Transcriptomic encoding of sensorimotor transformation in the midbrain

  1. Zhiyong Xie
  2. Mengdi Wang
  3. Zeyuan Liu
  4. Congping Shang
  5. Changjiang Zhang
  6. Le Sun
  7. Huating Gu
  8. Gengxin Ran
  9. Qing Pei
  10. Qiang Ma
  11. Meizhu Huang
  12. Junjing Zhang
  13. Rui Lin
  14. Youtong Zhou
  15. Jiyao Zhang
  16. Miao Zhao
  17. Minmin Luo
  18. Qian Wu  Is a corresponding author
  19. Peng Cao  Is a corresponding author
  20. Xiaoqun Wang  Is a corresponding author
  1. National Institute of Biological Sciences, China
  2. Institute of Biophysics, Chinese Academy of Sciences, China
  3. Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), China
  4. Capital Medical University, China
  5. Beijing Normal University, China
  6. National Institute of Biological Science, China
  7. National Institute of Biological Sciences, Beijing, China

Abstract

Sensorimotor transformation, a process that converts sensory stimuli into motor actions, is critical for the brain to initiate behaviors. Although the circuitry involved in sensorimotor transformation has been well delineated, the molecular logic behind this process remains poorly understood. Here, we performed high-throughput and circuit-specific single-cell transcriptomic analyses of neurons in the superior colliculus (SC), a midbrain structure implicated in early sensorimotor transformation. We found that SC neurons in distinct laminae express discrete marker genes. Of particular interest, Cbln2 and Pitx2 are key markers that define glutamatergic projection neurons in the optic nerve (Op) and intermediate gray (InG) layers, respectively. The Cbln2+ neurons responded to visual stimuli mimicking cruising predators, while the Pitx2+ neurons encoded prey-derived vibrissal tactile cues. By forming distinct input and output connections with other brain areas, these neuronal subtypes independently mediate behaviors of predator avoidance and prey capture. Our results reveal that, in the midbrain, sensorimotor transformation for different behaviors may be performed by separate circuit modules that are molecularly defined by distinct transcriptomic codes.

Data availability

The scRNA-seq data used in this study have been deposited in the Gene Expression Omnibus (GEO) under accession numbers GSE162404 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE162404).

The following data sets were generated

Article and author information

Author details

  1. Zhiyong Xie

    National Institute of Biological Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  2. Mengdi Wang

    Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  3. Zeyuan Liu

    Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0007-9874
  4. Congping Shang

    Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  5. Changjiang Zhang

    Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  6. Le Sun

    Capital Medical University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  7. Huating Gu

    National Institute of Biological Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  8. Gengxin Ran

    Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  9. Qing Pei

    National Institute of Biological Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  10. Qiang Ma

    Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  11. Meizhu Huang

    National Institute of Biological Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  12. Junjing Zhang

    Beijing Normal University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  13. Rui Lin

    National Institute of Biological Science, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  14. Youtong Zhou

    National Institute of Biological Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  15. Jiyao Zhang

    Beijing Normal University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  16. Miao Zhao

    National Institute of Biological Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  17. Minmin Luo

    National Institute of Biological Science, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3535-6624
  18. Qian Wu

    Beijing Normal University, Beijing, China
    For correspondence
    qianwu@ibp.ac.cn
    Competing interests
    The authors declare that no competing interests exist.
  19. Peng Cao

    National Institute of Biological Sciences, Beijing, China
    For correspondence
    caopeng@nibs.ac.cn
    Competing interests
    The authors declare that no competing interests exist.
  20. Xiaoqun Wang

    Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
    For correspondence
    xiaoqunwang@ibp.ac.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3440-2617

Funding

Ministry of Science and Technology of the People's Republic of China (2019YFA0110100; 2017YFA0103303)

  • Xiaoqun Wang

Ministry of Science and Technology of the People's Republic of China (2017YFA0102601)

  • Qian Wu

Chinese Academy of Sciences (XDB32010100)

  • Xiaoqun Wang

National Natural Science Foundation of China (31925019)

  • Peng Cao

National Natural Science Foundation of China (31771140; 81891001)

  • Xiaoqun Wang

BUAA-CCMU Big Data and Precision Medicine Advanced Innovation Center Project (BHME-2019001)

  • Xiaoqun Wang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All experimental procedures were conducted following protocols approved by the Administrative Panel on Laboratory Animal Care at the National Institute of Biological Sciences, Beijing (NIBS) (NIBS2021M0006) and Institute of Biophysics, Chinese Academy of Sciences (SYXK2019015).

