Cellular stress in brain organoids is limited to a distinct and bioinformatically removable subpopulation

Abstract

Organoids enable disease modeling in complex and structured human tissue, in vitro. Like most 3D models, 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 over 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. Our data show that adverse effects of cell stress can be corrected by bioinformatic analysis, improving developmental trajectories and resemblance to fetal data.

Forebrain Organoids to Model the Cell Biology of Basal Radial Glia in Neurodevelopmental Disorders and Brain Evolution

“…However, this can result in poor oxygenation and nutrient exchange within the organoid core causing a systematic cellular stress ( Bhaduri et al, 2020 ). A recent analysis, however, suggested that the cellular stress is a feature of a subpopulation of cells, which can be removed in subsequent computational analyses ( Vértesy et al, 2022 ).…”

Human organoids: New strategies and methods for analyzing human development and disease

“…Luckily, however, subsequent analyses revealed that cell stress reflects homeostatic gene expression changes not common to other organoid protocols, and that specific culture conditions robustly generate cell types which reflect the accurate repertoire of fetal brain cell identities (Gordon et al, 2021;Uzquiano et al, 2022). In addition, in datasets using culture conditions that result in the generation of mixed-identity cell types, these cells generally produce a restricted population that can be removed from the analysis by bio-informatic tools (Ve ´rtesy et al, 2022).…”

“…Networks of endothelial cells and blood vessels in different types of organoids have been generated using different strategies with varying degrees of success, for example by transplantation into mice (Mansour et al, 2018), by harnessing endogenous endothelial cells, or by co-culturing organoids with exogenously produced endothelial cells (Mansour et al, 2021). Until these types of co-cultures can be robustly and cost-effectively established in a high-throughput approach, other means, such as bio-informatic removal of stressed cells, are necessary to prevent bias from cells with high levels of oxidative stress (Ve ´rtesy et al, 2022).…”

FixNCut: single-cell genomics through reversible tissue fixation and dissociation

“…12 South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia. 13 Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain. 14 Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Barcelona, Spain.…”

“…Moreover, formaldehyde-based fixation generally impedes the application of single-cell multiome analysis, cellular indexing of transcriptomes, and epitopes sequencing (CITE-seq) [11,12] or immune repertoire profiling, which relies on 5′ end sequencing. Finally, post hoc computational tools such as machine learning algorithms have been developed to reduce or remove dissociation-induced artifacts [13]. For a more comprehensive description of methods to mitigate dissociation-induced artifacts, we recommend referring to the review by Machado et al [14].…”