Summary of data resources providing access to datasets described in this manuscript.

All datasets described in this manuscript are freely accessible in the form of interactive web apps and downloadable R/Bioconductor objects.

Experimental design to measure the landscape of gene expression in the postmortem human locus coeruleus (LC) using spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq).

(A) Brainstem dissections at the level of the LC were conducted to collect tissue blocks from 5 neurotypical adult human brain donors. (B) Inclusion of the LC within the tissue sample block was validated using RNAscope [31,32] for a pan-neuronal marker gene (SNAP25) and two NE neuron-specific marker genes (TH and SLC6A2). High-resolution H&E stained histology images were acquired prior to SRT and snRNA-seq assays (scale bars: 2 mm in H&E stained image; 20 μm in RNAscope images). (C) Prior to collecting tissue sections for SRT and snRNA-seq assays, tissue blocks were scored to enrich for the NE neuron-containing regions. For each sample, the LC region was manually annotated by visually identifying NE neurons in the H&E stained tissue sections. 100 μm tissue sections from 3 of the same donors were used for snRNA-seq assays, which included FANS-based neuronal enrichment prior to library preparation to enrich for neuronal populations.

Spatial gene expression in the human LC using SRT.

(A) Spots within manually annotated LC regions containing NE neurons (red) and non-LC regions (gray), which were identified based on pigmentation, cell size, and morphology from the H&E stained histology images, from donors Br2701 (top row) and Br8079 (bottom row). (B) Expression of two NE neuron-specific marker genes (TH and SLC6A2). Color scale indicates unique molecular identifier (UMI) counts per spot. Additional samples corresponding to A and B are shown in Supplementary Figures 1, 2A-B. (C) Boxplots illustrating the enrichment in expression of two NE neuron-specific marker genes (TH and SLC6A2) in manually annotated LC regions compared to non-LC regions in the N=8 Visium samples. Values show mean log-transformed normalized counts (logcounts) per spot within the regions per sample. Additional details are shown in Supplementary Figure 2C. (D) Volcano plot resulting from differential expression (DE) testing between the pseudobulked manually annotated LC and non-LC regions, which identified 32 highly significant genes (red) at a false discovery rate (FDR) significance threshold of 10−3 and expression fold-change (FC) threshold of 3 (dashed blue lines). Horizontal axis is shown on log2 scale and vertical axis on log10 scale. Additional details and results for 437 statistically significant genes identified at an FDR threshold of 0.05 and an FC threshold of 2 are shown in Supplementary Figure 7 and Supplementary Table 2. (E) Average expression in manually annotated LC and non-LC regions for the 32 genes from D. Color scale shows logcounts in the pseudobulked LC and non-LC regions averaged across N=8 Visium samples. Genes are ordered in descending order by FDR (Supplementary Table 2). (F-G) Cross-species comparison showing expression of human ortholog genes for LC-associated genes identified in the rodent LC [25,26] using alternative experimental technologies. Boxplots show mean logcounts per spot in the manually annotated LC and non-LC regions per sample in the human data.

Single-nucleus gene expression in the human LC using 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 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 numbers of nuclei per cluster are shown in horizontal bars on the right. Heatmap values represent mean logcounts per cluster. (B) UMAP representation of nuclei, with colors matching cell populations from heatmap. (C) DE testing between neuronal clusters identified a total of 327 statistically significant genes with elevated expression in the NE 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, 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 Table 4. Heatmap values represent mean logcounts in the NE neuron cluster and mean logcounts per cluster averaged across all other neuronal clusters. (D-E) Cross-species comparison showing expression of human ortholog genes for LC-associated genes identified in the rodent LC [25,26] 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 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 Table 5.

Spot-plot visualizations of manually annotated Visium spots within regions identified as containing LC-NE neurons in SRT data.

For each of the N=9 Visium capture areas (hereafter referred to as samples), the spots were manually annotated as being within the LC regions (red) or within the non-LC regions (gray) based on spots containing NE neurons, which were identified by pigmentation, cell size, and morphology on the H&E stained histology images.

Spatial expression of two NE neuron-specific marker genes in Visium samples for quality control (QC) in SRT data.

(A-B) Spot-plot visualizations of NE neuron marker gene expression (TH and SLC6A2, A and B, respectively) in the N=9 Visium samples. Color scale shows UMI counts per spot. One sample (Br5459_LC_round2) did not show clear expression of the NE neuron marker genes. This sample was excluded from subsequent analyses, leaving N=8 Visium capture areas (samples) from 4 out of the 5 donors. (C) Enrichment of NE neuron marker gene expression (TH and SLC6A2) within manually annotated LC regions compared to non-LC regions in the N=8 Visium samples. Boxplots show values as mean log-transformed normalized counts (logcounts) per spot within each region per sample, with samples represented by shapes.

Spot-level quality control (QC) data visualizations for Visium samples in SRT data.

(A) QC metrics, medians per sample (from left to right: sum of UMI counts per spot, number of detected genes per spot, and proportion of mitochondrial reads per spot). Boxplots show median for each QC metric per sample, with samples represented by shapes. (B) Applying thresholds of 3 median absolute deviations (MADs) to the sum of UMI counts and number of detected genes for each sample identified a total of 287 low-quality spots (red) (1.4% out of 20,667 total spots), which were removed from subsequent analyses. We did not use the proportion of mitochondrial reads for spot-level QC filtering (see Methods for more details).

Dimensionality reduction embeddings before and after batch integration across Visium samples in SRT data.

