Details

Autor(en) / Beteiligte

• Vértesy, Ábel
• Eichmüller, Oliver L
• Naas, Julia
• Novatchkova, Maria
• Esk, Christopher
• Balmaña, Meritxell
• Ladstaetter, Sabrina
• Bock, Christoph
• von Haeseler, Arndt
• Knoblich, Juergen A

Titel

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

Ist Teil von

• The EMBO journal, 2022-09, Vol.41 (17), p.e111118-n/a

Ort / Verlag

London: Nature Publishing Group UK

Erscheinungsjahr

2022

Quelle

Wiley Online Library All Journals

Beschreibungen/Notizen

• 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. Synopsis Cellular stress in 3D organoids due to insufficient oxygen transport can affect faithful lineage commitment and disease modeling. This work identifies a subpopulation of stressed cells characterized by a distinct gene expression signature that can be removed from scRNAseq datasets for better evaluation of fetal developmental trajectories. ER‐ and glycolytic stress are found accross organoid protocols. Stressed cells in 3D organoids form a separate cell state that is not found in vivo and that can be separated bioinformatically. The presence of stressed cells in organoids does not affect cell‐type specification or the maturation of non‐stressed neurons. Granular functional filtering (Gruffi) removes stressed cells from the single‐cell datasets but retains cell types found in vivo . Stress removal leads to clearer developmental trajectories. Graphical Abstract A unique stress signature found in in brain organoid samples but not in fetal samples can be quantified and removed from scRNAseq datasets using a new algorithm.