Single-cell analysis has shown consistently that a significant amount of transcriptional heterogeneity exists within and across differentiated cell-types. This transcriptional heterogeneity is pervasive across post-mitotic neurons in the brain and conflicts with the traditional expectation that a neuronal phenotype consists of functionally identical neurons that respond uniformly to synaptic and neuromodulatory inputs. High-throughput “-omic” level analysis suggests that transcriptionally heterogeneous neurons, within a neuroanatomical phenotype, function within a complex molecular organization in the brain. We aim to determine the functional relevance of this transcriptional heterogeneity (if it exists) in order to gain insight into how variability contributes to neurogenic control of physiological systems in health and disease.
To this end, we analyzed the transcriptional responses of individual neurons in distinct brain nuclei to specific physiological perturbations. We generated a high-dimensional gene expression dataset measured from hundreds of single neurons collected from the nucleus tractus solitarius (NTS), a brainstem nucleus involved in regulating cardiovascular homeostasis. NTS neurons were collected from rats undergoing an acute hypertensive challenge in order to measure their transcriptional responses to this blood pressure perturbation. We used multivariate analytical techniques such as principal component analysis and multidimensional scaling to analyze the high-dimensional gene expression dataset representing the transcriptional states of neurons, and identified a gradient of neuronal subtypes that aligned with synaptic input-types corresponding to blood pressure changes and to cardiovascular demands involved in homeostatic regulation. Also we used our recently developed fuzzy logic-based gene network modeling methodology to identify distinct gene networks that correspond to these neuronal subtypes and which drive heterogeneous gene expression across NTS neurons.
Additionally, single-cell analysis of the suprachiasmatic nucleus (SCN), a hypothalamic brain nucleus that regulates circadian rhythm, provided an alternative perspective of the organization of cell-interaction networks. Our analysis of hundreds of SCN neurons responding to a perturbed circadian cycle revealed a previously unidentified neuronal population, characterized by mRNA expression of pituitary adenylate cyclase-activating peptide (PACAP), a peptide typically thought to be an input to the SCN, rather than something produced endogenously. In addition to the traditionally defined SCN neuron-types that express arginine vasopressin (AVP) or vasoactive intestinal peptide (VIP) in a region-specific manner, the presence of the PACAP-expressing neuronal phenotype indicates that the SCN cellular interaction network is more complex than previously understood. Moreover, functional gene expression behavior across single SCN neurons correlated more strongly with receptor expression for AVP, VIP, and PACAP rather than with the mRNA expression of the neuropeptides themselves. This receptor-based correlation suggests that gene regulation driving the transcriptional state of SCN neurons is governed by inputs and coincides with the input-driven behavior observed in the NTS.
In conclusion, analysis of transcriptomic data of single neurons from distinct brain nuclei has revealed unexpected molecular organizational structures within neuroanatomical phenotypes. These organizational structures suggest that transcriptional heterogeneity reflects an adaptive response to inputs and provide a more complex perspective of how adaptive gene regulation drives neuronal state within and across phenotypic populations.
- Park J, Brureau A, Kernan K, Starks A, Gulati S, Ogunnaike B, Schwaber J, Vadigepalli R. 2014. Inputs drive cell phenotype variability. Genome Res.
- Park, J., Ogunnaike, B., Schwaber, J., & Vadigepalli, R. (2014). Identifying Functional Gene Regulatory Network Phenotypes Underlying Single Cell Transcriptional Variability. Progress in Biophysics and Molecular Biology, 117(1), 87–98.
Research Support: NIH (F31 AA023143-01 NIGMS R01GM083108, NHLBI R01HL111621, NIAAA T32 AA007463)