Relative cell type fraction estimates in bulk RNA-sequencing data are important to control for cell composition differences across heterogenous tissue samples. Current computational tools estimate relative RNA abundances rather than cell type proportions in tissues with varying cell sizes, leading to biased estimates. We present lute, a computational tool to accurately deconvolute cell types with varying sizes. Our software wraps existing deconvolution algorithms in a standardized framework. Using simulated and real datasets, we demonstrate how lute adjusts for differences in cell sizes to improve the accuracy of cell composition. Software is available from https://bioconductor.org/packages/lute.
Norepinephrine (NE) neurons in the locus coeruleus (LC) project widely throughout the central nervous system, playing critical roles in arousal and mood, as well as various components of cognition including attention, learning, and memory. The LC-NE system is also implicated in multiple neurological and neuropsychiatric disorders. Importantly, LC-NE neurons are highly sensitive to degeneration in both Alzheimer’s and Parkinson’s disease. Despite the clinical importance of the brain region and the prominent role of LC-NE neurons in a variety of brain and behavioral functions, a detailed molecular characterization of the LC is lacking. Here, we used a combination of spatially-resolved transcriptomics and single-nucleus RNA-sequencing to characterize the molecular landscape of the LC region and the transcriptomic profile of LC-NE neurons in the human brain. We provide a freely accessible resource of these data in web-accessible formats.
Full text Figures and data Side by side Abstract eLife assessment Introduction Results Discussion Materials and methods Data availability References Peer review Author response Article and author information Metrics Abstract Norepinephrine (NE) neurons in the locus coeruleus (LC) make long-range projections throughout the central nervous system, playing critical roles in arousal and mood, as well as various components of cognition including attention, learning, and memory. The LC-NE system is also implicated in multiple neurological and neuropsychiatric disorders. Importantly, LC-NE neurons are highly sensitive to degeneration in both Alzheimer's and Parkinson's disease. Despite the clinical importance of the brain region and the prominent role of LC-NE neurons in a variety of brain and behavioral functions, a detailed molecular characterization of the LC is lacking. Here, we used a combination of spatially-resolved transcriptomics and single-nucleus RNA-sequencing to characterize the molecular landscape of the LC region and the transcriptomic profile of LC-NE neurons in the human brain. We provide a freely accessible resource of these data in web-accessible and downloadable formats. eLife assessment This is an important initial study of cell type and spatially resolved gene expression in and around the locus coeruleus, the primary source of the neuromodulator norepinephrine in the human brain. The data are generated with cutting-edge techniques, and the work lays the foundation for future descriptive and experimental approaches to understand the contribution of the locus coeruleus to healthy brain function and disease. The empirical support for the main conclusions is solid. This paper, and the associated web application, will be of great interest to neuroscientists working on arousal-based behaviors and neurological and neuropsychiatric phenotypes. https://doi.org/10.7554/eLife.84628.3.sa0 About eLife assessments Introduction The LC is a small bilateral nucleus located in the dorsal pons of the brainstem, which serves as the brain's primary site for the production of the neuromodulator NE. NE-producing neurons in the LC project widely to many regions of the central nervous system to modulate a variety of highly divergent functions including attention, arousal, pain, mood, and the response to stress (Poe et al., 2020; Chandler et al., 2019; Sara, 2009; Morris et al., 2020; Suárez-Pereira et al., 2022; Ross and Van Bockstaele, 2020). The LC, translated as 'blue spot' (in recognition of its characteristic presence of neuromelanin pigment), comprises merely 3000 NE neurons in the rodent (~1500–1600 on each side of the brainstem) (Poe et al., 2020), and estimates in the human LC range from 19,000 to 46,000 total NE neurons (German et al., 1988). Despite its prominent involvement in a number of critical brain functions and its distinctive capacity to synthesize NE, the LC's small size and deep positioning within the brainstem have rendered it relatively intractable to a comprehensive cellular, molecular, and physiological characterization. The LC plays important roles in core behavioral and physiological brain