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Single-neuron analysis of dendrites and axons reveals the network organization in mouse prefrontal cortex

Nature Neuroscience volume 26, pages 1111–1126 (2023)Cite this article

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Abstract

The structures of dendrites and axons form the basis for the connectivity of neural network, but their precise relationship at single-neuron level remains unclear. Here we report the complete dendrite and axon morphology of nearly 2,000 neurons in mouse prefrontal cortex (PFC). We identified morphological variations of somata, dendrites and axons across laminar layers and PFC subregions and the general rules of somatodendritic scaling with cytoarchitecture. We uncovered 24 morphologically distinguishable dendrite subtypes in 1,515 pyramidal projection neurons and 405 atypical pyramidal projection neurons and spiny stellate neurons with unique axon projection patterns. Furthermore, correspondence analysis among dendrites, local axons and long-range axons revealed coherent morphological changes associated with electrophysiological phenotypes. Finally, integrative dendrite–axon analysis uncovered the organization of potential intra-column, inter-hemispheric and inter-column connectivity among projection neuron types in PFC. Together, our study provides a comprehensive structural repertoire for the reconstruction and analysis of PFC neural network.

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Fig. 1: Reconstruction and basic statistics for dendrites and axons of PFC neurons.
Fig. 2: Classification of PFC typical pyramidal projection neurons based on dendritic morphology.
Fig. 3: Classification of PFC atypical pyramidal projection neurons.
Fig. 4: Comparison of dendrite morphology of typical pyramidal neurons from PFC and sensorimotor cortical neurons.
Fig. 5: Joint analysis of dendrites and local axons.
Fig. 6: Relationship between dendrites and long-range axons.
Fig. 7: PFC connectivity map predicted from dendrite–axon pairing.

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Data availability

All reconstructed dendrites and their associated axons and metadata are available for public acces at https://mouse.braindatacenter.cn. Source data are provided with this paper.

Code availability

The codes used in the data analysis can be found at https://zenodo.org/record/7057167.

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Acknowledgements

We thank N. Xu, Y. Sun, H. Yao, C. Xu and C. Li (Institute of Neuroscience, Chinese Academy of Sciences) for data sharing and helpful discussions; Q. Luo (Hainan University), H. Gong and A. Li (Huazhong University of Science and Technology) for the help in fMOST imaging; X. Wang and his team (Institute of Neuroscience, Chinese Academy of Sciences) for managing the fMOST data and Q. Liu for including the dendrite data in the website for data browsing; and Z. Zeng and his team (Chengdu Huizhong Tianzhi Technology Co. Ltd.) for tracing the dendrites in this study. This work was supported by Major Project of the Science and Technology Innovation 2030 of China grants (STI2030-Major Projects 2021ZD0200204 and 2021ZD0204402 to J.Y.), a Shanghai Municipal Science and Technology Major Project grant (2018SHZDZX05 to J.Y.), a Lingang Laboratory grant (LG202104-01-06 to J.Y.) and a Strategic Priority Research Program of Chinese Academy of Sciences grant (XDB32040104 to J.Y.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Le Gao, Sang Liu.

Authors and Affiliations

  1. Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

    Le Gao, Sang Liu, Yanzhi Wang, Qiwen Wu, Lingfeng Gou & Jun Yan

  2. University of Chinese Academy of Sciences, Shanghai, China

    Sang Liu, Yanzhi Wang & Qiwen Wu

  3. School of Future Technology, University of Chinese Academy of Sciences, Beijing, China

    Sang Liu & Jun Yan

  4. Shanghai Research Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China

    Jun Yan

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Contributions

Design of the study: J.Y.; development of automatic reconstruction algorithms: L. Gou; dendrite reconstruction: Y.W., L. Gao and S.L.; electrophysiological recording: Q.W.; data processing, quality control and management: Y.W., L. Gao and S.L.; data analysis, data interpretation and generation of figures: L.Gao and S.L.; writing, reviewing and editing the manuscript: J.Y., L.Gao and S.L.; scientific direction and funding: J.Y.

