Figure 3. Single-nucleus gene expression in the human locus coeruleus (LC) using single-nucleus RNA-sequencing (snRNA-seq).

We applied an unsupervised clustering workflow to identify cell populations in the snRNA-seq data. (A) Unsupervised clustering identified 30 clusters representing populations including norepinephrine (NE) neurons (red), 5-HT neurons (purple), and other major neuronal and non-neuronal cell populations (additional colors). Marker genes (columns) were used to identify clusters (rows). Cluster IDs are shown in labels on the right, and the numbers of nuclei per cluster are shown in horizontal bars on the right. Percentages of nuclei per cluster are also shown in Figure 3—figure supplement 1D. Heatmap values represent mean logcounts per cluster. (B) UMAP representation of nuclei, with colors matching cell populations from heatmap. (C) Differential expression (DE) testing between neuronal clusters identified a total of 327 statistically significant genes with elevated expression in the NE neuron cluster, at a false discovery rate (FDR) threshold of 0.05 and fold-change (FC) threshold of 2. Heatmap displays the top 70 genes, ranked in descending order by FDR, excluding mitochondrial genes, with NE neuron marker genes described in text highlighted in red. The full list of 327 genes including mitochondrial genes is provided in Supplementary file 2C. Heatmap values represent mean logcounts in the NE neuron cluster and mean logcounts per cluster averaged across all other neuronal clusters (excluding ambiguous). (D–E) Cross-species comparison showing expression of human ortholog genes for LC-associated genes identified in the rodent LC (Mulvey et al., 2018; Grimm et al., 2004) using alternative experimental technologies. Boxplots show logcounts per nucleus in the NE neuron cluster and all other neuronal clusters. Boxplot whiskers extend to 1.5 times the interquartile range, and outliers are not shown. (F) DE testing between neuronal clusters identified a total of 361 statistically significant genes with elevated expression in the 5-HT neuron cluster, at an FDR threshold of 0.05 and FC threshold of 2. Heatmap displays the top 70 genes, ranked in descending order by FDR, with 5-HT neuron marker genes described in text highlighted in red. The full list of 361 genes is provided in Supplementary file 2F.

Figure 3—figure supplement 1. Distribution of nucleus-level quality control (QC) metrics across unsupervised clusters in snRNA-seq data.

(A) Sum of UMI counts per nucleus and cluster, (B) total number of detected genes per nucleus and cluster, (C) percentage of mitochondrial reads per nucleus and cluster, and (D) number and percentage of nuclei per cluster. We observed an unexpectedly high percentage of mitochondrial reads in the NE neuron cluster (cluster 6, red, C). Since NE neurons were of particular interest for analysis, we did not remove nuclei with a high percentage of mitochondrial reads during QC filtering.

Figure 3—figure supplement 2. Additional quality control (QC) evaluations for NE neuron cluster in snRNA-seq data.

Additional quality control (QC) comparisons for snRNA-seq data showing (A) sum of UMI counts and number of detected genes for the NE neuron cluster compared to other neurons (excluding ambiguous neurons), (B) sum of UMI counts and number of detected genes for the NE neuron cluster compared to all other cells (excluding ambiguous neurons), and (C-D) percent mitochondrial reads (y-axis) vs. (C) sum of UMI counts and (D) number of detected genes, showing individual nuclei for NE neurons (red), other neurons (blue), ambiguous neurons (gray), and other non-neuronal populations (black). The ambiguous neuron category (gray) includes a distinct group of droplets with the overall highest mitochondrial proportions and lowest number of detected genes, representing likely damaged nuclei and/or debris, which are clearly separated from the NE neurons (red). See also Figure 3—figure supplement 1 for QC metrics calculated for all clusters.

Figure 3—figure supplement 3. Supervised identification of NE neuron nuclei by thresholding on expression of NE neuron marker genes in snRNA-seq data.

