Many biosensing applications rely on characterization of
specific analytes such as proteins, viruses, bacteria, among many other
targets, which can be accomplished by using micro- or nano-scale particles. In
such biosensors, these particles are coated with a surface chemistry that makes
them stick to the target analyte forming clusters in response. The higher the
target analyte concentration is, the larger the number of clusters gets.
Therefore, monitoring and characterizing these particle clusters can tell us if
the target analyte is present in a sample and in what concentration. Current
methods to perform such an analysis are limited in that they are either capable
of only a coarse readout or rely on expensive and bulky microscopes, which
limit their applicability to address different biosensing needs, especially in
resource limited environments.
To overcome the shortcomings of the existing solutions, UCLA
researchers have developed a rapid and automated biosensing method based on
holography coupled with deep learning – currently, one of the most promising
and successfully used methods in artificial intelligence, AI. In this system,
all the particle clusters and individual micro-particles in a sample are first
imaged in 3D as holograms, all at the same time, and over a very large sample
area of more than 20 mm2, more than ten-fold larger than the imaging area of a
standard optical microscope. Next, a trained deep neural network processes
these holograms and rapidly reconstructs them into images of clusters similar
to those that could be obtained with a standard scanning microscope, but doing
this much faster and for a significantly larger sample volume. During this
process, all the particle clusters at the micro-scale (revealing the presence
of the target analyte) are automatically counted with a sensitivity similar to
a laboratory-grade microscope.
As a proof of concept, UCLA researchers successfully
demonstrated the application of this deep learning-based biosensing approach to
detect herpes simplex virus (HSV) and achieved a detection limit of ~ 5 viruses
per micro-liter, providing a clinically relevant level of sensitivity for HSV
detection. HSV is one of the most widespread viral infections that is estimated
to have affected more than 50% of the adults in the US.
This work was published as a cover article in ACS Photonics, a journal of the American Chemical
Society. The research was led by Dr. Aydogan Ozcan, an associate director of
the California NanoSystems Institute (CNSI) and the Chancellor’s Professor of
electrical and computer engineering at the UCLA Henry Samueli School of
Engineering and Applied Science, along with Yichen Wu, a graduate student, and
Aniruddha Ray, a postdoctoral scholar, at the UCLA electrical and computer
engineering department.
“Our work demonstrates an automated, inexpensive platform
for rapid read-out and quantification of a wide variety of particle
clustering-based biosensors. This unique capability enabled by deep learning
will help democratize biosensing instrumentation, making them suitable for
wide-scale use even in developing countries,” said Ozcan.
Other members of the research team were Alborz Feizi, Xin
Tong, Eva Chen and Yi Luo, members of the Ozcan Research Lab at UCLA, as well
as Dr. Qingshan Wei, an assistant professor at the department of chemical and
biomolecular engineering at the North Carolina State University.
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