A new study led by the researchers of Wyss Institute has developed a new technique for genetic analysis of cell using SNPs which are a’ natural barcode’ identifying cell from any individual.
SNPs stands for single nucleotide polymorphism is a variation in single nucleotide occurring at the particular position in the genome. These SNPs are inherited from the parents are rarely mutated and is of unique pattern in every human.
This new genetic analysis technique is faster and simpler way of tracking the cell.It helps to analyze cell function in any experimental condition enabling a large pool of cell to be analyzed simultaneously.
“There are numerous experiments that this technique could be applied to,” says first author Yingleong Chan, Ph.D., a Postdoctoral Fellow in George Church’s lab at the Wyss Institute and HMS. “You can test a cancer drug against different cell lines from different people, see whether a particular patient’s cell line responded well to the drug, and then use that drug for a targeted approach to treatment. We’ve effectively built a discovery tool to enable personalized medicine.” The research is reported in Genome Medicine.
This technique enabled researchers to perform the experiment on a cell from multiple people at the same time, as differences in how the cells respond can indicate that genetic variances between the individuals are conferring some kind of effect. This property gives this technique advantage over the currently used technique in which a unique tag or barcode is needed to attach to every individual cell which makes it costly and time-consuming for carrying out a multiplexed experiment. Furthermore, each cell line has to be frequently integrated with a barcode to identify cells during testing.
However, by taking advantage of all humans’ unique SNP profiles, the Wyss/HMS team achieved the same cell tracking without the cumbersome labeling process, and without modifying the cells’ DNA. It is been very difficult to unlocking SNP’s utility as a barcode. Any one of the SNP can differentiate between two individuals.
During their research, the team used cell lines whose genome was already sequenced in past studies and created a new method that combines genomic DNA extraction from a mixed pool of cells, whole-genome sequencing of the extracted DNA, and a computational algorithm that predicts the proportion of each individual cell line within the pool based on the cells’ known SNP allele profiles.
The team first tested their method by simulating a pool of cells and varying the number of samples, the quantity of SNPs analyzed, and number of times that the pool was sequenced. They found that, over several iterations, the algorithm converged to a fixed estimated proportion for each SNP profile in the pool that closely matched the simulated proportions. The algorithm was able to accurately estimate the proportions of pools of up to 1,000 different individuals by analyzing 500,000 SNPs and could handle samples of event more cell lines if either the number of SNPs analyzed or the depth of sequencing were increased.
“Testing the effects of drugs on multiple cancer cell lines is one application of this method that can be implemented immediately, based on this research,” says co-corresponding author George Church, Ph.D., who is a Founding Core Faculty member of the Wyss Institute, a Professor of Genetics at HMS, and Professor of Health Sciences and Technology at Harvard and MIT. “You can test a lot more people at once, which not only gives you more data but translates into significant time and cost savings.”