Currently, in order to see what type of bacteria (if any) are present in a liquid sample, bacterial cultures have to be grown in a lab over the course of several hours – or even a few days. A new laser technique, however, works in just minutes.
It was already known that when exposed to laser light, bacteria reflect the light back in a spectral pattern which is unique to that particular species.
The problem is, other microscopic items in the sample – such as blood cells or viruses – also reflect the light, putting their own unique spin on it. This means that the bacteria’s spectral “fingerprint” gets lost amongst the background noise, so it can’t be discerned.
Led by Assoc. Prof. Jennifer Dionne, a team of scientists at Stanford University have devised a solution to that problem.
Their technique incorporates a modified inkjet printer that utilizes acoustic pulses to print out tiny dots of the liquid in question. Printed onto a slide, each dot has a volume of just two trillionths of a liter. Because the dots are so small, they contain only a few dozen cells at most, so any bacteria that are present have very little competition for getting seen.
Additionally, gold nanorods which are added to the tiny samples attach themselves to the bacteria, serving as antennas that draw in the laser light. As a result, the bacteria’s reflected spectral fingerprint is 1,500 times stronger than it would be otherwise. This makes it very easy for machine-learning-based software to spot that fingerprint, and match it up to a specific type of bacteria.
And although the technology was developed mainly using infected mouse blood as the liquid, Dionne believes that it should be equally effective at analyzing other fluids – it could even be adapted to target other types of cells, such as viruses.
“It’s an innovative solution with the potential for life-saving impact,” said the study’s senior co-author Amr Saleh, a former postdoctoral scholar in Dionne’s lab and presently a professor at Cairo University. “We are now excited for commercialization opportunities that can help redefine the standard of bacterial detection and single-cell characterization.”
A paper on the research was recently published in the journal Nano Letters.
Source: Cornell University
