Midbrain dopamine (mDA) neurons comprise diverse cells with unique innervation targets and functions. This is illustrated by the selective sensitivity of mDA neurons of the substantia nigra compacta (SNc) in patients with Parkinson’s disease, while those in the ventral tegmental area (VTA) are relatively spared. Here we used single nuclei RNA sequencing (snRNA-seq) of approximately 70,000 mouse midbrain cells to build a high-resolution atlas of mouse mDA neuron diversity at the molecular level. The results showed that differences between mDA neuron groups could best be understood as a continuum without sharp differences between subtypes. Thus, we assigned mDA neurons to several “territories” and “neighborhoods” within a shifting gene expression landscape where boundaries are gradual rather than discrete. Based on the enriched gene expression patterns of these territories and neighborhoods, we were able to localize them in the adult mouse midbrain. Moreover, because the underlying mechanisms for the variable sensitivities of diverse mDA neurons to pathological insults are not well understood, we analyzed surviving neurons after partial 6-hydroxydopamine (6-OHDA) lesions to unravel gene expression patterns that correlate with mDA neuron vulnerability and resilience. Together, this atlas provides a basis for further studies on the neurophysiological role of mDA neurons in health and disease.
This study investigated transcriptional profiles of midbrain dopamine neurons using single nucleus RNA (snRNA) sequencing. The authors found more nuanced subgroups of dopamine neurons than previous studies, and identified some genes that are preferentially expressed in subpopulations that are more vulnerable to neurochemical lesions using 6-hydroxydopamine (6OHDA). The reviewers found the results are solid, and the study is overall valuable, providing critical information on the heterogeneity and vulnerability of dopamine neurons although the scope is somewhat limited because the result with snRNA is similar to previous results and cell deaths were induced by 6OHDA injections.
Dopamine (DA) neurons localized in the ventral midbrain (mDA neurons) comprise the brain’s largest and most extensive source of DA neurotransmission (Björklund and Dunnett, 2007). These neurons are known to perform a wide variety of functions, and distinct mDA neuron assemblies are known to play diverse functional roles. For example, mDA neurons of the substantia nigra pars compacta (SNc) are mainly associated with controlling motor functions. In contrast, those in the ventral tegmental area (VTA) and retrorubral field (RRF) have been associated more strongly with motivation, learning, and cognition (Björklund and Dunnett, 2007). Notably, mDA neurons are also characterized by their varying impact on disease. For example, the hallmark motor symptoms of Parkinson’s disease (PD) result from the degeneration of SNc neurons. On the other hand, mDA neurons in the VTA have been linked to neural pathways that are affected in addiction, schizophrenia, attention deficit hyperactivity disorder, and other psychiatric conditions (Björklund and Dunnett, 2007; Garritsen et al., 2023; Poulin et al., 2020).
The emergence of single-cell RNA sequencing and other methods that resolve differences in, e.g., chromatin state between single cells, have increased our understanding of mDA neuron diversity (Garritsen et al., 2023; Poulin et al., 2020). This diversity is of great significance in normal brain functions and disease, e.g., in forming multiple DA-controlled circuits enabling versatile DA neurotransmission and explaining different vulnerabilities of distinct groups of mDA neurons. However, methods dependent on capturing certain cell types, such as neurons sensitive to cell dissociation, can result in biased datasets where the most sensitive neuron groups are underrepresented. In addition, as the scRNAseq studies of mDA neurons published so far have analyzed a relatively low number of mDA neurons, the resolution of these datasets is still quite limited. Single-nucleus RNA sequencing (snRNA-seq) is a more efficient and reliable sampling alternative for individual neurons (Krishnaswami et al., 2016). Indeed, snRNAseq of human mDA neurons, enriched by sorting for NR4A2-labelled nuclei, yielded a 180-fold increase in absolute neuronal numbers compared to similar datasets based on scRNAseq (Kamath et al., 2022).
Our present study combined fluorescent sorting and snRNA-seq to explore the diversity of a large number of mouse mDA neurons, resulting in a comprehensive atlas of mDA neuron populations. By additionally analyzing nuclei from a partial toxin-induced mDA neuron degeneration model, we were able to investigate questions of selective and variable vulnerability among these distinct populations. Our findings provide a foundation for future investigations into the projections and functions of molecularly distinct mDA neuron groups.
Single nuclei RNA sequencing of the ventral midbrain
Perhaps partly because of the challenges in dissociating and isolating whole mDA neurons, previous studies have only analyzed a relatively small fraction of all mDA neurons from each dissected brain (Dougalis et al., 2012; Hook et al., 2018; Manno et al., 2016; Poulin et al., 2014; Tiklova et al., 2019). This raises concerns about the potential for sampling bias in those studies. To overcome this limitation, we used single nuclei RNA sequencing (snRNA-seq) for mice expressing nuclear mCherry under the control of the Slc6a3 (DAT, dopamine transporter) promoter (Figure 1A-C)(Hupe et al., 2014). Nuclei isolated by fluorescent sorting were sequenced using the 10X Genomics Chromium platform (10Xv3). Using this strategy, more than 20% of all mDA neurons were consistently collected and sequenced from each brain, an improvement of over 50X from our previous study (Tiklova et al., 2019).
In addition to assessing the diversity among the mDA neuron population, we also aimed to analyze how diverse mDA neuron groups were affected by cellular stress. Accordingly, mice (n=6) were unilaterally injected with 6-hydroxydopamine (6-OHDA, 0.7μg or 1.5μg) into the medial forebrain bundle, which leads to partial cell death of mDA neurons on one hemisphere of the brain (Figure 1A). To determine differential survival among diverse mDA neurons, nuclei from intact and lesioned hemispheres were collected six weeks after lesioning when the acute phase of cell death had already occurred. To avoid omitting low red fluorescent protein (RFP)-expressing nuclei from analysis, a relatively relaxed gating in fluorescently activated nuclear sorting (FANS) was used, resulting in an approximately equal number of mCherry+ and mCherry− nuclei from each sample per brain (Figure 1–figure supplement 1). After snRNAseq and quality control, 36,051 nuclei from the intact and 32,863 from the lesioned hemisphere were retained for bioinformatic analysis (see Methods, Figure 1-figure supplement 2).
Sequencing data were subjected to normalization, variance stabilization, and dimensional reduction and then visualized by uniform manifold approximation (UMAP; Figure 1D). Louvain clustering was adopted, dividing all nuclei into 26 clusters (Methods, Figure 1–figure supplement 3). A hierarchical clustering dendrogram illustrating the relationship between clusters was also constructed, and cluster-specific markers were identified and used to annotate previously known cell types (Figure 1D, E; Figure 1–figure supplement 3, Supplementary file 1). This analysis revealed the presence of different cell types, including oligodendrocytes; oligodendrocyte precursor cells; astrocytes; and various types of neurons known to be localized in the midbrain but also with some inclusion of cell types known to be localized both in the forebrain and hindbrain. Neuronal clusters were mainly comprised of different types of glutamatergic (GLU), gamma-aminobutyric acid (GABA), and dopamine neurons (Figure 1–figure supplement 3). This dataset is accessible via: http://184.108.40.206:5005/.
