Decision letter: Modified viral-genetic mapping reveals local and global connectivity relationships of ventral tegmental area dopamine cells

Field: Neuroscience (Life Sciences)

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Dopamine cells in the ventral tegmental area (VTADA) are critical for a variety of motivated behaviors. These cells receive synaptic inputs from over 100 anatomically defined brain regions, which enables control from a distributed set of inputs across the brain. Extensive efforts have been made to map inputs to VTA cells based on neurochemical phenotype and output site. However, all of these studies have the same fundamental limitation that inputs local to the VTA cannot be properly assessed due to non-Cre-dependent uptake of EnvA-pseudotyped virus. Therefore, the quantitative contribution of local inputs to the VTA, including GABAergic, DAergic, and serotonergic, is not known. Here, I used a modified viral-genetic strategy that enables examination of both local and long-range inputs to VTADA cells in mice. I found that nearly half of the total inputs to VTADA cells are located locally, revealing a substantial portion of inputs that have been missed by previous analyses. The majority of inhibition to VTADA cells arises from the substantia nigra pars reticulata, with large contributions from the VTA and the substantia nigra pars compacta. In addition to receiving inputs from VTAGABA neurons, DA neurons are connected with other DA neurons within the VTA as well as the nearby retrorubal field. Lastly, I show that VTADA neurons receive inputs from distributed serotonergic neurons throughout the midbrain and hindbrain, with the majority arising from the dorsal raphe. My study highlights the importance of using the appropriate combination of viral-genetic reagents to unmask the complexity of connectivity relationships to defined cells in the brain. Editor's evaluation By addressing shortcomings in rabies viral-mediated labeling of monosynaptic inputs to ventral tegmental area dopamine neurons, this study provides a previously unattained precision of local inputs to VTA dopamine neurons. Main findings include the preservation of a medial to lateral topography in the projection patterns within VTA microcircuitry, prominence of inhibition of DA neurons from the substantial nigra pars reticulata (SNr), DA-DA transmission, and inputs from raphe serotonin neurons. The precise local VTA connectivity described here is important for identifying how DA neurons compute reward, prediction, and movement-related signals during behavior. https://doi.org/10.7554/eLife.76886.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Ventral tegmental area (VTADA) cells mediate a variety of motivated behaviors, including reward and aversion (Björklund and Dunnett, 2007; Bromberg-Martin et al., 2010; Cohen et al., 2012; Lammel et al., 2012; Wise, 2004). Substantial effort has been made to map the brain regions and cell types that provide input to and receive projections from VTADA cells, a critical step toward understanding how VTADA cells effect behavioral consequences in response to a variety of stimuli. The recent advent of one-step rabies virus (RABV) has enabled the mapping of inputs onto defined cell types (Wickersham et al., 2007). This strategy was employed nearly a decade ago to map brain-wide inputs to DA cells in the VTA and the adjacent substantia nigra pars compacta (SNc) (Watabe-Uchida et al., 2012). However, DA neurons in the VTA and SNc are not homogenous, but rather are heterogenous in their molecular signatures, projection patterns, physiological properties, and behavioral functions (Kim et al., 2016; Lammel et al., 2008; Lammel et al., 2011; Lammel et al., 2012; Lerner et al., 2015). We therefore designed a method, Tracing the Relationship between Inputs and Outputs (TRIO) to map the input–output relationship of projection-defined VTADA cells (Beier et al., 2015; Lerner et al., 2015; Schwarz et al., 2015). TRIO revealed that different subtypes of VTADA cells received biased inputs, and that global input–output maps could be used to infer the behavioral contribution of particular input sites, such as the cortex (Beier et al., 2015). While these and more recent studies have mapped global inputs to VTADA and SNcDA cells (Beier et al., 2015; Faget et al., 2016; Lerner et al., 2015; Menegas et al., 2015; Watabe-Uchida et al., 2012) each study has the same limitation that only inputs located at a distance from the injection site in the midbrain can be assessed. This is because the Cre-dependent TVA protein that facilitates EnvA-mediated infection is also expressed at low levels in non-Cre-expressing cells near the site of injection (Miyamichi et al., 2013). Therefore, RABV virions that are pseudotyped with the EnvA protein can infect both Cre-expressing starter cells and non-Cre-expressing cells nearby that express low levels of TVA through leaky, non-Cre-dependent expression. This leaky gene expression is due to incomplete suppression of transcription/translation of TVA. While this may not be an issue with fluorescent molecules or chemogenetic effectors such as DREADDs (Botterill et al., 2021), in cases where only small amounts of a gene product are required to exert function, such