Uncovering Cell Type-Specific Expression Profiles in the Tumor Microenvironment with Ultra-Low Input RNA-Seq
About the Case Study
Since faithful characterization of the transcriptome depends largely on the quality and quantity of the input RNA, standard RNA-Seq approaches call for an ample amount (>500 ng) of intact RNA. Samples producing lower yields and degraded RNA typically require additional amplification steps as well as higher depths of sequencing to boost data output. These samples are prone to transcriptional bias and poor read mapping to exons.
Using an optimized extraction-to-sequencing pipeline in combination with our Ultra-Low Input RNA-Seq service, Azenta generated high quality transcriptomic data from approximately 50 sorted tumor cells. The quality and sensitivity of these results are commensurate with standard RNA-Seq experiments using micrograms of input RNA.
This Case Study discusses:
- Challenges and solutions for sequencing low-input RNA samples
- Generating high quality sequencing data from <1 ng of RNA
- Analyzing different cell types from the tumor microenvironment