function across the lifespan and in disease. Consistent with these roles, the LC-NE system is implicated in many neurodegenerative, neuropsychiatric, and neurological disorders (Morris et al., 2020; Weinshenker, 2018). The LC is one of the earliest sites of degeneration in both Alzheimer's disease (AD) and Parkinson's disease (PD), and profound loss of LC-NE neurons is evident with disease progression (Weinshenker, 2018; Mather and Harley, 2016; Chalermpalanupap et al., 2017). Moreover, maintaining the neural density of LC-NE neurons prevents cognitive decline during aging (Wilson et al., 2013). In addition, primary neuropathologies for AD (hyperphosphorylated tau) and PD (alpha-synuclein) can be detected in the LC prior to other brain regions (Grudzien et al., 2007; Andrés-Benito et al., 2017; Braak and Del Tredici, 2012; Del Tredici and Braak, 2013). However, the molecular mechanisms rendering LC-NE neurons particularly vulnerable to neurodegeneration are not well-understood. In addition to its role in neurodegenerative disorders, the LC-NE system is implicated in a number of other complex brain disorders. Noradrenergic signaling controls many cognitive functions, including sustained attention, and its dysregulation is associated with attention-deficit hyperactivity disorder (ADHD) (Asherson et al., 2016; Biederman and Spencer, 1999). Related to these findings, the NE reuptake inhibitor atomoxetine is the first non-stimulant medication that is FDA-approved for treating ADHD (Bouret and Sara, 2004; Bymaster et al., 2002; Newman et al., 2008). Disruption of noradrenergic signaling is also associated with anxiety, addiction, and responses to stress and trauma, and drugs that modulate noradrenergic signaling have been used in the treatment of post-traumatic stress disorder (PTSD), major depressive disorder (MDD), anxiety, and opioid withdrawal (Morris et al., 2020; Dell'Osso et al., 2011; Weinshenker and Schroeder, 2007; Berridge and Waterhouse, 2003; Urits et al., 2020; Paiva et al., 2021). Given the wide range of functions that are modulated by the LC-NE system, an improved understanding of the gene expression landscape of the LC and the surrounding region and delineating the molecular profile of LC-NE neurons in the human brain could facilitate the ability to target these neurons for disease prevention or manipulate their function for treatment in a variety of disorders. The recent development of single-nucleus RNA-sequencing (snRNA-seq) and spatially-resolved transcriptomics (SRT) technological platforms provides an opportunity to investigate transcriptome-wide gene expression at cellular and spatial resolution (Kamath et al., 2022; Maynard et al., 2021). SRT has recently been used to characterize transcriptome-wide gene expression within defined neuroanatomy of cortical regions in the postmortem human brain (Maynard et al., 2021), while snRNA-seq has been used to investigate specialized cell types in a number of postmortem human brain regions including medium spiny neurons in the nucleus accumbens and dopaminergic neurons in the midbrain (Kamath et al., 2022; Tran et al., 2021). Importantly, snRNA-seq and SRT provide complementary views: snRNA-seq identifies transcriptome-wide gene expression within individual nuclei, while SRT captures transcriptome-wide gene expression in all cellular compartments (including the nucleus, cytoplasm, and cell processes) while retaining the spatial coordinates of these measurements. While not all SRT platforms achieve single-cell resolution, depending on the technological platform and tissue cell density, spatial gene expression has been resolved at, for example, ~1–10 cells per spatial measurement location with a diameter of 55 μm in the human brain (Maynard et al., 2021). These platforms have been successfully used in tandem to spatially map single-nucleus gene expression in several regions of both neurotypical and pathological tissues in the human brain including the dorsolateral prefrontal cortex (Maynard et al., 2021) and the dopaminergic substantia nigra (Kamath et al., 2022). In this report, we characterize the gene expression signature of the LC and surrounding region at spatial resolution, and identify and characterize a population of NE neurons at single-nucleus resolution in the neurotypical adult human brain. In addition to NE neurons, we identify a population of 5-hydroxytryptamine (5-HT, serotonin) neurons, which have not previously been characterized at the molecular level in human brain samples (Okaty et al., 2019). We observe the expression of cholinergic marker genes within NE neurons, a finding which we confirm using multiplexed single-molecule fluorescence in situ hybridization (smFISH) with high-resolution imaging at cellular resolution. We compare our findings from the