Performing experiments on cells from multiple people simultaneously has advantages for studying the effects of genetic variation, but this process currently requires adding a unique identifying tag or barcode to each individual’s cells, which can be time-consuming and costly. According to a paper published in Genome Medicine, researchers have circumvented this cumbersome process using SNP profiling to track cells. The research was done by researchers at Harvard Medical School and Wyss Institute for Biologically Inspired Engineering.
SNPs have been used by scientists for decades, but their utility as barcodes has remained elusive due to their sparse distribution throughout the genome. Current high-throughput sequencing technologies have sequencing read-lengths of less than 1,000 base pairs, far less than what would be necessary to be able to distinguish between multiple individual genomes.
The new method gets around this by combining 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 within the pool based on the SNP allele profile of every known person’s cells. Many cell lines currently used by researchers already have whole-genome SNP allele profiles available for them, and individual profiles can be determined using genotyping arrays or low-coverage whole genome sequencing.
The SNP allele profiles can be used to track cells in experiments where cells are subjected to two or more different conditions. The algorithm for tracking them predicts the proportion of an individuals cells before and after the experiment and compares them to determine which cells are expressed differently, and which might have a specific genetic advantage.
The researchers performed several experiments with the cells wherein number of samples, quantities of SNPs analyzed, and number of sequencing runs was varied. After several of these experiments, the algorithm was able to provide a fixed estimated proportion for each SNP profile and was able to estimate proportions for up to 1,000 individuals.
"There are numerous experiments that this technique could be applied to," says Yingleong Chan, Ph.D., a Postdoctoral Fellow in the laboratory of George Church 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."
While this method will not work for samples where the different cell types come from the same person, it could offer great benefits for multiplexed testing of genetic variation.