Dopamine neuron diversity
Plotting known markers for mDA neurons in the UMAP revealed that a substantial number of all analyzed nuclei, clusters 1, 4, 7, and 10, were derived from mDA neurons (Figure 1–figure supplement 3). To focus the analysis on dopamine neurons, 33,052 nuclei expressing above threshold levels of either Th or Slc6a3 (Th+/DAT+) were selected for further computational analysis. Most of these filtered nuclei, visualized in a de novo UMAP, were positive for several genes known to be expressed in mDA neurons (Figure 2A, B). This dataset is accessible via: http://220.127.116.11:5006/.
K-means clustering, followed by hierarchical clustering, was used to divide Th+/DAT+ nuclei into 71 unique clusters whose relationship was also visualized in a dendrogram (Figure 2C). Although the analysis of gene expression in a dot-plot confirmed that most nuclei were dopaminergic, a smaller number of nuclei distributed in clusters expressing low levels of mDA neuron markers showed characteristics of GABAergic, glutamatergic neurons, or maturing myelinating oligodendrocytes (mODC). A few clusters remained unassigned (Figure 2A, C). Almost all clusters expressing genes required for synthesis and handling DA (dopaminergic) also expressed mDA neuron markers, including En1, Nr4a2, and Pitx3, the latter at a lower abundance, except one that expressed hypothalamic dopaminergic markers, including, Satb2, Prlr, and Gad1 (Figure 2A, C; referred to as HyDA neurons). The mDA neuron clusters form a major separate branch in the dendrogram, while HyDA neurons constitute a more distant branch (Figure 2C). Two closely related clusters (#12 and #26) form a distinctly separate group, predominantly (∼95%) composed of nuclei from the lesioned hemisphere. Upon closer inspection, they share the same parent node with mDA clusters marked by high abundance and expression level of Th. However, the expression of Th and other dopaminergic markers in these two lesion-specific clusters is relatively low (Figure 2C). The lesion-specific clusters and the consequences of lesioning will be further described below.
mDA neuron territories and neighborhoods
Illustration of diversity among different sub-categories of cells, such as mDA neurons, using a dendrogram can give a somewhat misleading impression that individual branching endpoints (referred to as “leaf nodes”) would represent transcriptionally distinct cellular subtypes. However, it may be more accurate to consider differences between closely related mDA neuron groups as a gradually shifting continuum within a gene expression landscape. Thus, we found using a hierarchical definition of the relationship more accurate when using the dendrogram to describe diversity. By this definition, mDA neurons are distinct and constitute a neuron type, clearly different from, for example, HyDA and other types of GABAergic and glutamatergic neurons. Within individual neuron types, such as the closely related mDA neurons, we define the next hierarchical level as “territories,” while “neighborhoods” is the most highly resolved level of diversity that, based on the bioinformatic analysis, can be defined meaningfully. According to this classification, each one of the territories and neighborhoods should constitute specified adjacent leaf nodes of the cluster tree with a common ancestor node in the hierarchical dendrogram. Notably, leaf nodes of a neighborhood or territory need not be sibling nodes.
Furthermore, they should be distinguishable by clearly defined gene expression signatures and occupy a relatively well-defined space, i.e., not scattered, in the UMAP. This classification subdivided mDA neurons into 7 territories and 16 neighborhoods (Figure 3A, C; Figure 3–figure supplement 1). The territories were named according to selected distinguishing genes (Sox6, Gad2, Fbn2, Pcsk6, Pdia5, Ebf1, and Otx2) and can be identified by additional markers as indicated in Figure 3 (see also Figure 3–figure supplement 1). It should be noted that these markers were typically not unique for a particular territory but, in combination, defined gene signatures that distinguished territories from each other.
The transcription factor Sox6 is known to be expressed in mDA neurons of the SNc and in some neurons of the VTA (Luppi et al., 2021; Panman et al., 2014; Poulin et al., 2014). The Sox6-territory included approximately one-third of all sequenced mDA neuron nuclei (Figure 3A). This territory expressed other previously defined SNc markers, including Kcnj6 (Girk2) (Figure 3–figure supplement 1) and additional genes representing a gene signature that included Col25a1 and Tigar, as well as Nwd2, Pex5l, Cdh11, and Serpine2 (Figure 3C; Figure 3–figure supplement 1). In contrast, expression of Calb1, encoding the known VTA marker Calbindin 1, was either absent or was expressed at low levels in Sox6-territory nuclei (Figure 3C). This almost non-intersecting, mutually exclusive expression of Sox6 and Calb1 defined a striking major division among all analyzed mDA neuron nuclei, following the neuroanatomical division of mDA neurons into SNc and VTA (Figure 3B).
The remaining six territories consisting of approximately 15,000 mDA nuclei were more diverse and, based on the prominent expression of Calb1 and absence of high Sox6 expression, presumably represented mDA neurons localized outside SNc. Indeed, these territories expressed additional VTA markers, including Otx2 (Figure 3C). Expression of mDA neuron markers such as Th, Ddc, and Slc6a3 was particularly weak in the Gad2-territory, which displayed typical features of GABAergic neurons, including Gad2 and Slc32a1.
Our next focus was to describe mDA neuron diversity within territories by dividing them into more highly resolved neighborhoods (NHs, Figure 3C, Figure 3–figure supplement 1). In rare cases, the smallest hierarchical domain for a neighborhood can be one dendrogram leaf node. Still, most consist of at least two leaf nodes. Four neighborhoods were defined within the Sox6-territory, two of which expressed high levels of Aldh1a1, a previously recognized marker for some SNc neurons (NH1 and NH4). Sox6+ neighborhoods were also distinguished by, Vcan and Col11a (NH1 and NH2), Anxa1 and Fam19a4 (NH1 and NH4), Grin2c, Ndnf and Vill (NH3 and NH4), and Aldh1a7 (NH4), among other markers.
The other six territories were each divided into two neighborhoods (Figure 3C, Figure 3-figure supplement 1). Gad2_NH1 expressed Chrm2 and Zfp536, whereas Gad2_NH2 expressed Megf11, Zeb2, and Met. Fbn2_NH1 was specified by Rxfp1 and Hs3st2 while Fbn2_NH2 by Col23a1, Dsg2 and Ism1. Pcsk6_NH1 was positive for Cald1, Pde11a and Cpne2. Pcsk6_NH2 contained Tacr3+ Aldh1a1− cells, which also expressed Igf1. There were also Tacr3+ Aldh1a1+cells, but they belonged to Otx2_NH1, which also expressed Grp, Eya1, and Plekhg1. Otx2_NH2, which was low for Slc6a3, was identified by Csf2rb2, Nfib, and Nfia. The Pdia5_NH1 expressed Jph1 Cpne5, and Pdia5_NH2 was positive for Npy1r, Postn, and Wnt7b. The Ebf1_NH1 expressed Col24a1, whereas Ebf1_NH2 was positive for Vip.
