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New Fluidigm Protocol Automates Processing for Single-Cell RNA-Seq


Fluidigm has released a system and protocols to automate the processing of cells for single-cell transcriptome sequencing. The protocol runs on the company's C1 system and utilizes the Clontech Ultra Low input kit for cDNA synthesis and Nextera XT for library preparation, allowing 96 cells to be processed in parallel.

Researchers from the Benaroya Research Institute in Seattle are using the system to do single-cell transcriptome sequencing to study autoimmune disease. They hope to better understand variation between individual cells in terms of predicting disease, understanding response to therapies, and relapse.

Vivian Gersuk, manager of Benaroya's genomics platform core laboratory, told In Sequence that the institute only recently began using Fluidigm's system and that it is its first attempt at single-cell sequencing.

She said the team decided to go with single-cell RNA sequencing using Fluidigm's system because it "makes it more straightforward to capture individual cells from a small population."

Previously, the institute had been doing flow sorting and real time PCR, said Peter Linsley, a scientist at Benaroya, but the Fluidigm system is "much more efficient."

The team is still in the process of generating libraries from single cells, so does not yet have any preliminary results, but plans to use the technique to study type 1 diabetes and multiple sclerosis.

For type 1 diabetes, "we're interested both in defining the cells, especially the T cells, at the time of diagnosis and within a year or two after diagnosis, and we're also interested in defining individuals who are at risk for type 1 diabetes because of family history or other factors," she said.

Additionally, she said, the team plans to study sequential samples to identify changes in the transcriptome as the disease progresses.

In the case of multiple sclerosis, they plan to study transcriptome changes as a response to therapy in individual cells, to eventually "predict response to therapy and monitor response."

As the cost of next-gen sequencing has come down, there has been an increased interest in single-cell sequencing. Recently, the National Institutes of Health awarded three groups from the University of California, San Diego (IS 11/27/2012); the University of Pennsylvania (IS 12/4/2012); and the University of Southern California (IS 12/11/2012) five-year grants between $9 million and $10 million each under its Single Cell Analysis Program for single-cell transcriptome sequencing projects.

Nevertheless, there is still much work that needs to be done to optimize single-cell sequencing protocols. One of the charges of the three groups funded by the NIH is to assess technical variability, which is typically difficult to do, since once a cell has been used for sequencing, it cannot be used again.

Linsley anticipates that determining technical variability will be a challenge as the Benaroya researchers proceed with single cell transcriptome sequencing. "When you're looking at lots of individual cells, that's obviously a big factor because if the individual variability is not greater than the technical variability then you won't see anything," he said.

According to Candia Brown, Fluidigm's director of product marketing for its single-cell genomic business, using a system to automate much of the cell processing work will help reduce technical variability, since some of that variability comes from the "integration of multiple technologies around enrichment, isolation, and library preparation."

Integrating all those steps into one automated process allows researchers to simply load the individual cells on the platform where they are isolated, washed, and stained to check for viability, Brown said. From there, the cells are lysed, reverse transcribed, and prepared for sequencing.

The whole process is "done in an automated way without much intervention," and 96 cells can be done at once.

Additionally, by standardizing the workflow, it becomes "more cost-effective to do multiple, individual cells," Brown said. Currently, most workflows require that the researcher has already done the extraction, isolation, and RNA purification, she said, all of which can be time-consuming and labor-intensive processes.

Benaroya's Gersuk agreed that the system has so far helped simplify the process and made it more practical to do large numbers of cells. There's also a cost advantage, she added, because the system has been optimized to work with small volumes of reagents — nanoliters, as opposed to microliters, she said.

Gersuk said that for its single-cell transcriptome sequencing experiments, the lab is currently using Illumina's HiScan SQ system, and that it did not have any current plans to purchase additional sequencing equipment, but would consider other systems.

For each of the institute's studies, it has not yet decided on a specific number of cells, but plans to take full advantage of the Fluidigm system's ability to process 96 cells at once and to do at least that many cells per sample.

"Initially, we're focusing on capturing 96 cells at a time," Gersuk said. "And as we begin to learn more and acquire more data, we'll have to address the question of how many cells need to be captured to measure the diversity of the population." The researchers will also have to figure out how many reads and transcripts will be needed for reliable coverage, Gersuk said.

She noted that as the researchers gain more experience with single-cell sequencing, much of the work that to date that has been focused on population-based sequencing could be converted to single-cell sequencing.

Single-cell sequencing enables "a finer and finer view at the diversity that's related to disease mechanisms and response to therapy," she said. "There will be an increased effort to better define the cells within populations that are driving the immune response."