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A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution
Preprint   Open access

A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution

Stefano Nardone, Roberto De Luca, Antonino Zito, Nataliya Klymko, Dimitris Nicoloutsopoulos, Oren Amsalem, Cory Brannigan, Jon M Resch, Christopher L Jacobs, Deepti Pant, …
bioRxiv : the preprint server for biology
11/17/2023
DOI: 10.1101/2023.09.18.558047
PMCID: PMC10680649
PMID: 38014113
url
https://doi.org/10.1101/2023.09.18.558047 View
Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kölliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled ~1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed many neuronal subtypes' unique marker genes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this study's translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard (http://harvard.heavy.ai:6273/) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data.

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