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Mesophasic organization of GABAA receptors in hippocampal inhibitory synapses

Nature Neuroscience volume 23, pages 1589–1596 (2020)Cite this article

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Abstract

Information processing in the brain depends on specialized organization of neurotransmitter receptors and scaffolding proteins within the postsynaptic density. However, how these molecules are organized in situ remains largely unknown. In this study, template-free classification of oversampled sub-tomograms was used to analyze cryo-electron tomograms of hippocampal synapses. We identified type-A GABA receptors (GABAARs) in inhibitory synapses and determined their in situ structure at 19-Å resolution. These receptors are organized hierarchically: from GABAAR super-complexes with a preferred inter-receptor distance of 11 nm but variable relative angles, through semi-ordered, two-dimensional receptor networks with reduced Voronoi entropy, to mesophasic assembly with a sharp phase boundary. These assemblies likely form via interactions among postsynaptic scaffolding proteins and receptors and align with putative presynaptic vesicle release sites. Such mesophasic self-organization might allow synapses to achieve a ‘Goldilocks’ state, striking a balance between stability and flexibility and enabling plasticity in information processing.

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Fig. 1: Identification and in situ structure of GABAAR in inhibitory synapses.
Fig. 2: GABAAR super-complexes.
Fig. 3: 2D networks of GABAARs.
Fig. 4: Mesophasic assembly of inhibitory PSD.

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Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request. The density map of GABAAR and receptor pair have been deposited in the Electron Microscopy Databank (EMDB) under accession numbers EMD-22365 and EMD-22366, respectively. Source data are provided with this paper.

Code availability

The code for random sampling and analysis of sub-tomograms is deposited at https://github.com/procyontao/cryoET-membrane-sampling. The code for simulation of receptor and gephyrin is deposited at https://github.com/alienPQ/Receptors-clustering. Source data are provided with this paper.

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Acknowledgements

We thank P. Ge and C. Xu for technical advice on cryoEM imaging and A. Tang and V. Lucic for valuable suggestions on the manuscript. This work was supported, in part, by grants from the National Natural Science Foundation of China (31630030, 31621002 and 31761163006 to G.-Q.B. and 31600606 to X.Z.), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB32030200 to G.-Q.B.), the National Key R&D Program of China (2017YFA0505300 to G.-Q.B. and 2016YFA0501100 to X.Z.), the China Postdoctoral Science Foundation (2018M640590 to C.-L.T.), the Natural Science Foundation of Anhui Province (1908085QC95 to C.-L.T.) and the Natural Science Foundation of Jiangsu Province (BK20160488 to W.X.). Research in the Zhou group is supported, in part, by the National Institutes of Health (NIH) (GM071940 to Z.H.Z.). We acknowledge the use of instruments at the Center for Integrative Imaging of Hefei National Laboratory for Physical Sciences at the Microscale and those at the Electron Imaging Center for Nanomachines of the University of California, Los Angeles that are supported by the NIH (S10RR23057 and S10OD018111 to Z.H.Z.) and the National Science Foundation (DMR-1548924 and DBI-133813 to Z.H.Z.). We thank the Bioinformatics Center of the University of Science and Technology of China for providing supercomputing resources for this project.

Author information

Author notes

  1. These authors contributed equally: Yun-Tao Liu, Chang-Lu Tao, Xiaokang Zhang.

Authors and Affiliations

  1. Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, China

    Yun-Tao Liu, Chang-Lu Tao, Cheng Xu, Rong Sun, Xiao-Wei Li & Guo-Qiang Bi

  2. CAS Key Laboratory of Brain Function and Disease, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China

    Yun-Tao Liu, Chang-Lu Tao, Dong-Qing Shi, Lei Qi, Pak-Ming Lau & Guo-Qiang Bi

  3. California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, USA

    Yun-Tao Liu & Z. Hong Zhou

  4. Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China

    Chang-Lu Tao, Xiaokang Zhang, Pak-Ming Lau & Guo-Qiang Bi

  5. School of Mathematical Sciences, Jiangsu University, Zhenjiang, China

    Wenjun Xia

  6. Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA

    Z. Hong Zhou

  7. CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

    Guo-Qiang Bi

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Contributions

Y.-T.L., C.-L.T., P.-M.L., Z.H.Z. and G.-Q.B. designed the research. C.-L.T. and L.Q. prepared cell culture. C.-L.T. and R.S. performed cryoET and cryoCLEM imaging. D.-Q.S. and X.-W.L. performed electrophysiological experiments. C.X. performed immunofluorescence experiments. W.X. and Y.-T.L. performed simulation. Y.-T.L., C.-L.T., X. Z. and G.-Q.B. analyzed data. Y.-T.L., C.-L.T., W.X., D.-Q.S., C.X., P.-M.L., Z.H.Z. and G.-Q.B. wrote the paper. All authors edited and approved the manuscript.

