Molecular and spatial profiling of the paraventricular nucleus of the thalamus
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This study uses single cell sequencing to characterize transcriptional profiles of cells in a brain region called the PVT that plays many roles in brain function. The authors combine these data with dataset of neuronal connectivity and conclude there are transcriptomically distinguishable populations of neurons in the PVT with different function. These data deepen our understanding of an important brain region.
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Abstract
The paraventricular nucleus of the thalamus (PVT) is known to regulate various cognitive and behavioral processes. However, while functional diversity among PVT circuits has often been linked to cellular differences, the molecular identity and spatial distribution of PVT cell types remain unclear. To address this gap, here we used single nucleus RNA sequencing (snRNA-seq) and identified five molecularly distinct PVT neuronal subtypes in the mouse brain. Additionally, multiplex fluorescent in situ hybridization of top marker genes revealed that PVT subtypes are organized by a combination of previously unidentified molecular gradients. Lastly, comparing our dataset with a recently published single-cell sequencing atlas of the thalamus yielded novel insight into the PVT’s connectivity with the cortex, including unexpected innervation of auditory and visual areas. This comparison also revealed that our data contains a largely non-overlapping transcriptomic map of multiple midline thalamic nuclei. Collectively, our findings uncover previously unknown features of the molecular diversity and anatomical organization of the PVT and provide a valuable resource for future investigations.
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eLife assessment
This study uses single cell sequencing to characterize transcriptional profiles of cells in a brain region called the PVT that plays many roles in brain function. The authors combine these data with dataset of neuronal connectivity and conclude there are transcriptomically distinguishable populations of neurons in the PVT with different function. These data deepen our understanding of an important brain region.
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Reviewer #1 (Public Review):
In this manuscript the authors use single nucleus sequencing together with in situ to profile neurons from the paraventricular nucleus of the thalamus. The PVT has been implicated in diverse functions and here the authors use snRNAseq to try to assign those functions to distinct cell types within the structure. They first use punches of PVT and iterative clustering and filtering to find neuronal clusters with known PVT markers. Other cell types and neurons from surrounding brain regions were also present in the dataset. These data both support the previous division of PVT neurons into Drd2+/- cells and suggest these two groups can be further subdivided into 5 distinct clusters. In a nice in situ experiment the authors assessed top marker gene expression for each cluster across the anterior-posterior axis of …
Reviewer #1 (Public Review):
In this manuscript the authors use single nucleus sequencing together with in situ to profile neurons from the paraventricular nucleus of the thalamus. The PVT has been implicated in diverse functions and here the authors use snRNAseq to try to assign those functions to distinct cell types within the structure. They first use punches of PVT and iterative clustering and filtering to find neuronal clusters with known PVT markers. Other cell types and neurons from surrounding brain regions were also present in the dataset. These data both support the previous division of PVT neurons into Drd2+/- cells and suggest these two groups can be further subdivided into 5 distinct clusters. In a nice in situ experiment the authors assessed top marker gene expression for each cluster across the anterior-posterior axis of the PVT. This showed that the five types were largely in distinct spatial locations. Follow-up in situ with an additional set of marker genes supported the same conclusion but also showed that expression of single genes even within a cell "type" can vary. The authors discuss how the transcriptomes of the cell types could map onto known function of anterior and posterior PVT neurons. Finally, the authors integrate their sequencing data with a dataset of thalamic neurons with specific known projection patterns. Of the cells that co-cluster between the datasets, they identify specific transcriptomic populations of cells that best overlap different cortical projection patterns. The authors identify Col12a1 as a marker of one particular population of PFC-projecting cells.
The idea of spatial gradients of transcription in brain regions rather than discrete cell "types" has been shown in a number of recent studies that combine transcriptomics and in situ hybridization. Application of this idea to other important functional areas of the brain like the PVT generally enhances understanding of the parcellation of neuronal function. Combining these data with mapping of projection patterns by a lab interested in the function of this region, will be of interest to other researchers who study PVT and its role in brain circuits. The data appear to be of high quality and the discussion is scholarly.
