Author
Listed:
- Jingtian Zhou
(The Salk Institute for Biological Studies
University of California San Diego)
- Zhuzhu Zhang
(The Salk Institute for Biological Studies
The University of Chicago)
- May Wu
(The Salk Institute for Biological Studies
University of California San Diego)
- Hanqing Liu
(The Salk Institute for Biological Studies)
- Yan Pang
(The Salk Institute for Biological Studies)
- Anna Bartlett
(The Salk Institute for Biological Studies)
- Zihao Peng
(Nanchang University
Henan Engineering Research Center of Intelligent Technology and Application, Henan University)
- Wubin Ding
(The Salk Institute for Biological Studies)
- Angeline Rivkin
(The Salk Institute for Biological Studies)
- Will N. Lagos
(The Salk Institute for Biological Studies)
- Elora Williams
(The Salk Institute for Biological Studies)
- Cheng-Ta Lee
(The Salk Institute for Biological Studies)
- Paula Assakura Miyazaki
(The Salk Institute for Biological Studies)
- Andrew Aldridge
(The Salk Institute for Biological Studies)
- Qiurui Zeng
(The Salk Institute for Biological Studies
University of California San Diego)
- J. L. Angelo Salinda
(The Salk Institute for Biological Studies)
- Naomi Claffey
(Flow Cytometry Core Facility, The Salk Institute for Biological Studies)
- Michelle Liem
(Flow Cytometry Core Facility, The Salk Institute for Biological Studies)
- Conor Fitzpatrick
(Flow Cytometry Core Facility, The Salk Institute for Biological Studies)
- Lara Boggeman
(Flow Cytometry Core Facility, The Salk Institute for Biological Studies)
- Zizhen Yao
(Allen Institute for Brain Science)
- Kimberly A. Smith
(Allen Institute for Brain Science)
- Bosiljka Tasic
(Allen Institute for Brain Science)
- Jordan Altshul
(The Salk Institute for Biological Studies)
- Mia A. Kenworthy
(The Salk Institute for Biological Studies)
- Cynthia Valadon
(The Salk Institute for Biological Studies)
- Joseph R. Nery
(The Salk Institute for Biological Studies)
- Rosa G. Castanon
(The Salk Institute for Biological Studies)
- Neelakshi S. Patne
(The Salk Institute for Biological Studies)
- Minh Vu
(The Salk Institute for Biological Studies)
- Mohammad Rashid
(The Salk Institute for Biological Studies)
- Matthew Jacobs
(The Salk Institute for Biological Studies)
- Tony Ito
(The Salk Institute for Biological Studies)
- Julia Osteen
(The Salk Institute for Biological Studies)
- Nora Emerson
(The Salk Institute for Biological Studies)
- Jasper Lee
(The Salk Institute for Biological Studies)
- Silvia Cho
(The Salk Institute for Biological Studies)
- Jon Rink
(The Salk Institute for Biological Studies)
- Hsiang-Hsuan Huang
(The Salk Institute for Biological Studies)
- António Pinto-Duartec
(The Salk Institute for Biological Studies)
- Bertha Dominguez
(The Salk Institute for Biological Studies)
- Jared B. Smith
(The Salk Institute for Biological Studies)
- Carolyn O’Connor
(Flow Cytometry Core Facility, The Salk Institute for Biological Studies)
- Hongkui Zeng
(Allen Institute for Brain Science)
- Shengbo Chen
(Henan Engineering Research Center of Intelligent Technology and Application, Henan University
Henan University)
- Kuo-Fen Lee
(The Salk Institute for Biological Studies)
- Eran A. Mukamel
(University of California San Diego)
- Xin Jin
(East China Normal University
New York University Shanghai)
- M. Margarita Behrens
(The Salk Institute for Biological Studies)
- Joseph R. Ecker
(The Salk Institute for Biological Studies
The Salk Institute for Biological Studies)
- Edward M. Callaway
(University of California San Diego
The Salk Institute for Biological Studies)
Abstract
Single-cell analyses parse the brain’s billions of neurons into thousands of ‘cell-type’ clusters residing in different brain structures1. Many cell types mediate their functions through targeted long-distance projections allowing interactions between specific cell types. Here we used epi-retro-seq2 to link single-cell epigenomes and cell types to long-distance projections for 33,034 neurons dissected from 32 different regions projecting to 24 different targets (225 source-to-target combinations) across the whole mouse brain. We highlight uses of these data for interrogating principles relating projection types to transcriptomics and epigenomics, and for addressing hypotheses about cell types and connections related to genetics. We provide an overall synthesis with 926 statistical comparisons of discriminability of neurons projecting to each target for every source. We integrate this dataset into the larger BRAIN Initiative Cell Census Network atlas, composed of millions of neurons, to link projection cell types to consensus clusters. Integration with spatial transcriptomics further assigns projection-enriched clusters to smaller source regions than the original dissections. We exemplify this by presenting in-depth analyses of projection neurons from the hypothalamus, thalamus, hindbrain, amygdala and midbrain to provide insights into properties of those cell types, including differentially expressed genes, their associated cis-regulatory elements and transcription-factor-binding motifs, and neurotransmitter use.
Suggested Citation
Jingtian Zhou & Zhuzhu Zhang & May Wu & Hanqing Liu & Yan Pang & Anna Bartlett & Zihao Peng & Wubin Ding & Angeline Rivkin & Will N. Lagos & Elora Williams & Cheng-Ta Lee & Paula Assakura Miyazaki & A, 2023.
"Brain-wide correspondence of neuronal epigenomics and distant projections,"
Nature, Nature, vol. 624(7991), pages 355-365, December.
Handle:
RePEc:nat:nature:v:624:y:2023:i:7991:d:10.1038_s41586-023-06823-w
DOI: 10.1038/s41586-023-06823-w
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