DOI: 10.5061/dryad.0k6djhbdf
title: Single-nucleus profiling highlights the all-brain echinoderm nervous system

description: 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.
description: # Single-nucleus profiling highlights the all-brain echinoderm nervous
 system Access this dataset on Dryad:
 [https://doi.org/10.5061/dryad.0k6djhbdf](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.