combinatorial indexing
DNA Barcodes Make for Better Single-Cell Analysis
Posted on by Dr. Francis Collins
Caption: Single-cell analysis helps to reveal subtle, but important, differences among human cells, including many types of brain cells.
Credit: Shutterstock, modified by Ryan M. Mulqueen
Imagine how long it would take to analyze the 37 trillion or so cells that make up the human body if you had to do it by hand, one by one! Still, single-cell analysis is crucial to gaining a comprehensive understanding of our biology. The cell is the unit of life for all organisms, and all cells are certainly not the same. Think about it: even though each cell contains the same DNA, some make up your skin while others build your bones; some of your cells might be super healthy while others could be headed down the road to cancer or Alzheimer’s disease.
So, it’s no surprise that many NIH-funded researchers are hard at work in the rapidly emerging field known as single-cell analysis. In fact, one team recently reported impressive progress in improving the speed and efficiency of a method to analyze certain epigenetic features of individual cells [1]. Epigenetics refers to a multitude of chemical and protein “marks” on a cell’s DNA—patterns that vary among cells and help to determine which genes are switched on or off. That plays a major role in defining cellular identity as a skin cell, liver cell, or pancreatic cancer cell.
The team’s rather simple but ingenious approach relies on attaching a unique combination of two DNA barcodes to each cell prior to analyzing epigenetic marks all across the genome, making it possible for researchers to pool hundreds of cells without losing track of each of them individually. Using this approach, the researchers could profile thousands of individual cells simultaneously for less than 50 cents per cell, a 50- to 100-fold drop in price. The new approach promises to yield important insights into the role of epigenetic factors in our health, from the way neurons in our brains function to whether or not a cancer responds to treatment.
