According to a story from MD Magazine, the team at Spatial Transcriptomics has utilized an innovative computational approach in order to map gene expression on the spinal cords of patients with amyotrophic lateral sclerosis, a rare and fatal neuromuscular disease. Transcriptomics was used to monitor the progression of a mouse model of the disease at four different stages and could allow researchers to learn about the illness in an entirely new way.
About Amyotrophic Lateral Sclerosis (ALS)
Amyotrophic lateral sclerosis, otherwise known as Lou Gehrig’s disease, is a rare, degenerative disease that causes the death of nerve cells associated with the voluntary muscles. Little is known about the origins of amyotrophic lateral sclerosis, with no definitive cause in about 95 percent of cases. The remaining five percent appear to inherit the disease from their parents. Symptoms initially include loss of coordination, muscle weakness and atrophy, muscle stiffness and cramping, and trouble speaking, breathing, or swallowing. These symptoms worsen steadily over time; most patients die because of respiratory complications. Treatment is mostly symptomatic and the medication riluzole can prolong life. Life expectancy after diagnosis ranges from two to four years, but some patients can survive for substantially longer. To learn more about amyotrophic lateral sclerosis, click here.
About The Study
The approach has revealed that gene expression changes that occur early in the course of amyotrophic lateral sclerosis could reveal new targets for therapy and potential indicators that could improve diagnosis. The process involved the collection of tens of thousands of spatial gene expression measurements. This massive pool of data allows for in-depth observation of interactions between cells. For example, while motor neurons are the principle affected cells in amyotrophic lateral sclerosis, it is important to see how these neurons are impacted by and engage with the cells around them.
This “gene expression atlas” is now available as an open source for researchers who can access it with an interactive portal for data exploration. The resource could also be useful in researching other diseases, such as Alzheimer’s, Huntington’s disease, and Parkinson’s disease.
Check out the original study here.