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

Stefano Nardone, Roberto Luca, Antonino Zito, Nataliya Klymko, Dimitris Nicoloutsopoulos, Oren Amsalem, Cory Brannigan, Jon M. Resch, Christopher L. Jacobs, Deepti Pant, Molly Veregge, Harini Srinivasan, Ryan M. Grippo, Zongfang Yang, Mark L. Zeidel, Mark L. Andermann, Kenneth D. Harris, Linus T. Tsai, Elda Arrigoni, Anne M. J. Verstegen (), Clifford B. Saper () and Bradford B. Lowell ()
Additional contact information
Stefano Nardone: Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School
Roberto Luca: Division of Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School
Antonino Zito: King’s College London
Nataliya Klymko: Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave
Dimitris Nicoloutsopoulos: University College London
Oren Amsalem: Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School
Cory Brannigan: HEAVY.AI, 100 Montgomery St Fl 5, San Francisco
Jon M. Resch: University of Iowa
Christopher L. Jacobs: Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School
Deepti Pant: Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School
Molly Veregge: Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School
Harini Srinivasan: Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School
Ryan M. Grippo: Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School
Zongfang Yang: Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School
Mark L. Zeidel: Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave
Mark L. Andermann: Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School
Kenneth D. Harris: University College London
Linus T. Tsai: Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School
Elda Arrigoni: Division of Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School
Anne M. J. Verstegen: Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave
Clifford B. Saper: Division of Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School
Bradford B. Lowell: Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School

Nature Communications, 2024, vol. 15, issue 1, 1-21

Abstract: 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 the unique marker genes of many neuronal subtypes. 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.

Date: 2024
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DOI: 10.1038/s41467-024-45907-7

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