Enhancer-driven cell type comparison reveals similarities between the mammalian and avian telencephalon
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ABSTRACT:
ORGANISM(S): Gallus gallus
PROVIDER: GSE275097 | GEO | 2024/08/24
REPOSITORIES: GEO
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Project description:Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We utilized deep learning to characterize these enhancer codes and devised three novel metrics to compare cell types in the telencephalon between mammals and birds. To this end, we generated single-cell multiome and spatially-resolved transcriptomics data of the chicken telencephalon. Enhancer codes of orthologous non-neuronal and GABAergic cell types show a high degree of similarity across vertebrates, while 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 solely based on genomic sequences.
2024-06-14 | GSE262321 | GEO
Project description:Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We utilized deep learning to characterize these enhancer codes and devised three novel metrics to compare cell types in the telencephalon between mammals and birds. To this end, we generated single-cell multiome and spatially-resolved transcriptomics data of the chicken telencephalon. Enhancer codes of orthologous non-neuronal and GABAergic cell types show a high degree of similarity across vertebrates, while 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 solely based on genomic sequences.
2024-09-16 | GSE262322 | GEO
Project description:The ability to form memories is a prerequisite for an organismâs behavioural adaptation to environmental changes. At the molecular level, the acquisition and maintenance of memory requires changes in chromatin modifications. In an effort to unravel the epigenetic network underlying both short- and long-term memory, we examined chromatin modification changes in two distinct mouse brain regions, two cell-types, and three time-points before and after contextual learning. Here we show that histone modifications predominantly change during memory acquisition and correlate surprisingly little with changes in gene expression. While long-lasting changes are almost exclusive to neurons, learning-related histone modification and DNA methylation changes occur also in non-neuronal cell types, suggesting a functional role for non-neuronal cells in epigenetic learning. Finally, our data provides evidence for a molecular framework of memory acquisition and maintenance, wherein DNA methylation could alter the expression and splicing of genes involved in functional plasticity and synaptic wiring. We examined chromatin modification changes in two distinct mouse brain regions (CA1 and ACC), two cell-types (neurons, non-neurons), and three time-points before and after contextual learning (naive, 1h, 4w).
2015-11-24 | E-GEOD-74966 | biostudies-arrayexpress
Project description:The ability to form memories is a prerequisite for an organismâs behavioural adaptation to environmental changes. At the molecular level, the acquisition and maintenance of memory requires changes in chromatin modifications. In an effort to unravel the epigenetic network underlying both short- and long-term memory, we examined chromatin modification changes in two distinct mouse brain regions, two cell-types, and three time-points before and after contextual learning. Here we show that histone modifications predominantly change during memory acquisition and correlate surprisingly little with changes in gene expression. While long-lasting changes are almost exclusive to neurons, learning-related histone modification and DNA methylation changes occur also in non-neuronal cell types, suggesting a functional role for non-neuronal cells in epigenetic learning. Finally, our data provides evidence for a molecular framework of memory acquisition and maintenance, wherein DNA methylation could alter the expression and splicing of genes involved in functional plasticity and synaptic wiring. We examined chromatin modification changes in two distinct mouse brain regions (CA1 and ACC), two cell-types (neurons, non-neurons), and three time-points before and after contextual learning (naive, 1h, 4w).
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Project description:Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. A single enhancer, of a few hundred base pairs in length, can autonomously and independently of its location and orientation drive cell-type specific expression of a gene or transgene. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Recently, deep learning models have yielded unprecedented insight into the enhancer code, and well-trained models are reaching a level of understanding that may be close to complete. As a consequence, we hypothesized that deep learning models can be used to guide the directed design of synthetic, cell type specific enhancers, and that this process would allow for a detailed tracing of all enhancer features at nucleotide-level resolution. Here we implemented and compared three different design strategies, each built on a deep learning model: (1) directed sequence evolution; (2) directed iterative motif implanting; and (3) generative design. We evaluated the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We then exploited this concept further by creating “dual-code” enhancers that target two cell types, and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the trajectories followed during state space searches towards functional enhancers, we could accurately define the enhancer code as the optimal strength, combination, and relative distance of TF activator motifs, and the absence of TF repressor motifs. Finally, we applied the same three strategies to successfully design human enhancers, finding highly similar design principles as in Drosophila. In conclusion, enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.
