07 Mar 2023
Stanford University develops AI-assisted approach for analysis of blood and waste water.
Recent studies have seen Raman platforms applied to the challenge of point-of-care Covid diagnosis and detection of tuberculosis bacteria, examples of what Jürgen Popp from the Leibniz Institute of Photonic Technology has called Raman's potential to "become a powerful analytical and diagnostic tool."
A project at Stanford University has now developed a technique for identification of bacteria that combines surface-enhanced Raman spectroscopy (SERS), machine learning, and a bioprinting approach to sample presentation. The work was published in Nano Letters.
The new method could lead to rapid, inexpensive, and more accurate microbial assays of many different fluids, according to the project, as an alternative to traditional culturing methods that can take hours or days.
"Not only does each type of bacterium demonstrate unique patterns of light, but virtually every other molecule or cell in a given sample does too," Stanford's Fareeha Safir. "Red blood cells, white blood cells, and other components in the sample are sending back their own signals, making it hard if not impossible to distinguish the microbial patterns from the noise of other cells."
Solving this problem required the team to consider how best to isolate the cells in extremely small samples, cutting out as much unwanted spectral information as possible. The answer was borrowed from the principles of inkjet printing, using a technique termed acoustic droplet ejection (ADE).
In ADE, ultrasonic waves are focused at the fluid−air interface to create radiation pressure that ejects a droplet from the surface, with droplet size inversely proportional to the frequency of the transducer.
Future point-of-care technologies
The Raman side of the platform makes use of gold nanorods for surface enhancement, introducing the nanorods into the sample liquid so that both bacteria and nanorods are deposited onto a gold-coated glass slide by the acoustic printing operation.
"This is the first demonstration of stable and precise high-frequency acoustic printing of multicomponent samples printed from both microscale biological entities along with nanoscale particles," commented the team in its published paper.
In trials, the Raman-based analysis was applied to samples of E. coli and Staphylococcus bacteria, as well as to samples of mouse red blood cells. Machine learning algorithms previously trained from uniform cell samples were then used to identify the Raman spectral signatures of the different species.
Results showed a greater than 99 percent classification accuracy from cellularly pure samples, and 87 percent accuracy from cellularly mixed samples. Tests with and without gold nanorods confirmed that the surface enhancement of Raman signals still occurred in the bioprinted samples, with amplifications of up to 1500× being recorded.
The Stanford method could help advance Raman-based research, clinical diagnostics and disease management, according to the team, offering minimally invasive, fluid-based biomarker detection for future point-of-care systems. The platform could also be applied to other fluids, such as drinking water for public health monitoring.
"It’s an innovative solution with the potential for life-saving impact, said team member Amr Saleh. "We are now excited for commercialization opportunities that can help redefine the standard of bacterial detection and single-cell characterization."