Corresponding authors

Correspondence to Pak-Ming Lau, Z. Hong Zhou or Guo-Qiang Bi.

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The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks Ryan Hibbs and Shigeki Watanabe for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Expression of GABAARs in cultured hippocampal inhibitory synapses.

a1, Current traces of synaptic IPSCs recorded from a neuron under voltage clamp. A nearby neuron was voltage clamped and stimulated to evoke the responses. a2, Current traces of autaptic IPSCs from a stimulated neuron. b, Representative recordings of spontaneous IPSCs (sIPSCs). c, Application of 1 µM strychnine (STR) did not affect evoked IPSC (eIPSC) amplitudes (N.S, p = 0.43, two-tailed paired t-test), while 20 µM bicuculline methiodide (BMI) could significantly block evoked IPSCs (**, p = 0.003, two-tailed paired t-test). Data are presented as mean values ± SEM. Each line represents an eIPSC (n = 6 neurons). d, 1 µM strychnine did not affect the frequency of sIPSCs (N.S, p = 0.90, two-tailed paired t-test), while 20 µM BMI almost totally abolished sIPSCs (*, p = 0.026, two-tailed paired t-test). Each line represents a recorded neuron (n = 8 neurons). e, 1 µM strychnine did not affect the amplitude of sIPSCs (N.S, p = 0.71, two-tailed paired t-test, n = 8 neurons). During BMI treatment, sIPSCs were so rare thus not included for comparison. Data are presented as mean values ± SEM. f, g, Example confocal fluorescent micrographs of cultured neurons with immunostaining of GABAAR γ2 subunits (red, f) and GlyR α1 subunits (green, g). h, The merged image from f and g. f1-h1, Magnified views of the boxed area in respective images showing putative receptor puncta along with dendrite segments. The experiments in f-h were repeated 3 times independently, and 10 neurons were imaged with similar results.

Extended Data Fig. 2 Flowchart illustrates identification and sub-tomogram averaging of GABAAR.

a, Steps for identifying GABAARs from sampling points. a1, Electron tomographic slice of an inhibitory synapse. a2, Electron tomographic slice superposed with sampling points on postsynaptic membrane. a3, Sampling points after each step. Red points are sampling points that will be discarded in the next step. For a1-a3, n = 72 synapses in 70 tomograms. b, Classification and refinement of GABAARs on postsynaptic membrane. Example 2D slices of sub-tomograms are from 171,374 (left, imaged with VPP) and 135,717 (right, imaged without VPP) sampled sub-tomograms. b1, Example 2D slices of aligned sub-tomograms of GABAARs (n = 5,811 sub-tomograms imaged with VPP, n = 3,807 sub-tomograms imaged without VPP). c, Structure of GABAAR emerged during iterative classification.

Extended Data Fig. 3 Performance estimation of template-free classification and refinement.

a, Number of classified GABAAR sub-tomograms plotted against the number of classes. b, Euler (psi and tilt) angles of all sub-tomograms used for the final sub-tomogram averaging. c, Distribution of Euler angles for sub-tomograms in two example synapses after first round of refinement. d, Percentage of outliers with opposite angles versus percentage of outliers with angles 3σ away from Gaussian core in each synapse. e, Percentage of all orientation outliers versus percentage of low score outliers in each synapse. f, Frequency distribution of sub-tomogram scores fitted with Gaussian curve (red curve). g, Number of sub-tomograms before and after removing both orientation and low score outliers. h, Normalized CC score distribution of sub-tomograms comparing with original sub-tomogram average and 36° rotated sub-tomogram average. i, Distribution of CC score differences for sub-tomograms comparing with original sub-tomogram average and 36° rotated sub-tomogram average.

Extended Data Fig. 4 Identification of GABAARs using sub-tomograms mixed with sub-tomograms on presynaptic membrane.

a, Steps for identifying GABAARs from dataset mixed with sub-tomograms on presynaptic membrane. a1, Electron tomographic slice of an inhibitory synapse. a2, Electron tomographic slices superposed with sampling points on postsynaptic membrane (top) or presynaptic membrane (bottom). b, Sub-tomogram sampling points on postsynaptic membrane (top) or presynaptic (bottom) after each step. Red points are sampling points that will be discarded in the next step. For a and b, n = 72 postsynaptic membranes, and n = 4 presynaptic membranes. c, 3D classification and refinement of sub-tomograms on 72 postsynaptic membranes and 4 presynaptic membranes. d, Receptor concentration from Analysis II (analysis of sub-tomograms mixed with presynaptic sub-tomograms) versus receptor concentration from Analysis I (analysis of sub-tomograms without presynaptic sub-tomograms). e, Concentration of identified receptors on postsynaptic membranes in Analysis I, Analysis II, and falsely identified receptors on presynaptic membranes in Analysis II for the 4 selected synapses.

