Harvard scientists encode GIF of galloping horse into DNA of living cells
14 Jul 2017
Harvard scientists have been able to encode a GIF of a galloping horse into the DNA of living cells, creating a man-made "biological recording."
The scientists inserted bits of information, a grainy photo of a hand and a GIF of a galloping horse, into the genetic code of bacteria and then managed to retrieve the biological recording and piece it back together with 90 per cent accuracy.
The ground-breaking achievement had become possible with "CRISPR," a complicated genome editing tool that scientists believe could one day treat HIV, remove malaria from mosquitoes and even cure cancer.
"This work demonstrates that this system can capture and stably store practical amounts of real data within the genomes of populations of living cells," detailed the study, which was published in the journal Nature. "By combining the principles of information storage in DNA with DNA-capture systems capable of functioning in living cells, we can create living organisms that capture, store, and propagate information over time."
Researcher Jeff Nivala told Wired in an interview that the real goal behind the study was to one day "enable cells to gather information about themselves and to store it in their genome for us to look at later," almost like a biological flash drive genetic scientists could use to understand how and why certain cells did the things they do.
Due to its dynamic, if brief, nature, encoding the historic horse GIF presented unique challenges something like sticking an old static image into living bacteria.
By utilising the technology's sequencing abilities, the researchers encoded the temporal order of the GIF frame by frame and were able to extract and put it back together with 90 per cent accuracy.
As the paper explains, "When harnessed, this system has the potential to write arbitrary information into the genome. Here we use the CRISPR–Cas system to encode the pixel values of black and white images and a short movie into the genomes of a population of living bacteria. In doing so, we push the technical limits of this information storage system and optimize strategies to minimize those limitations."
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