An updated comparison of microarray and RNA-seq for concentration response transcriptomic study: case studies with...

Area of Science:

• Toxicogenomics
• Computational Biology
• Molecular Toxicology

Background:

• Transcriptomic benchmark concentration (BMC) modeling is crucial for regulatory risk assessment of chemicals with limited data.
• RNA sequencing (RNA-seq) is increasingly favored over microarrays for transcriptomics due to its precision and dynamic range.
• The comparative advantage of RNA-seq over microarrays in concentration-response studies remains unclear.

Purpose of the Study:

• To compare the performance of microarray and RNA-sequencing (RNA-seq) platforms in concentration-response transcriptomic studies.
• To evaluate the utility of both platforms for toxicological risk assessment using cannabinoids as case studies.
• To determine if RNA-seq offers substantial advantages over microarrays for identifying compound-induced effects.

Main Methods:

• Comparative analysis of gene expression data from microarray and RNA-seq.
• Utilized two cannabinoids, cannabichromene (CBC) and cannabinol (CBN), as case studies.
• Applied gene set enrichment analysis (GSEA) and benchmark concentration (BMC) modeling to assess transcriptomic point of departure (tPoD) values.

Main Results:

• Both microarray and RNA-seq platforms identified similar gene expression patterns in response to CBC and CBN exposure.
• RNA-seq detected more non-coding RNA transcripts and differentially expressed genes (DEGs) with wider dynamic ranges.
• Gene set enrichment analysis (GSEA) revealed equivalent performance of both platforms in identifying impacted functions and pathways.
• Transcriptomic point of departure (tPoD) values derived from BMC modeling were comparable between microarray and RNA-seq.

Conclusions:

• Microarray and RNA-seq demonstrate equivalent performance for concentration-response transcriptomic studies and BMC modeling.
• Microarray remains a cost-effective and viable option for mechanistic pathway identification and concentration-response modeling.
• The choice between microarray and RNA-seq may depend on specific application needs, cost, and data analysis infrastructure.