Copyright

© 2021, Xie et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 4,503
    views
  • 818
    downloads
  • 33
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Zhiyong Xie
  2. Mengdi Wang
  3. Zeyuan Liu
  4. Congping Shang
  5. Changjiang Zhang
  6. Le Sun
  7. Huating Gu
  8. Gengxin Ran
  9. Qing Pei
  10. Qiang Ma
  11. Meizhu Huang
  12. Junjing Zhang
  13. Rui Lin
  14. Youtong Zhou
  15. Jiyao Zhang
  16. Miao Zhao
  17. Minmin Luo
  18. Qian Wu
  19. Peng Cao
  20. Xiaoqun Wang
(2021)
Transcriptomic encoding of sensorimotor transformation in the midbrain
eLife 10:e69825.
https://doi.org/10.7554/eLife.69825

Share this article

https://doi.org/10.7554/eLife.69825

Further reading

    1. Cell Biology
    2. Neuroscience
    Jun Sun, Francisca Rojo-Cortes ... Alicia Hidalgo
    Research Article

    Experience shapes the brain as neural circuits can be modified by neural stimulation or the lack of it. The molecular mechanisms underlying structural circuit plasticity and how plasticity modifies behaviour are poorly understood. Subjective experience requires dopamine, a neuromodulator that assigns a value to stimuli, and it also controls behaviour, including locomotion, learning, and memory. In Drosophila, Toll receptors are ideally placed to translate experience into structural brain change. Toll-6 is expressed in dopaminergic neurons (DANs), raising the intriguing possibility that Toll-6 could regulate structural plasticity in dopaminergic circuits. Drosophila neurotrophin-2 (DNT-2) is the ligand for Toll-6 and Kek-6, but whether it is required for circuit structural plasticity was unknown. Here, we show that DNT-2-expressing neurons connect with DANs, and they modulate each other. Loss of function for DNT-2 or its receptors Toll-6 and kinase-less Trk-like kek-6 caused DAN and synapse loss, impaired dendrite growth and connectivity, decreased synaptic sites, and caused locomotion deficits. In contrast, over-expressed DNT-2 increased DAN cell number, dendrite complexity, and promoted synaptogenesis. Neuronal activity modified DNT-2, increased synaptogenesis in DNT-2-positive neurons and DANs, and over-expression of DNT-2 did too. Altering the levels of DNT-2 or Toll-6 also modified dopamine-dependent behaviours, including locomotion and long-term memory. To conclude, a feedback loop involving dopamine and DNT-2 highlighted the circuits engaged, and DNT-2 with Toll-6 and Kek-6 induced structural plasticity in this circuit modifying brain function and behaviour.

    1. Neuroscience
    Mengqiao Cui, Xiaoyuan Pan ... Jun-Li Cao
    Research Article

    Memory impairment in chronic pain patients is substantial and common, and few therapeutic strategies are available. Chronic pain-related memory impairment has susceptible and unsusceptible features. Therefore, exploring the underlying mechanisms of its vulnerability is essential for developing effective treatments. Here, combining two spatial memory tests (Y-maze test and Morris water maze), we segregated chronic pain mice into memory impairment-susceptible and -unsusceptible subpopulations in a chronic neuropathic pain model induced by chronic constrictive injury of the sciatic nerve. RNA-Seq analysis and gain/loss-of-function study revealed that S1P/S1PR1 signaling is a determinant for vulnerability to chronic pain-related memory impairment. Knockdown of the S1PR1 in the dentate gyrus (DG) promoted a susceptible phenotype and led to structural plasticity changes of reduced excitatory synapse formation and abnormal spine morphology as observed in susceptible mice, while overexpression of the S1PR1 and pharmacological administration of S1PR1 agonist in the DG promoted an unsusceptible phenotype and prevented the occurrence of memory impairment, and rescued the morphological abnormality. Finally, the Gene Ontology (GO) enrichment analysis and biochemical evidence indicated that downregulation of S1PR1 in susceptible mice may impair DG structural plasticity via interaction with actin cytoskeleton rearrangement-related signaling pathways including Itga2 and its downstream Rac1/Cdc42 signaling and Arp2/3 cascade. These results reveal a novel mechanism and provide a promising preventive and therapeutic molecular target for vulnerability to chronic pain-related memory impairment.