Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We used deep learning to characterize these enhancer codes and devised three metrics to compare cell types in the telencephalon across amniotes. To this end, we generated single-cell multiome and spatially resolved transcriptomics data of the chicken telencephalon. Enhancer codes of orthologous nonneuronal and γ-aminobutyric acid-mediated (GABAergic) cell types show a high degree of similarity across amniotes, whereas excitatory neurons of the mammalian neocortex and avian pallium exhibit varying degrees of similarity. Enhancer codes of avian mesopallial neurons are most similar to those of mammalian deep-layer neurons. With this study, we present generally applicable deep learning approaches to characterize and compare cell types on the basis of genomic regulatory sequences.

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Publication Details

Journal
Science
Published
2025-02-13
DOI
https://doi.org/10.1126/science.adp3957
Citations
33
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
26.60
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article

Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

Gert Hulselmans, Stein Aerts, Ioannis Sarropoulos, Kristofer Davie et al.
33 citations
Science
Single-cell and spatial transcriptomics
26.60
article

Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

Gert Hulselmans, Stein Aerts, Ioannis Sarropoulos, Kristofer Davie, Valerie Christiaens, Julie De Man, Nikolai Hecker, Joris de Wit, Lars E. Borm, Carmen Bravo González‐Blas, Rani Moors, Suresh Poovathingal, Roel Vandepoel, Lynette Lim, David Mauduit, Niklas Kempynck, Elke Leysen, Darina Abaffyová, Sam Dieltiens, Jeroen Vandensteen
article en
33 citations

Abstract

Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We used deep learning to characterize these enhancer codes and devised three metrics to compare cell types in the telencephalon across amniotes. To this end, we generated single-cell multiome and spatially resolved transcriptomics data of the chicken telencephalon. Enhancer codes of orthologous nonneuronal and γ-aminobutyric acid-mediated (GABAergic) cell types show a high degree of similarity across amniotes, whereas excitatory neurons of the mammalian neocortex and avian pallium exhibit varying degrees of similarity. Enhancer codes of avian mesopallial neurons are most similar to those of mammalian deep-layer neurons. With this study, we present generally applicable deep learning approaches to characterize and compare cell types on the basis of genomic regulatory sequences.

ScienceVol. 387(6735)
Heidelberg University (DE), VIB-KU Leuven Center for Cancer Biology (BE), Computational Physics (United States) (US), VIB-KU Leuven Center for Brain & Disease Research (BE), Heidelberg University (US), KU Leuven (BE)
Openalex Percentile: Top 0%
Single-cell and spatial transcriptomics
26.60
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