NTS neurons regulate emotion, autonomic homeostasis, and stress responses. Multiple neuronal nuclei, ligands, receptors, and signaling dynamics are involved in these complex functions including NE, GLP-1, CRH, and GABA (Fig. 1A-B). Moreover, local glial-neuronal paracrine signaling via inflammatory cytokines like tumor necrosis factor-alpha (TNF-α) also play a role. We microdissected single Th + neurons, Th- neurons, and microglia from the rat NTS as 10-cell pooled samples using LCM and measured their expression of 96 gene transcripts in an alcohol withdrawal time series (Sup. Figure 1). Time points were chosen based on rat alcohol metabolism42. 8 hr wd represents the start of acute AWS, 32 hr wd represents the end of acute AWS, and 176 hr wd represents a protracted withdrawal state. We found that neurons that stained Th + had significantly elevated Th expression and labeled them NE neurons (Fig. 1D). Th- neurons had significantly elevated expression of the GLP-1 precursor transcript Gcg, and were labeled as neuronal samples enriched with GLP-1 + neurons. Likewise, CD11β + cells expressed the microglial markers Cd34 and Cx3cr1 at significantly elevated levels and were labeled microglia. In a dimension reduction analysis (LDA), these three cell types formed distinct clusters with microglia separating out from neurons along the x-axis and NE and GLP-1 neurons separating along the y-axis (Fig. 1E).
Further analysis of Th and Gcg expression showed an inverse relationship with respect to time point with Gcg expression demonstrating elevated expression levels only during withdrawal (Fig. 2A-B). However, expression of these neurotransmitter precursor genes did not organize the other genes assayed into distinct subphenotypes correlated to their expression levels (Fig. 2C-D). A data-driven approach to cellular subphenotype organization identified stark subphenotypes unique to each cell type likely with discrete functions (Figs. 3–5). Strikingly, these subphenotypes shared similarities in their expression of their inflammatory gene clusters (Sup. Table 4).
Gene cluster 4 in NE neurons, gene cluster 1 in GLP-1 neurons, and gene cluster 1 in microglia constituted these ‘inflammatory’ clusters (Sup. Table 4). 18 genes were shared across all of these co-expression clusters and only 5 genes were unique to a single cluster suggesting similar mechanisms across cell types that regulate their expression. In NE neurons, subphenotype C highly expressed this inflammatory cluster while subphenotype E had moderate inflammatory co-expression cluster elevation. At 8 hr wd, NE subphenotype C was 62.5% of the samples (5/8) and at 32 hr wd C and E combined to 62.2% of the samples (23/37). By 176 hr wd, subphenotype C was only 29% of NE neuron samples (5/17). This suggests that this subphenotype of NE neurons experiences a marked increase in inflammation during acute AWS, but that this subphenotype is not involved in protracted withdrawal symptoms such as low-grade anxiety12. This increase in local paracrine inflammation likely increases the excitability for this NE neuron subphenotype17. This is consistent with the observation of hypersympathetic activity in acute, but not prolonged, AWS1.
In GLP-1 neurons, subphenotype A highly expressed the ‘inflammatory’ gene cluster (gene cluster 1). The pattern of expression in this inflammatory subphenotype of GLP-1 neurons (A) is similar to the NE inflammatory subphenotypes (C and E). GLP-1 Subphenotype A makes up 33.3% of control samples (3/9) and 0% of EtOH samples. At 8 hr wd, 62.5% (5/8) of GLP-1 neurons are inflammatory subphenotype A and at 32 hr wd it is 55.2% (16/29). By 176 hr wd, this inflammatory subphenotype has decreased back near control levels: 35.7% (5/14) of GLP-1 neurons.
Surprisingly, microglia demonstrated a similar pattern. Microglia subphenotype A also highly expressed the inflammatory gene cluster (cluster 1). High gene expression in this cluster is indicative of M1 microglia phenotypes as this cluster includes the M1 markers Il1β, Il6, Nos1, Ptgs2, and TLRs 1,4, and 543. This phenotype made up 29.0% (9/31) of the control samples, 100% (13/13) of the 32 hr wd samples, and only 21.4% of the 176 hr wd samples. Of note, all NTS microglia sampled at the 32 hr wd time points demonstrated an M1 phenotype suggesting the importance of neuroinflammation in the NTS during the acute phase of AWS. Conversely, we observed fewer M1-like microglia at 176 hr wd compared to control samples which is unexpected based on our previous work on alcohol withdrawal in the amygdala9,44,45. We expected neuroinflammatory markers to be increased at the 176 hr wd time point, especially in microglia, but these data suggest that the NTS experiences inflammation during acute withdrawal only (8 hr and 32 hr time points) and recovers by the 176 hr time point. We observe this in all three cell types assayed and speculate that compensatory endogenous anti-inflammatory signaling may be driving this observation, though we cannot substantiate this claim with the genes measured in this study.
