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Help for experimental design
We proposed to detect the transcriptome/mirnaome of inner cell mass of porcine.The bottleneck for this study is that we could get very few cells ( about 10 from each blastocyst,~10 blastocyst for each fertilized porcine.We prepared 5 pigs for this experiment. So, we could get ~ 500 cells. ) Is so many cells enough for transcriptome/mirnaome analysis by Illumina Hiseq 2000? What else is worthy of note?
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Here are a couple links to resources for amplifying small amounts of RNA. I have used the first two, and they work well.
Nugen Ovation RNA-Seq:
Modified Illumina RNA-Seq protocol (look in the supplemental data):
Linear amplification of RNA:
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DSN protocol really works. We used non detectable amount of total RNA as an input for library prep and got ~30M passing reads from single GAIIx lane. Unique read was ~5M, so higher redundancy as you expected from extremely low input material, but this was great!Comment
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Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...
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