Researchers from the University of Pennsylvania plan to use single-cell transcriptome sequencing and functional genomics technology to study transcriptome variability in cardiac cells and neurons.
The team recently won a five-year, $10 million grant from the National Institutes of Health for the project, under the NIH's Single Cell Analysis Program. The Penn team is one of three groups that were awarded funding under the program.
The University of California, San Diego, is leading a project that will using single-cell transcriptome sequencing to construct a 3D map of gene activity in the human brain (IS 11/27/2012), and the University of Southern California is spearheading a project that will use single-cell RNA-seq to study how neuronal stimulation leads to changes in gene expression.
The Penn project is being led by Jim Eberwine and Junhyong Kim, codirectors of the Penn Genomic Frontiers Institute, and the goal is to characterize transcriptome variability, including the types of transcripts and their abundance, from heart and brain cells.
The team is working in conjunction with Sean Grady, chairman of neurosurgery at Penn's Perelman School of Medicine; Jai-Yoon Sul, assistant professor of pharmacology also at the Perelman School of Medicine; Tamas Bartfai at the Scripps Research Institute; and Bernhard Kuhn, assistant professor of pediatrics at Boston Children's Hospital.
Eberwine told In Sequence that the team will analyze thousands of cells that have been harvested from patients who have to undergo either neurosurgery or heart surgery. It's important to study the transcriptomes of cells in their natural environment because gene expression may be different in cells grown in culture, or cells that have been preserved, he said.
The lab has developed a linear RNA amplification process that does not use PCR. Eliminating PCR is "particularly critical for transcriptome work if you want to assess relative or absolute abundance of transcripts," Eberwine said. With PCR-based techniques, any error is exponentially amplified, as opposed to linearly amplified, so that some transcripts amplify better than others, skewing analysis.
The linear RNA amplification technique is available as a commercial kit, called TargetAmp, from Illumina's Epicentre business.
Sequencing will be done on the Illumina HiSeq 2500 once the lab's HiSeq 2000 is upgraded, and the researchers will multiplex between three and eight cells per lane in order to achieve 20x coverage of each transcriptome.
After sequencing, the researchers will use algorithms and computational methods that they have developed to analyze the transcriptomes and identify transcripts for further study.
Then, after identifying the most interesting transcripts, the researchers will develop mixtures with varying ratios of transcripts and inject them into cells of the same type and cells of different types to see how the different ratios impact phenotype.
For this part of the project, the researchers developed a technique called transcriptome-induced phenotype remodeling, or TIPeR, an RNA reprogramming technology that enables transcripts to be moved between live cells.
"The idea is that on those RNAs that are defined by our algorithms as the ones of interest, what are they doing when you transfer them into another cardiac cell and change their ratios, and what happens when you take them in the variability ratio and put them into a cell where their ratios might be completely different," said Eberwine.
The process will be done for both heart and brain cells. The project will initially focus on normal, healthy cells, said Eberwine, in order to characterize the phenotype of normal cells and to see how transcriptome variation impacts that phenotype. The team will make use of proteomics, transcriptomics, and functional assays that measure calcium uptake and electrophysiology to characterize the cells' phenotypes.
"We hope to characterize the normal phenotype of the cells very carefully and then by changing these variable genes, [determine what] we do to that phenotype," Eberwine said.
One potential outcome is that as the transcript makeup is changed, cells could develop a phenotype that is "reminiscent of a particular disease state," in which case the team could then study cells from that particular disease and see how changes to the transcriptome impact phenotype, he added.
Eberwine said that over the first year, all the methods and procedures will be tested and validated, and the team will start generating sequence data. Additionally, the researchers plan to set up a database with all the transcript data that will be freely accessible.