Wednesday, 16 March 2016

How many single cells are needed in a single-cell sequencing experiment

The landscape for gene expression analysis (and many other analyses) is moving away from measurements made in bulk tissues to single-cell methods. Bulk measurements are an average of the cells in the sample and so cannot truly reveal the subtleties of the biology in the sample, to get closer to the truth we do need to adopt single-cell methods, and this means making a choice as to which system you might run in your lab. Of course bulk measurements are still very powerful in understanding biology and we should not stop using the methods we've worked with for decades - but we should be thinking carefully about whether the specific question being asked could be better answered with, or only answered with single cell methods.

I started writing this post because I'm getting my head around the different methods for single-cell analysis. I'm trying to keep my focus on two areas right now, single-cell mRNA-seq analysis and copy number variation. For both a question that comes up all the time is "How many single cells are needed in my experiment?" and right now that is not a question I feel I can give a robust answer to!

In their Nature Reviews Genetics review article Shapiro, Biezuner and Linnarsson use some relatively simple back-of-the-envelope calculations that lead them to conclude "hundreds or thousands of single cells will need to be analysed to answer targeted questions in single tissues". The current systems on offer allow capture and sequencing of cells in this range so in theory any will be usable for experiments, I've briefly summarised some of the main contenders today including DROP-seq, Fluidigm, Wafergen and 10X Genomics.


The next few years are likely to see the continued development of single-cell systems. Which platform labs should invest in is going to be difficult to answer and this feels very much like the early days of NGS when we were choosing between Illumina and SOLiD; expensive instruments, rapidly developing technologies and an uncertainty about which will come out top-dog.

I'll be adding to this table over the next few months as I look into other systems, feel free to suggest other technologies to add and do point out inaccuracies where you see them.

7 comments:

  1. Few precisions about the Fluidigm C1 system.

    The C1 System with the 800 cell chip uses UMI, but in the actual state of the system, the doublet percentage is higher than 20%.
    A new 800 cell chip is in preparation to correct for doublets, as well as a new generation of 96 cell chips.
    The difference between the 96 and the 800 chip is that with the 96 cell chip you sequence all length of the mRNA and you prepare 1 library per cell, whereas with the 800 cell chip you sequence just a fragment, a specific barcode for each cell is added so that you have only one library to prepare for the 800 cells.

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  2. how about cellular research? FACSseq?

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  3. I'm not sure the 10X numbers you have are correct. Their old system can do 1k-6k cells per *lane*, or 48k cells per run. Their new Chromium system should bump that number up even further, maybe by 10-fold.

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  5. For those who want UMIs in a 96-cell C1 chip, have a look at C1 CAGE on Fluidigm's ScriptHub: https://www.fluidigm.com/c1openapp/scripthub/script/2015-07/c1-cage-1436761405138-3

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