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Correspondence to Jun Yan.

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Extended data

Extended Data Fig. 1 Analysis of basic dendrite morphology.

(a) Left, proportion of compartment length in the total dendrite length for IT (n = 686), PT (n = 525), and CT (n = 304) neurons. X-axis denotes individual neurons. Right, statistical results of proportion of compartment length among IT, PT, and CT neurons (two-way ANOVA with post hoc multiple comparison tests). Box plot: edges, 25th and 75th percentiles; central line, median; whiskers, 1.5× the interquartile range of the edges; dots, outliers. (b) Relationship of dendritic lengths among IT, PT, and CT neurons (linear regression, P value and R2 are shown). Shaded areas around the regression line indicate 95% confidence intervals. P values were calculated via two-sided t-test. (c) Boxplots show the difference of soma volume among IT (n = 1,023), PT (n = 502), and CT (n = 329) neurons and across 11 PFC subregions (two-way ANOVA with post hoc multiple comparison tests). Box plot: edges, 25th and 75th percentiles; central line, median; whiskers, 1.5× the interquartile range of the edges; dots, outliers. (d) Correlations between soma depth and soma volume, tuft length, trunk length, and basal length for IT, PT, and CT neurons, respectively (linear regression, P value and R2 are shown). Shaded areas around the regression line indicate 95% confidence intervals. P values were calculated via two-sided t-test. Right, the density plot showing the significant difference in soma volume, tuft length, trunk length, and basal length of IT, PT, and CT neurons (one-way ANOVA with post hoc multiple comparison test). (e) Correlation between total dendrite length of CT neurons and cortical thickness across PFC subregions (linear regression, P value and R2 are shown). P value was calculated via two-sided t-test. (f) Correlation between somatodendritic volume of CT neurons and neuron density across PFC subregions (linear regression, P value and R2 are shown). P value was calculated via two-sided t-test. (g) Correlation between cortical thickness and neuron density across PFC subregions (linear regression, P value and R2 are shown). Shaded areas around the regression line indicate 95% confidence intervals. P value was calculated via two-sided t-test. (h) Scaling laws of branch point number and dendrite length among IT (n = 1,049), PT (n = 537), and CT (n = 334) neurons and between apical and basal parts of dendrites. Left, difference of the powers in the scaling law of apical tufts among IT (n = 686), PT (n = 525), and CT (n = 304) neurons (linear regression, P value and R2 are shown). Middle, the difference of the powers in the scaling law of basal dendrites among IT (n = 686), PT (n = 525), and CT (n = 304) neurons. Shaded areas around the regression line indicate 95% confidence intervals. P values were calculated via two-sided t-test. Right, the powers of different compartments of dendrites and of IT (n = 686), PT (n = 525) and CT (n = 304) neurons. Data are presented as mean ± SEM. P values were shown, ANCOVA. (i) Scaling laws of spanning volume and density among IT (n = 1,049), PT (n = 537), and CT (n = 334) neurons and between apical and basal parts of dendrites. Left, the difference of the powers in the scaling law of apical tufts (linear regression, P value and R2 are shown). Shaded areas around the regression line indicate 95% confidence intervals. P values were calculated via two-sided t-test. Middle, the difference of the powers in the scaling law of basal dendrites. Right, the powers of different parts of dendrites and of IT, PT and CT neurons. Data are presented as mean ± SEM. P values were shown, ANCOVA.

Extended Data Fig. 2 Description and justification of dendrite classification.