Extended Data Fig. 5 Structure features of the sub-tomogram average of GABAAR.

a, Density of the sub-tomogram average fitted with atomic models of different subunit compositions or conformations26,29,30,31. b, Orthogonal slice views of sub-tomogram average of GABAAR. c, Left: Sub-tomogram average of GABAAR. Orange density is GABAAR density. Gray density is membrane bilayer. Right: Rotated view of sub-tomogram average of GABAAR displayed at low threshold. d, Classification of oversampled sub-tomograms without symmetry. Structures obtained from the 3D classification of VPP data. e, Orthogonal slice views of the structure boxed in d.

Extended Data Fig. 6 Two-dimensional networks of GABAARs.

a, Scatter plot of mean network size (number of receptors divided by number of networks) and number of receptors for each synapse. Colored dots (magenta, gray, cyan and red) correspond to the four synapses in Fig. 3b respectively. b, Distribution of network size. Y-axis is the plot in logarithm scale. c, Examples of Voronoi tessellation of receptors on postsynaptic membranes. Black dots represent the localizations of GABAARs. d, Cumulative frequency of Voronoi entropy for each synapse. The green line shows the Voronoi entropy of ‘networked’ receptors.

Extended Data Fig. 7 Mesophasic assembly of PSD.

a, Example of convex hull and smooth convex hull of linked receptors (n = 58 synapses). b, Examples of receptor distribution on the postsynaptic membranes and the corresponding 2D density profiles of scaffolding layer (n = 58 synapses). c, Scatter plot of area inside mesophase boundary and area of postsynaptic membrane, fitted with a dashed line.

Extended Data Fig. 8 Three types of interactions among gephyrin E and G domains in the simulation of gephyrin and GABAAR organization.

a, Direct trimerization of the gephyrin G domains. b, Gephyrin G domain trimerization through a dimer intermediate. c, Dimerization of gephyrin E domains.

Extended Data Fig. 9 Vesicle contacting sites on the presynaptic membrane correlate with mesophasic assembly of GABAARs.

Comparison between the number of tethered or contacting vesicles inside or outside of mesophase boundary with the corresponding number based on randomized vesicle distribution within the whole synapse (n = 58 synapses). For both inside and outside mesophase boundary, N.S, p = 0.20; **, p = 0.005, two-tailed paired t-test.

Extended Data Fig. 10 Synapses with two discretely separated receptor assemblies.

Synapses had narrow synaptic clefts (n = 2 synapses) (a, b) or deformed synaptic membranes (n = 1 synapse) (c). a1-c1, En face views of GABAARs (colored dots) on the postsynaptic membrane (transparent gray). a2-c2, Tomographic slices of the respective synapses. a3-c3, Side views superposed with the tomographic slices. Color indicates network size (n, the number of receptors in a network). Paired red dashed lines indicate the gap between two receptor assemblies and the corresponding synaptic cleft area.

Supplementary information

Reporting Summary

Supplementary Table 1

Proteomes on inhibitory postsynaptic membrane.

Supplementary Table 2

Synapse information. Sheet 1: the size of postsynaptic area and the number of receptors in each synapse. Sheet 2: receptor coordinates. Sheet 3: contacting vesicle coordinates. Sheet 4: tethered vesicle coordinates.

Supplementary Table 3

Parameters used for simulating GABAAR and gephyrin organization.

Supplementary Video 1

Tilt series of an inhibitory synapse. This video shows the tilt series of an inhibitory synapse (same data as in Fig. 1b) obtained using VPP, electron filter and counting technologies. Black dots of 15-nm diameter are gold beads used as fiducial markers for image alignment.

Supplementary Video 2

Structures of an inhibitory synapse. This video shows the tomogram of the same synapse as in Fig. 2b, displayed as z-stack and 3D surface rendering of the segmented structures. The structures were colored the same as Fig. 1c and Fig. 1d.

Supplementary Video 3

Example of Monte Carlo simulation of receptor and gephyrin organization. Magenta, blue and orange dots represent locations of GABAARs, gephyrin E domains and gephyrin G domains, respectively. Short black lines are linkers between the two domains. Dashed lines are boundaries of simulation (500 nm × 500 nm). The simulation parameters Kr = 0.001 and Kd = Kt = 0.00001. Numbers of steps are shown on the top left corner of each image.

Source data

Source Data Fig. 2

Statistical Source Data of Fig. 2a–c

Source Data Fig. 4

Statistical Source Data of Fig. 4b,e

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Liu, YT., Tao, CL., Zhang, X. et al. Mesophasic organization of GABAA receptors in hippocampal inhibitory synapses. Nat Neurosci 23, 1589–1596 (2020). https://doi.org/10.1038/s41593-020-00729-w

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