2.3 Metabolomics
Another tool used for systems wide analysis of innate immune pathways is metabolomics, which includes the profiling and quantification of metabolites predicated with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR). A metabolite can be defined as an intermediate or terminal product of metabolism. They are essential in the production of ATP that provides energy to cells through catabolism and can also directly impact cellular signaling dynamics in various pathways, especially those that are involved in immune cell signaling. The study of the role of metabolites within the immune system makes up the field known as immunometabolism, which is now emerging as a key field of study within metabolomics, and it has been shown that metabolic rewiring of immune cell populations can regulate their activation. Fluxes in the metabolic cycle within a cell, as well as the metabolites themselves, can modulate cellular signaling dynamics directly and indirectly through regulation of post translational modifications. For example, macrophages that are stimulated with lipopolysaccharide (LPS) have been shown to undergo metabolic reprogramming leading to a pro-inflammatory phenotype.32 Notably, they underwent a switch from oxidative phosphorylation-based ATP production to glycolysis, leading to a higher reactive oxygen species production (ROS) by repurposing their mitochondria to produce ROS in a diethyl succinate dependent manner. Elevated production of succinate within the LPS stimulated macrophages also directly impacts production of interleukin-1B by modulating HIF-1a activity via ROS dependent oxidation, independent of NF-κB signaling. Elevated succinate levels inhibit the production of anti-inflammatory cytokines such as IL-1RA and IL-10. Suggesting that upon macrophage activation, the accumulation of succinate directly enhances endogenous pro-inflammatory gene activation and inhibits anti-inflammatory gene expression.33 Like a proteomics workflow, a metabolomic LC-MS experiment can be conducted in a targeted or untargeted fashion, the experimental design is dependent on the chemical composition of the metabolites to be analyzed as well as metabolite absolute abundance. Due to the differences in structure, chemical composition and mass of peptides and metabolites, experimental parameters required for their analysis also differ to ensure proper fragmentation for the analyte. Untargeted or global approaches offer a wider detection window and do not require a pre-defined number of metabolites to be screened, unlike the targeted approach.34 Targeted metabolomics is usually geared toward a subset of metabolites within a biological pathway of interest. Both NMR and LC-MS techniques can be used to perform targeted metabolomics. The spectra obtained from 1H-NMR are usually compared to the spectra of a known chemical standard, as in a targeted LC-MS experiment, where quantification is based on the ratio of intensities for the detected metabolites, within the sample groups that match the pre-defined standards. Untargeted metabolomics offers an approach aimed at simultaneously measuring the most metabolites within a given sample with as little bias as possible. Each peak in an untargeted LC-MS run corresponds to a unique mass-to-charge (m/z) ratio and retention time known as a metabolite feature. The data sets that are generated are gigabytes in size when high resolution instrumentation is utilized, and a particular metabolite may have multiple unique metabolic features as well. This complicates data analysis and makes manual inspection impractical. However, developments in bioinformatics software designed for this specific purpose have revolutionized the way in which biological conclusions can be drawn from global scale metabolomic experiments. A more comprehensive review on analysis of metabolomics data with systems biology approaches can be accessed here.35 For quantification of metabolites, and for measuring metabolic fluxes or the rate at which they occur, label-based LC-MS approaches prove to be robust. Quantification of metabolites is an informative approach to metabolomics; however, it does not consider the non-linear relationship of the production and consumption of metabolites, i.e., the metabolic flux, which dictates metabolic pathway activities. Metabolic flux analysis (MFA) helps elucidate the rates at which metabolites interconvert by analyzing the patterns associated with labeling frequencies of isotope tracers and their target metabolites to infer fluxes in the metabolic network.36 In this approach, the rate at which the metabolite is labeled corresponds to the rate at which flux occurs.37­­­­­
SINGLE CELL OMICS:
The field of multiomics has seen consistent progress over the years as new “omics” technologies continue to be added to the arsenal. Most recently, with the development of the single cell techniques, it is now possible to explore the heterogeneity between cells of the same population. Macrophages act as the primary regulators of inflammation in the innate immune response. They can adopt stimulus induced phenotypes because of their functional plasticity. This allows macrophages to appropriately respond to diverse pathogens and aid in tissue repair following acute damage.38 M0 monocytes represent the common progenitor lineage for the pro-inflammatory M1 phenotype and anti-inflammatory M2 phenotype of macrophages. This differentiation is dependent on induction with polarizing cytokines. The M1 phenotype is a result of stimulation with interferon gamma (IFNγ) and granulocyte macrophage colony stimulating factor (GM-CSF). M1 macrophages secrete pro-inflammatory cytokines such as IL-1β, TNF-α, and IL-12, all of which recruit immune cells to the site of infection and aid in pathogen clearance. However, this classification has been proposed to be less rigid as there are cells displaying characteristics of both and the macrophages have been reported to transition from one phenotype to another.39 This would normally be overlooked during global omics-based analyses, as the target molecule is pooled together with many different cells. For example, during the global transcriptomic analyses, cells are lysed together, and the RNA extract is pooled. This relies on the assumption that the cell population of interest is homogenous in nature. However, that is not the case, and with single cell resolution, one can assess quantifiable changes from cell to cell in the population of interest.40 Recent developments have now made it possible to extract multiple layers of omics data from a single cell. For example, obtaining the transcriptomic profile and doing targeted proteomics on the same cell. Single cell techniques can also provide insight on the spatial and temporal organization, as well as the population architecture of each compartment of the cell, tissue, or the organ.41 This is a powerful tool for studying systems biology, especially in the context of innate immunity, as spatio-temporal organization within cells is imperative for relaying cellular messages, leading to activation of immune signaling pathways.42 Isolation of single cells is the biggest limitation when it comes to any of the various downstream omics analyses. This presents a constant battle between the loss of cellular material during the isolation process and conservation of the spatial temporal information. Low throughput methods for single cell sorting such as laser capture microdissection or manual micromanipulation are costly, with output below a thousand cells for a given study and are time consuming, but they provide the advantage of decent spatio-temporal resolution.43 High throughput methods such as fluorescent-activated cell sorting (FACS) are less costly, automated, and provide a high yield of isolated cells. However, they do not retain spatio-temporal resolution since the tissues are homogenized during the pooling of cell suspensions. Thus, choosing the appropriate isolation method for the single cell omics analyses is a key part of a successful experiment.
Barcoding of cells for library preparation also represents a key component of single cell omics experiments, it allows for libraries generated by each single cell to be pooled and sequenced together which saves time and lowers the cost. However, this requires downstream bioinformatics analysis to be able to distinguish barcoded single cells from one another with a high confidence interval35. An overview on the multiomics based single cell approaches with detailed insights into each category can be accessed here.44