human LC and adjacent region to molecular profiles of LC and peri-LC neurons that were previously characterized in rodents using alternative technological platforms (Mulvey et al., 2018; Grimm et al., 2004; Luskin et al., 2023), and observe partial conservation of LC-associated genes across these species. Results Experimental design and study overview of postmortem human LC We selected five neurotypical adult human brain donors to characterize transcriptome-wide gene expression within the LC at spatial and single-nucleus resolution using the 10x Genomics Visium SRT (10x Genomics, 2022a) and 10x Genomics Chromium snRNA-seq (10x Genomics, 2022b) platforms (see Supplementary file 1 for donor demographic details). After all quality control (QC) steps (described below), the final SRT and snRNA-seq datasets used for analyses consisted of samples from 4 and 3 donors, respectively. In each tissue sample, the LC was first visually identified by neuroanatomical landmarks and the presence of the pigment neuromelanin on transverse slabs of the pons (Figure 1A). Prior to SRT and snRNA-seq assays, we ensured that the tissue blocks encompassed the LC by probing for known NE neuron marker genes (Counts and Mufson, 2012). Specifically, we cut 10 μm cryosections from tissue blocks from each donor and probed for the presence of a pan-neuronal marker gene (SNAP25) and two NE neuron-specific marker genes (TH and SLC6A2) by multiplexed single-molecule fluorescence in situ hybridization (smFISH) using RNAscope (Maynard et al., 2020; Wang et al., 2012; Figure 1B). Robust mRNA signal from these markers, visualized as puncta on imaged tissue sections, was used as a quality control measure in all tissue blocks prior to proceeding with inclusion in the study and performing SRT and snRNA-seq assays. Figure 1 Download asset Open asset 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 five neurotypical adult human brain donors. (B) Inclusion of the LC within the tissue sample block was validated using RNAscope (Maynard et al., 2020; Wang et al., 2012) for a pan-neuronal marker gene (SNAP25) and two norepinephrine (NE) neuron-specific marker genes (TH and SLC6A2). High-resolution hematoxylin and eosin (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 three 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. After all quality control (QC) steps, the final SRT and snRNA-seq datasets used for analyses consisted of samples from 4 and 3 donors, respectively. For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform (10x Genomics, 2022a; Figure 1C). Fresh-frozen tissue sections were placed onto each of four capture areas per Visium slide, where each capture area contains approximately 5000 expression spots (spatial measurement locations with diameter 55 μm and 100 μm center-to-center, where transcripts are captured) laid out in a honeycomb pattern. Spatial barcodes unique to each spot are incorporated during reverse transcription, thus allowing the spatial coordinates of the gene expression measurements to be identified (10x Genomics, 2022a). Visium slides were stained with hematoxylin and eosin (H&E), followed by high-resolution acquisition of histology images prior to on-slide cDNA synthesis, completion of the Visium assay, and sequencing. For our study, 10 μm tissue sections from the LC-containing tissue blocks were collected from the five brain donors, with assays completed on 2–4 tissue sections per donor. Given the small size of the LC compared to the area of the array, tissue blocks were scored to fit 2–3 tissue sections from the same donor onto a single capture area to maximize the use of the Visium slides, resulting in a total of n=9 Visium capture areas (hereafter referred to as samples). For 3 of the 5 donors, we cut additional 100 μm sections from the same tissue blocks to profile transcriptome-wide gene expression at single-nucleus resolution with the 10x Genomics Chromium single cell 3' gene expression platform (10x Genomics, 2022b; Figure 1C). Prior to collecting tissue sections, the tissue blocks were scored to enrich for NE neuron-containing regions. Neuronal enrichment was employed with fluorescence-activated nuclear sorting (FANS) prior to library preparation to enhance the capture of neuronal population diversity, and snRNA-seq assays were subsequently completed. Supplementary file 1 provides a summary of SRT and snRNA-seq sample information and demographic characteristics of the donors. Spatial gene expression in the human LC After applying the 10x Genomics Visium SRT platform (10x Genomics, 2022a), we executed several analyses to characterize transcriptome-wide gene expression at spatial resolution within the human LC. First, we manually annotated spots within regions identified as containing LC-NE neurons, based on pigmentation, cell