Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data.

Journal: Cell reports
PMID:

Abstract

Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demanding but still highly informative. We thus developed a region-CNN-based deep learning method to identify, segment, and reconstruct synapses and mitochondria to explore the structural plasticity of synapses and mitochondria in the auditory cortex of mice subjected to fear conditioning. Upon reconstructing over 135,000 mitochondria and 160,000 synapses, we find that fear conditioning significantly increases the number of mitochondria but decreases their size and promotes formation of multi-contact synapses, comprising a single axonal bouton and multiple postsynaptic sites from different dendrites. Modeling indicates that such multi-contact configuration increases the information storage capacity of new synapses by over 50%. With high accuracy and speed in reconstruction, our method yields structural and functional insight into cellular plasticity associated with fear learning.

Authors

  • Jing Liu
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Junqian Qi
    School of Life Science and Technology, ShanghaiTech University, Shanghai, China; Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of the Chinese Academy of Sciences, Beijing 100049, China.
  • Xi Chen
    Department of Critical care medicine, Shenzhen Hospital, Southern Medical University, Guangdong, Shenzhen, China.
  • Zhenchen Li
    National Laboratory of Pattern Recognition, Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, School of Future Technology, University of the Chinese Academy of Sciences, Beijing 101408, China.
  • Bei Hong
  • Hongtu Ma
    National Laboratory of Pattern Recognition, Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Guoqing Li
    College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, People's Republic of China. Electronic address: gqli@scau.edu.cn.
  • Lijun Shen
    Department of radiation oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Danqian Liu
    Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China.
  • Yu Kong
    Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China.
  • Hao Zhai
    National Laboratory of Pattern Recognition, Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, School of Future Technology, University of the Chinese Academy of Sciences, Beijing 101408, China.
  • Qiwei Xie
  • Hua Han
    Department of Orthopaedic Surgery, the Second Hospital &Clinical Medical School, Lanzhou University, Lanzhou, Gansu Province, China.
  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.