We applied a supervised strategy to identify NE neuron nuclei by simply thresholding on expression of NE neuron marker genes (selecting nuclei with ≥1 UMI counts of both DBH and TH). We observed a higher than expected proportion of mitochondrial reads within this set of nuclei, and did not filter on this parameter during QC processing, in order to retain these nuclei. (A) Percentage of mitochondrial reads within the supervised set of nuclei by donor (Br2701, Br6522, and Br8079). (B) Histogram showing percentage of mitochondrial reads within the supervised set of nuclei across all donors. (C) Venn diagram showing overlap between NE neuron cluster identified by unsupervised clustering (left) and NE neuron population identified by supervised thresholding (right). Values display the number of nuclei.

Figure 3—figure supplement 4. Expression of NE neuron marker genes in individual cells using RNAscope and high-magnification confocal imaging.

We applied RNAscope (Wang et al., 2012) and high-magnification confocal imaging to visualize expression of NE neuron marker genes (TH in green, DBH in yellow, and SLC6A2 in pink, with white representing all three colors overlapping), DAPI stain for nuclei (blue), and lipofuscin (teal) on additional tissue sections from an additional independent donor, Br8689. Top row: merged channels, DAPI, and lipofuscin. Bottom row: individual channels for NE neuron marker genes in separate panels. The figure displays a region within the LC region from a single tissue section, demonstrating clear co-localization of expression of the three NE neuron marker genes (white points in merged channels) within individual cells. Scale bar: 20 μm.

Figure 3—figure supplement 5. DE testing results between neuronal clusters in the LC and surrounding region in snRNA-seq data.

(A) Volcano plot showing 327 statistically significant DE genes (FDR <0.05 and FC >2) elevated in expression within the NE neuron cluster compared to all other neuronal clusters (excluding ambiguous) captured in this region. The significant DE genes include known NE neuron marker genes (DBH, TH, SLC6A2, and SLC18A2) and mitochondrial genes. (B) Volcano plot showing 361 statistically significant DE genes (FDR <0.05 and FC >2) elevated in expression within the 5-HT neuron cluster compared to all other neuronal clusters (excluding ambiguous) captured in this region. The significant DE genes include known 5-HT neuron marker genes (TPH2 and SLC6A4). Vertical axes are on reversed log₁₀ scale, and horizontal axes are on log₂ scale. Additional details are provided in Supplementary file 2C, E.

Figure 3—figure supplement 6. DE testing results between NE neuron cluster and all other clusters in snRNA-seq data.

(A) Volcano plot showing 427 statistically significant DE genes (FDR <0.05 and FC >2) elevated in expression within the NE neuron cluster compared to all other clusters (neuronal and non-neuronal, excluding ambiguous neuronal). The significant DE genes include known NE neuron marker genes (DBH, TH, SLC6A2, and SLC18A2) and mitochondrial genes. (B) Heatmap displaying top 120 genes, ranked in descending order by FDR, excluding mitochondrial genes, with NE neuron marker genes described in text highlighted in red. The full list of 427 genes including mitochondrial genes is provided in Supplementary file 2D. Heatmap values represent mean logcounts in the NE neuron cluster and mean logcounts per cluster averaged across all other clusters (excluding ambiguous neuronal). (C) Venn diagram showing overlap between sets of statistically significant DE genes identified between NE neuron cluster and other neuronal clusters (left) and between NE neuron cluster and all other clusters (right). Values display the number of genes.

Figure 3—figure supplement 7. Overlap and comparison between DE genes identified in SRT and snRNA-seq data.

(A) Venn diagram showing overlap between sets of statistically significant DE genes identified in SRT data (pseudobulked LC vs. non-LC regions, left) and snRNA-seq data (NE neuron cluster vs. all other clusters excluding ambiguous neuronal, right). Values display the number of genes. (B) Comparison between log₂ fold change (FC) for 51 genes identified as statistically significant DE genes (FDR <0.05 and FC >2) in both SRT data (pseudobulked LC vs. non-LC regions, vertical axis) and snRNA-seq data (NE neuron cluster vs. all other clusters excluding ambiguous neuronal, horizontal axis). Additional details for these 51 genes are provided in Supplementary file 2E.

Figure 3—figure supplement 8. Unsupervised clustering results showing additional inhibitory neuronal, miscellaneous, dopaminergic, and cholinergic marker genes in snRNA-seq data.

