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Old 05-20-2014, 03:40 AM   #18
Simone78
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Location: Basel (Switzerland)

Join Date: Oct 2010
Posts: 202
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Hi,
what you saw here is nothing new for us and we still canīt explain what it is. we also saw a peak around 1.8 Kb when optimizing the protocol using HEK293T (human) cells. When we looked for over-represented sequences in the data we found out that the peak corresponded to a specific transcript called "humanin", which is mitochondrial (!) but has several homologous copies on the genome. While itīs annoying having such a library, the results were not affected. Below please find an example of what we got when we were optimizing Smart-seq2. There you can see we had not only this humanin peak but also a fair amount of primer dimers, but still the performance of our method was superior to the Clontech kit (primer dimers get tagmented with the Nextera kit as well and end up in the final library, thus "wasting" sequencing reads). Sometimes we see it also with mouse cells, but this obviously canīt be humanin which, as the name says, is present only in humans. For the mouse we never found out what it is...what we know is that itīs not a contamination because some of our collaborators reported the same. On the other hand, even when working with single cells this peaks doesnīt always come up. Maybe it depends on how stressed the cells are or how they were sorted? I would also be interested in finding it out!
In conclusion, Smart-seq2 is better than Smart-seq but itīs not perfect, sorry!

I saw that you tried to reduce the amount of TSO. I did few trials on the amount of the different primers. While reducing ISPCR and SMART dT30VN sometimes (sometimes!) helps in reducing the amount of primer dimers, decreasing the TSO usually leads to lower cDNA yield after preampl. Maybe the strand-switch reaction is inefficient and you need to have such a huge amount of TSO even when working with single cells!
Have you tried to sequence some of these libraries? I donīt know what you are interested in, but if you need a lot of reads you might simply multiplex less samples per lane. For most applications (diff expression, detection of isoforms/splice variants) 1 million reads/sample are sufficient and you would get that amount even when pooling 96 samples on a Illumina HiSeq 2000.

We had some concatamer problem in the beginning, but they were due to too much adaptors (TSO, ISCR or oligo dT) compared to the RNA of a cell and we observed that only with very small cells (mostly immune cells which have very little mRNA). If it is an issue, you might try to block the TSO at the 5īend as done by Kapteyn J et al (BMC Genomics 2010, 11:413). Blocking the 5īshould prevent concatamerization of the TSO.

Best,
Simone

Last edited by Simone78; 08-24-2017 at 06:42 AM.
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