DOI: 10.5061/dryad.wh70rxwv4
title: Data from: Whole-brain spatial organization of hippocampal single-neuron projectomes

description: Mapping hippocampal single-neuron projections is essential for
 understanding brain-wide circuit organization and diverse functions of the
 hippocampus, a brain structure underlying episodic memory and cognition.
 Here, we reconstructed 10,100 single-neuron projectomes of the mouse
 hippocampus, identified rostral and caudal axon pathways that
 preferentially innervated cortical vs. subcortical areas, and classified
 43 projectome subtypes with distinct axon targeting patterns. Notably, the
 soma locations along hippocampal longitudinal and transverse axes
 determined the number of their target areas and the spatial distribution
 and complexity of their axon arbors within the targets. We defined
 selective hippocampal subdomains based on spatial transcriptomic profiles
 and found that many projectome subtypes were enriched in specific
 subdomains. Next, we defined the wiring diagram for hippocampal neurons
 exclusively projecting to hippocampal formation (HPF) and those projecting
 to both intra- and extra-HPF targets with coordinated projection
 strengths. Furthermore, bi-hemispheric projecting hippocampal neurons
 generally projected to one pair of homologous targets with ipsilateral
 preference. These organization principles of single-neuron projectomes
 provide a structural basis for understanding diverse but coordinated
 functions of hippocampal neurons.
description: In brief, male mice (C57BL/6J, 11 weeks
 old) were perfused with artificial cerebrospinal fluid at -4℃. Brain
 tissue blocks were cut from left brain hemisphere, embedded in OCT (4583#,
 Sakura), frozen with dry ice and stored in -80℃ fridge. Brain sections
 (10-μm thick) for Stereo-seq were cut from brain tissue blocks in a pre-cooled
 cryochamber at -20℃ (Thermo Fisher Cryostar NX50) at various
 bregma coordinates (100-μm interval). The sections were carefully placed
 on a pre-cooled Stereo-seq chip (-20℃) and moved from one end to the other
 with a finger gently pressed under the chip. The temperature of the finger
 kept the section close to the chip, reducing bubbles and wrinkles. The RNA
 RIN value of total RNA extracted from adjacent brain sections were above
 9, indicating qualified brain sections for spatial transcriptome
 experiments. Brain sections covering hippocampus (Bregma -0.9 – -4.4 mm)
 were laid on Stereo-seq chips for mRNA collection and followed by gene
 sequencing.
description: # Whole-brain spatial organization of hippocampal single-neuron
 projectomes Code and data
 for mouse hippocampal single-neuron projectome and spatial transcriptome
 analysis and visualization. ## Description of the data and file structure
 The neuronal data is represented as tree-structured swc files following
 specifications as:
 . All the swcs are registered to the Allen Template Brain CCF 2017 (Wang, Quanxin, et al. "The Allen mouse brain common coordinate framework: a 3D reference atlas." Cell 181.4 (2020): 936-953) with brain regions following Allen naming conventions, e.g. HPF: Hippocampal formation; HIP: Hippocampal region; CA1: Field CA1; CA3: Field CA3; DG: Dentate gyrus; SUB: Subiculum. We organize the repository following research article figure groups; at the same time, we provide description for our data and scripts below for easy and independent access to our resources. The projectome data are mainly presented in: 1. readable tabular and JSON files, with description listed below; 2. pickle objects that can be read using Python `pickle.load(open($file_path, 'rb'))` function, stored within each figure with variable description in *[note for pkl files.ipynb]* under its designated directory. The spatial transcriptome data are stored as Seurat object that can be read using R `readRDS($file_path)` function. The main structure for the repository: * archive `###pre-requisites to run python files.` ├─ requirements.txt `###data and scripts (*.ipynb, *.py, *.R) for analysis grouped by figures.` ├─ figures `###Projectome subtype classifications of hippocampal neurons.