Extended form of heatmap displayed in Figure 3A, showing additional inhibitory neuronal marker genes (light blue), miscellaneous marker genes including neuropeptides and receptors included for comparison with Luskin et al., 2023 (dark blue-purple), cholinergic marker genes (yellow), dopaminergic marker genes (pink), and additional 5-HT neuron marker genes for comparison with Ren et al., 2019 (dark purple; SLC6A18 is not shown since we observed zero expression of this gene in the nuclei that passed filtering). We observed diversity in expression of inhibitory neuronal marker genes across inhibitory neuronal subpopulations (additional results in Figure 3—figure supplement 13), and we observed expression of cholinergic marker genes within NE neurons (additional results in Figure 3—figure supplement 17). We did not observe expression of dopaminergic marker genes within the NE neuron cluster (additional results in Figure 3—figure supplements 15 and 16). Percentages of nuclei per cluster are shown in Figure 3—figure supplement 1D.

Figure 3—figure supplement 9. Spatial expression and enrichment analysis of 5-HT neuron marker genes in Visium SRT samples.

(A-B) We visualized the spatial expression of 5-HT (5-hydroxytryptamine or serotonin) neuron marker genes (TPH2 and SLC6A4) in the n=9 initial Visium SRT samples within the Visium SRT samples, which showed that the population of 5-HT neurons was distributed across both the LC and non-LC regions. The annotated LC regions are shown in Figure 2—figure supplement 1. (C) Enrichment of 5-HT neuron marker gene expression (TPH2 and SLC6A4) within manually annotated LC regions compared to non-LC regions in the n=8 Visium SRT samples that passed QC (see Figure 2—figure supplement 3). Boxplots show values as mean log-transformed normalized counts (logcounts) per spot within each region per sample, with samples represented by shapes.

Figure 3—figure supplement 10. Spatial expression of additional marker genes for 5-HT neurons in Visium SRT samples.

We visualized the spatial expression of (A-B) additional marker genes for 5-HT neurons (SLC18A2, FEV) and (C-D) 5-HT autoreceptor genes (HTR1A, HTR1B) in the n=9 initial Visium samples. SLC6A18 is not shown since we observed zero expression of this gene in the Visium samples. Color scale shows UMI counts per spot.

Figure 3—figure supplement 11. Expression of NE neuron and 5-HT neuron marker genes using RNAscope.

We applied RNAscope (Wang et al., 2012) to visualize expression of an NE neuron marker gene (TH) as well as 5-HT neuron marker genes (TPH2 and SLC6A4) within an additional tissue section from donor Br6522, demonstrating that the NE and 5-HT marker genes were expressed within distinct cells and that the NE and 5-HT neuron populations were not localized within the same regions. Scale bar: 500 μm.

Figure 3—figure supplement 12. Expression of 5-HT neuron marker genes using RNAscope.

We applied RNAscope (Wang et al., 2012) and high-magnification confocal imaging to visualize expression of two 5-HT neuron marker genes (TPH2 and SLC6A4) and an NE neuron marker gene (TH) within an additional tissue section from donor Br6522, demonstrating that the 5-HT neuron marker genes were co-expressed within individual cells. Image region corresponds to a small region within the RNAscope image displayed in Figure 3—figure supplement 11. Scale bar: 50 μm.

Figure 3—figure supplement 13. Inhibitory neuronal subpopulations identified by secondary unsupervised clustering on inhibitory neurons in snRNA-seq data.

We applied a secondary round of unsupervised clustering to the inhibitory neuron nuclei identified in the first round of clustering. This identified 14 clusters representing inhibitory neuronal subpopulations. (A) Expression of neuron marker genes (black) and inhibitory neuron marker genes (light blue) (columns) in the 14 clusters (rows). (B) Expression of neuron marker genes (black) and additional set of GABAergic neuron marker genes from a recent publication using snRNA-seq in mice (Luskin et al., 2023) (light blue) (columns) in the 14 clusters (rows). Cluster IDs are shown in labels on the right, and numbers of nuclei per cluster are shown in horizontal bars on the right. Heatmap values represent mean log-transformed normalized counts (logcounts) per cluster.

Figure 3—figure supplement 14. Spot-level deconvolution to map the spatial coordinates of snRNA-seq populations within the Visium SRT samples.

