Single cell transcriptomics reveals unique metabolic profiles of ependymoma subgroups

• Published in: Gene Vol. 820; p. 146278
• Main Authors: Batchu, Sai, Patel, Karan, Yu, Siyuan, Mohamed, Aleem T., Karsy, Michael
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 30-04-2022
• Subjects: Ependymoma; Metabolism; Oxidative phosphorylation; Single-cell RNA sequencing; ST; Oxidative phosphorylation; Single-cell RNA sequencing; PF; ST-RELA; ST-YAP; Metabolism; Ependymoma
• ISSN: 0378-1119; 1879-0038
• DOI: 10.1016/j.gene.2022.146278

•Dimensionality reduction of metabolic profiles exhibited clustering of tumor subgroups.•Over 75 pathways had significantly different activities between ependymoma subgroups.•Oxidative phosphorylation accounts for major metabolic variation .
Ependymomas are biologically diverse tumors with five major genomic subgroups. However, intratumor heterogeneity continues to be poorly understood. The present study characterized the metabolic landscapes of ependymoma subgroups at the single-cell level.
Expression profiles from 11,200 ependymoma single cells derived from the five major subgroups and 7,200 ependymoma-derived non-neoplastic cells were computationally analyzed using a robust workflow to elucidate relative differences in metabolic pathway activities.
Dimensionality reduction using metabolic expression profiles exhibited clustering corresponding to each tumor subgroup, but non-neoplastic cells exhibited no discernable differences between subgroups.
From the 80 metabolic pathways examined, over 75 pathways had significantly different activity scores between ependymoma subgroups. Further analysis of metabolic heterogeneity suggests that mitochondrial oxidative phosphorylation accounts for considerable metabolic variation within tumor subgroups and non-neoplastic cells of the same cell type. Drug metabolism pathways, specifically those involving cytochromes P450, were also found to be major contributors to heterogeneity.
Ependymoma subgroups display distinct metabolic differences as evaluated through gene expression profiles with certain pathways contributing greatly to intra-subgroup variation. These results may account for variation in tumor metabolism, treatment response, and potential targeting approaches that disrupt metabolic signalling.