size, and morphology from the H&E stained histology images (Figure 2A and Figure 2—figure supplement 1). Analysis of cell segmentations of the H&E images (Figure 2—figure supplement 2A) revealed that the median number of cells per spot within the LC regions ranged from 2 to 5 per sample (Figure 2—figure supplement 2B). Next, we performed additional sample-level QC on the initial n=9 Visium capture areas (hereafter referred to as samples) by visualizing the expression of two NE neuron-specific marker genes (TH and SLC6A2) (Figure 2B), which identified one sample (Br5459_LC_round2) without clear expression of these markers (Figure 2—figure supplement 3A–B). This sample was excluded from subsequent analyses, leaving n=8 samples from 4 out of the 5 donors. For the n=8 Visium samples, the annotated regions were highly enriched in the expression of the NE neuron marker genes (TH and SLC6A2) (Figure 2C and Figure 2—figure supplement 3C), confirming that these samples captured dense regions of LC-NE neurons within the annotated regions. We performed spot-level QC to remove low-quality spots based on QC metrics previously applied to SRT data (Maynard et al., 2021; Amezquita et al., 2020; Lun et al., 2016) (Methods). Due to the large differences in read depth between samples (Figure 2—figure supplement 4A, Supplementary file 1, Methods), we performed spot-level QC independently within each sample. After filtering low-expressed genes (Methods), this resulted in a total of 12,827 genes and 20,380 spots across the n=8 samples used for downstream analyses (Figure 2—figure supplement 4B). Figure 2 with 9 supplements see all Download asset Open asset Spatial gene expression in the human locus coeruleus (LC) using spatially-resolved transcriptomics (SRT). (A) Spots within manually annotated LC regions containing norepinephrine (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 Figure 2—figure supplements 1 and 3A, 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 Figure 2—figure supplement 3C. (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 Figure 2—figure supplement 8 and Supplementary file 2A. (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 file 2A). (F–G) 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 mean logcounts per spot in the manually annotated LC and non-LC regions per sample in the human data. To investigate whether the LC regions could be annotated in a data-driven manner, we applied a spatially-aware unsupervised clustering algorithm (BayesSpace, Zhao et al., 2021) after applying a batch integration tool (Harmony, Korsunsky et al., 2019) to remove sample-specific technical variation in the molecular measurements (Figure 2—figure supplement 5). The spatially-aware clustering using k=5 clusters (across eight samples) identified one cluster that overlapped with the manually annotated LC regions in several samples. However, the proportion of overlapping spots between the manually annotated LC region and this data-driven cluster (cluster 4, colored red in Figure 2—figure supplement 6A) was relatively low and varied across samples. We quantitatively evaluated the clustering performance by calculating the precision, recall, F1 score, and adjusted Rand index (ARI) for this cluster in each sample (see Methods for definitions). We found that while precision was >0.8 in 3 out of 8 samples, recall was <0.4 in all samples, the F1 score was <0.6 in all samples, and the ARI was <0.5 in all samples (Figure 2—figure supplement 6B). Therefore, we judged that the data-driven spatial domains identified from BayesSpace were not sufficiently reliable to use for the downstream analyses, and instead proceeded with the histology-driven manual annotations for all further analyses. In addition, we note that using the manual annotations avoids potential issues due to inflated false discoveries resulting from circularity when performing differential gene expression testing between sets of cells or spots defined by unsupervised clustering, when the same genes are used for both clustering and differential testing (Gao et al., 2022). Next, in addition to the manually annotated LC regions, we also manually annotated a set of individual spots that overlapped with NE neuron cell bodies identified within the LC regions, based on pigmentation, cell size, and morphology from the H&E histology images (Figure 2—figure supplement 7A). However, we observed relatively low overlap between spots with expression of NE neuron marker genes and this second set of annotated individual spots. For example, out of 706 annotated spots, only 331 spots had ≥2 observed UMI counts of TH (Figure 2—figure supplement 7B). We hypothesize that this may be due to technical factors including sampling variability in the gene expression measurements, partial