High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labeling

Abstract

Deciphering patterns of connectivity between neurons in the brain is a critical step toward understanding brain function. Imaging-based neuroanatomical tracing identifies area-to-area or sparse neuron-to-neuron connectivity patterns, but with limited throughput. Barcode-based connectomics maps large numbers of single-neuron projections, but remains a challenge for jointly analyzing single-cell transcriptomics. Here, we established a rAAV2-retro barcode-based multiplexed tracing method that simultaneously characterizes the projectome and transcriptome at the single neuron level. We uncovered dedicated and collateral projection patterns of ventromedial prefrontal cortex (vmPFC) neurons to five downstream targets and found that projection-defined vmPFC neurons are molecularly heterogeneous. We identified transcriptional signatures of projection-specific vmPFC neurons, and verified Pou3f1 as a marker gene enriched in neurons projecting to the lateral hypothalamus, denoting a distinct subset with collateral projections to both dorsomedial striatum and lateral hypothalamus. In summary, we have developed a new multiplexed technique whose paired connectome and gene expression data can help reveal organizational principles that form neural circuits and process information.

Data availability

Raw gene expression, barcode count matrices and metadata were available from the Gene Expression Omnibus (GSE210174). The computational code used in the study is available at GitHub (https://github.com/MichaelPeibo/MERGE-seq-analysis). The data needed to evaluate the conclusions in the paper can be downloaded at https://figshare.com/projects/High-throughput_mapping_of_single-neuron_projection_and_molecular_features_by_retrograde_barcoded_labeling/150207. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials and source data files.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Peibo Xu

    CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
    For correspondence
    michaelxupb@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
  2. Jian Peng

    School of Life Science and Technology, ShanghaiTech University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  3. Tingli Yuan

    CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6676-8790
  4. Zhaoqin Chen

    CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  5. Hui He

    CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  6. Ziyan Wu

    CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  7. Ting Li

    School of Life Science and Technology, ShanghaiTech University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  8. Xiaodong Li

    CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2459-8778
  9. Luyue Wang

    CAS Key Laboratory of Computational Biology, Chinese Academy of Sciences, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  10. Le Gao

    CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  11. Jun Yan

    Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shangjai, China
    Competing interests
    The authors declare that no competing interests exist.
  12. Wu Wei

    CAS Key Laboratory of Computational Biology, Chinese Academy of Sciences, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  13. Chengyu T Li

    CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
    For correspondence
    tonylicy@lglab.ac.cn
    Competing interests
    The authors declare that no competing interests exist.
  14. Zhen-Ge Luo

    School of Life Science and Technology, ShanghaiTech University, Shanghai, China
    For correspondence
    luozhg@shanghaitech.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5037-0542
  15. Yuejun Chen

    CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
    For correspondence
    yuejunchen@ion.ac.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4625-2604

Funding

National Key Research and Development Program of China (2018YFA0108000)

  • Yuejun Chen

National Natural Science Foundation of China (32170806)

  • Yuejun Chen

National Natural Science Foundation of China (32130035)

  • Zhen-Ge Luo

Thousand Young Talents Program of China

  • Yuejun Chen

National Key Research and Development Program of China (2021ZD0202500)

  • Zhen-Ge Luo

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 animal experiments were conducted according to a protocol approved by the IACUC at the Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technol- ogy of the Chinese Academy of Sciences (Shanghai, China). (reference number for approval: NA-034-2022).

Copyright

© 2024, Xu 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.

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  1. Peibo Xu
  2. Jian Peng
  3. Tingli Yuan
  4. Zhaoqin Chen
  5. Hui He
  6. Ziyan Wu
  7. Ting Li
  8. Xiaodong Li
  9. Luyue Wang
  10. Le Gao
  11. Jun Yan
  12. Wu Wei
  13. Chengyu T Li
  14. Zhen-Ge Luo
  15. Yuejun Chen
(2024)
High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labeling
eLife 13:e85419.
https://doi.org/10.7554/eLife.85419

Share this article

https://doi.org/10.7554/eLife.85419

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