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Identification of RCC subtype-specific microRNAs – meta-analysis of high-throughput RCC tumor microRNA expression data [miRNA-Seq]

ABSTRACT: Identification of RCC subtype-specific microRNAs – meta-analysis of high-throughput RCC tumor microRNA expression data [miRNA-Seq]

PROVIDER: PRJNA635803 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR11873709.fastq.gz	Fastqsanger.gz
	SRR11873710.fastq.gz	Fastqsanger.gz
	SRR11873711.fastq.gz	Fastqsanger.gz
	SRR11873712.fastq.gz	Fastqsanger.gz
	SRR11873713.fastq.gz	Fastqsanger.gz

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Project description:During development, the human brain orchestrates the generation of hundreds of diverse cell types, and recent transcriptomic atlases of the developing human cortex provide new opportunities to identify novel regulators of cell fate specification. We have conducted an integrated meta-analysis of seven recently published single-cell transcriptomic profiles of the developing human cortex, generating a meta-atlas of 225 meta-modules. By tracking the activity of these meta-modules throughout development and comparing this to datasets from the adult human as well as the developing and adult mouse, we identified modules with potential roles in the transition from neurogenesis to gliogenesis and neuronal subtype maturation. The cell type and developmental state at which these modules exist was validated in primary samples from the developing human brain. Of particular interest, we identified and validated a module associated with both developing human deep layer neurons and human adult deep layer neuronal subtypes expressing the terminal differentiation marker FEZF2. Though FEZF2 is neither a member of nor co-expressed with our deep layer-associated gene module, almost half of module genes are putative FEZF2 targets, including TSHZ3, a transcription factor associated with neurodevelopmental disorders such as autism. Knockdown experiments in human cortical organoid models confirm that both FEZF2 and TSHZ3 are necessary for deep layer neuron specification. Importantly, we observe that subtle manipulations of these transcription factors result in slight changes in module activity, but together yield strong differences in cell fate. Our analyses therefore reveal a gene network that required to generate deep layer neuronal subtypes, demonstrating the ability of our meta-atlas to engender further mechanistic analyses of cortical fate specification. We anticipate this resource being useful for the study of co-expression programs across diverse biological questions related to the developing neocortex.

Dataset Information

Identification of RCC subtype-specific microRNAs – meta-analysis of high-throughput RCC tumor microRNA expression data [miRNA-Seq]

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