Anatomical distribution of diverse mDA neurons
Next, we investigated the anatomical distribution of the seven mDA neuron territories and their neighborhoods in the adult mouse midbrain. We based our spatial mapping on Allen Mouse Brain Atlas (mouse.brain-map.org) and on our own in situ hybridization and immunohistochemical analyses. In situ hybridization experiments used single probes on individual sections combined with immunohistochemistry. We also used an in situ sequencing strategy that included 49 selected probes, hybridized simultaneously on seven sections across the entire midbrain (Figure 4B-E, Figure 4–figure supplements 1-3, Supplementary files 2 and 3)(Gyllborg et al., 2020; Krzywkowski et al., 2017; Qian et al., 2020). We also used coronal in situ Allen Mouse Brain Atlas data (atlas.brain-map.org) to guide when relevant data were available. For the anatomical localization of territories and neighborhoods, we mainly followed Paxinos and colleagues (Fu et al., 2011) and the coronal reference atlas from Allen Mouse Brain Atlas. Together these analyses provided the basis of the schematic in Figure 4F, showing the distribution of different territories in rostrocaudal levels of the midbrain.
Consistent with previously published results (Panman et al., 2014; Poulin et al., 2014), Sox6 territory neurons were confined to the SNc and its subdomains (Figure 4F, Figure 4– figure supplement 1, See also Supplementary Results). Cells of the Ebf1 territory were scattered within the periaqueductal grey (PAG), parabrachial pigmented nucleus (PBP), rostral linear nucleus (RLi), and para nigral nucleus (PN) (Figure 4F, Figure 4–figure supplement 2), consistent with previous single-cell studies identifying Vip-expressing mDA neurons within the PAG (Dougalis et al., 2012; Hook et al., 2018; Manno et al., 2016; Poulin et al., 2014; Tiklova et al., 2019). Cells of the Pdia5 territory were mapped to the dorsal VTA and the most lateral SN tip (Figure 4F, Figure 4–figure supplement 2). Consistent with previous analyses (Hook et al., 2018; Manno et al., 2016; Poulin et al., 2014; Saunders et al., 2018; Tiklova et al., 2019), Otx2 territory was localized in the ventral tier of VTA, including PBP, PN, and interfascicular nucleus (IF), but some cells were also found in RLi and caudal linear nucleus (CLi) (Figure. 4, Figure 4–figure supplement 2). Gad2 territory was primarily localized in the most medial VTA, especially to IF, RLi, and CLi (Figure 4A, F, Figure 4– figure supplement 3), supporting findings from previous studies (Garritsen et al., 2023; Poulin et al., 2020). A few scattered neurons matching the smallest territory, Fbn2, were detected in the anteromedial PBP (Figure 4A, F, Figure 4–figure supplement 3). Finally, neurons of the Pcsk6 territory were localized broadly in VTA, although being most numerous in PBP (Figure 4F, Figure 4–figure supplement 3).
The analysis of the anatomical localization of neighborhoods is described further in Supplementary Results.
Selective vulnerability to 6-OHDA lesioning
As mentioned, nuclei for snRNAseq were sampled from animals that received a unilateral injection of 6-OHDA in the medial forebrain bundle. To assess the extent of mDA neuron fiber loss in the lesioned hemisphere, striatal tissue was also isolated, sectioned, and analyzed by DAT-binding autoradiography. Animals with partial lesions were chosen for sampling and RNA sequencing of nuclei (Methods, Figure 5–figure supplement 1). The nuclei that expressed either Th or Slc6a3 (mDA nuclei dataset, in total 33,052 nuclei) were visualized by UMAP and split by condition to indicate their origin from either lesioned or intact hemispheres (Figure 5A, B). Unsurprisingly, fewer nuclei (8,243) were recovered from the lesioned compared to the intact hemisphere (24,809).
Two conclusions were immediately apparent from the UMAP visualization: First, many nuclei from the lesioned hemisphere were distributed outside of the mDA neuron territories (Figure 5A-B, F, see Figures 2 and 3), and, as expected, the numbers of nuclei in these territories were less affected by 6-OHDA. Second, most mDA neuron nuclei remaining from the lesioned hemispheres were evenly dispersed together with nuclei from the non-lesioned hemispheres within mDA neuron territories and neighborhoods, indicating that surviving cells were relatively similar to non-lesioned cells at the gene expression level (Figure 5A, B). Next, normalized cell loss per territory and neighborhood was calculated based on the sub-clusters cellular composition per condition, accounting for the global FANS yield per condition and sub-cluster size (see methods, Supplementary file 4). The results demonstrated a pervasive loss in the Sox6 territory (Figure 5C), which is consistent with previous studies showing that Sox6-expressing cells of the SNc are more vulnerable to pathological stress in rodents and in PD (Luppi et al., 2021; Panman et al., 2014). Cell loss was also notably high in the Pcsk6 territory. The Ebf1 territory showed the lowest level of cell loss, while the other territories were affected at intermediate levels (Figure 5C, D). Analyses of normalized cell loss were also calculated for neighborhoods. Within the Sox6 territory, a consistent and high level of cell loss was seen in all neighborhoods except Sox6_NH3, which showed a somewhat milder but still significant decrease (Fig. 5E). Notably, different levels of cell loss were often seen within individual territories e.g., Gad2_NH1, and Otx2_NH1 as compared to Gad2_NH2 and Otx2_NH2 (Figure 5E). As described above, the corresponding mDA neurons were mostly confined to regions within the VTA, demonstrating that high vulnerability to 6-OHDA was not exclusive to neurons distributed within the SNc.
Identification of genes associated with vulnerability or resilience
Next, we set out to identify genes that might predict either mDA neuron vulnerability or resilience to 6-OHDA. Because the goal was to identify genes with predictive value expressed in normal mDA neurons, only gene expression in nuclei from the non-lesioned hemisphere was analyzed. Thereby direct effects of the toxin were excluded from the analysis. Accordingly, genes that are commonly and significantly enriched in intact nuclei from clusters with either >90% (vulnerability genes) or <50% (resilience genes) normalized cell loss were identified (Figure 6–figure supplement 1, Supplementary file 4). 39 genes fulfilled the stringent cutoff criteria for vulnerability genes. In contrast, only ten genes fulfilled the cutoff criteria for resilience genes (Methods). The selected genes were ranked based on average Log2 fold change, and vulnerability and resilience gene modules were constructed, consisting of 20 and 8 most significantly enriched genes, respectively (Figure 6F, G, Supplementary file 5). The composite average expression of the gene sets (modules) was visualized in violin plots for neighborhoods and territories (Figure 6A-D, Supplementary File 5). As expected, the vulnerability and resilience gene module expression in territories and neighborhoods corresponded to the distribution of the clusters used as input for identifying the gene modules (Figure 6–figure supplement 1). However, linear regression analysis also demonstrated a highly significant overall correlation between cell loss and the vulnerability module scores (p-value = 3.89e-10), even when the input clusters were excluded from analysis (Figure 6–figure supplement 1C). Thus, vulnerability and presumably resilience gene modules can predict cell loss in the data set. This suggests that the identified module genes may be functionally associated with either sensitivity to or protection against 6-OHDA-induced cell stress. One of the vulnerability genes, Slc6a3, encodes the dopamine transporter used to import 6-OHDA to the cytosol. Thus, it is unsurprising that neurons of the Gad2 and Ebf1 territories expressing low levels of Slc6a3 are less vulnerable. However, when regression analysis was repeated using a vulnerability gene module in which Slc6a3 had been excluded, correlation to cell loss remained highly significant (Figure 6–figure supplement 1D, p-value = 1.73e-09). Therefore, the other genes of the vulnerability module may influence sensitivity to toxic stress.
