Single-nucleus profiling highlights the all-brain echinoderm nervous system
Data files
Oct 07, 2025 version files 9.64 MB
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Clustering_analysis_2wpm_Plividus_juvenile.R
26.70 KB
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mapping_table_pl-sp-merged.csv
5.36 KB
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mapping_table_pl-sp.csv
12.07 KB
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pl_cellranger_forced.sh
623 B
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Pl_cross-species_comparison.rmd
3.67 KB
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README.md
7.22 KB
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Supplementary_file_1.xlsx
6.74 MB
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Supplementary_file_2.xlsx
9.99 KB
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Supplementary_file_3.xlsx
2.40 MB
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Supplementary_file_4.xlsx
432.99 KB
Abstract
Metazoans comprise diverse tissues and cell types, each essential for the survival of the organism. Most of these types are established early in embryogenesis and persist into adulthood. In indirectly developing sea urchins, however, the continuity between embryonic and adult stages is interrupted by a planktonic larval stage that undergoes complete metamorphosis. While gene regulatory networks controlling embryonic and larval lineages are well studied, the molecular and morphological identities of post-metamorphic cell types remain poorly understood. Here, we reconstructed the cell type atlas of post-metamorphic Paracentrotus lividus juveniles using single-nucleus transcriptomics, revealing conservation of regulatory mechanisms. We identified signatures of eight distinct cell type groups and analyzed 29 neuronal families, including 15 unique photoreceptor types. By combining transcriptomics, spatial analysis, and ultrastructure, we identified vertebrate neuronal and opsin homologues expressed across the sea urchin juvenile. These findings show the echinoderm body plan is predominantly head-like and exhibits an “all-brain” organization.
Access this dataset on Dryad: https://doi.org/10.5061/dryad.0k6djhbdf
This repository contains scripts and Supplementary Materials related to the analyses
Description of the data and file structure
This repository contains scripts used for the manuscript "Single Nucleus Profiling Highlights the All-Brain Echinoderm Nervous System" by Periklis Paganos, Jack Ullrich-Lüter, Alba Almazán, Danila Voronov, Jil Carl, Anne-C. Zakrzewski, Berit Zemann, Maria Lorenza Rusciano, Tiphaine Sancerni, Maria Schauer, Oğuz Akar, Filomena Caccavale, Maria Cocurullo, Giovanna Benvenuto, Jenifer Carol Croce, Carsten Lüter, Maria Ina Arnone. It also contains files associated with the mapping of snRNA-seq data (pl_cellranger_forced.sh), the analyses reported in the manuscript (Clustering_analysis_2wpm_Plividus_juvenile.R), and the larva versus juvenile cross-species comparison (Pl_cross-species_comparison.rmd). In detail, the Clustering_analysis_2wpm_Plividus_juvenile R script is used to implement a complete single-nucleus RNA sequencing (snRNA-seq) analysis pipeline using the Seurat framework and related visualization and clustering tools. The analysis integrates three P.lividus juvenile single-cell datasets and performs clustering, dimensional reduction, integration, and downstream biological interpretation, including marker gene identification.
The Pl_cross-species_comparison Rmd file documents the SAMap cross-species cell-type mapping built on Seurat outputs from P. lividus juveniles and S. purpuratus 3 dpf larvae. In R, the integrated Seurat objects (pljuv_integrated_umap, sp_3dpf_integrated_umap) are prepared for SAMAP and exporting to H5Seurat and h5ad via SeuratDisk (SaveH5Seurat → Convert), while the SAMap is performed in Python. SAMAP is initialized with the two .h5ad files and a precomputed gene map directory; orthology/homology maps are created with map_genes.sh using species proteome FASTA files. The pipeline runs pairwise SAMAP, stitches the species graphs, and computes mapping scores (get_mapping_scores) keyed by Seurat cluster labels (or by a merged neuronal label for the second analysis), then exports the full mapping table to CSV. Finally, in R, the mapping table is reshaped (melted), cluster order is explicitly set for readability, and a heatmap of alignment scores is plotted with ggplot2 (diverging palette, fixed aspect, labeled axes).
Moreover, it contains the following supplementary files:
- Supplementary file 1 containing conversion gene IDs and IDs used to generate the plots of the figures (Supplementary_file_1.xlsx).
