(F1072) CHALLENGES IN APPLYING SPATIAL TRANSCRIPTOMICS TO ORGANOIDS: GENERATION OF A BENCHMARKING DATASET FOR A HIGH-RESOLUTION SPATIAL WHOLE-TRANSCRIPTOME, ACROSS MULTIPLE ORGANOID MODELS

Abstract: Spatial transcriptomics (ST) is a transformative technology enabling the exploration of gene regulatory networks with spatial and temporal resolution. The insight provided by ST platforms promises to shed new light on developmental biology across an array of tissue types including organoids. Organoids offer the opportunity to model development and disease while overcoming limitations of animal models or 2D cell cultures. As organoids can be cultured from genetically engineered stem cell and patient cell lines, they permit the analysis of the transcriptional perturbations that underlie disease pathogenesis. Organoids display 3D organisation in vitro, making them prime candidates for exploration with ST. However, limitations of current ST technologies and the small size of organoids have led to relatively few studies using ST to investigate organoids, typically restricted to a single organoid type at a time. Here we present the use of Stereo-seq, currently the only ST platform to offer sub-cellular resolution while capturing transcriptome-wide information, to perform multi-organoid profiling of brain, heart, kidney, lung, cartilage, blood and engineered heart valve organoids. Our data demonstrates the versatility of Stereo-seq and its potential to determine the organisation of cell populations within this range of pluripotent stem cell derived organoids, with certain tissues requiring further optimisation. Furthermore, we provide examples of profiling multiple organoids from a single chip to maximise data capture. Benchmarking of quality metrics, such as the read depth and the number of genes and counts obtained in the different experiments, showed variable performance of Stereo-seq which were dependent on organoid types and sizes. While the sub-cellular resolution revealed limited gene expression within individual cells, by analysing gene expression at the regional level, rather than focusing on single cells, we were able uncover distinct gene expression patterns, particularly between the inner and outer regions of organoids. By providing a systematic overview of ST data across these varied organoid types, this study illustrates the power and limitations of ST to improve our understanding of organoids and their applications for modelling development and disease.

Funding Source: The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW) is supported by a Novo Nordisk Foundation grant number NNF21CC0073729

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