Donate to Science & Enterprise

S&E on Mastodon

S&E on LinkedIn

S&E on Flipboard

Please share Science & Enterprise

System Slashes Time to Detect Bacteria in Blood

Study authors

L-R, Stephanie Fraley with graduate students and co-authors Hannah Mack and Daniel Ortiz (Univ of California, San Diego)

9 February 2017. A biomedical engineering lab developed a compact system that detects harmful bacteria in blood samples in less than four hours, a process that now takes days. A team from University of California in San Diego describes the system in the 8 February issue of the journal Scientific Reports.

Researchers led by engineering professor Stephanie Fraley are seeking faster and more reliable methods for detecting bacteria and other pathogens in blood samples. Not only do current lab tests take several days, they require making guesses about the suspected bacteria, when other pathogens in varying quantities may be present and go undetected. And while genomic sequencing techniques are available, those technologies also take days to complete, are expensive, and require highly trained staff.

The system designed by Fraley and colleagues combines a number of technologies: microfluidic chips, DNA sequencing, analytical chemistry, and machine learning algorithms. It takes 1 milliliter of blood (0.03 ounces), with the DNA in the sample then isolated, and inserted into a microfluidic device, or lab-on-a-chip. The minute amounts of DNA are amplified on the chip with polymerase chain reactions, a genomic analysis technique, and chemically enhanced for further analysis.

The researchers then slowly increased the temperature of the samples to 50 to 90 degrees C (120 to 190 F), which melts the DNA, breaking the strands and causing them to unwind. The bonds holding the DNA strands differ in strength, with the unwinding behavior of the strands also varying in unique ways. A fluorescent dye added to the samples makes it possible to track these differences in melting reactions, and capture them as a unique signature for each DNA.

These unique DNA signatures are analyzed further by algorithms using machine learning developed by Fraley and colleagues at UC San Diego and Johns Hopkins University. These models identify the precise DNA signatures in the blood, which in a study published in January 2016 were able to successfully identify 37 different bacteria with near 100 percent accuracy under some conditions.

In lab tests, researchers tested mock clinical blood samples with listeria, bacteria responsible for some 1,600 food poisoning cases each year in the U.S., and Streptococcus pneumoniae bacteria that cause a number of diseases from ear and sinus infections to pneumonia and meningitis. The team successfully identified the bacteria in the blood, even with the “background noise” of human DNA, and returned the results in less than 4 hours.

“Analyzing this many reactions at the same time at this small a scale had never been attempted before,” says Fraley in a university statement. “Most molecular tests look at DNA on a much larger scale and look for just one type of bacteria at a time. We analyze all the bacteria in a sample.”

The researchers plan to continue refining the system, making it more compact for desktop use in doctors’ offices or clinics. They also expect to test the system on real rather than mock blood samples, and extend the targets to viruses and fungal pathogens. The university also filed a patent for the technology.

Read more:

*     *     *

Comments are closed.