The Th+/DAT+ dataset of 33,052 nuclei was re-analyzed by the Seurat integration method treating nuclei from lesioned and intact hemispheres as two different experimental conditions (Stuart et al., 2019). The main goal of this approach was to identify common transcriptomic states in nuclei from the different conditions via a learned joint structure (see Methods). We used this integrated dataset to validate further the predictive value of the identified vulnerability and resilience gene sets. Vulnerability and resilience modules were plotted across the integrated clusters and were color-coded by territory identities (Figure 6– figure supplement 2). In the integrated dataset, the clusters are completely independent of the merged data-based annotations assigned to integrated territories and neighborhoods. Therefore, the distribution of the two modules’ scores across the integrated clusters is a good indicator of how predictive they can be, given the territorial identities of the integrated clusters. Of note, the module scores corresponded well to the clusters, as integrated clusters with the Sox6 territory identity and most of the clusters with the Pcsk6 territory identity showed considerably high and low values of the vulnerability and resilience modules, respectively.
Conversely, integrated clusters with Gad2 and Ebf1 territory identities showed high resilience module values and low vulnerability module values (Figure 6–figure supplement 2). In addition, most of the pairwise comparisons of module scores across the mDA-integrated clusters were significant (Figure 6–figure supplement 2, Supplementary file 6). Non-mDA clusters were excluded from this statistical analysis. Thus, a strong predictive correlation between the expression of vulnerability and resilience modules is apparent across the snRNA-seq data.
Long-lasting gene expression changes in response to 6-OHDA
As mentioned above, cells from lesioned and intact hemispheres were relatively evenly distributed within mDA neuron territories in the UMAP diagram, indicating that most of the surviving cells from the lesioned hemisphere were transcriptionally similar to corresponding cells on the intact hemisphere (Figure 5A, B). However, two clusters consisted almost exclusively of nuclei from the lesioned hemisphere. To distinguish them from territories harboring nuclei characteristic of the normal non-lesioned /intact hemisphere of the brain, they were named mostly lesion (ML) clusters (Figure 7A, B). Although expression of typical mDA neuron markers such as Th and Slc6a3 was low, the ML-clusters were part of the mDA neuron dendrogram branch and shared a parent node with Sox6-territory (Figure 3A, C). Thus, these nuclei were likely derived from surviving mDA neurons that have become transcriptionally altered due to cellular stress. We took advantage of the integrated dataset described above to analyze this further. Using original territory labels, visualizing the integrated data by UMAP was consistent with data from non-integrated original analysis and demonstrated that the mDA neuron nuclei clustered together (Figure 7C, Figure 6-figure supplement 2). Next, the integrated data territories were annotated based on the cluster composition of inherited merged (original) territory identities and the enriched markers for the original territories (see Figure 3C). Notably, the nuclei forming the ML-clusters were now entirely integrated within the mDA neuron territories, supporting the hypothesis that they originated from healthy mDA neurons. In addition to ML clusters being predominantly made up of lesioned nuclei, Sox6 expression level and abundance were also clearly higher than Calb1 (Figure 7D, E). Both visualizations of ML-clusters nuclei in the integrated UMAP and quantifications demonstrated that most (>80%) of all ML nuclei were confined to two territories, Sox6 and Pcsk6. It thus seems plausible that the ML nuclei originated from altered neurons in these two territories. Notably, these territories were also the most vulnerable when exposed to 6-OHDA (Figure 7F-H, also see Figure 6A, C).
Differential gene expression analysis by condition was performed in two different ways. First, gene expression in all nuclei from the lesioned hemisphere was compared to all nuclei from the intact hemisphere. Consistent with the similar distribution of most of the lesioned and intact nuclei in the UMAP, relatively few (242 in total) significantly differentially expressed genes were identified (Figure 7–figure supplement 1, Supplementary file 7). Second, nuclei from the ML-clusters were compared to nuclei from all other territories. Consistent with a more dramatic and long-lasting influence from lesioning in the ML-clusters, 1577 genes were found to be differentially expressed in this comparison. Several of the most strongly upregulated genes, including Atf3, Creb5, Xirp2, Clic4, Sprr1a, and Mmp12, have in previous studies been associated with cellular stress, cell death, and cell signaling (Figure 7I, Figure 7-figure supplement 1, Supplementary file 7). In situ hybridization analysis combined with immunohistochemistry shows apparent upregulation of ML-enriched genes in the lesioned tissue compared to control (Figure 7J-M).
Although 6-OHDA was injected unilaterally, a potential influence on cells in the intact hemisphere could interfere with some of our conclusions. Therefore, to assess whether the stereotactic injection had any effect on the transcriptional profile of the nuclei from the intact hemisphere, we created and analyzed a separate dataset including all Th+/DAT+nuclei from the intact hemisphere of the brain, merged with a new dataset of 6,001 mCherry+ nuclei isolated from the ventral midbrain of untreated mice. UMAP visualization of this combined data set indicated an almost complete overlap between the two groups of nuclei. The dataset was annotated similar to the procedure above and all major cell types were also identifiable in it (Figure 7–figure supplement 2). Moreover, comparative differential gene expression analysis also supported the conclusion that any differences between the two groups were attributable to undefined technical parameters in experimental conditions rather than to the effects of the toxin (Figure 7–figure supplement 2, Supplementary file 7).
Relationship between dopamine neuron groups in the mouse and human brain
Understanding how the emerging knowledge of mouse mDA neuron diversity compares to the molecular landscape in the human brain is of major interest. We, therefore, integrated and reanalyzed our mouse data with human snRNAseq data from a recently published paper (Kamath et al., 2022). Canonical correlation analysis (CCA) was used to integrate 25,003 nuclei from human control and diseased (PD and Lewy body disease) donors with our lesioned and intact nuclei from the Th+/DAT+ dataset (see methods). UMAP visualization revealed a relatively large overlap between nuclei from the two species, despite some regions being predominated by one specific species or condition. Detection of mouse-specific UMAP regions is not surprising, given the comprehensiveness of the mDA-enriched mouse dataset. However, human specific UMAP regions were also observed (Figure 8). Next, we used the mouse intact nuclei from the mDA territories (see Figure 3) to build a reference UMAP structure onto which the human control nuclei (query dataset) were projected (see methods). The results demonstrated that almost 75% of the human control nuclei are mapped to mouse Sox6 territory (Figure 8-figure supplement 1A-D). Upon using the mouse intact nuclei, only from Sox6 territory as a reference, and the human control nuclei that were mapped to Sox6 territory in the previous step as query, most of this human query subset were mapped to Sox6-NH2, while almost no nuclei mapped to Sox6-NH4 (Figure 8-figure supplement 1E-H). The findings that most human nuclei bear the closest resemblance to mouse Sox6 territory nuclei were expected, as the human nuclei were derived from SNc.
