Tuesday, 3 July 2012

Is genomic analysis of single cells about to get a whole lot easier?

A couple of months ago Fluidigm disclosed the latest addition to their microfluidic chip technology, the C1 single-cell analysis system.

At the disclosure in May the details of the C1 were a little sketchy but the system was planned to take cell suspensions, separate and capture single-cells for nucleic-acid analysis on the Biomark system. The disclosure also hinted at the ability to process single-cells for NGS applications such as transcriptome and copy-number analysis. One rather worrying detail was the fact that the C1 was lumped together with the Biomark and FACS instruments as far as likely cost is concerned.

This means it is probably going to be an expensive instrument, which could limit its uptake. Like many sample prep systems only a few labs will have the capacity to run the box at full tilt. This often makes the investment hard to justify and labs end up sharing systems or using a service.
 

How does the system work: The C1 will take cells in suspension and isolate 96 single-cells. On the system you will be able to stain and image captured cells to determine what sort of cells are present from your population. Cells are then lysed and you can perform molecular biology on the nucleic acids (mRNA RT and pre-amp for now). Finally nucleic acids are recovered from the inlet wells for further analysis.



What is most interesting from my perspective is the ability to tie the C1 to NGS sample prep, either through transcriptome and/or Nextera based library prep, targeted resequencing through Access Array (see our recent paper for ideas) or WGA. If we can prepare libraries with 96 barcodes then I can see some wonderful experiments coming out of work in Flow Cytometry and Genomics labs.

What do I want to do with it: There are so many unanswered questions in biology that are hampered by using data from heterogeneous pools of cells; e.g. Tumours. Being able to dissociate cells, sort by flow cytometry and analyse with NGS is going to be powerful. From my experience of flow cytometry it is nearly always the case that any population of cells can be further subdivided. Being able to perform copy-number, mutation profiling or differential gene expression analysis on these populations is going to help get a better understanding of how much subdivision is required. Being able to capture perhaps 200 circulating tumour cells from a patient and sequence each of these is going to help us understand cancer evolution and metastasis. There are so many possibilities.

The C1 may also help one complex type of experiment that is often not replicated highly enough, low cell number gene expression. Some of my users provide flow sorted cells from populations that vary in number. Being able to run a pool of perhaps 100 cells from each population in a single C1 chip and get high quality GX data is going to remove much of the technical bias and enable more powerful statistical analysis.

Unfortunately this is going to mean lots of sequencing. Even if we can get away with 2M reads per sample for differential gene expression (genes, not exons or isoforms), then we’ll need to run a lane per C1 chip. It is not clear if we be able to generate copy-number analysis of sufficient quality from 0.25x coverage of a genome, although I have some ideas about that I'll post about later.

However for groups working on model organisms where cells can be grown in suspension or dissociated before analysis the C1 could be a phenomenal advance. Imagine individual C.elegans dissociated, cells identified by labelling, tagged by transcriptome library prep and sequenced. Fate-mapping on steroids anyone!

My lab has been working with Fludigm for two and a half years on the Biomark and Access Arrays and we did hear early on about their single-cell projects. I am excited to see a product is now available and look forward to working with the technology. Now all I have to do is find out just how much it will cost to complete an experiment!

1 comment:

  1. Amazing to see the world developing they way it does. I like the way you elaborated your post, very comprehensible.

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