The research team, led by investigators at Stanford University including Chew Chai, Jesse Gibson, Pengyang Li, Brennan D. McDonald, Anusri Pampari, Aman Patel, Anshul Kundaje, and Bo Wang, set out to address a fundamental question in evolutionary biology: what mechanisms define and maintain families of related cell types across deep evolutionary time? Cell types can be organized into related families based on their functional and molecular properties, yet the regulatory underpinnings that sustain these families across hundreds of millions of years of divergence have remained largely unknown. By integrating single-nucleus multi-omic sequencing data from three species of flatworms and comparing it with vertebrate data, the researchers were able to identify hundreds of sequence motifs that dictate chromatin accessibility and partition into distinct, conserved sets referred to as vocabularies.

The concept of motif vocabularies represents a significant conceptual advance in the field. Each vocabulary is associated with a specific cell type family, meaning that the short DNA sequences recognized by transcription factors are not randomly distributed across the genome but instead cluster into coherent sets that define broad categories of cellular identity. When the researchers examined the combinatorial relationships among these motifs, they found that the particular combinations preferred by individual cell types are largely species-specific. This means that while the building blocks, the individual motifs themselves, remain stable across vast evolutionary distances, the ways in which those blocks are assembled into functional regulatory programmes evolve rapidly and independently in each lineage.

To dissect this layered organization, the team employed ChromBPNet, a deep-learning architecture designed to model chromatin accessibility at base resolution while factoring out technical biases introduced by the Tn5 transposase used in ATAC-seq library preparation. Models trained on chromatin accessibility data from one species accurately predicted family-level chromatin accessibility in distantly related species, demonstrating that the vocabulary-level information encoded in DNA sequences is conserved in a functionally meaningful way. However, interpretability analyses of the model predictions revealed a striking pattern: the deep-learning models frequently relied on different motifs from the shared vocabularies to arrive at convergent predictions. In other words, two species might achieve the same regulatory outcome for a given cell type family, but they do so by drawing on different members of the same motif vocabulary rather than by using identical regulatory elements.

The picture changes dramatically when the resolution of analysis shifts from the cell type family level to the individual cell type level. Models trained on chromatin accessibility data from a specific cell type within one species lost their predictive power when applied to the corresponding cell type in a distantly related species. This loss of cross-species transferability indicates that the regulatory syntax governing cell type-level identity, the precise arrangements and combinations of motifs that specify an individual cell type, evolves much more rapidly than the vocabulary-level constraints that define broader cell type families. The researchers refer to this hierarchical organization as a collective maintenance model, in which the identity of a cell type family is preserved not by any single conserved regulatory element but by the collective stability of a large pool of conserved regulatory factors.

This collective maintenance framework draws a compelling parallel to the concept of developmental homology in evolutionary biology. In homology, a character identity persists across species through conservation at the network level, even as the individual components of the network undergo extensive rewiring. The flatworm and vertebrate data suggest that cell type family identity operates under a similar logic: the vocabulary of sequence motifs defining a family is evolutionarily stable, yet the recombination of these motifs generates cell type-specific regulatory programmes that can differ substantially between species. This decoupling of family-level conservation from cell type-level innovation provides a mechanistic explanation for how new cell types can arise during evolution without disrupting the fundamental identities of existing cell type families.

The technical rigor underlying these conclusions is substantial. The researchers generated single-nucleus multi-omic sequencing data, capturing both gene expression and chromatin accessibility from the same individual nuclei, across three flatworm species: Schmidtea mediterranea, Schistosoma mansoni, and Macrostomum lignano. These species span a considerable range of evolutionary divergence within the flatworm phylum, providing a robust framework for comparative analysis. The team also extended their analysis to vertebrate systems by leveraging existing single-cell multi-omic data from mouse and zebrafish, two species separated by approximately 450 million years of evolution. The cross-species comparison between flatworms and vertebrates is particularly informative because these lineages diverged over 550 million years ago, representing one of the deepest evolutionary comparisons feasible with single-cell genomics.

Among the findings that emerge from this analysis is the observation that chromatin accessibility is conserved within cell type families even when the individual regulatory elements driving that accessibility are not. This means that the overall pattern of which parts of the genome are open and accessible in a given cell type family remains similar across species, but the specific DNA sequences responsible for opening those regions differ. The deep-learning models captured this distinction with remarkable fidelity, as they were able to predict family-level accessibility patterns across species using different combinations of motifs from the same conserved vocabulary. When the researchers examined neural cell types specifically, they documented extensive turnover in combinatorial motif usage, with individual neural cell types in different species employing different sets of motif pairs from the shared neural vocabulary to achieve their specific regulatory identities.

The implications of this work extend beyond basic evolutionary biology into the realm of biomedical research. Understanding that the vocabulary-level organization of regulatory motifs is conserved across species suggests that insights gained from model organisms about cell type family regulation may be more broadly transferable than previously appreciated, provided the analysis is conducted at the appropriate level of biological organization. Conversely, the rapid evolution of cell type-specific regulatory syntax means that extrapolating detailed regulatory mechanisms from one species to another requires caution, particularly for cell types that have undergone significant diversification. The collective maintenance model also raises intriguing questions about the evolutionary dynamics that maintain vocabulary stability while permitting combinatorial flexibility, and whether disruptions to vocabulary-level conservation might underlie certain developmental disorders or diseases.

As the field of single-cell genomics continues to expand the catalog of cell types across the tree of life, the framework developed in this study provides a conceptual scaffold for interpreting cross-species comparisons at multiple levels of resolution. The finding that conserved motif vocabularies constrain genome access while their flexible recombination drives cell type innovation bridges a persistent gap between the stability of cellular identities and the remarkable diversity of cell types observed across the animal kingdom. The research team has made all data and computational tools publicly available, including the single-cell multi-ome datasets deposited in the Sequence Read Archive, the ChromBPNet models shared through figshare, and the complete analysis code archived on GitHub and Zenodo, ensuring that the broader scientific community can build upon these findings to further unravel the regulatory architecture of cell type evolution.

Subject of Research: Evolutionary conservation of regulatory motif vocabularies governing chromatin accessibility and cell type family identity across flatworms and vertebrates

Article Title: Flexible use of conserved motifs constrains genome access in cell type evolution

Article References: Flexible use of conserved motifs constrains genome access in cell type evolution. (n.d.). https://doi.org/10.1038/s41559-026-03164-5

Image Credits: AI Generated

DOI: 10.1038/s41559-026-03164-5

Keywords: cell type evolution, chromatin accessibility, deep learning, single-cell multi-omics, motif vocabularies, regulatory syntax, flatworms, vertebrates, developmental homology, gene regulation, Nature Ecology & Evolution, transcription factors