Article 
Benefits of Multiple Methods in Gene Expression Research
As outlined above, qPCR, Droplet Digital™ PCR, and NGS rely on different mechanistic principles. Hence, each technique has distinct advantages and disadvantages and is suitable for specific research questions.
All three methods allow quantifying gene expression levels of all or one specific target sequence. However, Droplet Digital PCR does not require a standard curve, which facilitates absolute quantification studies. On the other hand, to quantify the levels of multiple genetic features, NGS should be the method of choice since it determines all molecules in a sample.
Other factors to consider are sensitivity, reproducibility, and challenging sample types. The PCR enzymes and reactions are highly sensitive to inhibiting molecules and contaminants. Due to sample partitioning, Droplet Digital PCR can handle such contaminants without perfect amplification and is thus the better alternative when qPCR fails or gives inconclusive results.
Similarly, NGS and Droplet Digital PCR enable measuring small fold changes of genetic sequences reliably as they enrich for rare targets. NGS and ddPCR technologies can also handle small sample volumes, while Droplet Digital PCR was even shown to give meaningful results for single-cell samples.
While all approaches can be automated, ddPCR and NGS experiment setups are more labor-intensive, harder to train researchers on, and, thus, more prone to error. However, qPCR has been the standard procedure in many laboratories. Protocol transfer and adaptation are easy as many scientists have experience running qPCR experiments.
Lastly, ddPCR and NGS experiments require specific machines, materials, and assays that add to the cost of running a lab. Also, additional software is needed to analyze complex NGS data, while data interpretation is easier for qPCR and ddPCR experiments.
Comparison of qPCR, Droplet Digital PCR, and NGS Methods
| Feature | qPCR | Droplet Digital PCR | NGS |
|---|---|---|---|
| Quantification | Relative, absolute, and standard curves needed | Absolute | Relative, absolute |
| Target numbers | Multiple (up to 5) | Multiple (up to 12) | Multiple variants detected (limitless) |
| Target numbers | Multiple (up to 5) | Multiple (up to 12) | Multiple variants detected (limitless) |
| Sample matrix complexity | Simple matrices | Simple to complex matrices | Simple to complex matrices |
| Sample volume | High volume needed | Low to high | Low |
| Reproducibility | Standardized | Standardized | Multiple steps, prone to errors |
| Workflow setup | Automated, fast | Automated, fast | Automated, labor-intensive, hard to train researchers on |
| Output | Data interpretation easy | Data interpretation easy | Complex, specialized data analysis needed |
Assessing the Optimal Gene Expression Analysis Method for Later Research Stages
Having explored the different gene expression profiling methods and their advantages, one can now choose the appropriate method for their research question. NGS is the obvious choice to identify and characterize potential targets as it looks at all present molecules in a sample. However, understanding a target’s impact on the pathway requires focusing on several specific genetic sequences. In such cases, qPCR or ddPCR technology should be used to give insightful results.
Especially during drug optimization stages, reproducible results are needed with standardized workflows and little room for errors. This is where ddPCR technology excels, as it can be fully automated, giving fast and reliable results.
Gene expression profiling techniques are essential throughout all biopharma research stages. However, each method can be suitable to tackle a specific research question. Therefore, one should consider the costs, sample type, and required information to decide on their method of choice.
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