Review: DNA Barcode-based High-throughput Mesoscale Connectomics
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1)State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China;2)College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

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This work was supported by grants from Science and Technology Innovation 2030 (2022ZD0206900), and The National Natural Science Foundation of China (32271143).

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    Abstract:

    Connectomics, a research field in neuroscience studying the synaptic connectivity patterns between neurons across different brain regions, is crucial for understanding neural computations underlying complex functions such as emotion, learning, and cognition. Specifically, micrometer-resolution mesoscale connectomics has become the most widely used technology in rodent neuroscience due to its unique advantages, and it also has the potential to transform brain research in non-human primates. Traditional mesoscale connectome techniques typically use fluorescence labeling and optical imaging to perform anterograde or retrograde tracing of neural circuits. To achieve single-cell resolution, methods for sparse labeling of neurons have been developed. However, it remains challenging to trace neurons in high throughput in individual animals and integrate multi-omics data across modalities. In the past decade, high-throughput mesoscale connectome technologies based on DNA barcoding have made significant progress. These technologies have provided novel tools to map single cell connectome, with higher throughput, lower cost, and multi-omics compatibility. Here we review several mature mesoscale connectome technologies based on DNA barcoding, discussing their principles, applications, advantages and disadvantages. We also propose future directions for barcoding-based connectomics.

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HU Peng-Kai, HUANG Long-Wen. Review: DNA Barcode-based High-throughput Mesoscale Connectomics[J]. Progress in Biochemistry and Biophysics,2024,51(10):2369-2378

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History
  • Received:June 30,2024
  • Revised:August 31,2024
  • Accepted:August 21,2024
  • Online: August 24,2024
  • Published: October 20,2024