New method could advance precision medicine

Scientists have devised a new computational method that reveals genetic patterns in the massive jumble of individual cells in the body, which will be useful in discerning patterns of gene expression across many kinds of disease, including cancer, according to new research published in the journal eLife.

Scientists from the Oregon Health & Science University worked out the formulation by testing tissue taken from the testes of mice. Results in hand, they're already applying the same analysis to biopsies taken from men with unexplained infertility.

"There have been very few studies that attempt to find the cause of any disease by comparing single-cell expression measurements from a patient to those of a healthy control,” said Donald Conrad, PhD, co-senior author and an associate professor and chief of the Division of Genetics in the Oregon National Primate Research Center. “We wanted to demonstrate that we could make sense of this kind of data and pinpoint a patient's specific defects in unexplained infertility.”

Conrad said he expects the new method will advance the field of precision medicine, where individualized treatment can be applied to the specific nuance of each patient's genetic readout.

The scientists made the breakthrough by applying a method recently developed at the University of Oxford to gene expression data from the massive trove of individual cells comprising even minuscule tissue biopsies. The method is known as sparse decomposition of arrays (SDA).

The new study applied the method to 57,600 individual cells taken from the testes of five lines of mice, four that carry known genetic mutations causing defects in sperm production and one with no sign of genetic infertility. Researchers said they wanted to see whether it was possible to sort this massive dataset based on the variation in physiological traits resulting from differences in the genes expressed in the RNA, or ribonucleic acid, of individual cells.

Researchers found they were able to cut through the statistical noise and sort many thousands of cells into 46 genetic groups.

"It's a data-reduction method that allows us to identify sets of genes whose activity goes up and down over subsets of cells," Conrad said. "What we're really doing is building a dictionary that describes how genes change at a single-cell level."

The work will immediately apply to male infertility. Infertility affects an estimated 0.5 percent to 1 percent of the male population worldwide. Current measures to treat male infertility involve focus on managing defects in the sperm itself, including through in vitro fertilization. However, those techniques don't work in all cases.

This new technique could open new opportunities to diagnose a specific genetic defect and then potentially rectify it with new gene-editing tools such as CRISPR. Identification of a specific cause would be a vast improvement over the current state of the art in diagnosing male infertility, which amounts to a descriptive analysis of testicular tissue biopsies.

"The opportunity provided by CRISPR,” Conrad said, “coupled to this kind of diagnosis, is really a match made in heaven.”