RNA: From Sidekick to Center Stage
The dogma of DNA→RNA→protein had been thoroughly revised.
In the 1960s, biology textbooks painted a clear, almost hierarchical picture of life’s molecular underpinnings. DNA was the master molecule, the blueprint of life. RNA, its faithful assistant, ferried genetic instructions to ribosomes, where proteins—the real workhorses of biology—got things done (Crick, 1958).
By the 1980s, cracks appeared in this simple narrative. Scientists discovered that RNA was not just a passive messenger but an active participant in cellular function. Ribozymes—RNA molecules with catalytic activity—shattered the assumption that only proteins could serve as enzymes (Cech et al., 1981; Guerrier-Takada et al., 1983). Meanwhile, small nuclear RNAs (snRNAs) and other RNA species emerged as essential players in gene expression and RNA processing (Sharp, 1985).
By the 2000s, RNA research exploded. Non-coding RNAs, microRNAs, and long non-coding RNAs (lncRNAs) demonstrated regulatory roles at nearly every level of cellular function (Bartel, 2004; Guttman et al., 2009). RNA was no longer a humble intermediary; it was a master regulator. Advances in transcriptomics painted a picture of a cell teeming with diverse RNA species, each with its own nuanced function. The dogma of DNA→RNA→protein had been thoroughly revised.
Now, in the 2020s, our understanding of RNA is at an inflection point. Life increasingly appears to be a seething mass of RNA-driven interactions, with DNA serving as an archival system rather than the command center we once imagined. This paradigm shift has fueled the rise of RNA-based technologies, from CRISPR-Cas gene editing to mRNA vaccines to RNA interference (RNAi)-based therapeutics. These advances have transformed medicine and biotechnology, opening doors to targeted therapies and synthetic biology applications that were unthinkable just a few decades ago.
As our understanding of RNA and its vast and complex roles continues to expand, we need new and improved ways to cut through the noise and hone in on what we really care about. We need specificity to capture the important signals from the irrelevant ones, and scalability to paint the whole picture.
Poly(A) enrichment was developed over half a century ago…
What we do differently here at MultiTarga achieves just that, we leverage AI-derived genomic and transcriptomic motifs to differentiate numerous target sequences from numerous off-target sequences. This discriminative selection can be used in cDNA synthesis and bead-capture enrichment in bulk RNA-seq or scRNA-seq workflows to point a telescope at the RNA species you care about. Our approach allows for these targets and off-targets to be adaptively defined providing a custom solution for each organism, multi-organismal sample, or specific research context. Poly(A) enrichment was developed over half a century ago, and can no longer keep up with the demands of cutting edge transcriptomics research.