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Nov 12, 2019

Yale-MIT-Harvard collaboration maps out cellular culprits of blindness

By: Kyoung A Lee

According to statistics reported by Mayo Clinic, there are more than 200,000 new cases of macular degeneration in the United States each year.

It is no secret that vision impairment is common among the elderly — age-related macular degeneration, or AMD, which results in the loss of central vision in the retina, is one of the leading causes of blindness in the United States.

But a study written by a joint research team from Yale, MIT and Harvard has recently identified the cellular culprits of this incurable disease, bringing the scientific community a step closer to finding more permanent therapeutics for macular degeneration patients.

Published in Nature Communications on Oct. 25, the study reported that cells expressing high-risk genes for macular degeneration were not limited to cells directly responsible for vision, but also included supporting cells of the eye such as glia and vascular cells. Using a highly efficient, high-resolution RNA sequencing technology, the researchers created a single-cell atlas of the human retina that identifies individual retinal cell types associated with macular degeneration.

“It is the first time the human retinal cells have been sequenced at the single-cell level,” said Brian Hafler, assistant professor of ophthalmology and pathology at the School of Medicine and co-senior author of the study. “We were able to look at each individual retinal cell’s RNA and how the expression of specific genes varies from other cells. We found which cells expressed the known genetic risk factors for macular degeneration. It’s exciting to be able to assign the AMD-associated genes with the specific cell types they act on.”

Single cell sequencing technologies have transformed the possibilities of molecular discoveries in recent years.

In traditional sequencing, “bulk” data is generated with large quantities of cells amounting to an average signal, making it impossible to differentiate or observe variation within the bulk of cells. But with single-cell sequencing, it is possible to add another dimension to the data by capturing the unique properties of each cell being sequenced.

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Source: Yale Daily News

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