Uncovering Cell Type-Specific Expression Profiles in the Tumor Microenvironment with Ultra-Low Input RNA-Seq

Uncovering Cell Type-Specific Expression Profiles in the Tumor Microenvironment with Ultra-Low Input RNA-Seq

Accurate characterization of the transcriptome depends heavily on both the quality and quantity of input RNA, yet standard RNA-Seq workflows typically require more than 500 ng of intact RNA. Samples with limited input or degraded RNA often demand additional amplification and deeper sequencing to compensate, increasing the risk of transcriptional bias, reduced exon coverage, and suboptimal read mapping.

By leveraging an optimized extraction-to-sequencing pipeline together with Ultra-Low Input RNA-Seq, GENEWIZ from Azenta Life Sciences generated high-quality transcriptomic data from approximately 50 sorted tumor cells. These results demonstrate sensitivity, gene detection, and exon mapping rates comparable to conventional RNA-Seq experiments performed with microgram-level RNA inputs, enabling reliable profiling of rare cell populations within the tumor microenvironment.

This case study discusses:

• Key challenges and solutions for sequencing ultra-low input RNA samples derived from rare or sorted cells
• How to generate high-quality RNA-Seq data from less than 1 ng of total RNA without introducing amplification bias
• Strategies for distinguishing cell type-specific expression profiles across immune and tumor cell populations within the tumor microenvironment