as TVA, the problem becomes magnified. Only a single functional unit – presumably three TVA molecules bound to an EnvA trimer (Alsteens et al., 2017) – is required to enable infection of EnvA-pseudotyped RABV. Injection of adeno-associated viruses (AAVs) expressing TVA and RABV-G 2 weeks prior to injection of EnvA-pseudotyped RABV can result in thousands of infection events near the injection site, even in Cre− animals that should not express TVA (Beier et al., 2015). Notably, these infections do not occur if AAVs are not injected, demonstrating that the off-target labeling is TVA-mediated. Therefore, whether RABV-labeled neurons near the injection site are bona-fide inputs to starter cells or cells that are infected via the viral inoculum in previous VTADA mapping experiments cannot be distinguished. One solution to this problem is to reduce the efficiency of TVA-mediated infection. A mutant version of TVA with a single-point mutation (Glu66 → Thr, or TC66T) that exhibits only 10% of the efficiency of the wild-type TVA dramatically reduces local off-target labeling (Miyamichi et al., 2013; Rong et al., 1998). Using this variant enables analysis of the quantitative contribution of local inputs to defined cell types, such as VTADA neurons. Here, I quantified the inputs to VTADA neurons, comparing long-distance inputs to local inputs. Specifically, I quantified the percent of virally labeled inputs to VTADA cells that were located in 57 different brain regions. I used the TC66T variant in place of the wild-type TVA, TCB, in my tracing studies and found that almost half of the inputs to VTADA cells are located near the VTA; many of these represent inputs that were missed or may have been misquantified in the previous studies. I then performed quantitative analyses to identify the biases that local inputs have onto different sets of VTADA cells. I then examined the sources of local GABAergic input to VTADA cells, explored potential connections between DA cells in the ventral midbrain, and quantified the sources of serotonergic inputs to VTADA cells. My analysis is the first of its kind to detail the quantitative contributions of inhibition and neuromodulatory influence from local cells in the midbrain and hindbrain onto VTADA cells, providing a comprehensive global picture of the main cell populations that influence and control VTADA cells. Results I performed one-step RABV mapping using TC66T in place of TCB (Miyamichi et al., 2013; Figure 1A). I first injected a combination of Cre-dependent AAVs encoding the RABV glycoprotein, RABV-G, and TC66T into the VTA of DAT-Cre mice that express the Cre recombinase in DA neurons. Two weeks later, I injected EnvA-pseudotyped, G-deleted, GFP-expressing RABV into the VTA. I then allowed 5 days for RABV spread to input cells before terminating the experiment. Figure 1 with 2 supplements see all Download asset Open asset Use of a modified viral-genetic strategy to map local and global inputs to VTADA neurons. (A) Strategy for viral mapping. On day 1, a combination of adeno-associated viruses (AAVs) expressing the mutated TVA protein fused to mCherry, TC66T, and RABV-G were injected into the VTA. Two weeks later, EnvA-pseudotyped RABV expressing GFP was injected into the VTA. Animals were sacrificed 5 days later. (B) Representative image of local virally labeled inputs at anterior–posterior −3.9 mm from bregma. Scale, 1 mm. (C) Representative image of local virally labeled inputs at anterior–posterior −4.2 mm from bregma. Scale, 1 mm. (D) Representative image of a midbrain section including starter cells in the VTA as well as local inputs. Scale, 1 mm. (E) Starter cell distributions for each of the four experiments. The center of each oval represents the center of mass of starter cells, and the horizontal and vertical radii of the oval represent 1 SD of starter cells in the medial–lateral and dorsal–ventral axes, respectively. (F) The fraction of starter cells located in each region in the ventral midbrain. 58% ± 6% were located in the parabrachial pigmented nucleus (PBP), 27% ± 6% in the paranigral nucleus (PN), and 5% ± 2% in the IF nuclei of the VTA, 8% ± 3% in the SNc, 2% ± 1% in the Rli, and 1% ± 0.3% in all other regions. (G) Linear regression of RABV-labeled inputs vs. number of starter cells. (H) The convergence index, or ratio of inputs to starter cells, for RABV input mapping experiments using TCB as reported in Beier et al., 2015, or TC66T. Only long-range inputs were considered to enable direct comparison between conditions. p = 0.42, 95% CI −21.25 to 10.24, unpaired t-test; n = 4 for each condition. (I) The percentage of each local and long-distance input relative to all RABV-labeled inputs is shown. (J) The fraction of inputs that were long-range inputs (54% ± 3%), as mapped by us in the previous studies, and local inputs (46% ± 3%), is shown. All error bars in this figure and others throughout this manuscript represent ±1 standard error of the mean (SEM) (Beier et al., 2015). Control experiments in non-Cre-expressing mice were performed to examine the local background of non-Cre-mediated, TC66T-facilitated infection. We previously observed using TCB that, on average, 3,183 cells were infected in a non-Cre-dependent, TCB-facilitated fashion