Way of Identifying Neurons Essential to Walking, Other Abilities Found

“However, investigators currently lack bespoke tools to identify cell types affected by perturbation,” the researchers wrote.

To fill this gap, Courtine’s team, in collaboration with colleagues in Canada and the U.S., developed Augur, a new and improved way of prioritizing those cell types most responsive to biological perturbations in single-cell datasets.

“We reasoned that cell types most responsive to a perturbation should be more separable, within the multidimensional space of single-cell measurements, than less affected ones, and that the relative difficulty of this separation would provide a quantitative basis for cell type prioritization,” the researchers stated.

Augur is a machine-learning method, meaning that it is a form of artificial intelligence that uses algorithms to analyze data, learn from its analyses, and then make a prediction about something.

In this case, Augur showed it is capable of learning to pinpoint those types of cells that best reflect differences between two conditions by automatically considering the activity of thousands of genes.

After validating this method on three distinct single-cell datasets and showing that it outperformed existing methods, the researchers aimed to demonstrate how Augur could be used to discover new biological mechanisms.

Courtine’s team had previously used targeted electrochemical stimulation of the spinal cord to restore locomotion and walking abilities in both rats and people with paralysis caused by spinal cord injury.

“However, the neural circuits engaged by this treatment remain enigmatic,” the researchers wrote.

They used Augur to predict which neurons, among the 50 types that exist in the spinal cord of both rodents and humans, displayed the greatest differences between paralyzed mice and those that regained mobility after the stimulation treatment.

“The more accurately Augur can assign a particular type of neuron to the two groups of mice, the more relevant those particular nerve cells are. They are therefore more likely to be involved in gait recovery,” Michael Skinnider and Jordan Squair, the study’s co-first authors, said.

The types of neurons prioritized by Augur were further confirmed to be activated in response to neurostimulation-enabled walking, while those not prioritized showed minimal activation.

With such information, researchers may better understand the underlying mechanisms of locomotion recovery and of the neurons that need to be specifically targeted, increasing the likelihood of a treatment’s effectiveness.

“These results illustrate the value of Augur to expose neural circuits underlying complex behaviors,” the researchers wrote.

Augur “is a robust statistical method that can be applied to any perturbation,” Skinnider and Squair added.

According to the team, Augur may help in the development of new therapeutic approaches and make treatments even more effective by targeting the most relevant cell types in several areas of biomedical research, including in MS.