Additional details:
ref_id= reference identifier.
ref_gene= reference gene.
tid= transcript identifier.
gid= gene identifier.
pl_name= P. lividus gene name.
pliv_name= P. lividus gene name from legacy annotation.
spu_id_pl= S. purpuratus gene identifier corresponding to P. lividus genes.
sp_name_pl= S. purpuratus gene name corresponding to P. lividus genes.
spu_id_pliv= S. purpuratus gene identifier corresponding to P. lividus legacy gene identifiers.
sp_name_pliv= S. purpuratus gene name corresponding to P. lividus legacy gene identifiers.
whl_id_pl= S. purpuratus transcript identifier corresponding to P. lividus genes.
whl_id_pliv= S. purpuratus transcript identifier corresponding to P. lividus legacy gene identifiers.
pfam_id_pl= protein family identifier for P. lividus genes.
pfam_id_pliv= protein family identifier for P. lividus legacy gene identifiers.
function_id_pl= functional annotation for P. lividus genes.
function_id_pliv= functional annotation for P. lividus legacy gene identifiers.
ncbi_desc_pliv= = NCBI-based description associated to P. lividus legacy gene identifiers.
ncbi_desc_pl= NCBI-based description associated with P. lividus genes.
pl_sc_name= identifiers used to plot in the single-nucleus RNA-seq data.
NA=not applicable (no blast hit found)
- Supplementary file 2 containing information on HCR probes (Supplementary_file_2.xlsx)
- Supplementary file 3 containing gene markers of the juvenile atlas (Supplementary_file_3.xlsx)
Details on headers:
p_val= p-value
avg_log2FC= average log2 fold change
pct.1= percentage of cells within a designated group
pct.2= percentage of cells in another group
p_val_adj= adjusted p-value
cluster= cluster name
gene= single cell-based gene name
gene_id= gene identifier
- Supplementary file 4 containing Photoreceptor cells' marker genes (Supplementary_file_4.xlsx)
Details on headers:
p_val= p-value
avg_log2FC= average log2 fold change
pct.1= percentage of cells within a designated group
pct.2= percentage of cells in another group
p_val_adj= adjusted p-value
cluster= cluster name
gene= single cell-based gene name
gene_id= gene identifier
- Supplementary Table 1. Mapping table of S. purpuratus and P. lividus clusters (mapping_table_pl-sp.csv).
- Supplementary Table 2. Mapping table of S. purpuratus and P. lividus clusters, in which P. lividus neuronal clusters are merged into a single cluster (mapping_table_pl-sp-merged.csv).
Sharing/Access information
The raw snRNA-seq data generated for this work and the resulting rds objects are deposited on NCBI Gene Expression Omnibus (accession number GSE292747).
Code/Software
The Clustering_analysis_2wpm_Plividus_juvenile.rmd file contains the workflow to analyze in R the single-nucleus data generated. The workflow relies on packages, including Seurat and SeuratWrappers for data processing, normalization, integration, clustering, dimensional reduction (PCA, UMAP), and visualization. Additional utility and visualization packages include ggplot2, cowplot, patchwork, pheatmap, and viridis for high-quality plots; reshape2, tidyverse, tidyr, and dplyr for data wrangling; and matrixStats for efficient matrix operations. The pipeline also incorporates ape, bioDist, ggtree, treeio, and tidytree for cell type tree construction and annotation using neighbor-joining methods.
The Pl_cross-species_comparison.rmd file described the steps to perform cross-species comparison of single-nucleus/cell atlases. This analysis utilizes several R and Python packages across both environments. In R, the workflow relies on Seurat for managing single-cell/nucleus objects, SeuratData for handling datasets, SeuratDisk for converting Seurat objects to the .h5ad format compatible with Python, and reshape2 for data reshaping before visualization. For plotting and visualizing mapping results as heatmaps, it employs ggplot2 alongside reshape2 to generate high-quality, publication-ready figures. In Python, the main package is samap (Alexander J Tarashansky Jacob M Musser Margarita Khariton Pengyang Li Detlev Arendt Stephen R Quake Bo Wang (2021) Mapping single-cell atlases throughout Metazoa unravels cell type evolution eLife 10:e66747.), which includes analytical tools such as get_mapping_scores, GenePairFinder, sankey_plot, chord_plot, CellTypeTriangles, ParalogSubstitutions, FunctionalEnrichment, convert_eggnog_to_homologs, and GeneTriangles for cross-species cell-type mapping and gene orthology analysis.
