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