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Reviewer #2 (Public Review):
This manuscript by Gao, Penzo and colleagues provides a first pass characterization of PVT neurons using single-cell RNA sequencing. Following identification and characterization of likely unique PVT cell types, the authors use multiplexed in situ hybridization to confirm the existence of differentially expressed genes and their spatial location along the AP, ML, and DV axes of the PVT. Finally, the authors compared their sequencing dataset to an existing single cell sequencing atlas, which includes projection-specific sequencing. Within these experiments, the authors describe the expression and spatial location of unique gene sets that are enriched within the clustered cell types. The authors use hierarchical clustering to suggest the existence of two main cell branches in PVT, with each of those branches …
Reviewer #2 (Public Review):
This manuscript by Gao, Penzo and colleagues provides a first pass characterization of PVT neurons using single-cell RNA sequencing. Following identification and characterization of likely unique PVT cell types, the authors use multiplexed in situ hybridization to confirm the existence of differentially expressed genes and their spatial location along the AP, ML, and DV axes of the PVT. Finally, the authors compared their sequencing dataset to an existing single cell sequencing atlas, which includes projection-specific sequencing. Within these experiments, the authors describe the expression and spatial location of unique gene sets that are enriched within the clustered cell types. The authors use hierarchical clustering to suggest the existence of two main cell branches in PVT, with each of those branches having subclassifications for a total of 5 identified cell populations.
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Reviewer #3 (Public Review):
This paper from Gao et al., uses single nuclei RNA sequencing to identify cell types and their putative gene markers for the paraventricular thalamus, a small midline brain region important for arousal and motivation. The dataset, collected from male mice, contains ~13,000 single nuclei transcriptomes from the PVT and surrounding regions. Overall, the collected data itself is generally of high quality, and the authors describe some gene markers and putative cell types in the PVT. The authors then go on to characterize PVT cell types from ~4,000 nuclei they identified from the first round of clustering as cell originating from the PVT. They go onto to use fluorescence in situ hybridization to show the spatial patterning of 5 putative marker genes they identified and provide summary disk plot data for the …
Reviewer #3 (Public Review):
This paper from Gao et al., uses single nuclei RNA sequencing to identify cell types and their putative gene markers for the paraventricular thalamus, a small midline brain region important for arousal and motivation. The dataset, collected from male mice, contains ~13,000 single nuclei transcriptomes from the PVT and surrounding regions. Overall, the collected data itself is generally of high quality, and the authors describe some gene markers and putative cell types in the PVT. The authors then go on to characterize PVT cell types from ~4,000 nuclei they identified from the first round of clustering as cell originating from the PVT. They go onto to use fluorescence in situ hybridization to show the spatial patterning of 5 putative marker genes they identified and provide summary disk plot data for the expression of genes for neuromodulator receptors, ion channel subunits, calcium binding proteins, and neuromodulators. The authors then integrate the data with a published 'thalamoseq' dataset of an additional ~2K neurons to show there may be some overlap with cell types identified in previous thalamic sequencing attempts and the current data. Overall, this is a nice start for understanding cell types in the PVT. While the data collected so far is of high quality, and will likely be of interest to the field, the total number of putative PVT cells are quite low (4K or so), which may be impacting the ability to accurately identify cell types. Consistent with this, it is unclear whether the data is best explained by 5 unique PVT neuronal cell types as they describe, or whether the clustering resolution is set too high, which is forcing cells into somewhat arbitrary clusters. By eye, the clusters in Figure 2 do not seem well separated in Umap space. This would likely be improved by additional cells added to the dataset or by demonstrating by other means that the current clustering resolution is appropriate. Alternatively, repeated data integration steps used to try and correct for batch effects may also be causing this.
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