When histochemical methods for mapping catecholaminergic cells and projections were developed (Falck et al., 1962), it became clear that mDA neurons consist of heterogeneous groups of cells with distinct anatomical locations and innervation targets. However, we now know that a substantial diversity exists beyond the anatomically defined cell groups, supported by studies focused on mDA neurons using rapidly emerging single-cell methods to characterize cellular diversity (Garritsen et al., 2023; Hook et al., 2018; Manno et al., 2016; Poulin et al., 2020, 2014; Tiklova et al., 2019). Here the goal was to create a comprehensive gene expression atlas over the mouse mDA neuron gene expression landscape by using snRNA-seq for the analyses. In addition, we wished to address the differential vulnerability to neuronal stress within the diverse groups of mDA neurons.
Unsurprisingly, global gene expression patterns differ substantially between cell types such as astrocytes, neurons, or endothelial cells. In addition, different neuron types with distinct developmental origins usually differ substantially in scRNA sequencing analyses. Thus, DA neurons from the hypothalamus, which have a distinct developmental origin from mDA neurons, differ from mDA neurons by many differentially expressed genes. Our study was primarily motivated by the more challenging need to describe differences across the population of related mDA neurons, which have a common developmental origin and a high degree of molecular similarities among them. The challenge in the analysis is evident from UMAP visualizations, as presented here, where mDA neurons are distributed in a shifting gene expression landscape without clearly distinct borders between groups of mDA neurons. Thus, defining distinctive sub-types within such a nuanced gene expression landscape may be misleading, as noted in several previous single-cell studies (Kozareva et al., 2021; Saunders et al., 2018; Tasic et al., 2018; Yao et al., 2021). Therefore, we chose to use a nomenclature that better reflects continuity between mDA neuron groups by referring to categories of mDA neurons as belonging to “territories” and “neighborhoods.” We do not propose that the taxonomy presented here should be viewed as universal or a definitive endpoint, but rather should be seen as high-resolution gene expression analysis that will help to guide further studies that use strategies to understand connectivity, neurophysiology, and behavior in investigations of relevant neuronal sub-circuits influenced by mDA neurons.
The mDA neuron atlas presented here is based on the sequencing of a large number of mDA neurons (40,000 in total), which was achieved by using snRNA-seq, which we found to be superior to scRNA-seq for reducing sampling bias. Furthermore, in support of a robust sampling, a large proportion of mDA neurons population was analyzed from each dissected brain (consistently above 20%), a 50-100 times improvement from our previous single-cell study (Tiklova et al., 2019). Indeed, the neurons belonging to the SNc (Sox6 territory) corresponded to roughly one-third of all mDA neurons, a proportion that conforms to previous stereological cell counting of mouse mDA neurons (Björklund and Dunnett, 2007; Zaborszky and Vadasz, 2001). Thus, we assume that the cellular stoichiometry, as presented in the present snRNA-seq atlas, roughly corresponds to the distribution of mDA neurons in the adult mouse brain.
Previous scRNA-seq studies have described mDA neuron groups characterized by gene expression patterns that resemble those in different territories and neighborhoods. Comparisons to those studies are described and referenced in Supplementary Results and show that distinguishing gene expression features seen in six of the seven territories described here are similar to those found in previous single-cell studies (Hook et al., 2018; Manno et al., 2016; Poulin et al., 2014; Saunders et al., 2018; Tiklova et al., 2019). However, the presented mDA neuron atlas illustrates previously unknown diversity within these groups. Thus, the Sox6 territory was divided into four neighborhoods with partly unique anatomical localizations within the SNc. In addition, the gene expression signature of the Pcsk6 territory, comprising a relatively large number of cells, did not correspond well to previously described mDA neuron groups. Thus, the Pcsk6 territory neurons apparently correspond to an mDA neuron group previously not recognized.
Moreover, while Vip+ mDA neurons were identified in several previous single cell studies, we show here that the Ebf1 territory can be divided into two related neighborhoods encompassing Vip+and Col24a1+ mDA neurons, respectively. Although these cells were most enriched in PAG, they were also found scattered in other regions such as PBP and CLi. Col24a1+ likely corresponds to a specific population of Npw+ mDA neurons, localized in PAG, VTA, and the dorsal raphe, which project to the extended central amygdala and modulate mouse behavior under stress (Motoike et al., 2016).
We also extended our analysis to neurons exposed to neuronal stress by sampling nuclei from the lesioned after partial unilateral exposure to the neurotoxin 6-OHDA. The mechanisms of neuronal stress leading to the degeneration of mDA neurons in PD differ from events following exposure to neurotoxins (Blesa and Przedborski, 2014). However, it is known that most insults to mDA neurons, including those resulting from 6-OHDA exposure, affect SNc more severely than neurons of the VTA, indicating an inherent higher vulnerability of these neurons. This is evident in our analysis, where normalized cell loss was most severe within the Sox6 territory. However, a notable high vulnerability was also seen outside the Sox6 territory, e.g., within the Pdia5 and Pcsk6 territories. Indeed, the Pcsk6_NH1, apparently localized within the PBP, is even more vulnerable than the Sox6_NH3. Within the VTA territories, resilient neighborhoods included several that express Slc17a6 encoding the glutamate transporter VGLUT2, a protein linked to increased and decreased dopamine neuron vulnerability (Kouwenhoven et al., 2020; Steinkellner et al., 2018).
The information on normalized cell death was used to identify genes enriched in highly vulnerable or resilient clusters. Regression analysis showed a highly significant correlation between the vulnerability module scores and normalized cell loss in neuron groups, i.e., clusters that were not used as input to identify the genes used in calculating the module. We note that several genes in these modules may be functionally linked to either vulnerability or resilience. For example, Kcnj6 is a known marker for SNc neurons and has, in previous studies, been shown to be associated with the vulnerability of these neurons (Liss et al., 2005). In addition, the protein encoded by the Vsnl1 gene is a calcium sensor that is known to be increased in cerebrospinal fluid in Alzheimer’s patients and may be linked to disease progression (Liao et al., 2022; Zetterberg, 2017). Therefore, the analysis of 6-OHDA vulnerability complements the atlas in ways that will be valuable for understanding how the molecular gene expression landscape influences sensitivity to pathological stress. It will be important in future studies to extend these studies to mouse neurons exposed to pathology that more resembles that seen in PD.
To avoid extensive influence on gene expression from the acute toxin effects in dying cells, brain tissue was collected for nuclei isolation several weeks after 6-OHDA exposure. Most nuclei derived from the lesioned hemispheres of dissected brains were scattered in UMAP analysis with nuclei from the intact hemisphere, suggesting that the toxin had not caused any severe gene expression alterations. However, nuclei in the ML clusters were an exception, as they were derived almost exclusively from neurons dissected from the lesioned hemisphere. These toxin-exposed neurons displayed more profound and long-lasting alterations in gene expression. We addressed the origin of these cells in an integrated data set, and it is plausible from this analysis that most of these altered neurons were derived from neurons belonging to the two most vulnerable Sox6 and Pcsk6 territories. Gene expression changes in ML-clusters’ included severe down-regulation of typical mDA neuron gene markers such as Th and Slc6a3. In addition, many enriched genes have previously been associated with neurodegeneration or response to neuron stress. For example, Atf3, Lifr, Sprr1a have been reported to be part of a regeneration-associated gene program in peripheral nerves (Kanaan et al., 2015; Seijffers et al., 2007). Atf3 is also known to be upregulated as a result of activation of the unfolded protein response pathway via PERK (Jiang et al., 2004). Ngf, encoding the nerve growth factor, is known to be neurotrophic to mDA neurons and is dysregulated in PD (Mogi et al., 1999). Thus, part of this stress response is likely to be neuroprotective and likely reflects a cellular survival response in affected cells.