per mouse (Beier et al., 2015). Using TC66T, I found an average of 2.67 cells per brain (Figure 1—figure supplement 1). This background infection was indistinguishable from controls where no AAV was injected (Figure 1—figure supplement 1). When experiments were performed in DAT-Cre mice, between 900 and 2,400 starter cells were observed, and an average of 23,000 total input cells were labeled per experiment (Figure 1B, C). I next examined the location of starter neurons within the ventral midbrain (Figure 1D–F). The majority of starter cells were located within the parabrachial pigmented nucleus (PBP: 58% ± 6%) and paranigral nucleus (PN: 27% ± 6%) of the VTA. The rest of the starter neurons were located within the medial aspect of the SNc (8% ± 3%), interfascicular nucleus of the VTA (IF: 5% ± 2%), and rostrolinear nucleus (Rli: 2% ± 1%). These subregions are largely defined by connectivity patterns and cytoarchitectural features of these cells. While not exclusive, DA neurons that project to different forebrain sites are largely located within different substructures of the VTA (Oades and Halliday, 1987; Trutti et al., 2019). For example, DA neurons projecting to the medial prefrontal cortex and medial shell of the nucleus accumbens are mostly located in the PN, IF, and medial PBP; those projecting to the amygdala are interspersed throughout the PBP; and those projecting to the lateral shell of the nucleus accumbens are largely located in the lateral PBP (Lammel et al., 2008). I then examined the ratio of inputs to starter cells, also known as the convergence index, and compared it to tracing performed using TCB. I used only the long-range inputs quantified in my previous studies in order to enable comparison of TC66T with TCB (Beier et al., 2015; Beier et al., 2019). The number of RABV-labeled inputs scaled with the number of starter cells, as expected (Figure 1G). The average convergence index using TC66T was approximately 8, which was not significantly different from tracing performed using TCB (Figure 1H). Notably, this was also similar to the convergence index of 7 reported by the study from Watabe-Uchida et al. using a nonmutated TVA that reported only a small number of inputs local to the VTA (Watabe-Uchida et al., 2012). However, in my study nearly half (46%) of the inputs to VTADA cells arose from local regions that were not quantified in my previous analysis. Though Faget et al. excluded the VTA, SNc, substantia nigra pars reticulata (SNr), red nucleus (RPC), and interpeduncular nucleus (IPN) likely for precisely the reason that concerned us, these regions combined to yield 20% of the total inputs to the VTA. This included about 10% of the total inputs within the VTA itself, 6% in the adjacent substantia nigra, and 4% in other nearby regions. However, in addition to this 20% of inputs in regions immediately adjacent to the injection site, given that AAVs can exhibit substantial spread from the injection site I was concerned that non-Cre-dependent, EnvA-mediated infection extended beyond these regions to several other midbrain regions. To assess if this was indeed a problem, I compared my data to that published by Faget et al. for three separate groups of brain sites: (1) those excluded in the previous study, (2) midbrain/hindbrain regions excluded by us previously but not by the previous study, and (3) long-range inputs included by both studies. Using dimensional reduction techniques such as Uniform Manifold Approximation and Projection (UMAP) to compare the overall labeling patterns within these three groups, I found that my data mixed with that from the Faget et al. study for long-range inputs (comparison 3) suggesting that the data were comparable. However, data from the two studies segregated for both the excluded (comparison 1) and questionable (comparison 2) brain regions (Figure 1—figure supplement 2). Data mixing in UMAP space supports the conclusion that the variances between the datasets are similar, and thus the datasets can be compared. In contrast, separation of the two datasets in UMAP space suggests that these datasets cannot be readily compared as the variances between input sites in the datasets are different. These results support the likelihood that the method of using a modified, less efficient version of TVA is necessary to properly examine the local input landscape in the ventral midbrain. Local inputs to VTADA cells have differential associations with long-range inputs Given the large fraction of total inputs to VTADA that are local, I wanted to explore the nature of these connections and by extension, how they may influence VTADA cells. Long-range inputs to DA neurons measured using RABV tracing have been published several times (Beier et al., 2015; Faget et al., 2016; Watabe-Uchida et al., 2012). More recently, DA neurons have been subdivided by output site, and the inputs to particular subpopulations compared to one another (Beier et al., 2015; Beier et al., 2019; Derdeyn et al., 2021; Lammel et al., 2012; Menegas et al., 2015). These analyses have enabled us to understand how input patterns relate to one another, and which inputs are biased onto which sets of VTADA cells. However, much less is known about the patterns of innervation from local