` │ ├─ fig1 │ │ ├─ fig1_D │ │ ├─ fig1_E │ │ ├─ fig1_panelD.ipynb `###Code for heatmap showing the whole-brain projection strength of caudal part and rostral part for each single neuron` │ │ └─ fig1_panelE.ipynb `###Code for hierarchical clustering based on the whole-brain projection patterns.` │ │ └─ note for pkl files.ipynb `###projection target-dependent soma locations of hippocampal neurons.` │ ├─ fig2 │ │ ├─ fig2_A │ │ ├─ fig2_A.ipynb `###Code for the top 50 most frequent single-cell target patterns` │ │ ├─ fig2_C │ │ ├─ fig2_C.ipynb `###Code for the distribution of LEC-MEC selectivity for each ENT projecting neurons and their distribution in CA1, SUB, ProS and SUBr neurons` │ │ ├─ fig2_G │ │ ├─ fig2_G.ipynb `###Code for showing the neuron number and soma locations in given joint projection patterns and subtypes` │ │ ├─ fig2_H │ │ ├─ fig2_H.ipynb `###Code for showing the soma locations for specified projection patterns among given target areas.` │ │ └─ note for pkl files.ipynb `###Spatial correlation between transcriptome and projectome subtypes;` │ ├─ fig3 │ │ └─ figure_3.xlsx `###list of data and functions linked to spatialTranscriptome/R_data and spatialTranscriptome/python_data;` `###Bi-hemispheric projecting neurons in hippocampus.` │ ├─ fig4 │ │ ├─ fig4_A │ │ ├─ fig4_A.ipynb `###Code for showing the distribution of bilaterally projecting neurons in different subtype` │ │ ├─ fig4_E │ │ ├─ fig4_E.ipynb `###Code for showing the distribution of CA3 neurons and CA1 neurons that bilaterally projecting to HIP` │ │ ├─ fig4_H │ │ ├─ fig4_H.ipynb `###Code for showing the projection strength in different layers of bilateral LEC and MEC for ProS,PAR,PRE,POST neurons that could bilaterally projecting to RHP` │ │ ├─ fig4_I │ │ ├─ fig4_I.ipynb `###Code for showing intersection of neurons that bilaterally projecting to different areas.` │ │ ├─ fig4_J │ │ ├─ fig4_J.ipynb `###Code for showing the neuron number, soma distribution in sub-hip regions and ipsilateral or contralateral projection strength in different target areas ` │ │ ├─ fig4_L │ │ ├─ fig4_L.ipynb `###Code for showing the ipsi-preference index of neurons having bilateral projections` │ │ ├─ fig4_M │ │ ├─ fig4_M.ipynb `###Code for showing the ratio of ipsi-only,contra-only and bilateral projection neurons` │ │ └─ note for pkls.ipynb `###Projection patterns of CA1, SUB and SUBr neurons` │ ├─ fig5 │ │ ├─ fig5_A │ │ ├─ fig5_A.ipynb `###Code for showing enriched target patterns in CA1, SUB, ProS and SUBr neurons.` │ │ ├─ fig5_B │ │ ├─ fig5_B.ipynb `###Code for showing the neuron number percentage which have different target number` │ │ ├─ fig5_C │ │ ├─ fig5_C.ipynb `###Code for showing the collateral index of CA1,SUB,ProS and SUBr neurons projecting to different downstream areas.` │ │ ├─ fig5_EFG │ │ ├─ fig5_EFG.ipynb `###Code for showing the correlation motif of downstream areas for CA1,SUB,ProS and SUBr neurons` │ │ ├─ fig5_H │ │ ├─ fig5_H.ipynb `###Code for showing the intra-HPF projection patterns and extra-HPF projection patterns of neurons preferring different intra-HPF targets. ` │ │ ├─ fig5_I │ │ ├─ fig5_I.ipynb `###Code for showing the collateral index of neurons preferring different intra-HPF target` │ │ ├─ fig5_K │ │ ├─ fig5_K.ipynb `###Code for showing the correlation of projection strength in intra-HPF tragets and extra-HPF targets` │ │ └─ note for pkl files.ipynb `###Longitudinal spread of Schaffer collaterals and mossy fiber axons` │ ├─ fig6 │ │ ├─ fig6_C │ │ ├─ fig6_D │ │ ├─ fig6_F │ │ ├─ fig6_H │ │ ├─ fig6_I │ │ ├─ fig6_J │ │ ├─ fig6_K │ │ ├─ fig_6C.ipynb `###Code for showing the T-L index of axon projections in ipsilateral and contralateral CA3 from every single CA3 neuron ` │ │ ├─ fig_6D.ipynb `###Code for showing the T-L index of axon projections in ipsilateral and contralateral CA1 from every signle CA3 neuron ` │ │ ├─ fig_6F.ipynb `###Code for showing the T-L index of axon projections in DG and CA3 from every signle DG neuron ` │ │ ├─ fig_6H.ipynb `###Code for showing the statistic of overall T-L index for all CA3 neurons` │ │ ├─ fig_6J.ipynb `###Code for showing the statistic of overall T-L index for all DG neurons` │ │ ├─ fig_6i.ipynb `###Code for showing the statistic of overall span for all CA3 neurons` │ │ └─ fig_6k.ipynb `###Code for showing the statistic of overall span for all DG neurons` │ │ └─ note for pkl files.ipynb `#Dependence of axon arbor distribution on the transverse location of soma.