The past 20 years have witnessed a steady influx of tools enabling scientists to study gene function by controlling its expression or manipulating the underlying genomic sequences. What is now a dizzying array of options to affect gene modulation or perform gene editing, began with RNAi techniques becoming available in the early 2000s. The subject of the 2006 Nobel Prize in Physiology, RNAi, quickly became an essential tool to interrogate the central dogma of molecular biology—the flow of information from DNA to RNA to protein. Simply, siRNAs act against a gene of interest to catalytically destroy native mRNA transcripts within a cell and thereby temporarily knockdown gene expression.
The advent of CRISPR-Cas9-guided systems brought forth another method for precision-guided gene manipulation. Here, guide RNA directs the Cas9 nuclease to the gene of interest and mediates a double-stranded DNA break that can knockout the target gene, or when in the presence of donor DNA with sequence homology near the cut site, knock-in a change to the genome. CRISPR has proven a powerful tool for gene manipulation in eukaryotic cells, not only for its ability to edit target genes permanently but also for its relative ease-of-use, carrying it from the molecular biologist’s specialized toolkit into mainstream biology labs.
Due to this proliferation of tools to help accomplish gene editing and gene modulation experiments, it’s more important than ever to make sure the operations are well planned and conducted with a streamlined workflow.
Setting up the experiment
The standard workflow for any RNAi or CRISPR experiment resembles many scientific efforts—the biological question is posed, assays are designed and run, and finally, the resultant data yields insights. However, there are many decisions to make along the experimental path. A carefully designed experiment should control all biological variables as narrowly as possible, including delivery method (typically by transfection by lipid-based reagents, electroporation, or viral transduction) and cell growth conditions. In particular, special care must be given during delivery optimization to maintain cell viability while providing the most thorough knockdown or editing.
Data from these experiments will likely include phenotypic analysis and, in the case of RNAi, quantitative measurement of mRNA levels by RT-PCR. Scientists can then analyze observations from the treated population against untreated cells, mock-transfected cells, and well-characterized positive and negative controls to provide insights into target gene function.
Minimizing off-target effects
The application of modern bioinformatic approaches dramatically reduces the concern of off-target effects by generating designs that target the gene of interest with high efficiency. This is accomplished by stringent specificity checks that look for mismatches, alignment issues, and gaps in sequence between the target gene and other similar genes.
Additionally, one should consider using multiple siRNAs or guide RNAs as a method to confirm that the phenotypic effects observed are due solely to the gene of interest, and not an off-target event.
Lastly, chemically modifying the double-stranded RNA structure of siRNAs can minimize antisense seed-region mediated off-target interactions.
Working in biologically relevant cell models
Adding unique, lipid-like modifications to the molecules can achieve self-delivery of the siRNA into a cell, and act as an alternative to traditional viral, lipid, or instrument-mediated delivery. Using self-delivering siRNA is particularly useful as a tool for gene knockdown in more biologically relevant primary cells, or even in vivo.
Modifying immune cells for immunotherapy has seen a rapid rise due to the ease of targeting multiple proteins simultaneously using CRISPR. This application commonly uses synthetic guide RNAs pre-complexed with Cas9 protein, also known as a ribonucleoprotein (RNP). RNPs start editing immediately upon transfection into cells, and due to their DNA-free nature, dramatically reduce off-target editing due to their inability to integrate into the cell’s genome, which can happen with viral-based guide RNAs and/or Cas9 expression methods.
Some cell types are inherently challenging to transfect with standard lipid and electroporation techniques, and therefore viral transduction may be necessary. In this case, having multiple selection methods, e.g., antibiotic resistance markers and fluorescent tags, is essential. Scientists often choose to combine antibiotic-resistant lentiviral guide RNA, with fluorescent lentiviral-based Cas9, to enrich for cells with the highest likelihood of having a functional protein knockout. A streamlined workflow, along with appropriate reagents, will deliver successful outcomes regardless of how difficult the cell type may be to transfect.
Using RNAi & CRISPR together
While there are some unique experimental considerations between RNAi and CRISPR techniques, combining them can give researchers superior confidence in their results. Seeing concordant data when temporarily knocking down the expression of a gene in a heterogeneous pool of cells, and generating a clonal population of cells with a functional protein knockout using CRISPR is a means to validate the observed phenotype further. One should note that this dual approach is not feasible with essential genes, as generating functional knockouts in essential genes using CRISPR leads to cell death. In this case, validation should focus on replicating phenotypes across multiple cell lines using siRNA.
Conclusion
The molecular biologist’s toolbox has blossomed into a staggering array of tools to interrogate gene function. This has led to many thousands of breakthroughs in core biological understanding and new insights into disease pathology and, importantly, treatments. The ease of access to today’s techniques has enabled any lab to perform these compelling studies. Scientists should consider their goal, along with all available tools, select the right ones to accomplish gene knockdown, gene activation, gene editing, etc., and then streamline their RNA workflow as best possible.
Steve Smith is Product Manager for Gene Modulation Products and Ryan Donnelly is Product Manager for Gene Editing Products at Horizon Discovery.