In conclusion, the work presented here provides a comprehensive atlas over mDA neuron diversity in mice. Combined with the included anatomical analysis and assessment of neuron vulnerability after toxin stress, we anticipate that this will be a useful resource in studies to understand the intricate functions of neurocircuitries involving dopamine neurotransmission functionally.
Materials and Methods
Mouse lines and genotyping
DatCre (Ekstrand et al., 2007), Pitx3GFP(Zhao et al., 2004) Rpl10a-mCherry (TrapCfl) (Hupe et al., 2014) and their genotyping have been described before. DatCreand TrapCfl lines were intercrossed to generate DatCre/+; TrapCfl/fl mice. All experimental procedures followed the guidelines and recommendations of Swedish animal protection legislation and were approved by Stockholm North Animal Ethics board.
Partial lesions with 6-OHDA
C57BL6 female mice were pretreated (30 min before surgery) with 25 mg/kg desipramine (i.p.; Sigma–Aldrich) and 5 mg/kg pargyline (i.p.; Sigma–Aldrich), anesthetized with 2-2.5% isoflurane, placed in a stereotaxic frame, and unilaterally injected, over 2 min, with 0.7 or 1.5 μg of 6-OHDA in 0.01% ascorbate (Sigma–Aldrich) into the medial forebrain bundle (MFB) of the right hemisphere. The coordinates for injection were AP, −1.1 mm; ML, −1.1 mm; and DV, −4.75 mm relative to bregma and the dural surface (Paxinos and Franklin, 2001). The tissues were collected six weeks after 6-OHDA administration.
DAT binding autoradiography
Fresh frozen thaw-mounted striatal sections (12 μm thick) were initially incubated in 50 mM Tris–HCl/120 mM NaCl (pH 7.5) for 20 min. Then the same sections were incubated in binding buffer (50 mM Tris– HCl/120 mM NaCl, pH 7.5/1 μM fluoxetine) containing 50 pM of the DAT ligand (125I) RTI55 (PerkinElmer Life Sciences, Boston, USA) for 60 min. To detect nonspecific binding, 100 μM nomifensine was added to the assay. The sections were washed 2 × 10 s in ice-cold binding buffer, rapidly dipped in deionized water, air-dried, and exposed to autoradiographic films in X-ray cassettes at room temperature for 2 days.
Fluorescent RNA in situ hybridization and immunohistochemistry
Mice were deeply anesthetized, and the brains collected after intracardial perfusion with +37C PBS, followed by 4% PFA in PBS. The midbrain piece was coronally cut out using a brain matrix and post-fixed for 2-3 days in PFA at RT, changing the PFA solution daily. The tissues were dehydrated and processed into Histosec wax (Merck) using an automated tissue processor (Leica TP1020), and coronal 6 μm sections were collected at Superfrost Plus slides (Thermo-Scientific) using an automated waterfall microtome (Epredia HM355S). Immunohistochemistry and fluorescent RNA in situ hybridization were performed essentially as described previously (Tiklova et al., 2019), except for Pdia5 antibody Triton X-100 in the washing buffers and blocking solutions was replaced by 0.1% Tween-20. For the analysis of Tacr3, parallel slides were stained with an antibody for Aldh1a1. 2-3 animals were used for each experiment. Each ML-territory probe was tested on lesioned (n=3) and control DatCre; TrapC fl/fl mice.
The ML-clusters specific probes Clic4, Creb5, Mmp12 and Sprr1a were cloned fragments from the respective protein coding sequences of mouse cDNA. Size of fragments: Clic4 722 bp, Creb5 591 bp, Mmp12 759 bp and Sprr1a 598 bp. The probe used for Vip was cloned with primers from Allen Mouse Brain Atlas (experiment 77371835). The Gad2 probe was a cloned 869 bp fragment from the protein coding sequence of mouse Gad2 cDNA. Tacr3 probe was a 992 bp Genestrings fragment (Invitrogen) from the protein coding sequence of mouse Tacr3 cDNA. All cDNA fragments were cloned into pCR-Blunt II TOPO vector (Invitrogen 450245) and DIG-UTP labelled cRNA probes synthetized from linearized plasmids using either SP6 or T7 RNA polymerases (Roche) according to manufacturer’s instructions. The list of Allen Mouse Brain Atlas experiments of which images were shown in Fig 4 and in Figure 1-figure supplement 3, Figure 4-figure supplements 1-3, as well as the list of primary and secondary antibodies used, are in Supplementary Materials (Supplementary file 2).
Image capture and processing
Images of fluorescent in situ hybridizations and immunostainings were taken using a Zeiss confocal microscope LSM700. Tiling of the individual images was done with Fiji (ImageJ) Pairwise stitching plug-in (Preibisch et al., 2009). Brightness and contrast were adjusted after tiling, if needed.
In situ RNA sequencing
The in situ RNA sequencing experiments were performed as described (Gyllborg et al., 2020). In brief, fresh-frozen 10 μm thick mouse brain sections were sectioned on a cryostat and stored at −80 °C until fixation. After fixation in 3% (w/v) paraformaldehyde (Sigma) in DEPC-treated PBS (DEPC-PBS) for 10 min at RT, tissues were washed in DEPC-PBS and reverse transcription was performed at 37 °C for 16 hrs. Tissues were then treated with RNaseH at +37°C for 45 min to make single-stranded cDNAs, followed by padlock probe (IDT Corelville, Iowa; 4nmole, standard desalting, 5 prime phosphorylated; Supplementary file 3) hybridization and ligation at 45 °C for 60 min. Rolling circle amplification was performed at 30 °C for 16 hrs. Rolling circle products were detected by using fluorescently labeled detection oligo (Supplementary file 3, IDT Corelville, Iowa).
Imaging and data processing of ISS
Images were acquired with Leica DMi8 epifluorescence microscope equipped with an external LED light source (Lumencor® SPECTRA X light engine), automatic multi-slide stage (LMT200-HS), sCMOS camera (Leica DFC9000 GTC), and objective (HCX PL APO 40X/1.10 W CORR). A series of images (10% overlap between neighboring images) were obtained, and maximum intensity projected to two-dimensional images. These images were aligned between cycles and stitched together using Ashlar algorithm. Stitching was followed by retiling to get smaller (6000×6000 pixel) images. Those images were used for decoding using PerRoundMaximumChannel Decode Spots Algorithm. The resulting spots were filtered based on minimum quality. The preprocessing and decoding pipeline can be found on the Moldia GitHub page (https://github.com/Moldia).
