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.

中文翻译：

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Gruffi：一种从大脑类器官转录组数据集中计算去除压力细胞的算法

类器官能够在体外复杂发育过程和疾病病理学的建模。与大多数 3D 培养物一样，类器官缺乏足够的氧气供应，因此会受到细胞压力。这些负面影响在复杂模型中尤其突出，例如大脑类器官，并且会影响谱系承诺。在这里，我们分析来自已发布和新数据集的大脑类器官和胎儿单细胞 RNA 测序 (scRNAseq) 数据，总计约 190,000 个细胞。我们在所有类器官样本的数据中发现了一个独特的压力特征，但在胎儿样本中却没有。我们证明，细胞应激仅限于特定的细胞亚群，这是类器官独有的，不会影响神经元的规格或成熟。我们开发了一种计算算法 Gruffi，它使用粒度功能过滤以无偏见的方式从任何类器官 scRNAseq 数据集中识别和去除压力细胞。我们使用来自不同类器官协议和早期大脑的六个额外数据集验证了我们的方法，并展示了它对包括视网膜类器官在内的其他类器官系统的有用性。我们的数据表明，细胞应激的不利影响可以通过生物信息学分析来纠正，以改善发育轨迹的描绘和相似性体内数据。