Research into the genetic factors behind certain disease mechanisms, illness
progression, and response to new drugs is frequently carried out using tiny multicellular
animals such as nematodes, fruit flies, or zebra fish. Often, progress relies
on the microscopic visual examination of many individual animals to detect
mutants worthy of further study.
Now, scientists have demonstrated an automated system that uses artificial
intelligence and cutting-edge image processing to rapidly examine large numbers
of individual Caenorhabditis elegans, a species of nematode widely
used in biological research. Beyond replacing existing manual examination steps
using microfluidics and automated hardware, the system’s ability to detect
subtle differences from worm-to-worm—without human intervention—can identify
genetic mutations that might not have been detected otherwise.
By allowing thousands of worms to be examined autonomously in a fraction of
the time required for conventional manual screening, the technique could change
the way that high-throughput genetic screening is carried out using C.
Details of the research were reported in Nature Methods.
“While humans are very good at pattern recognition, computers are much
better than humans at detecting subtle differences, such as small changes in
the location of dots or slight variations in the brightness of an image,” says Hang
Lu, the project’s lead researcher and an associate professor in the School of
Chemical and Biomolecular Engineering at the Georgia Institute of Technology. “This technique found differences that would have been almost impossible to
pick out by hand.”
Lu’s research team is studying genes that affect the formation and
development of synapses in the worms, work that could have implications for
understanding human brain development. The researchers use a model in which
synapses of specific neurons are labeled by a fluorescent protein. Their
research involves creating mutations in the genomes of thousands of worms and
examining the resulting changes in the synapses. Mutant worms identified in this
way are studied further to help understand what genes may have caused the
changes in the synapses.
One aspect the researchers are studying is why synapses form in the wrong
locations, or are of the wrong sizes or types. The differences between the mutants
and the normal or “wild type” worms indicate inappropriate developmental
patterns caused by the genetic mutations.
Because of the large number of possible genes involved in these
developmental processes, the researchers must examine thousands of worms—perhaps
as many as 100,000—to exhaust the search. Lu and her research group had earlier
developed a microfluidic “worm sorter” that speeds up the process of examining
worms under a microscope, but until now, there were two options for detecting
the mutants: a human had to look at each animal, or a simple heuristic
algorithm was used to make the sorting decision. Neither option is objective or
adaptable to new problems.
Lu’s system, an optimized version of earlier work by her group, uses a
camera to record 3D images of each worm as it passes through the
sorter. The system compares each image set against what it has been taught the “wild type” worms should look like. Worms that are even subtly different from
normal can be sorted out for further study.
“We feed the program wild-type images, and it teaches itself to recognize
what differentiates the wild type. It uses this information to determine what a
mutant type may look like—which is information we didn’t provide to the system—and
sorts the worms based on that,” explains Matthew Crane, a graduate student who
performed the work. “We don’t have to show the computer every possible mutant,
and that is very powerful. And the computer never gets bored.”
While the system was designed to sort C. elegans for a specific
research project, Lu believes the machine learning technology—which is borrowed
from computer science—could be applied to other areas of biology that use model
genetic organisms. The system’s hardware and software are currently being used
in several other laboratories beyond Georgia Tech.
“Our automated technique can be generalized to anything that relies on detecting
a morphometric—or shape, size, or brightness difference,” Lu says. “We can
apply this to anything that can be detected visually, and we think this could
be expanded to studying many other problems related to learning, memory, neurodegeneration,
and neural developmental diseases that this worm can be used to model.”
Individual C. elegans are less than a millimeter long and thinner
than a strand of hair, but have 302 neurons with well-defined synapses. While
research using single cells can be simpler to do, studies using the worms are
good in vivo models for many important processes relevant to human health.
The autonomous processing facilitated by the new system could allow
researchers to examine more animals more rapidly, potentially opening up areas
of study that are not feasible today.
“We are hoping that the technology will really change the approach people
can take to this kind of research,” says Lu. “We expect that this approach will
enable people to do much larger scale experiments that can push the science
forward beyond looking what individual mutations are doing in a specific
Source: Georgia Institute of Technology