We applied a spot-level deconvolution algorithm (cell2location, Kleshchevnikov et al., 2022) to integrate the snRNA-seq and SRT data by estimating the cell abundance of the snRNA-seq populations, which are used as reference populations, at each spatial location (spot) in the Visium SRT samples. While this approach mapped (A) NE neurons (cluster 6) and (B) 5-HT neurons (cluster 21) to the spatial regions where these populations were previously identified based on expression of marker genes (Figure 2—figure supplement 3 and Figure 3—figure supplement 9), the overall mapping performance was relatively poor. We note that these are relatively rare populations, with relatively subtle expression differences compared to other neuronal populations, and NE neurons are characterized by large size and high transcriptional activity, which may have affected performance of the algorithm. The annotated LC regions are shown in Figure 2—figure supplement 1.

Figure 3—figure supplement 15. Spatial expression of dopamine (DA) neuron marker genes in Visium SRT samples.

We visualized the spatial expression of DA neuron marker genes (A) SLC6A3 (encoding the dopamine transporter), (B) ALDH1A1, and (C) SLC26A7 in the n=9 initial Visium SRT samples, which showed that these genes were not strongly expressed within the annotated LC regions. Color scale shows UMI counts per spot. The annotated LC regions are shown in Figure 2—figure supplement 1.

Figure 3—figure supplement 16. Expression of NE neuron marker genes, 5-HT neuron marker gene, and DA neuron marker gene in individual cells using RNAscope and high-magnification confocal imaging.

We applied RNAscope (Wang et al., 2012) and high-magnification confocal imaging (at 40 x magnification) to samples from 3 independent donors, (A) Br8689, (B) Br5529, and (C) Br5426, to visualize expression of two NE neuron marker genes (DBH and TH), one 5-HT neuron marker gene (TPH2), and one DA neuron marker gene (SLC6A3) within individual cells within the LC region in each sample. We do not observe expression of the DA neuron marker gene (SLC6A3, encoding the dopamine transporter) within individual NE neurons (identified by co-expression of DBH and TH). Scale bar: 40 μm.

Figure 3—figure supplement 17. High-resolution images demonstrating co-expression of cholinergic marker gene within NE neurons.

We applied RNAscope (Wang et al., 2012) and high-resolution imaging at 63 x magnification to visualize expression of SLC5A7 (cholinergic marker gene encoding the high affinity choline transporter, shown in pink) and TH (NE neuron marker gene encoding tyrosine hydroxylase, shown in green), and DAPI stain for nuclei (blue), within the LC region in a tissue section from donor Br8079. This confirmed co-expression of SLC5A7 and TH within individual cells. Scale bar: 25 μm.

Figure 3—figure supplement 18. Spatial expression of cholinergic marker genes in Visium SRT samples.

We visualized the spatial expression of cholinergic marker genes (A) SLC5A7 and (B) ACHE in the n=9 initial Visium SRT samples, which showed that these genes were expressed both within and outside the annotated LC regions. Color scale shows UMI counts per spot. The annotated LC regions are shown in Figure 2—figure supplement 1.

Figure 3—figure supplement 19. Overview of interactive web-accessible data resources.

All datasets described in this manuscript are freely accessible via interactive web apps and downloadable R/Bioconductor objects (see Table 1 for details). (A) Screenshot of Shiny (Chang et al., 2019) web app providing interactive access to Visium SRT data. (B) Screenshot of iSEE (Rue-Albrecht et al., 2018) web app providing interactive access to snRNA-seq data. For instructions on how to use the web apps to search for and display individual genes, see Figure 3—figure supplements 20 and 21.

Figure 3—figure supplement 20. Instructions to display individual genes in Visium SRT data app.

Example displaying screenshots and sequential instructions for how to search for individual genes and show spatial gene expression in Shiny (Chang et al., 2019) web app providing interactive access to Visium SRT data.

Figure 3—figure supplement 21. Instructions to display individual genes in snRNA-seq data app.

Example displaying screenshots and sequential instructions for how to search for individual genes and show gene expression by cluster or cell population in iSEE (Rue-Albrecht et al., 2018) web app providing interactive access to snRNA-seq data.