Advancements in next generation sequencing (NGS) technologies have improved our understanding of transcriptome dynamics and gene expression patterns. However, standard RNA-Seq approaches require a significant amount of input RNA, which can be challenging when working with limited starting material, such as formalin-fixed paraffin-embedded tissue, sorted cells, and microdissected tissue.
This case study from Azenta Life Sciences describes how Azenta developed an optimized extraction-to-sequencing pipeline combined with ultra-low input RNA-Seq, generating high-quality transcriptomic data from approximately 50 cells. The method selectively amplifies full-length transcripts with minimal bias, resulting in sequencing reads with strong quality scores and impressive mapping rates, detecting over 17,000 genes per sample. This breakthrough allows researchers to uncover gene expression patterns and assess transcriptomic heterogeneity in scarce source material, addressing common limitations in oncology, clinical research, and developmental biology.
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