Tissue processing for FANS
DatCre TrapC fl/fl (n=6 for lesion cohort, n=6 for control cohort) mice were euthanized with CO2, and brains were rapidly removed and transferred into cold PBS. The midbrain was dissected and snap-frozen on dry ice. For lesioned brains, the lesioned hemisphere was separated from the intact hemisphere before being snap-frozen. Tissue processing for FANS was performed as described previously (Toskas et al., 2022), with the addition of adding 0.1% Triton and 5mM CaCl2 to the lysis buffer. For the control cohort, resuspended and filtered nuclei were stained with NeuN-647 conjugated (1:1000, MAB377 Millipore Corp) antibody for a few hours before being sorted. Nuclei were sorted using a FACSAria Fusion or FACSAriaIII cell sorter with the FACSDiva software (BD Biosciences). The nuclei were identified by forward- and side-scatter gating, a 405-nm laser with a 450/50 or 450/40 filter, a 561-nm laser with a 610/20 filter, and a 633-nm laser with a 660/20 filter, quantifying DNA content per event to assure that only singlets were collected. A 100 μm nozzle and an acquisition rate of ∼3000 events per second was used. Nuclei were stained with DAPI (1:200, Merck) 5 min before sorting and preserved in 2% BSA until downstream procedures.
Library preparations and sequencing
Ventral midbrains tissues from 6 mice (6 lesioned, 6 non-lesioned (intact) hemispheres) were used to obtain 70464 nuclei (36789 un-lesioned (intact) and 33675 lesioned). Ventral midbrain tissue from 6 control mice were used to obtain 8311 nuclei. Chromium Single Cell 3′ v3, dual indexing (10x Genomics) was used to make single-nucleus libraries, in accordance with the manufacturer’s protocols. Libraries were sequenced on a NovaSeq 6000 system (ESCG, SciLifeLab, Karolinska Institute). Library preparations and sequencing for the nuclei from the intact and lesioned ventral midbrain hemispheres were done together but separately from the control nuclei.
Sequenced reads were demultiplexed and aligned to a CellRanger customized reference package “transcriptome: mm10-3.0.0_premrna”, with pre-mRNA added to ensure both premature and mature mRNAs are mapped accurately, using CellRanger Pipeline version 3.1.0 from 10x Genomics (Zheng et al., 2017). A count matrix (filtered_feature_bc_matrix) per sample was generated using CellRanger Pipeline version 3.1.0. Seurat package version 4 was used for downstream analyses of the data (Hao et al., 2021). Seurat Read10x() was used to load the sparse count matrices of 10X genomics CellRanger output and CreateSeuratObject() function was used to make one Seurat Object per sample. All 12 samples (6 lesioned, 6 un-lesioned) were merged to generate one Seurat object for the entire data. Quality control and filtering of data were done in the following steps. First, the percentage of transcripts that map to mitochondrial genes and ribosomal genes were calculated using Seurat function PercentageFeatureSet and the corresponding values added to the created percent_mito% and percent_ribo% metadata columns, respectively. Then, CellCycleScoring function was used to score cell cycle phases, and phase metadata was calculated and added based on the expression of G2-M and S phase canonical markers. In the next step, the ratio of a gene’s unique molecular identifier (UMI) counts to the sum of all UMIs per library (nucleus), was calculated as the average relative expression of each gene per library. Malat1, a lncRNA gene, was expressed in all libraries with a mean fraction of total UMI counts per library of about 7%. Polyadenylate captured RNA-seq libraries, irrespective of the used protocol, show a relatively high detection of Malat1 reads. However, compared to other methods, the detection is even higher in snRNA-seq due to its nuclear localization and therefore Malat1 expression is used to estimate the nuclear proportion of total mRNA (Bakken et al., 2018). Malat1 was filtered out for downstream analysis. Genes with reads present in fewer than 10 nuclei (libraries) were removed. Libraries with detected genes below 500 and above 10,000 (doublets), and with percent_mito above 0.5 were also removed from the analysis.
After the filtering steps (above), 68914 nuclei remained (36,051 intact, 32,863 lesioned). The remaining nuclei after the quality control constituted the all-nuclei dataset and had UMIs per library in the range of 574 - 122753, genes per library in the range of 501 - 9991, with average UMIs per library of 17630, and average genes per library of 4762. The average UMI/gene was 3.58, with a range of 1.14 - 13.31 (Figure 1-figure supplement 2).
Dopaminergic nuclei dataset
The filtered dataset (all-nuclei, above) was then subject to selection based on the expression of two genes, Th and Slc6a3 (DAT). Nuclei expressing either Th or DAT (Th+/DAT+) were selected. The remaining 24809 and 8243 nuclei from the respective intact and lesioned hemispheres were used to create the dopaminergic dataset.
Combined intact and control nuclei dataset
Count matrices for the 6 intact samples as well as the 6 control samples were loaded separately and merged to create a dataset of 12 samples (6 intact, 6 control). After the QC steps, described above, and selection of libraries based on either Th or DAT (Th+ | DAT+) expression, 24809 nuclei from the intact hemisphere and 6001 nuclei from the control remained which were used to make the final intact-control dataset. Interestingly, the ratio of the Th+ | DAT+filtering for both groups was 0.7.
Normalization, variance stabilization and dimensionality reduction
SCTransform package (Hafemeister and Satija, 2019) was used to normalize, scale, and find the top 1000 variable genes, with the intension to replace the heuristic approaches of adding pseudo-counts and log transformation. This method of normalization and variance stabilization is meant to improve the disentanglement of biological heterogeneity from the confounding effects of the technical variables, using the Pearson residuals of the regularized negative binomial regression, with the sequencing depth as a covariate in a generalized linear model. The residuals of the SCTransform were used as input for dimensionality reduction. Principal component analysis (PCA) was applied, followed by the JackStraw procedure and ElbowPLot to visualize the standard deviations of the principal components. PCs 1:30 were used in the UMAP embedding.
SNN graph-based Clustering
Seurat FindNeighbors function was applied for SNN graph construction, followed by FindClusters function, using the Louvain algorithm, to identify the clusters. For the all-nuclei dataset, the resolution 0.1 was used since the goal was to identify the major cell classes. BuildClusterTree function was used to make a phylogenetic tree, with assay = “SCT”, slot = “data” parameters for the all-nuclei dataset. For the different datasets in this research, a range of resolutions were tested to estimate the optimal cluster numbers, as a heuristic approach based on known markers, neuroanatomical regions and hierarchical dendrograms. These estimated values were used in determining k (centers) parameters in k-means clustering when applicable (below).
K-means and hierarchical clustering
A matrix of PCA values based on PCs 1:30 was generated to be used as input for the kmeans clustering function, with “MacQueen” algorithm. The computed clusters (centroids) were added to the object metadata and then were subject to hierarchical clustering, with distance matrix computation method set as “Euclidean” and the agglomeration method as “ward.D2”.
Integration analysis mouse dopaminergic dataset
For the dopaminergic dataset, it was split based on condition (intact / lesioned), SCTransform approach was used to independently, normalize and identify the top 1000 variable genes per condition. Same number of genes (1000) were selected as integration features to be used as “anchors” in data integration, using SCT as the normalization method. PCs 1:30 were used in the UMAP embedding. K-means and hierarchical clustering were performed as described above.
