A single-cell analysis approach to understanding molecular organization and plasticity in the brain
Date
2016
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Delaware
Abstract
Single-cell transcriptional heterogeneity pervades the fully differentiated brain.
This heterogeneity is particularly prevalent in brain nuclei involved in the autonomic
regulation of physiological functions such as cardiovascular homeostasis. Because
neuronal function largely depends on its transcriptome, such heterogeneity confounds
our understanding of how heterogeneous neurons contribute to their broader
phenotypic function. In addition to the transcriptome, functional connectivity and in
vivo anatomical environment are additional factors central to defining a neuron’s
functional state. Given their importance, these factors may provide the added context
necessary to understand how a distribution of heterogeneous neurons contributes to
phenotypic function. Consequently, the overall goal of this work is to establish an
organizational framework that characterizes single-neuron heterogeneity within a
brain nucleus and elucidates its functional relevance. ☐ Towards this goal, we have taken a combined experimental and computational
approach to determine the organizing principles driving complex interaction networks
within and among transcriptionally diverse neurons within a brain nucleus. First, we
generated a large-scale gene expression dataset from several hundred neurons, selected
on the basis of their synaptic input types, taken from the nucleus tractus solitarius
(NTS), a brainstem nucleus involved in the central regulation of blood pressure. Our
analysis of these neurons revealed an organizational structure in which transcriptional
variability aligns with synaptic input type along a continuum of graded gene
expression. This continuum is populated by distinct neuronal subtypes characterized
by gene groups exhibiting correlated expression. ☐ In order to identify the molecular mechanisms driving this correlated behavior,
we next developed a fuzzy logic modeling-based methodology to model quantitatively
causal gene interaction networks from single-cell transcriptomic data. Our modeling
results suggest that distinct input stimuli operating on distinct network structures
corresponding to these subtypes can drive neurons through various transcriptional
states. These results suggest that transcriptional heterogeneity represents a neuron’s
adaptive response to various inputs. Based on these results, we propose that neuronal
adaptation may be a mechanism through which the NTS robustly regulates blood
pressure and cardiovascular homeostasis. ☐ To test this proposal, we examined what impact adaptation to neuronal
subtypes in the NTS and brainstem would have on the short-term autonomic
regulation of cardiovascular homeostasis under the simulated disease state of systolic
heart failure via mathematical modeling. We developed a closed-loop control model
characterizing neuronal regulation of the cardiovascular system by integrating
previous quantitative models that simulated various aspects of the cardiovascular
system. Because the goal of this study was to investigate the effects of neuronal
subtype adaptation, we incorporated brainstem neuronal subtypes, such as those
identified in our analysis of the NTS. Modeling simulation results suggest that
adaptation of these neuronal components can compensate for an impaired
cardiovascular state due to systolic heart failure by decreasing neuronal inhibition (i.e.
parasympathetic tone) of cardiac contractility. ☐ Finally, we tested the utility of a single-cell analysis approach to interpret
single-cell heterogeneity throughout the brain by identifying a cellular network
organization in a distinct brain nucleus – the suprachiasmatic nucleus (SCN), which
regulates circadian rhythms in mammals. Similar to our analysis of the NTS, we
generated and analyzed a high-dimensional gene expression dataset consisting of
hundreds of transcriptionally heterogeneous SCN neurons. Our multivariate analysis
of these neurons revealed both known and previously undescribed SCN neuron-types,
which organize into a neuronal interaction network via known paracrine signaling
mechanisms underlying the synchronizing functions of the SCN. ☐ Based on the analysis of heterogeneous single neurons, we have identified an
organizational framework with which we can now interpret single-cell heterogeneity;
a heterogeneous neuronal population comprises a mixture of distinct neuronal
subtypes whose adaptive response to inputs is driven by distinct regulatory networks.
Such adaptation provides a mechanism in which the brain is able to regulate robustly
physiological functions by providing compensatory effects under perturbed or
challenged states.