Abstract
Animal cell types are defined by differential access to genomic information—a process orchestrated by the combinatorial activity of transcription factors that bind to cis-regulatory elements (CREs) to control gene expression. Changes in these gene regulatory networks (GRNs) underlie the origin and diversification of cell types, yet the regulatory logic and specific GRNs that define cell identities remain poorly resolved across the animal tree of life. Cnidarians, as early-branching metazoans, provide a critical window into the early evolution of cell type-specific genome regulation. Here we profiled chromatin accessibility in 60,000 cells from whole adults and gastrula-stage embryos of the sea anemone Nematostella vectensis. We identified 112,728 putative CREs and quantified their activity across cell types, revealing pervasive combinatorial enhancer usage and distinct promoter architectures. To decode the underlying regulatory grammar, we trained sequence-based models predicting CRE accessibility and used these models to infer cell type similarities that reflect known ontogenetic relationships. By integrating sequence motifs, transcription factor expression and CRE accessibility, we reconstructed the GRNs that define cnidarian cell types. Our results show the regulatory complexity underlying cell differentiation in a morphologically simple animal and highlight conserved principles in animal gene regulation. This work provides a foundation for comparative regulatory genomics to understand the evolutionary emergence of animal cell type diversity.
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Data availability
Raw and processed files will be available in GEO repository under accession number GEO: GSE294388. In addition, the atlas can be explored in an interactive database: https://sebelab.crg.eu/nematostella-cis-regulatory-atlas/ and also in an interactive genome browser: https://sebelab.crg.eu/nematostella-cis-reg-jb2/.
Code availability
Scripts to reproduce the data processing and downstream analysis are available via Zenodo at https://doi.org/10.5281/zenodo.17425383 (ref. 93). Unless otherwise specified, scripts are based on R v.4.2.2 and Python v.3.8.10, and the language-specific libraries specified in Methods.
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Acknowledgements
We thank I. Kim, A. de Mendoza, S. Montgomery, M. Irimia and N. Maeso for critical comments on the paper, as well as all members of the Sebe-Pedros group for discussion and suggestions. We thank F. Rentzsch for access to Nematostella Elav1::mOrange transgenic line. We are grateful to D. Cañas-Armenteros for taking care of Nematostella cultures and to the CRG Flow Cytometry, Genomics and ALMU facilities for technical support. Research in A.S-P. group has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement number 851647) and the Spanish Ministry of Science, Innovation and Universities (PID2021-124757NB-I00 funded by MICIU /AEI /10.13039/501100011033 / FEDER, UE). We acknowledge support of the Spanish Ministry of Science and Innovation through the Centro de Excelencia Severo Ochoa (CEX2020-001049-S, MCIN/AEI /10.13039/501100011033), the Generalitat de Catalunya through the CERCA program and to the EMBL partnership. A.E. was supported by FPI PhD fellowship from the Spanish Ministry of Science and Innovation (PRE2019-087793SO funded by MCIN/AEI/10.13039/501100011033 and FSE+). M.I. has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement number 75442. X.G-B. was supported by the European Union’s H2020 research and innovation program under Marie Skłodowska-Curie grant agreement 101031767.
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A.S.-P. conceived and supervized the study. M.I. performed single-cell experiments and generated transgenic reporter lines. A.E. analysed scATAC-seq data, performed motif analyses and trained sequence models with the support of L.M. and S.A. G.Z. and X.G.-B. performed phylogenetic and comparative genomics analyses. A.E. created visualizations. A.E., M.I. and A.S.-P. interpreted the data and wrote the paper with contributions from all authors.
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Nature Ecology & Evolution thanks Maria Ina Arnone, Ferdinand Marlétaz and Juan Tena for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 scATAC-seq dataset QC, clustering and annotation.
a, Number of cells (top) and unique fragments per cell (bottom), b, scATAC-seq fragment size distribution for each sample. c, TSS enrichment signal for each sample. d, UMAP projection of single cells and metacells for gastrula dataset. e, UMAP projection of single cells and metacells for adult dataset. f, NJ clustering of metacells for adult and gastrula together, only for adult (g) and only for gastrula (h). i, Heatmap showing peak accessibility per cell type. j, Annotation transfer heatmap for adult scATAC-seq clusters. k, Annotation transfer heatmap for gastrula scATAC-seq clusters. l, Comparison of ATAC and RNA correlations for multiome (left) and separately profiled scATAC-seq and scRNA-seq data (right).
Extended Data Fig. 2 Cell type markers.
Comparison between accessibility scores and expression for selected marker genes.
Extended Data Fig. 3 Cis-regulatory element classification.
a, Decision tree used to classify CRE into different promoter types. b, Fraction of gastrula CREs classified as promoters and enhancers. Promoters are further classified as constitutive promoters (CP), specific promoters (SP) and alternative promoters (AP). Enhancers are classified based on their overlap with different genomic regions. c, Distance to the nearest TSS distributions for different types of CREs. d, Summary peak statistics. Number of peaks per gene (top-left) and number of genes that each peak gets assigned to (bottom-left), correlation across metacells between peak accessibility and expression of the genes they are assigned to (top-right), and co-accessibility across cell clusters of all pairs of peaks (bottom-right). e, V-plots showing tagmentation fragment size distributions (y-axis) at different distances (x-axis) around CP, SP and distal CREs in adult and gastrula stages.