(a) Co-clustering matrix from 1,000 runs of hierarchical clustering with 70% subsampled data. Top and left of heatmap show the hierarchical dendrogram of final classification of 24 dendrite subtypes based on the co-clustering matrix. Colors on the top denote the types of IT, PT, and CT neurons. Colors on the bottom denote the 24 dendrite subtypes. (b) Confusion matrix of actual dendrite subtypes (rows) and predicted dendrite subtypes (columns) by random forest classifier. Prediction accuracy of each dendrite subtype is shown in brackets in row names. (c) Difference of dendrite morphology among IT (n = 1,049), PT (n = 537), and CT (n = 334) neurons in terms of the lengths of apical tuft, apical trunk and basal dendrites (one-way ANOVA with post hoc multiple comparison tests). Box plot: edges, 25th and 75th percentiles; central line, median; whiskers, 1.5× the interquartile range of the edges; dots, outliers. (d) Boxplots show the difference in sphericity index of 3D soma shape for 24 dendrite subtypes (two-sided Wilcoxson rank sum test with FDR correction). Colors denote the main projection types of each dendrite subtypes as in Fig. 2d. See Source Data Extended Data Fig. 2 for full table of P value. Exact number of neurons for each dendrite subtype can be found in Supplementary Table 4. Box plot: edges, 25th and 75th percentiles; central line, median; whiskers, 1.5× the interquartile range of the edges; dots, outliers. (e) The conditional inference tree shows the most distinctive morphological feature at each level of binary partition on the hierarchical tree dendrogram in (a). The boxes at each node and the values on each edge show the most distinctive feature and the split condition at each binary partition. The leaf nodes represent the major dendrite subtype and corresponding accuracy reached at the final split.

Source data

Extended Data Fig. 3 Classification and morphological analysis of atypical pyramidal neurons and spiny stellate neurons.

(a) Confusion matrix of actual dendrite subtypes (rows) and predicted dendrite subtypes (columns) for atypical pyramidal neurons by random forest classifier. Prediction accuracy of each dendrite subtype is shown in brackets in row names. (b) Schematic diagram shows the workflow to classify L2/3 atypical pyramidal neuron subtypes. (c) Morphological differences between L2/3 atypical pyramidal neuron subtypes and L2/3 typical pyramidal neurons (one-way ANOVA, P values were shown). Exact number of neurons for each dendrite subtype can be found in Supplementary Table 4. Box plot: edges, 25th and 75th percentiles; central line, median; whiskers, 1.5× the interquartile range of the edges; dots, outliers. (d) Left, box plots show sphericity index of soma shape for atypical pyramidal subtypes and spiny stellate neurons. Red line denotes the mean value of sphericity index of all neurons. Right, raw images of somata of two example neurons (L4 spiny stellate and L2/3 vertical three main dendrites). Two-sided Wilcoxson rank sum test with FDR correction. Exact number of neurons for each dendrite subtype can be found in Supplementary Table 4. Box plot: edges, 25th and 75th percentiles; central line, median; whiskers, 1.5× the interquartile range of the edges; dots, data points. (e) Sholl analysis of L6 atypical neuron subtypes. Bipolar neurons are significantly more branching than other L6 atypical neuron subtypes (one-sided Wilcoxon rank sum test). Data presented as mean ± SEM. Exact number of neurons for each dendrite subtype can be found in Supplementary Table 4. (f) Morphological differences among L6 atypical pyramidal neuron subtypes (one-way ANOVA, P values are shown). Exact number of neurons for each dendrite subtype can be found in Supplementary Table 4. Box plot: edges, 25th and 75th percentiles; central line, median; whiskers, 1.5× the interquartile range of the edges; dots, outliers.

Extended Data Fig. 4 The laminar distribution of local axons and long-range axons for each dendrite subtype.

(a) Correlations of local axon lengths and long-range axon length for IT, PT, and CT neurons, respectively. Linear regression, P values and R2 are shown, the shaded areas around the regression line indicate 95% confidence intervals. P values were calculated via two-sided t-test. (b) Laminar distributions of local axons and long-range axons for each dendrite subtype. Red, local axons. Blue, long-range axons. The shade areas around the line indicate SEM. Bottom, laminar difference between local axons and long-range axons. Data are presented as mean ± SEM. Statistical analysis was performed by two-side Wilcoxon rank sum test, FDR correction for multiple comparisons. Exact number of neurons for each dendrite subtype can be found in Supplementary Table 4.