inputs. Previous analyses using dyes and nonviral tracers have been limited by the ambiguity of not knowing if nearby neurons were labeled from the initial inoculum, or from retrograde tracer spread, and/or if labeled inputs formed synaptic connections with DA cells or rather to other cells or even nearby brain regions. It is generally more difficult to unambiguously label local connections than long-distance ones. This is because previous viral approaches to label monosynaptic inputs have been hampered by off-target AAV expression, while anterograde methods also suffer from a lack of specificity, as it is difficult to exclusively target small regions in the midbrain without contamination from nearby brain regions, including the VTA itself. My approach here was to leverage my previous analyses of long-range inputs to explore with which long-range inputs each set of local inputs associates. This analysis would tell us if local inputs have a medial/lateral bias within the VTA and by extension, if they preferentially target particular sets of DA cells that are located in different subregions of the VTA. If the percentage of inputs from local inputs covaries with long-range inputs that target lateral subregions of the VTA, I would expect the local inputs to also target lateral aspects of the VTA, and if they covary with medial-targeting long-range inputs, I would expect a medial bias in VTA targeting. I first used UMAP to identify the clustering relationships of both local and long-distance input sites based on all 57 quantified input (22 long distance and 35 local) regions in my RABV tracing data (Figure 1I). This approach harnesses intrinsic variation within the dataset, which likely arises from slight differences in injection location between animals. UMAP clusters regions based on those with similar variance across the mice in the dataset; regions that covary across mice likely innervate similar populations of cells and project to similar regions of the VTA. I found that local input sites intermingled with long-distance inputs (Figure 2A). This is expected, and suggests that local inputs co-organize with long-distance inputs, rather than being a separate set of circuits. Given that I used only four brains for these UMAP analyses, which is a relatively small number for accurately capturing variance in the dataset, I wanted to test how robust the UMAP embedding was in capturing the relationships between brain regions. I recently performed a similar analysis on a 76-brain dataset that included long-distance RABV-labeled inputs to VTA cells based on projection, neurochemical phenotype, or a combination of these factors (Derdeyn et al., 2021). Since UMAP embeddings can be somewhat stochastic due to their reliance on initial seeding conditions, I also computed the distance between points relative to the maximum distance between any two points in each embedding, over 20 embeddings, then averaged across all embeddings for my four brain dataset, as done previously for the 76 brain dataset (Derdeyn et al., 2021). I found that the region associations were robust, with only three long-range input regions – the nucleus accumbens medial shell (NAcMed), nucleus accumbens core (NAcCore), and extended amygdala (EAM) associating with different groups of inputs across conditions (Figure 2B, C). While the results were not identical, these data suggest that my four brain dataset could be compared with reasonable confidence to my previous dataset. Similar to my 76 brain dataset, I observed three clusters of input regions (Figure 2D). The first is the set of brain regions that predominately project lateral or dorsal to the VTA (Derdeyn et al., 2021). The regions that project laterally include inputs from the basal ganglia such as the dorsal striatum (DStr), nucleus accumbens lateral shell (NAcLat), globus pallidus external segment (GPe), and cortex. Those that project dorsally include the entopeduncular nucleus (EP), zona incerta (ZI), and deep cerebellar nuclei (DCN). The local inputs that associate with this cluster include mostly those located dorsally and laterally to the VTA, including the periaqueductal gray (PAG), midbrain reticular nucleus (mRT), interstitial nucleus of Cajal (InC), subpeduncular tegmental nucleus (SPTg), subbrachial nucleus and substantia nigra pars To test if the local inputs I indeed laterally to the VTA, I mapped the relative innervation by the and of the VTA across the medial–lateral To do I used data from the with (Figure Beier et al., 2019). In the a GFP-expressing AAV is injected into a site, and labeled are throughout the brain. By on the medial–lateral of the VTA, one can assess if each input site preferentially particular of the VTA or I two or three for each input brain I indeed observed that three inputs in cluster 1 – the and – a lateral bias in the VTA (Figure Figure supplement 1). Figure 2 Download asset Open asset of local inputs to VTADA cells with long-range inputs. (A) regions are in Uniform Manifold Approximation and Projection (UMAP) with to across mouse represent inputs with similar patterns of variation across the (B) of across 20 UMAP for the RABV input including only long-distance inputs to enable comparison to 76-brain dataset, where only long-distance inputs were (C) of