overlap between spots and cell bodies, potential diffusion of mRNA molecules between spots, as well as biological variability in the expression of these marker genes. Therefore, we instead used the LC region-level manual annotations for all further analyses. Next, to identify expressed genes associated with the LC regions, we performed DE testing between the manually annotated LC and non-LC regions by pseudobulking spots, defined as aggregating UMI counts from the combined spots within the annotated LC and non-LC regions in each sample (Maynard et al., 2021). This analysis identified 32 highly significant genes at a false discovery rate (FDR) threshold of 10–3 and expression FC threshold of 3 (Figure 2D and Figure 2—figure supplement 8A). This includes known NE neuron marker genes including DBH (the top-ranked gene by FDR within this set), SLC6A2 (ranked 6th), TH (ranked 7th), and SLC18A2 (ranked 14th). Out of the 32 genes, 31 were elevated in expression within the LC regions, while one (MCM5) was depleted. The set includes one long noncoding RNA (LINC00682), while the remaining 31 genes are protein-coding genes (Figure 2E and Supplementary file 2A). Alternatively, using standard significance thresholds of FDR <0.05 and expression FC >2, we identified a total of 437 statistically significant genes (Figure 2—figure supplement 8B and Supplementary file 2A). As a second approach to identify genes associated with LC-NE neurons in an unsupervised manner, we applied a method to identify spatially variable genes (SVGs), nnSVG (Weber et al., 2023c). This method ranks genes in terms of the strength in the spatial correlation in their expression patterns across the tissue areas. We ran nnSVG within each contiguous tissue area containing an annotated LC region for the n=8 Visium samples (a total of 13 tissue areas, where each Visium sample contains 1–3 tissue areas) and combined the lists of top-ranked SVGs for the multiple tissue areas by averaging the ranks per gene. In this analysis, we found that a subset of the top-ranked SVGs (11 out of the top 50) were highly-ranked in samples from only one donor (Br8079), which we determined was due to the inclusion of a section of the choroid plexus adjacent to the LC in these samples (based on expression of choroid plexus marker genes including CAPS and CRLF1) (Figure 2—figure supplement 9A–C). In order to focus on LC-associated SVGs that were replicated across samples, we excluded the choroid plexus-associated genes by calculating 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 a list of 32 highly-ranked, replicated LC-associated SVGs. These genes included known NE neuron marker genes (DBH, TH, SLC6A2, and SLC18A2) as well as mitochondrial genes (Figure 2—figure supplement 9D and Supplementary file 2B). We also compared the expression of LC-associated genes previously identified in the rodent LC from two separate studies. The first study used translating ribosomal affinity purification sequencing (TRAP-seq) using an SLC6A2 bacTRAP mouse line to identify gene expression profiles of the translatome of LC neurons (Mulvey et al., 2018). The second study used microarrays to assess gene expression patterns from laser-capture microdissections of individual cells in tissue sections of the rat LC (Grimm et al., 2004). We converted the lists of rodent LC-associated genes from these studies to human orthologs and calculated the average expression for each gene within the manually annotated LC and non-LC regions. A small number of genes from both studies were highly associated with the manually annotated LC regions in the human data, including DBH, TH, and SLC6A2 from Mulvey et al., 2018, and DBH and GNAS from Grimm et al., 2004. However, the majority of the genes from both studies were expressed at low levels in the human data, which may reflect species-specific differences in the biological function of these genes as well as differences due to the experimental technologies employed (Figure 2F–G). Single-nucleus gene expression of NE neurons in the human LC To add cellular resolution to our spatial analyses, we characterized gene expression in the human LC and the surrounding region at single-nucleus resolution using the 10x Genomics Chromium single cell 3' gene expression platform (10x Genomics, 2022b) in 3 of the same neurotypical adult donors from the SRT analyses. Samples were enriched for NE neurons by scoring tissue blocks for the LC region and performing FANS to enhance the capture of neurons. After raw data processing, doublet removal using scDblFinder (Germain et al., 2021), and standard QC and filtering, we obtained a total of 20,191 nuclei across the three samples (7957, 3015, and 9219 nuclei respectively from donors Br2701, Br6522, and Br8079) (see Supplementary file 1 for additional