Source data

Extended Data Fig. 5 Relationship between dendrites and long-range axonal projection for PT and CT neurons.

(a) Correlation between total lengths of dendrites and axons for IT, PT, and CT neurons (linear regression, P value and R2 are shown). Shaded areas around the regression line indicate 95% confidence intervals. P values were calculated via two-sided t-test. (b) Correlations between the pair-wise distances of dendrite morphology based on multi-dimensional morphological features and the pair-wise distances of axon projections calculated by FNT-dist (Methods) for IT, PT, and CT neurons, respectively (linear regression, P value and R2 are shown). P values were calculated via two-sided t-test. (c) Pearson’s correlation between dendrite morphological features and axon projection bias (MY vs ACB) of PT neurons in AI/ORBl/ORBvl. (d) Representative MY-projecting or ACB-projecting neurons of different dendrite/axon subtype. (e) Soma distributions of the neurons in different PT dendrite subtypes and the gradient of their dendrite morphology/axon projection pattern. Left, soma distributions of PT neurons on the PFC flatmap with dendrite subtypes color-coded. The shaded area denotes the AI/ORBl/ORBvl. Middle and right, correlations of apical trunk length between soma position (in lateral-medial axis of AI/ORBl/ORBvl) or axon projection pattern (MY-projecting or ACB-projecting) for the PT dendrite subtypes (linear regression, P value and R2 are shown). Shaded areas around the regression line indicate 95% confidence intervals. P values were calculated via two-sided t-test. (f) The distributions of axon projection strength to major targets of CT dendrite subtypes with their soma distributions in PFC subregions. Top, hierarchical tree of CT dendrite subtypes based on their soma distribution (Euclidean distance, Ward’s linkage). Middle, dot plot shows the number of neurons (represented by dot size) in PFC subregions. Bottom, violin plot shows the axon projection strength. Colored by dendrite subtypes. (g) Pearson’s correlation between dendrite morphological features and axon projection bias to ventral medial nucleus of thalamus (VM) vs. reticular nucleus of thalamus (RT) of CT neurons in ACA/PL/MOs. (h) Representative VM-projecting or RT-projecting neurons of different dendrite/axon subtype. (i) Soma distributions of the neurons in different CT dendrite subtypes and the gradient of their dendrite morphology/axon projection pattern. Left, soma distributions of CT neurons on the PFC flatmap with dendrite subtypes color-coded. The shaded area denotes the ACA/PL/MOs. Middle and right, correlations of the height of dendrite between soma position (in anterior-posterior axis of ACA/PL/MOs) or axon projection pattern (VM-projecting or RT-projecting) for the CT dendrite subtypes (linear regression, P value and R2 are shown). Shaded areas around the regression line indicate 95% confidence intervals. P values were calculated via two-sided t-test.

Source data

Extended Data Fig. 6 Relationship between dendrite and long-range axonal projection for IT neurons.

