Universal Molecular Diagnostics by Affinity

Rice University researchers have invented a technology that could potentially identify hundreds of bacterial pathogens simply, quickly and at low cost using a single set of random DNA probes. Richard Baraniuk, Amirali Aghazadeh and Rebekah Drezek whomped up a batch of five random probes and used them to identify 11 known strains of bacteria, providing a genomic-based test for identity of pathogens.  This is a big deal because usually each species required its own DNA probe.

Their new study includes several computer simulations, including one that shows how a random selection of five probes can identify 40 different strains of bacteria, and another that demonstrates how the system can accurately differentiate between 24 different species of Staphylococcus.

Rather than identifying a target strain based on a 100 percent match with a specific probe, Rice’s system tests how well the target DNA binds with several different random segments of complementary DNA. UMD uses a mathematical technique called compressive sensing, which was pioneered in the field of digital signal processing. With compressive sensing, the disease DNA need not bind with 100 percent of the probes. Instead, the new system measures how well the disease DNA binds with each of the random probes and creates a specific binding profile for the test organism. It then uses deductive reasoning to determine whether that profile matches the profile of any known pathogens.

With larger numbers of probes, it works even better:

No special hardware is required for this approach, other than the tried and true PCR with which we have become familiar over the last twenty years (thank you, Kary Mullis and LSD). The special sauce is the computer code which figures out the relative affinities.  This can be made available everywhere pretty cheaply, versus specialized DNA probes which require expensive facilities and a lot more regulatory testing.

Homework: Universal microbial diagnostics using random DNA probes, Amirali Aghazadeh1,*, Adam Y. Lin1,*, Mona A. Sheikh1,*, Allen L. Chen1, Lisa M. Atkins2, Coreen L. Johnson2, Joseph F. Petrosino2, Rebekah A. Drezek1 and Richard G. Baraniuk1, Science Advances  28 Sep 2016:Vol. 2, no. 9, e1600025 DOI:10.1126/sciadv.1600025