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Old 10-08-2019, 10:58 AM   #3
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Location: NYC

Join Date: Oct 2019
Posts: 2

Thanks for all the advice, I really appreciate it.

The reason I'm getting total cDNA from single cells (primary B cells in this case) is for the reason you mentioned: banking the cDNA for analysis of multiple genes.

Okay, thanks for letting me know. I am screening single B cells for their allelic usage. I want to see whether they commit to one allele, or co-express the alleles. I can discriminate between the alleles by a simple RE digest, so I was planning to TOPO clone, and do colony PCR/digest ~4-5 clones per cell, rather than Sanger sequencing. I'm not concerned about the precise ratio, but more interested in whether single cells completely favor one allele or not. Is there an easier way to do this? Basically, I'm most interested in using Smart-Seq2 for its library prep.

What do you mean by "bad annotation"?

Originally Posted by cmbetts View Post
1. Sure, you can swap in different length RT primers without a noticeable impact on performance
2. SSII is definitely better than SSIII, but I don't know about IV
3. I personally like Takara's SeqAmp, but I used to work there and am biased

A followup question. Why are you adding the cost/complexity of preamplification when gene specific RT-PCR works at the single cell level? Banking the cDNA or assessing multiple genes per cell?

Also, are you sure you don't need NGS level resolution for when you're describing? Single cell methods are going to have lots of allelic dropouts that have nothing to do with actual biological regulation, just good ol' sampling noise, especially for anything that isn't highly expressed. The cost of the number of cells, TOPO cloning, and Sanger reactions required to get a remotely robust answer is going to approach the cost of an NGS run real fast (especially if you use targeted amplicons).

Regardless of sequencing method, I also highly recommend putting UMIs in your RT primer and targeting the 3'UTR, RACE style, if there distinguishing alleles close enough to the end of the transcript. Just watch out for alternative polyA usage and bad annotation. Otherwise, you may falsely assume you've reached a high enough count to assess statistical significance when you've really just been looking at a much smaller population that won the sampling lotto (and PCR jackpot)
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