details). For nucleus-level QC processing, we used standard QC metrics including the sum of UMI counts and detected genes (Amezquita et al., 2020) (see Methods for additional details). We observed an unexpectedly high proportion of mitochondrial reads in nuclei with expression of NE neuron marker genes (DBH, TH, and SLC6A2), which represented our rare population of interest, and hence we did not remove nuclei based on the proportion of mitochondrial reads (Figure 3—figure supplement 1, Figure 3—figure supplement 2 and Figure 3—figure supplement 3, additional details described below). We identified NE neuron nuclei in the snRNA-seq data by applying an unsupervised clustering workflow adapted from workflows used for snRNA-seq data in the human brain (Tran et al., 2021), using a two-stage clustering algorithm consisting of high-resolution k-means and graph-based clustering that provides sensitivity to identify rare cell populations (Amezquita et al., 2020). The unsupervised clustering workflow identified 30 clusters, including clusters representing major neuronal and non-neuronal cell populations, which we labeled based on the expression of known marker genes (Figure 3A–B). This included a cluster of NE neurons consisting of 295 nuclei (168, 4, and 123 nuclei from donors Br2701, Br6522, and Br8079, respectively), which we identified based on the expression of NE neuron marker genes (DBH, TH, and SLC6A2). In addition to the NE neuron cluster, we identified clusters representing excitatory neurons, inhibitory neurons, astrocytes, endothelial and mural cells, macrophages and microglia, oligodendrocytes, and oligodendrocyte precursor cells (OPCs), as well as several clusters with ambiguous expression profiles including pan-neuronal marker genes (SNAP25 and SYT1) without clear expression of excitatory or inhibitory neuronal markers, which may represent damaged neuronal nuclei and debris (Figure 3A–B and Figure 3—figure supplement 1). Further evaluation of QC metrics revealed that standard QC metrics (sum of UMI counts and detected genes) for the NE neuron cluster fell within the ranges of values observed for the other neuronal and non-neuronal clusters (Figure 3—figure supplement 2A, B), and that the ambiguous neuronal category included a clear subset of measurements with the overall highest mitochondrial proportions and lowest number of detected genes (likely damaged nuclei and/or debris), which remained separate from the NE neuron cluster (Figure 3—figure supplement 2C, D), thus providing additional confidence that the high mitochondrial proportions observed for the NE neuron cluster were not due to mis-classified damaged nuclei and/or debris. Figure 3 with 21 supplements see all Download asset Open asset 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 neu
The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.
Norepinephrine (NE) neurons in the locus coeruleus (LC) make long-range projections throughout the central nervous system, playing critical roles in arousal and mood, as well as various components of cognition including attention, learning, and memory. The LC-NE system is also implicated in multiple neurological and neuropsychiatric disorders. Importantly, LC-NE neurons are highly sensitive to degeneration in both Alzheimer's and Parkinson's disease. Despite the clinical importance of the brain region and the prominent role of LC-NE neurons in a variety of brain and behavioral functions, a detailed molecular characterization of the LC is lacking. Here, we used a combination of spatially-resolved transcriptomics and single-nucleus RNA-sequencing to characterize the molecular landscape of the LC region and the transcriptomic profile of LC-NE neurons in the human brain. We provide a freely accessible resource of these data in web-accessible and downloadable formats.
Norepinephrine (NE) neurons in the locus coeruleus (LC) make long-range projections throughout the central nervous system, playing critical roles in arousal and mood, as well as various components of cognition including attention, learning, and memory. The LC-NE system is also implicated in multiple neurological and neuropsychiatric disorders. Importantly, LC-NE neurons are highly sensitive to degeneration in both Alzheimer’s and Parkinson’s disease. Despite the clinical importance of the brain region and the prominent role of LC-NE neurons in a variety of brain and behavioral functions, a detailed molecular characterization of the LC is lacking. Here, we used a combination of spatially-resolved transcriptomics and single-nucleus RNA-sequencing to characterize the molecular landscape of the LC region and the transcriptomic profile of LC-NE neurons in the human brain. We provide a freely accessible resource of these data in web-accessible and downloadable formats.
