-chip, to replace animal testing in drug development workflows, to probing the outcomes of fast genetic editing experiments using CRISPR in the optimization of synthetic biology for fine and commodity chemical production. While enormous challenges in phenomics remain, there is immense promise in terms of diagnostics and predictive capabilities for health and medicine, to help better understand what makes us human.

References

1. Sherrod, S. D.; McLean, J. A. Systems-Wide High Dimensional Data Acquisition and Informatics Using Structural Mass Spectrometry Strategies. Clin. Chem. 2016, 62 (1), 77–83. DOI: 10.1373/clinchem.2015.238261
2. Holmes, E.; Wilson, I. D.; Nicholson, J. K. Metabolic Phenotyping in Health and Disease. Cell 2008, 134 (5), 714–717. DOI: 10.1016/j.cell.2008.08.026
3. May, J. C.; Goodwin, C. R.; McLean, J. A. Ion Mobility-Mass Spectrometry Strategies for Untargeted Systems, Synthetic, and Chemical Biology. Curr. Opin. Biotechnol. 2015, 31, 117–121. DOI: 10.1016/j.copbio.2014.10.012
4. May, J. C.; Gant-Branum, R. L.; McLean, J. A. Targeting the Untargeted in Molecular Phenomics with Structurally-Selective Ion Mobility-Mass Spectrometry. Curr. Opin. Biotechnol. 2016, 39, 192–197. DOI: 10.1016/j.copbio.2016.04.013
5. Eng, J. K.; McCormack, A. L.; Yates, J. R. An Approach to Correlate Tandem Mass Spectral Data of Peptides with Amino Acid Sequences in a Protein Database. J. Am. Soc. Mass Spectrom. 1994,5 (11), 976–989. DOI: 10.1016/1044-0305(94)80016-2
6. Giddings, J. C. Two-Dimensional Separations: Concept and Promise. Anal. Chem. 1984, 56 (12), 1258A–2170A. DOI: 10.1021/ac00276a003
7. May, J. C.; McLean, J. A. Ion Mobility-Mass Spectrometry: Time-Dispersive Instrumentation. Anal. Chem. 2015, 87 (3) 1422–1436. DOI: 10.1021/ac504720m
8. May, J. C.; Morris, C. B.; McLean, J. A. Ion Mobility Collision Cross Section Compendium. Anal. Chem. 2017, 89 (2), 1032–1044. DOI: 10.1021/acs.analchem.6b04905
9. Valentine, S. J.; Kulchania, M.; Barnes, C. A. S.; Clemmer, D. E. Multidimensional Separations of Complex Peptide Mixtures: A Combined High-Performance Liquid Chromatography/Ion Mobility/Time-of-Flight Mass Spectrometry Approach. Int. J. Mass Spectrom. 2001, 212 (1–3), 97–109. DOI: 10.1016/S1387-3806(01)00511-5
10. McLean, J. A. The Mass-Mobility Correlation Redux: The Conformational Landscape of Anhydrous Biomolecules. J. Am. Soc. Mass Spectrom. 2009, 20 (10), 1775–1781. DOI: 10.1016/j.jasms.2009.06.016

John A. McLean is Stevenson Professor and Chair of the Department of Chemistry and Associate Provost at Vanderbilt University in the USA. He also serves as Director of the Center for Innovative Technology, which facilitates phenomics research for large-scale consortia projects in health and medicine. He studied at the University of Michigan and George Washington University, and subsequently performed postdoctoral research at Forschungszentrum Jülich in Germany and Texas A&M University. He is a fellow of the National Academy of Inventors and the American Association for the Advancement of Science. He has published over 200 manuscripts in the areas of technology development in ion mobility, mass spectrometry, and omics research.

John A. Mclean will present his plenary lecture on Sunday 18 June at 17:30 in Room 1 (Auditorium).