RNA sequencing can prove extremely challenging when working with limited material. Total RNA isolated from single cells, tumors, laser capture microdissections (LCM), FFPE or dead tissue can often be in limited amounts and low quality. Since many of these sources are of clinical importance, there is a dire need for sequencing methods that can handle the specialized nature of these samples. Consistency of results and quality of data suffers when low input samples are handled in the same way as regular high quality RNA.
One must be cautious to avoid dropping low input and low quality samples into the same bucket. The problems they pose are vastly different and addressing their specific limitations can lead to a significant improvement in results. Typically RNA-seq in eukaryotes begins with an enrichment for poly-A transcripts. This method works best in high quality RNA samples where poly-A tails are still intact. Samples that are partially degraded and exhibit low quality will either lack poly-A tails or enrich for the 3′ end of the transcript, generating a library with significant 3′ bias. In low input libraries the limiting amount of material can create significant PCR duplication and ultimately a library with very low complexity. Therefore, an understanding of the differences in these samples is important to design the most optimal strategy to tackle them.
Several techniques have been developed to deal with low input and low quality samples. Most of these strategies involve an amplification step to generate enough material for library construction. Nugen’s ovation RNA-seq techniqueinvolves an amplification of the transcriptome at the 3′ end as well as randomly along the transcript by a proprietary Single Primer Isothermal amplification (SPIA) method. NuGen’s line of RNA-seq solutions are designed to deal with a variety of low input sample types: single cells, FFPE and even prokaryotic sources. The SMARTer ultra low kitis another solution from Clontech that can start with as low as 10pg. Other approaches involve RNase H, probe based rRNA depletion (Epicentre) or double stranded nuclease (DSN) techniques.
Whatever the technique, hurdles exist. Ineffective rRNA depletion is one of the most challenging to overcome. Since most popular low input and low quality RNA sequencing techniques involve selective amplification rather than selective removal (like probe based rRNA removal or poly-A selection), a higher percentage of rRNA coming through is inevitable. One method to address the problem, as we’ve suggested before, is to sequence more
Another hurdle to overcome when dealing with low-input samples is library complexity. Dealing with low complexity is not a straightforward problem to solve, as it is difficult to both measure and create metrics for. Two common approaches are duplication rate and evenness of coverage, which help to assess library complexity during data analysis. At the library construction stage it is a bit trickier. Observing the cDNA spread and reducing amplification cycles are things to pay attention to. Careful assessment of the library at each checkpoint is always important, and even more so with low-input RNA sequencing.
At the end of the day, it is possible to get clean results with good resolution from low-input samples. However, it is important to identify the relative merits of each method as compared to a standard high-input, high-quality control and then determine which method is suited best for your low-input sample.
If you are working with low-input RNA and have questions about how to best tackle your sample or interpret your results, get in touch.