Calculation of cell loss
The ratio of the FANS yield for the nuclei passing the QC, for condition (intact to lesioned) was calculated and the quotient (1.097) was set as a yield normalization coefficient (β = 1.097) for lesioned subclusters. The calculation of cell loss was done per subcluster for the original kmeans clusters in the dopaminergic nuclei dataset. Based on the hierarchical dendrogram and class annotation, the non-dopaminergic clusters were excluded from the analysis. Louvain modularity optimization algorithm was applied with resolution of 0.5 to obtain the subclusters. For a couple of clusters that did not resolve with res=0.5, incrementally higher resolutions were applied. Most clusters were resolved into subclusters with no or only one identified singleton, while no clusters were resolved into subclusters with higher than 3 singletons. The cell composition of each subcluster per condition was determined, considering the FANS yield coefficient. For each cluster the average normalized cell loss was calculated, considering the subcluster size, according to the formula below, with normalized cell loss per cluster; A, determined by subcluster size by condition; sl and si lesioned and intact respectively, N; number of subclusters per cluster, and β.
The A = 1 denotes total cell loss while A = 0, indicates no cell loss at all. The subcluster cell loss values were used to calculate the normalized cell loss per cluster, neighborhood, and territory. Subclusters with , were set to NA. The statistical tests used for cell loss analyses include Shapiro-Wilk normality test, either Levene’s Test or Fligner-Killeen (for non-normally distributed data) tests of homogeneity of variances, Kruskal-Wallis rank sum test, and the Conover-Iman test, with Benjamini-Hochberg p adjustment method, for multiple pairwise comparisons across clusters, territories, and the neighborhoods within Sox6 territory. For all other territories of only two neighborhoods, Levene’s Test for Homogeneity of Variance and either Two Sample t-test or Wilcoxon rank sum test were applied. For the Conover-Iman test, the default (altp=FALSE) was set as p-value = P(T ≥ |t|), and the null hypothesis (H0) was rejected at p ≤ α/2, which is 0.025.
Vulnerability and resilience modules
Vulnerable (normalized cell loss > 90%) and resilient (normalized cell loss < 50%) clusters were identified and gene sets each module were created based on being commonly upregulated (adjusted p value < 0.05 & average Log2FC > 0.5) in either vulnerable or resilient clusters. The Seurat function AddModuleScore was used to calculate the module scores. This function assigns a numerical value to every library for a given module. A positive value indicates an expression level above the cell population average, while a negative value indicates the opposite. Shapiro-Wilk normality test, or for cases with n>5000, the D’Agostino Skewness and Kurtosis Normality Tests from the “burrm/lolcat” R package were applied. Next, either Levene’s Test or Fligner-Killeen tests of homogeneity of variances were used. Because data was neither normally distributed nor homoscedastic (homogeneity of variances), both the Kruskal-Wallis rank sum test and Welch’s ANOVA (One-way analysis of means, not assuming equal variances) were applied. Both tests rejected the null hypothesis, indicating that there is significant difference among groups. Finally, the Conover-Iman test, with Benjamini-Hochberg p adjustment method, for multiple pairwise comparisons across dopaminergic clusters, neighborhoods and territories was applied.
Correlation between cell loss and vulnerability module score
Two linear regression models were fitted using normalized cell loss as the response variable and vulnerability module either with or without DAT (Slc6a3) as the predictor. The module scores were square root (sqrt) transformed to make their distribution normal, prior to fitting. The plots show the R2, the estimated model equations and the 95% confidence interval limits.
Differential gene expression analysis
Differentially expressed genes between two or more groups were identified using Wilcoxon Rank Sum test in the Seurat FindMarkers or FindAllMarkers functions with the following set parameters; logfc.threshold = 0.25, min.pct = 0.1, min.diff.pct = -Inf, only.pos = FALSE, max.cells.per.ident = Inf. The calculated p-values were adjusted for multiple comparisons using the Bonferroni correction.
GO enrichment analysis
The ML territory enriched genes (806 upregulated and 661 downregulated) were used for this analysis by removing the overlapping genes enriched in the DE between all lesioned and all intact (Figure 7-figure supplement 1). The “enrichR” package as the R interface to the Enrichr database, version 3.1 was used with GO_Biological_Process_2021 as the imported gene-set library (Kuleshov et al., 2016). The top 20 enriched terms, ranked by p value, were selected per group for visualization.
Generation of human dataset
Human dataset including count matrix, barcodes, features and metadata files were downloaded from the link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE178265. Genes with reads present in fewer than 1 nucleus (libraries), and Malat1 were removed. Libraries with detected UMIs below 650 and UMI/Gene below 1.2 and, with percent_mito above 10 were also removed. Reciprocal PCA (RPCA) approach was used to integrate individual SNc samples separately per condition (control vs diseased), by which each sample is projected into the others’ PCA space, and the integration anchors are defined by the requirements of mutual neighborhood. Next the control and diseased dataset were integrated into one dataset, using RPCA approach. Louvain resolution 0.1 was used for clustering and the clusters of dopaminergic nuclei were identified by plotting typical markers. These dopaminergic clusters of 25003 nuclei (control=17039, LBD=4642, PD=3322 nuclei) were subset for downstream analysis.
Integration of human and mouse dataset
Orthology mapping package “Orthology.eg.db”, version 3.13.0 was used to map NCBI gene IDs between species. Human genes were converted to their mouse orthologs and the ones without a mouse ortholog, were also kept. Data from human dopaminergic nuclei, subset in the previous step (above), containing 18 donors (8 control, 6 PD, 4 LBD) and data of the 12 mouse samples from 6 dissected midbrains (6 intact, 6 lesioned) from the dopaminergic dataset (Th+/DAT+), was first log-normalized, highly variable genes found (method=vst) separately per species, prior to being integrated using the Seurat canonical correlation analysis (CCA).
Mapping and annotating human control nuclei query dataset
Unimodal UMAP Projection approach from Seurat v4. was used to compute a UMAP reference from the mouse intact, mDA territories nuclei, using the first 30 PCs after log-normalization, finding highly variable genes (method=vst) and scaling of the mouse reference data. Then human control dopaminergic nuclei were used as query to be projected onto the reference UMAP structure, using the MapQuery() function. Next, the mouse intact only Sox6 territory nuclei were used to make the reference UMAP, with the same parameters as above and the human nuclei with the transferred label “Sox6” from the previous step were used as a subset query to be mapped with a higher resolution to the Sox6 neighborhoods (Figure 8-figure supplement 1).
Sequencing Fastq files, raw and normalized count matrixes, and supplementary files are accessible via GEO accession number xxxxx. All-nuclei and mDA-nuclei datasets are accessible for visualization and exploration via the links to our lab’s local server, http://18.104.22.168:5005/ and http://22.214.171.124:5006/ respectively.
This work was supported by Swedish Research Council (VR 2020-00884), The Swedish Brain Foundation (Hjärnfonden), and Torsten Söderbergs Foundation to T.P., and a postdoctoral grant from Karolinska Institutet’s Strategic research area neuroscience (StratNeuro, Karolinska Institutet) to B.Y.S. The computational analyses, data handling, and data curation were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) in the project NAISS 2023/23-181 and the Swedish National Infrastructure for Computing (SNIC) in the project SNIC 2022/23-171 at UPPMAX, Uppsala University, partially funded by the Swedish Research Council through grant agreements no. 2022-06725 and no. 2018-05973. The authors wish to thank SciLifeLab Eukaryotic Single Cell Genomics as well as SciLifeLab In Situ Sequencing Infrastructure Units. We also would like to thank Petter Woll, at the Center for Hematology and Regenerative Medicine (HERM) in Neo, Karolinska Institutet, for advice.
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