We applied a batch integration tool (Harmony [36]) to remove technical variation in the molecular measurements between the N=8 Visium samples from 4 donors. The integrated measurements were subsequently used as the input for spatially-aware clustering using BayesSpace [35]. (A) Principal component analysis (PCA) (top 2 PCs) calculated on molecular expression measurements, with spots labeled (left to right) by donor ID, round ID, and sample ID, without applying any batch integration. (B) Harmony embeddings (top 2 Harmony embedding dimensions) after applying Harmony batch integration on sample IDs, with spots labeled (left to right) by donor ID, round ID, and sample ID, demonstrating that the technical variation has been reduced.

Identifying LC and non-LC regions in a data-driven manner by spatially-aware unsupervised clustering in SRT data.

We applied a spatially-aware unsupervised clustering algorithm (BayesSpace [35]) to investigate whether the LC and non-LC regions in each Visium sample could be annotated in a data-driven manner. (A) Using BayesSpace with k=5 clusters, we clustered spots from the N=8 Visium samples using the Harmony batch-integrated molecular measurements. Cluster 4 (red) corresponds most closely to the manually annotated LC regions. (B) BayesSpace clustering performance evaluated in terms of concordance between cluster 4 (red) and the manually annotated LC region in each sample. Clustering performance was evaluated in terms of precision, recall, F1 score, and adjusted Rand index (ARI) (see Methods for definitions).

Comparison of spot-level and region-level manual annotations in SRT data.

(A) We manually annotated individual Visium spots (black) overlapping with NE neuron cell bodies within the previously manually annotated LC regions (red), based on pigmentation, cell size, and morphology from the H&E stained histology images, in the N=8 Visium samples. (B) We observed relatively low overlap between spots with expression of the NE neuron marker gene TH (>=2 observed UMI counts per spot) and the set of annotated individual spots. The differences included both false positives (annotated spots that were not TH+) and false negatives (TH+ spots that were not annotated). Therefore, we did not use the spot-level annotations for subsequent analyses, and instead used the LC region-level annotations for all further analyses.

Results from differential expression (DE) analysis to identify expressed genes associated with LC regions in SRT data.

We performed DE testing between the manually annotated LC and non-LC regions by pseudobulking spots, defined as aggregating UMI counts from the combined set of spots, within the annotated LC and non-LC regions in each sample. (A) Using a false discovery rate (FDR) significance threshold of 10−3 and an expression fold-change (FC) threshold of 3 (dashed blue lines), we identified 32 highly significant genes (red points). (B) Using standard significance thresholds of FDR < 0.05 and expression FC > 2, we identified 437 significant genes (red). Vertical axes are on reversed log10 scale, and horizontal axes are on log2 scale. Additional details are provided in Supplementary Table 2.

Results from applying nnSVG to identify spatially variable genes (SVGs) in SRT data.

We applied nnSVG [38], a method to identify spatially variable genes (SVGs), in the Visium SRT samples. We ran nnSVG within each contiguous tissue area containing a manually annotated LC region (13 tissue areas in the N=8 Visium samples) and calculated an overall ranking of top SVGs by averaging the ranks per gene from each tissue area. (A) The top 50 ranked SVGs from this analysis included a subset (11 out of 50) of genes that were highly ranked in samples from only one donor (Br8079, genes highlighted in maroon). We determined that this was due to the inclusion of a section of the choroid plexus adjacent to the LC for this donor. Bars show the number of times (out of 13 tissue areas) each gene was included within the top 100 SVGs. Rows are ordered by overall average ranking in descending order. (B) Spatial expression of CAPS, a choroid plexus marker gene, in the N=8 Visium samples. (C) Histology image showing the two tissue areas for sample Br8079_LC_round3. (D) In order to focus on LC-associated SVGs, we calculated an overall average ranking of SVGs that were each included within the top 100 SVGs in at least 10 out of the 13 tissue areas, which identified 32 highly-ranked, replicated LC-associated SVGs. Boxplots show the ranks in each tissue area. Rows are ordered by the overall average ranking in descending order.

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 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.

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 number of nuclei.

Expression of NE neuron marker genes in individual cells using RNAscope and high-magnification confocal imaging.

We applied RNAscope [32] and high-magnification confocal imaging to visualize expression of NE neuron marker genes (DBH in yellow, TH in green, and SLC6A2 in pink, with white representing all three colors overlapping) and DAPI stain for nuclei (blue) on additional tissue sections from an additional independent donor, Br8689. The figure displays a region from a single tissue section, demonstrating clear co-localization of expression of the three NE neuron marker genes (white points) within individual cells. Scale bar: 20 μm.

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 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 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 Tables 4, 5.

Unsupervised clustering results showing additional inhibitory neuronal, miscellaneous, 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 [27] (dark blue-purple), and cholinergic marker genes (yellow). We observed diversity in expression of inhibitory neuronal marker genes across inhibitory neuronal subpopulations (additional results in Supplementary Figure 16), and we observed expression of cholinergic marker genes within NE neurons (additional results in Supplementary Figure 18).

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. (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. Boxplots show values as mean log-transformed normalized counts (logcounts) per spot within each region per sample, with samples represented by shapes.

Expression of NE neuron and 5-HT neuron marker genes using RNAscope.

We applied RNAscope [32] 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.

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. Heatmap displays expression of neuronal marker genes (black) and inhibitory neuron marker genes (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.

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 [41]) 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. This correctly 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 (Supplementary Figures 2 and 14). However, the estimated absolute cell abundance of these populations per spot was higher than expected.

High-resolution images demonstrating co-expression of cholinergic marker gene within NE neurons.

We applied RNAscope [32] and high-resolution imaging at 63x 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), in a tissue section from donor Br8079. This confirmed co-expression of SLC5A7 and TH within individual cells. Scale bar: 25 μm.

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.

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 [42] web app providing interactive access to Visium SRT data. (B) Screenshot of iSEE [43] web app providing interactive access to snRNA-seq data.