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Gruffi removes stressed cells from brain organoid datasets

ABSTRACT: Gruffi removes stressed cells from brain organoid datasets

PROVIDER: PRJNA846315 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR19560262_1.fastq.gz	Fastqsanger.gz
	SRR19560262_2.fastq.gz	Fastqsanger.gz
	SRR19560263.fastq.gz	Fastqsanger.gz
	SRR19560264.fastq.gz	Fastqsanger.gz
	SRR19560265.fastq.gz	Fastqsanger.gz

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Gruffi removes stressed cells from brain organoid datasets

Project description:Organoids enable disease modeling in complex and structured human tissue, in vitro. Like most 3D cultures, they lack sufficient oxygen supply, leading to cellular stress. These negative effects are particularly prominent in complex models, like brain organoids, where they can prevent proper lineage commitment. Here, we analyze brain organoid and fetal single cell RNA sequencing (scRNAseq) data from published and new datasets totaling about 190,000 cells. We describe a unique stress signature found in all organoid samples, but not in fetal samples. We demonstrate that cell stress is limited to a defined organoid cell population, and present Gruffi, an algorithm that uses granular functional filtering to identify and remove stressed cells from any organoid scRNAseq dataset in an unbiased manner and validate our findings on six further datasets from different organoid protocols and early brains. Our data show that adverse effects of cell stress can be corrected by bioinformatic analysis, improving developmental trajectories and resemblance to fetal data.

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Gruffi: an algorithm for computational removal of stressed cells from brain organoid transcriptomic datasets.

Project description:Organoids enable in vitro modeling of complex developmental processes and disease pathologies. Like most 3D cultures, organoids lack sufficient oxygen supply and therefore experience cellular stress. These negative effects are particularly prominent in complex models, such as brain organoids, and can affect lineage commitment. Here, we analyze brain organoid and fetal single-cell RNA sequencing (scRNAseq) data from published and new datasets, totaling about 190,000 cells. We identify a unique stress signature in the data from all organoid samples, but not in fetal samples. We demonstrate that cell stress is limited to a defined subpopulation of cells that is unique to organoids and does not affect neuronal specification or maturation. We have developed a computational algorithm, Gruffi, which uses granular functional filtering to identify and remove stressed cells from any organoid scRNAseq dataset in an unbiased manner. We validated our method using six additional datasets from different organoid protocols and early brains, and show its usefulness to other organoid systems including retinal organoids. Our data show that the adverse effects of cell stress can be corrected by bioinformatic analysis for improved delineation of developmental trajectories and resemblance to in vivo data.

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