(a) The distributions of axon projection strength to major targets of IT dendrite subtypes with their soma distributions in PFC subregions. Top, hierarchical tree of IT dendrite subtypes based on their soma distribution (Euclidean distance, Ward’s linkage). Middle, dot plot shows the number of neurons (represented by dot size) in PFC subregions. Bottom, violin plot shows the axon projection strength. Colored by dendrite subtypes. (b) Pearson’s correlation between dendrite morphological features and axon projection bias to striatum (STR) or not to STR of L5 IT neurons in ACA/PL. (c) Representative retrosplenial (RSP) area-projecting and STR-projecting IT neurons in PL/ACA regions with distinct dendritic morphology. (d) Soma distributions of the neurons in different L5 IT dendrite subtypes and the gradient of their dendrite morphology/axon projection pattern. Left, soma distributions of L5 IT neurons on the PFC flatmap with dendrite subtypes color-coded. The shaded area denotes the ACA/PL. Middle and right, correlations of the length of basal dendrite between soma position in anterior-posterior axis of ACA/PL or axon projection pattern projecting to STR or not for the L5 IT dendrite subtypes (linear regression, P value and R2 are shown). Shaded areas around the regression line indicate 95% confidence intervals. P values were calculated via two-sided t-test. (e) Axon subtype 21 and 38 are intermixed in the t-SNE plot based on dendrite morphology dissimilarity (top) but are separated in the t-SNE plot based on the dissimilarity of their axon projections (bottom). (f) Representative IT neurons in PL/ACA regions with similar dendritic morphology but distinct axon projection patterns, color-coded by axon subtypes. Red, axon subtype 38 with only ipsilateral caudate putamen (CP) projection. Blue, axon subtype 21 with both ipsilateral and contralateral CP projection. Top, dendrite morphology. Bottom right, axon projection pattern. Bottom left, violin plots show the indistinguishable dendrite morphology of axon subtype 21 and 38 (P values are shown, two-sided Wilcoxon rank sum test). Top right, (g) Representative L4-like spiny stellate neuron and a nearby pyramidal neuron with similar axon projection pattern.

Source data

Extended Data Fig. 7 Intra-column and homotopic contralateral connectivity.

(a) Heatmap shows the connectivity map between five neuron classes (L2/3 IT, L5 IT, L5 PT, L6 IT, and L6 CT) in single cortical columns. (b) Heatmap shows the connectivity map between five neuron classes (L2/3 IT, L5 IT, L5 PT, L6 IT, and L6 CT) between homotopic contralateral columns. (c) Laminar distribution of axons from L2/3, L5, and L6 IT neurons in contralateral homotopic regions. Line and shaded area denotes mean ± SEM. (d) Left, diagram shows the main subdomains of dendrites of a typical PT neuron. Right, difference of number of potential synapses formed on apical tuft, apical trunk and oblique, and basal dendrites for L2/3 IT and L5 IT, respectively (****P < 0.0001, two-sided Wilcoxon rank sum test). (e) Top, laminar distribution of apical tuft of PT neurons and axons from homotopic contralateral regions for each PFC subregion. Line and shaded area denotes mean ± SEM. Pearson’s correlation coefficients between two mean distributions for each PFC subregion are shown. Bottom, morphology of dendrites of PT neurons with apical tufts highlighted in red and axons from contralateral homotopical regions colored in cyan. (f) Columns with different connectivity patterns (pattern 1: n = 22, pattern 2: n = 16, pattern 3: n = 30, pattern 4: n = 43, pattern 5: n = 17) show different inter-column distances (two-sided Wilcoxon rank sum test). Box plot: edges, 25th and 75th percentiles; central line, median; whiskers, 1.5× the interquartile range of the edges; dots, outliers. (g) The observed global hierarchical score of ipsilateral connectivity network was significantly higher than that of shuffled networks (P < 2⨯10−6, two-sided Wilcoxon rank sum test).

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Reporting Summary

Supplementary Tables 1–6

Supplementary Table 1: Abbreviations of brain structures used in this study. Supplementary Table 2: Morphological features used for dendrite classification. Supplementary Table 3: Regional distribution of dendrite subtypes. Supplementary Table 4: Summary of dendrite subtypes defined in this study and correspondence with that defined in previous dendrite studies. Supplementary Table 5: Electrophysiological features of recorded PCG-projecting and SCm-projecting neurons in PL/ACA. Supplementary Table 6: Information of mice and virus injection.

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Gao, L., Liu, S., Wang, Y. et al. Single-neuron analysis of dendrites and axons reveals the network organization in mouse prefrontal cortex. Nat Neurosci 26, 1111–1126 (2023). https://doi.org/10.1038/s41593-023-01339-y

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