Abstract Parkinson’s disease (PD) is the second most common neurodegenerative disorder that results in motor dysfunction and eventually, cognitive impairment. α-Synuclein protein has been known to be the most culprit protein, but the underlying pathological mechanism still remains to be elucidated. As an effort to clarify the pathogenesis mechanism by α-synuclein, various PD mouse models with α-synuclein overexpression have been developed. However, the systemic analysis of protein abundance change by the overexpressed α-synuclein in the whole proteome level has been still lacking. To address this issue, we established two sophisticated mouse models of PD by injecting α-synuclein preformed fibrils (PFF) or by inducing overexpression of human A53T α-synuclein to discover overlapping pathways, which could be altered in the two different types of PD mouse model. For more accurate quantification of mouse brain proteome, stable isotope labeling with amino acid in mammal-based quantification was implemented. As a result, we have successfully identified a total of 8,355 proteins from both of the mouse models; ∼6,800 and ∼7,200 proteins from α-synuclein PFF injected mice and human A53T α-synuclein transgenic mice, respectively. From the pathway analysis of the differentially expressed proteins in common, the complement and coagulation cascade pathway were determined as the most enriched ones. This is the first study that highlights the significance of the complement and coagulation pathway in the pathogenesis of PD through proteome analyses with two sophisticated mouse models of PD.
Abstract Hepatocyte growth factor (HGF) and its receptor, cMet, activate biological pathways necessary for repair and regeneration following kidney injury. Because HGF is a highly unstable molecule in its biologically active form, we asked whether a monoclonal antibody (Ab) that displays full agonist activity at the receptor could protect the kidney from fibrosis. We attempted to determine whether the cMet agonistic Ab might reduce fibrosis, the final common pathway for chronic kidney diseases (CKD). A mouse model of kidney fibrosis disease induced by unilateral ureteral obstruction was introduced and subsequently validated with primary cultured human proximal tubular epithelial cells (PTECs). In kidney biopsy specimens from patients with CKD, cMet immunohistochemistry staining showed a remarkable increase compared with patients with normal renal functions. cMet Ab treatment significantly increased the levels of phospho-cMet and abrogated the protein expression of fibrosis markers such as fibronectin, collagen 1, and αSMA as well as Bax2, which is a marker of apoptosis triggered by recombinant TGF-β1 in PTECs. Remarkably, injections of cMet Ab significantly prevented kidney fibrosis in obstructed kidneys as quantified by Masson trichrome staining. Consistent with these data, cMet Ab treatment decreased the expression of fibrosis markers, such as collagen1 and αSMA, whereas the expression of E-cadherin, which is a cell-cell adhesion molecule, was restored. In conclusion, cMet-mediated signaling may play a considerable role in kidney fibrosis. Additionally, the cMet agonistic Ab may be a valuable substitute for HGF because it is more easily available in a biologically active, stable, and purified form.
Norepinephrine (NE) neurons in the locus coeruleus (LC) make long-range projections throughout the central nervous system, playing critical roles in arousal and mood, as well as various components of cognition including attention, learning, and memory. The LC-NE system is also implicated in multiple neurological and neuropsychiatric disorders. Importantly, LC-NE neurons are highly sensitive to degeneration in both Alzheimer’s and Parkinson’s disease. Despite the clinical importance of the brain region and the prominent role of LC-NE neurons in a variety of brain and behavioral functions, a detailed molecular characterization of the LC is lacking. Here, we used a combination of spatially-resolved transcriptomics and single-nucleus RNA-sequencing to characterize the molecular landscape of the LC region and the transcriptomic profile of LC-NE neurons in the human brain. We provide a freely accessible resource of these data in web-accessible and downloadable formats.
Norepinephrine (NE) neurons in the locus coeruleus (LC) make long-range projections throughout the central nervous system, playing critical roles in arousal and mood, as well as various components of cognition including attention, learning, and memory. The LC-NE system is also implicated in multiple neurological and neuropsychiatric disorders. Importantly, LC-NE neurons are highly sensitive to degeneration in both Alzheimer's and Parkinson's disease. Despite the clinical importance of the brain region and the prominent role of LC-NE neurons in a variety of brain and behavioral functions, a detailed molecular characterization of the LC is lacking. Here, we used a combination of spatially-resolved transcriptomics and single-nucleus RNA-sequencing to characterize the molecular landscape of the LC region and the transcriptomic profile of LC-NE neurons in the human brain. We provide a freely accessible resource of these data in web-accessible and downloadable formats.
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