Extended Data Fig. 4 Motif enrichment analysis.
Dotmap of representative motifs enriched in different cell types. Significantly enriched motifs (fold change > 1, padj < 0.001) are grouped by motif-motif similarity and top enriched representative motifs for each cluster are indicated (hypergeometric test, FDR adjustment for multiple testing).
Extended Data Fig. 5 Sequence models.
a, Five-fold cross-validation (CV) area under the curve (AUC, top) and test set AUC (bottom) for gkm-SVM classifiers trained on adult cnidocytes. b, Area under receiver operator curve (AUC ROC, top) and area under precision recall curve (AUC PR, bottom) for all cell type gkm-SVM classifiers. c, Spearman correlation for ChromBPNet predicted accessibility counts in test set peaks. d, Spearman correlation for crested predicted accessibility counts in test set peaks. e, Nucleotide importance scores for top scored CREs in different cell types.
Extended Data Fig. 6 Sequence motif discovery.
a, Heatmap showing pairwise motif similarities used to generate motif archetypes from enriched motifs. b, Examples of motif clusters. c, Examples of motif archetypes. d, Same as (a) and (b) for patterns discovered with sequence models. e, Number of motifs per archetype (left) and number of archetypes composed of motifs from different sources (right). f, Number of patterns per archetype (left) and number of archetypes composed of patterns from different sequence models (right). g, Comparison of motif enrichment fold change (left) and adjusted p-value (right) for archetypes versus best scoring motif in each archetype cluster (hypergeometric test, FDR adjustment for multiple testing). h, For all pattern archetypes and their most similar motif archetype, Jensen-Shannon divergence (JSD) calculated across the best pairwise alignment of archetypes (x-axis, JSDcomplete), and calculated across the best alignment spanning the length of shorter archetype (y-axis, JSDmin). Based on these two metrics, pattern archetypes are classified as being novel motifs, having novel context or resembling known motifs from motif enrichment analysis. i, Fraction of pattern archetype classes defined in h, Euler diagrams summarizing the source of pattern archetypes (that is sequence models) for each of these three categories are shown on top. j, Fraction of enriched motifs found with each of the sequence models, k, Co-occurrence network of pattern archetypes with contribution in cnidocytes. Size of the node reflects its cell type contribution, and width of the connection scales with the number of CREs in which two motifs co-occur. l, Correlation-based approach for assigning motifs to TFs. For each TF, we rank motifs based on correlation of motif activity to TF accessibility and expression, and assign it top ranking motif of the same structural class. m, Euler diagram showing TF coverage using different motif-to-TF assignment methods for expressed Nematostella TFs. n, Final motif assignment sources for expressed Nematostella TFs.
Extended Data Fig. 7
Examples of TF expression and TF motif activity correlations for selected marker genes.
Extended Data Fig. 8 Cell type gene regulatory networks.
a, Number of TFs in GRNs inferred for each broad cell type (top), number of genes targeted by each TF (middle), and fraction of overlapping target genes for each pair of TFs (bottom). b, Overlap of target genes for the same TF across cell types, plotted for groups of TFs active in different number of cell types. Selected TFs are highlighted on the plot and overlap of their target genes is shown as Euler diagrams below. c, Number of CREs per target gene (x-axis) compared to number of CREs of the same gene with any single TF motif (y-axis). Most TFs have binding motif in a single CREs of their target genes. d-g, Additional inferred GRN and TF connectivity measurements for neuro-secretory cell types: GATA/Islet neurons (d-e), Pou4/FoxL2 neurons (f-g) and gland cells (h-i). Asterisks highlight TFs known to be involved in neurosecretory development.
Supplementary information
Supplementary Tables 1–3 (download XLSX )
Table 1. scATAC-seq library information. Table 2. Annotation of putative CREs assigned to Nematostella genes in broad cell types. Peaks are classified as CP, SP, AP or NO (not a promoter, putative enhancer). Table 3. N. vectensis TF motif assignment.
Supplementary Data 1 (download XLSX )
Active TFs per cell type. Thresholds used for TF filtering: gene expression FC > 0.4 quantile of cell type expression values (exp_thrs column), ChromVAR deviation Z-score > 4.
Supplementary Data 2 (download XLSX )
Cell type-specific GRNs. Thresholds used for filtering cell type-specific networks: gene expression FC > 0.4 quantile of cell type expression values (exp_thrs column), gene accessibility > 0.4 quantile of cell type accessibility values (acc_thrs column), ChromVAR deviation Z-score > 4. Note that in Fig. 3 and Extended Data Fig. 8, for simplicity, cell type GRNs are further filtered for genes with expression FC > 1.2.
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Elek, A., Iglesias, M., Mahieu, L. et al. Decoding cnidarian cell type gene regulation. Nat Ecol Evol 10, 140–153 (2026). https://doi.org/10.1038/s41559-025-02906-1
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DOI: https://doi.org/10.1038/s41559-025-02906-1