` │ ├─ fig7 │ │ ├─ HIP_CA1_CA3_proximodistal_analysis.py `###code for HPF transverse plane analysis` │ │ ├─ allen_annotation │ │ │ └─ allen_struct_info.json `###json file that store the Allen CCFv3 structure information` │ │ ├─ resource │ │ │ ├─ CA1_ipsi_targets_proximodistal_info.csv `###table of CA1 neuron's location on CA1 proximodistal and longitudinal position, mean terminal location in specific target regions.` │ │ │ ├─ CA1_soma_info.csv `###table of CA1 neuron's soma information, for example, neuron id, neuron's soma location, neuron's laterality, neuron's depth in CA1.` │ │ │ ├─ CA1_terminal_info.csv `### table of CA1 neuron's terminal information, for example, terminal location, laterality, CCFv3 structure id that the terminal located in.` │ │ │ ├─ CA3_soma_info.csv `###table of CA3 neuron's soma information, for example, neuron id, neuron's longitudinal location in HIP, neuron's laterality, neuron's proximodistal location in CA3.` │ │ │ └─ CA3_terminal_info.csv `### table of CA3 neuron's terminal information, for example, laterality, CCFv3 structure id that the terminal located in, terminal's longitudinal position and depth in CA1.` │ │ └─ utils │ │ ├─ **init**.py │ │ └─ allen_struct_info.py `### map function between Allen CCFv3 structure informations, for example, map CCFv3 structure id to CCFv3 structure acronym.` `###Dependence of axonal arbor distribution on the longitudinal location of soma` │ ├─ fig8 │ │ ├─ fig8_C │ │ ├─ fig8_D │ │ ├─ fig8_EF │ │ ├─ fig8_G │ │ ├─ fig_8C.ipynb `###Code for showing the topographic projection correlation between the somalocations and the axon terminal locations for all ENT projecting neurons` │ │ ├─ fig_8D.ipynb `###Code for showing the whole-brain topographic projection correlation between the somalocations and the axon terminan locations ` │ │ ├─ fig_8EF.ipynb `###Code for showing the correlation between somalocations and the target number,the correlation between the longitudinal span and the target number` │ │ └─ fig_8G.ipynb `###Code for showing the whole brain topographic projection patterns for all the neurons` │ │ └─ note for pklfiles.ipynb `###dendrite analysis` │ ├─ fig_s10 │ │ ├─ CA1_to_MSC_dendritic_analysis.py `### Code for dendritic morphological feature analysis of MSC projecting CA1 neurons.` │ │ └─ resource │ │ ├─ CA1_dendritic_cluster.csv `### Dendritic cluster of all CA1 neurons.` │ │ ├─ CA1_projection_strength.csv `### MSC projecting CA1 neurons' projection strength in different target regions.` │ │ ├─ CA1_to_MSC_dendritic_cluster.csv `###Dendritic clusters of MSC projecting CA1 neurons` │ │ ├─ CA1_to_MSC_dendritic_features.csv `###Dendritic mophological features of MSC projecting CA1 neurons` │ │ ├─ CA1_to_MSC_feature_importance.csv `###Dendritic feature importance for dendritic classicication.` │ │ ├─ CA1_to_MSC_soma_info.csv `###Location information of MSC projecting CA1 neurons` │ │ └─ MSC_projecting_axon_length.csv `### CA1 neurons' axon length in MSC` `###Intra-HPF target-specific preference in hippocampal axon projections.` │ ├─ fig_s11 │ │ ├─ figS11_A.ipynb `###Code for clustermap of intra-HPF projection patterns.` │ │ ├─ figS11_B.ipynb `###Code for intra-HPF projection subtype distribution in CA1, SUB, ProS, and SUBr neuron groups.` │ │ ├─ figS11_DEFG.ipynb `###Code for intersection among neurons having different target numbers, neurons projecting different targets and intra-HPF projection subtype ` │ │ ├─ figS11_H.ipynb `###Code for co-projecting neuron numbers of intra-HPF areas and extra-HPF areas` │ │ ├─ figS11_I.ipynb `###Code for the fraction of SUBr neurons in each intra-HPF prjectiono subtype ` │ │ ├─ fig_s11A │ │ ├─ fig_s11B │ │ ├─ fig_s11DEFG │ │ ├─ fig_s11H │ │ ├─ fig_s11I │ │ └─ note for pklfiles.ipynb `###superficial/deep CA1 neuron analysis` │ ├─ fig_s12 │ │ ├─ S12.py `### Code for demostrate the projection pattern difference of superficial CA1 neurons and deep CA1 neurons.` │ │ └─ resource │ │ ├─ S12_deep_superficial_projection_features.csv `### Projection strength in different target regions of deep superficial CA1 neurons.` │ │ └─ S12_dorsal_CA1_soma_depth_and_projection_features.csv `### Table of neurons' location and projection strength in target regions.