By Ben Butkus
A team from the Stanford University School of Medicine has developed a protocol for using Fluidigm's BioMark HD platform and Dynamic Array chips to conduct qPCR-based profiling of gene expression in single neuronal cells.
According to the researchers, the method enables simple and inexpensive profiling of several hundred transcripts from a single cell, making it ideal for use in any experimental system where researchers wish to interrogate a large number of scant samples against a large number of probes, such as stem cell biology.
The research group described the method in a research paper published last month in Nature Protocols. According to Ami Citri, first author on the paper and a postdoctoral research fellow in the department of psychiatry and behavioral science at the Stanford University School of Medicine, the group identified the BioMark platform as ideal from unrelated neuroscience work Citri had been doing.
"I started off using the Fluidigm [platform] for a different application — basically the same idea as using it for single cells, but querying multiple different samples of limiting initial material against a large number of probes," Citri said.
"I had done microarray experiments looking at a certain region of the brain that integrates reward signaling, and this was in the context of models of addictive behaviors," he added. "So we'd be exposing a [mouse] to cocaine and then analyzing gene expression within this region."
Citri explained that the particular brain region is relatively small, and as such, using a small sample obtained from mouse "can only be stretched so far in terms of validation and gene expression … using standard qPCR approaches. Every sample is extremely valuable, and … from microarray experiments I [was interested in] about 300 or 400 genes, and I wanted to validate them and look at them with a little more depth using a different method than microarrays and with reduced cost. That's where I found the Fluidigm platform to be very effective."
From there, Citri became involved with another group at Stanford led by Zhiping Pang, who was attempting to use single-cell qPCR gene profiling to better understand the induction of human neuronal cells by defined transcription factors. Citri said that his success using the BioMark HD platform with other sample types with a limiting initial quantity of RNA led them to believe that the platform would be ideal for the neuronal cell induction work.
The BioMark HD system comprises the BioMark HD real-time PCR instrument and the company's Dynamic Array or Digital Array integrated fluidic circuit consumable or recyclable chips. Each Dynamic Array chip, which is what the Stanford researchers used, can be formatted in a 48-by-48 or 96-by-96 array, enabling the equivalent of 2,304 or 9,216 independent qPCR reactions on a single chip.
The company has been touting the platform for a variety of high-throughput gene-expression studies where cost and a limiting sample size are crucial, but users seem to have identified single-cell gene expression as a particularly sweet spot. Notably, in November startup Quanticel Pharmaceuticals said it would be using the BioMark HD as part of its single-cell genomic analysis work for drug discovery (PCR Insider, 11/10/11).
In their Nature Protocols paper, the Stanford researchers described how they used the platform to explore variation in neuronal gene expression patterns of individual induced neuronal cells. The group wrote that characterizing lineage-specific reprogrammed human neuronal cells requires the use of an assay with single-cell sensitivity, which can provide evidence of the identity of generated neurons, such as their expression of specific lineage markers and neurotransmitter identities.
More specifically, the group aspirated single cells into glass pipettes, then conducted target-specific amplification using a mix of primers to the sequences of interest. Although the BioMark system is compatible with commercially available TaqMan probes, the group found that in-house-designed primers coupled with DNA-binding dyes such as EvaGreen was more cost-efficient and flexible.
Using their method in the early part of their research, the researchers were able to successfully convert human skin fibroblasts into functional neurons and identify the subtype specificity of the newly converted neurons — work that they detailed in a paper published in May in Nature.
The BioMark would be ideal, Citri noted, in any situation where there is a scant initial amount of RNA, "[from] single cells through anything that's microdissected. You could do these experiments using qPCR in a standard 96-well or 384-well format, but it would be more laborious and you'd probably end up using all your material in one experiment. Here I basically make a library that I can keep querying with a bunch of probes, and it becomes more efficient and cost-effective."
Even though the BioMark seemed to fit their needs from the start, Citri and colleagues did investigate competing platforms, such as the QuantaLife (now Bio-Rad) Droplet Digital platform; and Life Technologies' OpenArray system.
The former, Citri said, is "fantastic for digital PCR and for getting absolute quantities, but for looking at a large number of samples and querying them across a large number of probes, that's just not the platform to use." Meantime, a limitation of the OpenArray was that its pre-fabricated TaqMan arrays were more costly and less flexible for the team's particular application, Citri said.
The researchers highlighted a few limitations of their BioMark protocol, most of which had little to do with the performance of the platform per se. However, Citri noted that good primer design was particularly important — as it is in all qPCR experiments — "but here it's a little more extreme because you're starting out with such small samples, and you're basically amplifying enormously. So anything that's not perfect to begin with will be amplified accordingly. The potential of creating an artifact is larger … so you need to be a little more careful and stringent than you normally would when you're working with such small initial quantities."
Citri, who recently accepted a position as an assistant professor at Hebrew University in Jerusalem, will continue single-cell analysis work, but will refocus on his original addiction research. "I will be studying single cells from brain sections and querying the variability there and its role in developing addiction." The BioMark platform, he added, should prove useful in that context, as well.
"I think that the platform is good for any system where you're interested in addressing variability at the single-cell level … and for applications where you want to query a large number of samples across a large number of probes," he said.
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