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Project description:Here, we combine comparative regulatory genomics with machine learning to investigate enhancer logic in melanoma. Through epigenomics profiling of 26 melanoma cell lines across six species, we examine the conservation of the two main melanoma states and underlying master regulators. By training a deep neural network on topic models derived from the human lines, we were able to classify not only human melanoma enhancers, but also regulatory regions in the other species. The deep learning model revealed important genomic features (i.e. TF binding motifs) for the different melanoma states, how they co-occur within melanoma enhancers, and where they are placed with respect to the central enhancer nucleosome. This in-depth analysis of the melanoma enhancer code allowed us to propose a mechanistic model of TF binding in MEL melanoma enhancers. Finally, by exploiting the deep layers of our model, we are able to identify causal mutations for melanoma enhancer loss and gain through evolution, not only affecting enhancer accessibility but also activity.
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Project description:The evolution of the six-layered neocortex is often credited with an increased capacity for complex behaviors and cognition in humans and other mammals. However, birds, despite lacking a laminated neocortex, display complex and non-instinctual behaviors including vocal learning, tool use, and problem-solving1,2. The evolution of brain circuits and cell-types supporting advanced behavioral repertoires remains poorly understood. Here in songbirds, we use single-cell RNA-sequencing to characterize the molecular identities of cells in the song motor pathway, a pallial circuit with function and connectivity that has been likened to the mammalian neocortex1,2. We find that each song region contains different glutamatergic excitatory projection neurons but similar sets of GABAergic inhibitory interneurons, similar to patterns of neuronal diversity in mammals and reptiles3,4. Song motor pathway glutamatergic neurons have gene expression patterns similar to those described in neocortical projection neurons, but at the level of transcription factor expression, display stronger similarity to neurons in the ventral pallium. We observed multiple GABAergic neuron classes that are conserved across amniotes, yet the most abundant class strongly resembles a cell-class that in mammals is not found in the neocortex but is present in non-neocortical pallial regions3,4. Thus, although pallial song-control regions contain neurons with similar molecular profiles to neocortical neurons, these pallial areas have a regional identity distinct from neocortex, indicating that complex behaviors such as vocal learning have evolved through the use of different brain circuits under similar functional constraints.
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Project description:Cellular diversification is a fundamental feature of the brain that is critical for physiological functions of the nervous system including perception, motor control, and learning and memory. Advances in single cell RNA-sequencing have led to characterization of transcriptomic profiles of distinct major types of neurons in the brain. However, how transcriptomic profiles diversify within a specific population of neurons and their links to function remain poorly understood. Purkinje neurons represent some of the most iconic cells in the brain with decades of research characterizing their anatomy, cell biology, physiology, and roles in plasticity, learning and memory, as well as in diseases of the nervous system. In this study, we deployed an approach of isolating nuclei tagged in specific cell types followed by cell sorting and single nuclear RNA sequencing to profile Purkinje neurons and their response to motor activity and learning in adult mice. We uncovered the molecular map of two major subpopulations of Purkinje neurons, identified by the hallmark genes Aldoc and Plcb4, which bear distinct transcriptomic features. Remarkably, Plcb4+, but not Aldoc+, Purkinje neurons display robust plasticity of gene expression in mice subjected to sensorimotor and learning experience with downregulation of gene clusters related to chromatin regulation and synaptic organization and upregulation of gene clusters related to neuronal activity and synaptic transmission and neuron-immune interactions. Using in vivo calcium imaging and optogenetics perturbation approaches, we show that activation of Plcb4+ Purkinje neuron plays a crucial role in associative motor learning. Upon integrating single nuclear RNA-seq datasets with weighted gene network analysis, we also identify a motor activity and learning specific gene module that includes components of the FGFR2-SOS1-MAPK signaling pathway in Plcb4+ Purkinje neurons. Knockout of FGFR2 in Plcb4+ Purkinje neurons in adult mice by a CRISPR approach dramatically disrupts motor learning. Our findings provide a platform for identification of subpopulations of neurons and define how diversification of Purkinje neurons in the cerebellum links to their responses to motor learning. Our study, and by extension similar studies in the human brain, will provide the foundation for understanding the selective vulnerability of Plcb4+ Purkinje neurons to neurological diseases.
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