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Deep learning of enhancer codes highlights similarities between mammalian and avian telencephalon cell types [mpra]

ABSTRACT: Deep learning of enhancer codes highlights similarities between mammalian and avian telencephalon cell types [mpra]

PROVIDER: PRJNA1091334 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR28431159_1.fastq.gz	Fastqsanger.gz
	SRR28431159_2.fastq.gz	Fastqsanger.gz
	SRR28431160_1.fastq.gz	Fastqsanger.gz
	SRR28431160_2.fastq.gz	Fastqsanger.gz
	SRR28431161_1.fastq.gz	Fastqsanger.gz

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Deep learning of enhancer codes highlights similarities between mammalian and avian telencephalon cell types [mpra]

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.

2024-12-03 | GSE262320 | GEO

Deep learning of enhancer codes highlights similarities between mammalian and avian telencephalon cell types [stereoseq]

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Deep learning of enhancer codes highlights similarities between mammalian and avian telencephalon cell types [multiome]

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Deep learning of enhancer codes highlights similarities between mammalian and avian telencephalon cell types [stereoseq]

Project description:Deep learning of enhancer codes highlights similarities between mammalian and avian telencephalon cell types [stereoseq]

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Deep learning of enhancer codes highlights similarities between mammalian and avian telencephalon cell types [multiome]

Project description:Deep learning of enhancer codes highlights similarities between mammalian and avian telencephalon cell types [multiome]

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Enhancer-driven cell type comparison reveals similarities between the mammalian and avian telencephalon

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Enhancer-driven cell type comparison reveals similarities between the mammalian and avian telencephalon

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Granulins Regulate Aging Kinetics in the Adult Zebrafish Telencephalon

Project description:Granulins (GRN) are secreted factors that promote neuronal survival and regulate inflammation in various pathological conditions. However, their roles in physiological conditions in the brain remain poorly understood. To address this knowledge gap, we analysed the telencephalon in Grn-deficient zebrafish and identified morphological and transcriptional changes in microglial cells, indicative of a pro-inflammatory phenotype in the absence of any insult. Unexpectedly, activated mutant microglia shared part of their transcriptional signature with aged human microglia. Furthermore, transcriptome profiles of the entire telencephali isolated from young Grn-deficient animals showed remarkable similarities with the profiles of the telencephali isolated from aged wildtype animals. Additionally, 50% of differentially regulated genes during aging were regulated in the telencephalon of young Grn-deficient animals compared to their wildtype littermates. Importantly, the telencephalon transcriptome in young Grn-deficent animals changed only mildly with aging, further suggesting premature aging of Grn-deficient brain. Indeed, Grn loss led to decreased neurogenesis and oligodendrogenesis, and to shortening of telomeres at young ages, to an extent comparable to that observed during aging. Altogether, our data demonstrate a role of Grn in regulating aging kinetics in the zebrafish telencephalon, thus providing a valuable tool for the development of new therapeutic approaches to treat age-associated pathologies.

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