Cellular Research released their latest development: Resolve for single cell mRNA-seq sample-prep at under £1 per cell. A paper in today's Science describes the method: Combinatorial labeling of single cells for gene expression cytometry. Using CytoSeq (why not Cyto-seq) 10,000 or 100,000 cells can be analysed. Like the C1 cells need to be flow sorted, but unlike the C1 CytoSeq does not apply as stringent a restriction on cell size or morphology - if you can sort it, CytoSeq can sequence it. The paper presents data from several hematopoietic systems but solid tissue, e.g. tumours, should be analysable if they can be mechanically or enzymaticly disaggregated.
Who Are Cellular Research: The company was set up by Steve Fodor (hence the Affy link in the title of this post) in 2011 at the same time a PNAS paper first described the molecular indexing approach. I was first alerted to Cellular Research by a contact who'd moved from Fluidigm in 2013. The publication in 2014 of a PNAS paper by Glenn Fu on molecular indexing in RNA-seq showed what Cellular Research might be delivering, and in the past few months we've begun testing of the Precise assay for targeted RNA-seq. The workflow in the lab is great and expected costs are just £10 per sample for up to 130 genes.
|Precise assay workflow|
How does Cyto-seq work: Cells in suspension are loaded into 20 picolitre wells, such that most wells are empty but those that do contain cells only have one. Oligonucleotide coated beads deliver the molecular index for each cell, and the molecular indexes for the mRNAs at the same time. mRNAs bind to the oligos ready for 1st strand cDNA synthesis; and similarly to the Precise protocol all 10,000 cells are pooled for downstream processing as a single reaction through reverse transcription, cDNA amplification and finally sequencing. Figure 1 from the Science paper describes the basic approach.
The first experiment described in the Science paper was a mixture analysis of K562 (myelogenous leukemia) and Ramos (Burkitt’s lymphoma) cells using a panel of 12 genes: five genes specific for K562 cells, six genes specific for Ramos cells, and the common housekeeping gene GAPDH. Other experiments reported against panels of 93, 98 and 111 genes. Not quite whole transcriptome, but only 1-5M reads per experiment makes CytoSeq the first single-cell transcriptome MiSeq application.
The method again uses the power of molecular indexing to tag multiple cDNAs from single cells and apply unique indexes to both mRNAs and the cells they come from. You'll be able to run CytoSeq in your lab from 2016 when Cellular Research will release an instrument to perform the library-prep workflow in cartridges of 5-10,000 cells per run.
|Figure 1 from Fu et al 2015.|
How much might Cyto-seq cost: The combination of a Resolve cartridge for 10,000 cells at £1 each plus a single MiSeq run at £600 comes to a little over £10,000 for a 100 gene panel. It is not clear how scalable the number of genes is and whole transcriptome may be a ways off yet. But assuming you can do this, and you stick with the 1-3M reads per cell that the major single-cell labs (and Fluidigm) are suggesting then each cell would cost about £3-5 to sequence on HiSeq 2500 today. So a 10,000 cell CytoSeq total mRNA-seq experiment would cost £6000 for library prep and £30,000+ for sequencing (price per M reads here). Not cheap, but the impact on some biological questions will be impressive, and new questions can be asked if we can do this kind of work routinely.
Clincal applications of CytoSeq: Will the method be applicable to blood cancers as a new screening tool? Will gene expression analysis of disaggregated solid tumours be possible in real time and at a cost that can make an impact on patient care? I am sure people are already working on these kind of questions.
Why is molecular indexing important: I think molecular indexing is a big leap forward for NGS. Being able to clearly identify single-molecules on an Illumina sequencer means the need to develop single molecule sequencers is significantly lessened for most of us. Molecular indexes should allow us to reduce the impact of technical artifacts in PCR amplification and resolve copy-number amplifications and deletions, mRNA DGA, Chromatin binding peaks, and exome allele calls much better than we can today.