from an UMAP analysis of 76 brain dataset, from Derdeyn et al., for of (D) of for the RABV input including both local and long-distance inputs. are to are to the clusters in the UMAP Figure with 1 supplement see all Download asset Open asset from local inputs the medial–lateral of the VTA. (A) of how relative innervation of the medial–lateral of the VTA was Scale, 1 mm. used for of the data in were from the (B) innervation across the medial–lateral for Uniform Manifold Approximation and Projection (UMAP) cluster (C) innervation across the medial–lateral for UMAP cluster (D) innervation across the medial–lateral for UMAP cluster (E) innervation across the medial–lateral for each UMAP the average of the three quantified input regions in bars represent 1 standard error of the mean The cluster of inputs mostly regions that project across the VTA, including the lateral and nucleus of the (Derdeyn et al., 2021). The local inputs associating with this cluster include inputs from all of the subregions of the VTA PN, and and other inputs located the raphe medial nucleus as well as those located laterally to the such as the red nucleus (RPC), ventral tegmental nucleus and nucleus While more in their the and show a more projection across the medial–lateral of the VTA (Figure The cluster of inputs mostly those that project within the VTA. This the medial and lateral nucleus and Local inputs associating with this include such as the interpeduncular nucleus raphe nucleus near the such as the nucleus tegmental nucleus more lateral such as the retrorubal and tegmental nucleus or inputs with such as the tegmental nucleus and the dorsal nucleus of the lateral The and all a medial bias in the VTA (Figure The in medial–lateral the three clusters was averaged across the three regions and (Figure These data in suggest a of inputs to the VTA that to both long-distance and local inputs, inputs based on their projection or laterally within the VTA, as have found previously with long-range inputs (Beier et al., 2015; Beier et al., 2019; Derdeyn et al., 2021). Here, I these by including all inputs to the VTA, of distance from injection site. My approach thus us to infer connectivity biases of local inputs that are difficult to using other viral or nonviral Local and distributed GABAergic inhibition to VTADA cells I next wanted to assess the location of local GABAergic inhibition to the VTA. The majority of studies of inhibition to VTADA cells have on neurons within the VTA or on an anatomically defined to as the tegmental nucleus et al., et al., et al., While efforts have been made to map inputs, including cell inputs to the VTA et al., 2007; and and 2010; et al., these methods the connectivity information by RABV. studies have performed functional mapping of local inhibition to the VTA. For example, of local GABAergic cells in the VTA DA cells and is et al., 2019; et al., 2012; et al., 2012). These GABAergic populations are likely as they receive differential innervation from input and influence different sets of DA cells et al., Notably, DA neurons located more laterally in the VTA received a input than DA cells located more et al., the likelihood of a DA cell being by an to the location of the of those cells, which the number of GABAergic these cells received et al., 2012). Notably, the GABAergic was for located in the than the SNc, that the location of connections in the ventral midbrain the of whether they were or These data all to the of different subpopulations of local neurons and local GABAergic innervation different populations of DA cells. However, much about the sources of GABAergic inputs to DA cells and how they may different sets of cells in the VTA. A necessary step to understanding how inhibition subpopulations of DA cells is to identify all of the sources of local I therefore performed local viral tracing experiments in combination with fluorescent in the GABAergic and in order to provide a quantitative map of inhibition onto VTADA cells (Figure Figure 4 Download asset Open asset Local and distributed GABAergic inhibition to VTADA cells. (A) Strategy for viral mapping combined with (B) fluorescent in with for and (C) section of the midbrain near the injection site. = red = for = TC66T. Scale, 1 mm. (D) of starter cell location in different midbrain ± 1% were located within the parabrachial pigmented nucleus of the VTA ± 1% in the VTA PN, ± 2% in the SNc, 6% ± 1% in the VTA IF, 3% ± 1% in the Rli, and 2% ± 0.3% in all other (E) image of labeled cells, with the to a Scale, (F) of GABAergic input location throughout the ventral midbrain. to the total of local GABAergic inputs located in that particular Data from each are reported The injection in the (G) the total fraction of GABAergic inputs located in each Data from both are ± of all inputs are from the ± from the VTA SNc ± VTA IF ± VTA ± ± ± ± ± ± ± ± ± ± and ± in all other regions. To assess how this local input map would relate to global maps in Figure 1, I first compared the location of starter neurons in the VTA between experiments (Figure The majority of starter neurons were located within the PBP nucleus of the VTA ± by VTA ± SNc ± 2%), VTA IF ± and ± 1%). This was a similar to the mapping experiments (Figure direct comparison of these I all