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Spatial-transcriptomics technologies have demonstrated exceptional performance in characterizing brain and visceral organ tissues, as well as brain and retinal organoids. However, it has not yet been proven whether spatial transcriptomics can effectively characterize primary tissue-derived organoids, as the standardized tissue sectioning or slicing methods are not applicable for such organoids. Herein, we present a technique, lamination-based organoid spatially resolved transcriptomics (LOSRT), for organoid-spatially resolved transcriptomics based on organoid lamination. Primary mouse lung and liver-derived organoids were used in this study. The organoids were formulated using the droplet-engineering method and laminated using a homemade device with weight compression. This technique preserved most cells in individual organoids while maintaining delicate epithelium structures in laminated domains that can be recognized through visual segmentation. The mouse lung and liver organoids were resolved comprising various cell types, including alveolar cells, damage-associated transient progenitor cells, basal cells, macrophages, endothelial cells, fibroblasts, hepatocytes, and hepatic stellate cells. The distribution and count of cells were confirmed using immunohistology and identified with spatial transcriptomic features. This study reports an automated and integrated spatial transcriptomics method for primary organoids. It has the potential to standardize and rapidly characterize primary tissue-derived organoids. [ABSTRACT FROM AUTHOR]

Background: Renal ischemia/reperfusion injury (RIRI) is an inevitable consequence of kidney transplantation and has a negative impact on both short-term and long-term graft survival. The identification of key markers in RIRI to improve the prognosis of patients would be highly advantageous. Methods: Gene expression profile data of GSE27274 were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were analyzed using the Limma package. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment of DEGs were performed. Support vector machine-recursive feature elimination and least absolute shrinkage and selection operator regression modeling were both performed to identify potential biomarkers. The GSE148420 dataset, quantitative reverse transcriptase-PCR, and western blotting results of kidney tissue samples were used to validate the bioinformatic analysis. Lastly, exploring differences between different groups through gene set enrichment analysis and using DsigDB database to identify potential therapeutic drugs targeting hub genes. Results: A total of 160 upregulated and 180 downregulated DEGs were identified. Functional enrichment analysis identified significant enrichment in processes involving peroxisomes. As a subunit of Polycomb Repressive Complex l(PRCl), chromobox 6(Cbx6) was identified as a potential biomarker with an area under the receiver operating characteristic curve of 0.875 (95% confidence interval 0.624-1.000) in the validation cohort, and it was highly expressed in the RIRI group (p < 0.05). In the high expression group Cbx6 was more enriched in the toll-like receptor signaling pathway. We predicted 15 potential drugs targeting hub genes of RIRI. Conclusions: Vie identified Cbx6 as a potential biomarker for RIRI and 15 potential drugs for the treatment of RIRI, which might shed a light on the treatment of RIRI. [ABSTRACT FROM AUTHOR]