The 176 hr wd time point is meant to measure long term changes in gene expression that occur in protracted withdrawal. At this time point, some similarities across cell types were observed in the subphenotypes that highly expressed GABAR subunits as was observed at other time points. NE neuron cluster 5, GLP-1 neuron cluster 2 and microglia cluster 2 contained the majority of the GABAR subunit genes, and the makeup of this ‘GABAR’ co-expression cluster was not as consistent as the inflammatory cluster across cell types—16 genes are shared across all cell types and 16 genes are unique to a single cell type within its respective GABAR cluster (Sup. Table 4). GLP-1 neurons in subphenotype B upregulates this co-expression cluster in the control treatment, but the relative level of expression of this cluster decreases throughout the time series within this subphenotype (Fig. 4). At the 176 hr wd time point, this GABAR cluster is only moderately expressed suggesting long term changes to this neuronal subphenotype as a result of alcohol dependence and withdrawal. The decrease in expression of inhibitory GABAR gene transcripts, along with the concurrent upregulation of co-expression cluster 3, which contains Gcg, suggests that this GLP-1 neuronal subphenotype experiences long term functional changes such that its neurotransmission increases. Literature indicates that GLP-1 signaling from the NTS to the amygdala and other nuclei is anxiogenic29. Taken together, these data suggest that this GLP-1 neuronal subphenotype is not primarily involved the acute withdrawal process characterized by inflammation, but rather experiences GABAR subunit downregulation over a longer process potentially leading to increased anxiety and susceptibility to stress in protracted AWS.
Microglia also showed elevated GABAR expression at the 176 hr wd time point, but the pattern of increased GABAR expression was unexpected. Control microglia in subphenotype C show moderate expression of both cluster 1 (inflammatory) and cluster 2 (GABAAR) (Fig. 5). Expression of both of these clusters increases at the 176 hr wd time point. This suggests elevated inflammation, but not by distinct M1 phenotype microglia (subphenotype A), and also elevated GABAR expression. These findings are best visualized in Fig. 8. Of note, there many genes in microglia cluster 2 that are not GABAR subunits. Moreover, microglial Tnf expression was significantly elevated in control, EtOH and 8 hr wd treatments compared to 176 hr wd independent of subphenotype (Sup. Table 3). Indeed, Tnf expression by microglia did not fit neatly into a gene cluster. Cluster C has some cells that demonstrate high tnf in both control and 176 hr wd, where Cluster A showed a decrease in Tnf expression between these two time points. Cluster B, conversely, increased its expression of Tnf from control to 176 hr wd. This apparent absence of a pattern in microglia Tnf expression suggests that in microglia this gene that is central to neuroinflammation is constrained by a mechanism that is independent of other gene expression regulation. Further, the decrease in overall microglia Tnf expression at 176 hr wd as measured by an average of –ΔΔCt values and two-tailed heteroscedastic t-tests may be misleading. A single-cell analysis reveals that overall expression may not be the best indicator of inflammation. Rather, shifts in subphenotype proportion, and the number of cells showing a moderately increased Tnf expression, as seen in subphenotype C, may have more of a physiologic impact than total gene expression levels.
Cell diagrams in Figs. 6,7,8 average the expression of a gene within a subphenotype designated by color and display that color in a location on the cell cartoon that corresponds to the protein function. This method of data presentation allows for analysis of receptor-ligand interactions within and between subphenotypes. For example, Fig. 6 suggests that subphenotype C experiences an increase in both CCL-CCR and CXCL10-CXCR signaling at 32 hr wd as the ligand and receptor genes for these proteins are highly expressed. Figure 6 also provides clarity in subphenotype D upregulation of Mapk1 at 176 hr wd suggesting long term transcription is altered during protracted withdrawal in this subphenotype. Moreover, transcription factor genes cFos, Junb, NfkB, and Stat3 have increased expression in subphenotype D2 suggesting this subset of NE neurons undergoes substantial long-term changes in transcription following alcohol withdrawal. Microglia in subphenotype C upregulate IL1a, IL1b, and IL1r1 at 176 hr wd in subphenotype C as compared to control, while subphenotype B downregulate these genes at 176 hr wd compared to control (Fig. 8). This dynamic provides indirect evidence that subphenotype B provides an anti-inflammatory function that is most active in protractracted withdrawal. Moreover, it suggests that microglia subphenotype C, identified here as a microglia subset that can function in a multitude of processes whether inflammatory or anti-inflammatory based on their lack of a clear co-expression module pattern in control, is pushed towards an inflammatory state in protracted withdrawal.
This dataset has allowed the identification of cellular subphenotypes and their gene expression dynamics in alcohol withdrawal through time. Analysis has revealed valuable insights in both neurotransmission signaling and local paracrine signaling processes that relate to what is observed clinically in the context of what is already established about such neurotransmission. The dataset is unique in that microfluid RT-qPCR, a method lower in throughput but more reliable than RNA-seq46, is combined with anatomic and staining specificity using LCM for single-cell selection in a time series. This allows for analysis of complex signaling dynamics at multiple levels, and the influence of such signaling dynamics on both acute AWS and protracted withdrawal based on the clinical symptoms at that time point.
We have collected the data, validated the accuracy of the dataset, and identified cellular subphenotypes and their major signaling dynamics. However, signaling dynamics measured in our dataset can be further investigated and may identify clinical targets to treat acute or protracted AWS and potentially alcohol dependence itself. Future studies analyzing these signaling dynamics with the addition of female rats that also include other brain cell types such as astrocytes and endothelial cells are needed to further understand the underlying pathophysiology of AWS and dependence.
Lastly, these findings are consistent with the hypothesis that neuroinflammation in the visceral-emotional neuraxis contributes to antireward which motivates alcohol, and opioid, dependence (Fig. 1A-B; Sup. Figure 7)6. In brief, this hypothesis suggests that neuroinflammation in the NTS and amygdala stimulates antireward which contributes to negative reinforcement. This study does not only provides evidence of neuroinflammation in the NTS in acute and protracted alcohol withdrawal, but also an understanding of the emergence of this neuroinflammation and its relation to neurotransmission and AWS. Improved understanding of such processes in alcohol withdrawal lends insights into targets that may mitigate inflammation, decrease antireward in AWS, and treat substance dependence.