Top Breakthrough Discovery | Published June 2020 in Nature

An image of Kyle Ferchen, BS, H. Leighton Grimes, PhD, Nathan Salomonis, PhD, and Andre Olsson, PhD.

Kyle Ferchen, BS, H. Leighton Grimes, PhD, Nathan Salomonis, PhD, and Andre Olsson, PhD

Changes in DNA can have very different consequences—some cause disease, while others have no effect. So how can we determine which mutations are important and which aren’t?

Single-cell methods hold the key to finding answers, according to a breakthrough study in the journal Nature led by H. Leighton Grimes, PhD, of the Division of Immunobiology, and Nathan Salomonis, PhD, of the Division of Biomedical Informatics.

The study introduces a new workflow to tackle the challenge of linking gene mutations with disease-causing processes. Researchers and clinicians can use this platform to study the single-cell genomics of a variety of diseases—potentially improving the precision and effectiveness of genetic-based diagnoses in the clinic.

Finding Missing Links

Advances in genetics and sequencing technologies have revealed many gene alterations that are associated with disease. Despite these insights, determining causality between genetics and disease can be tedious and difficult.

“We can already sequence a child’s genome and link a DNA sequence difference to the disease,” says Grimes. “However, determining which of these DNA changes is a real disease-causing mutation is difficult, but critical to understanding the molecular mechanisms of a disease, and driving toward a cure.”

Why are these connections so tricky to identify? The more we learn about gene and protein expression in each cell at a given moment, the more complex the task becomes. Single-cell analyses are vastly increasing the number of cellular populations to be studied, and each can be affected by mutations in unique ways.

To get a complete picture of how cells work, accurate models of genetic disease are essential. By analyzing these models at a single-cell level, researchers can evaluate how mutations and medications impact different cell states.