` `###Morphological features of neurons in hippocampal subregions.` │ ├─ fig_s2 │ │ ├─ fig_s2_morphology_parameters.ipynb `###Code for box plot for morphology features quantification.` │ │ └─ morphology_parameters.xlsx `###Morphology features quantification.` `###Neuron clustering.` │ ├─ fig_s4 │ │ ├─ fig_s4D │ │ ├─ fig_s4E │ │ ├─ figs4_D.ipynb `###Code for dot plot of downstream target patterns along axon trajectories of axon arbor templates.` │ │ ├─ figs4_E_caudal.ipynb `###Code for hierarchical clustering of caudal projection patterns ` │ │ ├─ figs4_E_combine.ipynb `###Code for generate whole-brain projection patterns by combining caudal projection patterns and rostral projection patterns ` │ │ └─ figs4_E_rostral.ipynb `###Code for hierarchical clustering of rostral projection patterns ` │ │ └─ note for pklfiles.ipynb `###Discrete longitudinal distribution of projectome subtypes.` │ ├─ fig_s7 │ │ ├─ fig_s7_plot_distribution.ipynb `###Code for distribution plot for projectome subtypes.` │ │ ├─ neuronInfo.csv `###Neuron sample information.` │ │ ├─ scatter.csv `###Downstream projection patterns for subtype5 and subtype21 neurons.` │ │ ├─ slopes.csv `###Distribution quantification for each projectome subtypes.` │ │ └─ slopes_region.csv `###Distribution quantification for each projectome subtypes per region.` `###The long-range projection from neurons in DG, CA2 and CA3.` │ ├─ fig_s8 │ │ ├─ figS8_B.ipynb `###Code for pie plot and distribution plot for DG long-range projection neurons of subtype 5, 34, 35, 39.` │ │ ├─ figS8_C.ipynb `###Code for violin plot of axon lengths in target areas (HPF, LSX and MSC) for DG long-range projection neurons of subtype 5, 34, 35, 39.` │ │ ├─ figS8_E.ipynb `###Code for stacked bar plot of DG-mo, DG-po, and DG-sg neuron numbers in local and long-range projection DG neurons.` │ │ ├─ figS8_I.ipynb `###Code for pie plot and distribution plot for CA3 neurons of subtype 5, 34, 35, 39, and 43.` │ │ ├─ figS8_J.ipynb `###Code for violin plot of axon lengths in target areas (HPF, LSX and MSC) for CA3 neurons of subtype 5, 34, 35, 39, and 43.` │ │ ├─ figS8_LMN.ipynb `###Code for neuron distribution and downstream projection patterns for CA2 neurons of subtype 34, 39, and 43.` │ │ ├─ fig_s8B │ │ ├─ fig_s8C │ │ ├─ fig_s8E │ │ ├─ fig_s8I │ │ ├─ fig_s8J │ │ ├─ fig_s8LMN │ │ └─ note for pklfiles.ipynb `###Spatial transcriptome analysis for CA1 sections.` │ └─ fig_s9 │ └─ figure_s9.xlsx `###list of data and functions linked to spatialTranscriptome/R_data and spatialTranscriptome/python_data.` `###data and scripts (*.R, *.ipynb) to analyze spatial transcriptome and its correlation to projectome, including Seurat objects for mouse CA1 spatial transcriptome analysis and documents on how to use them, mainly used for fig3 and fig_s9.` └─ spatialTranscriptome ├─ How to process transcriptomic data.pptx ├─ R_data │ ├─ bregma.txt `###chip bregma coordinates.` │ ├─ cluster │ │ └─ cluster.rds `###Seurat object of CA1 spatial bin-resolution cells` │ ├─ cluster_pipeline.R `###code for CA1 cells processing and clustering` │ ├─ mask_data `###Allen brain region mask.` │ ├─ subcluster │ │ ├─ CA_subcluster_transcriptomic_proportion.csv `###transcriptomic proportion in CA1 subcluster C1.` │ │ ├─ CA_subtype_projectomic_proportion.csv `###projectome proportion in bregma.` │ │ └─ subcluster.rds `###Seurat object of CA1 subcluster C1 spatial bin-resolution cells` │ └─ subcluster_pipeline.R `###code for CA1 subcluster C1 cells processing and clustering` └─ python_data ├─ fig3_GI_figS9_J.ipynb `###code to plot cell distribution in coronal slice` ├─ for_qc_heatmap_and_cor.csv `###sample information` └─ mask.csv `###Allen brain region mask.` ## Sharing/Access information This data is also available at: * \[] (Brain Science Data Center, Chinese Academy of Sciences) ## Code/Software All data can be used independently. All scripts can be run seperately using desired functions and data with pre-requisite libraries installed properly. The pre-requisites to run the Python code are listed in requirements.txt, the Python version used is Python 3.8.18. The pre-requisites to run the R code are listed below with R 4.3.1: * Seurat 4.4.0 * tidyverse 2.0.0 * pheatmap 1.0.12 * reshape2 1.4.4 * harmony 1.0.3