Cedars-Sinai investigators have created the most bio-realistic and complex computer models of individual brain cells -- in unparalleled quantity. Their research, published today in the peer-reviewed journal Cell Reports, details how these models could one day answer questions about neurological disorders -- and even human intellect -- that aren't possible to explore through biological experiments.
From: Cedars-Sinai Medical Center
August 9, 2022 -- "These
models capture the shape, timing and speed of the electrical signals that
neurons fire in order to communicate with each other, which is considered the
basis of brain function," said Costas Anastassiou, PhD, a research
scientist in the Department of Neurosurgery at Cedars-Sinai, and senior author
of the study. "This lets us replicate brain activity at the single-cell
level."
The models are the
first to combine data sets from different types of laboratory experiments to
present a complete picture of the electrical, genetic and biological activity
of single neurons. The models can be used to test theories that would require
dozens of experiments to examine in the lab, Anastassiou said.
"Imagine that you
wanted to investigate how 50 different genes affect a cell's biological processes,"
Anastassiou said. "You would need to create a separate experiment to
'knock out' each gene and see what happens. With our computational models, we
will be able to change the recipes of these gene markers for as many genes as
we like and predict what will happen."
Another advantage of
the models is that they allow researchers to completely control experimental
conditions. This opens the possibility of establishing that one parameter, such
as a protein expressed by a neuron, causes a change in the cell or a disease
condition, such as epileptic seizures, Anastassiou said. In the lab,
investigators can often show an association, but it is
difficult to prove a cause.
"In laboratory
experiments, the researcher doesn't control everything," Anastassiou said.
"Biology controls a lot. But in a computational simulation, all the
parameters are under the creator's control. In a model, I can change one
parameter and see how it affects another, something that is very hard to do in
a biological experiment."
To create their models,
Anastassiou and his team from the Anastassiou Lab -- members of the Departments
of Neurology and Neurosurgery, the Board of Governors Regenerative Medicine
Institute and the Center for Neural Science and Medicine at Cedars-Sinai, used
two different sets of data on the mouse primary visual cortex, the area of the
brain that processes information coming from the eyes.
The first data set
presented complete genetic pictures of tens of thousands of single cells. The
second linked the electrical responses and physical characteristics of 230
cells from the same brain region. The investigators used machine learning to
integrate these two datasets and create bio-realistic models of 9,200 single
neurons and their electrical activity.
"This work represents
a significant advancement in high-performance computing," said Keith L.
Black, MD, chair of the Department of Neurosurgery and the Ruth and Lawrence
Harvey Chair in Neuroscience at Cedars-Sinai. "It also gives researchers
the ability to search for relationships within and between cell types and to
glean a deeper understanding of the function of cell types in the brain."
The study was conducted
in collaboration with the Allen Institute for Brain Science in Seattle, which
also provided data.
"This work led by
Dr. Anastassiou fits in well with Cedars-Sinai's dedication to bringing
together mathematics, statistics, and computer science with technology to
address all the important questions in biomedical research and
healthcare," said Jason Moore, PhD, chair of the Department of
Computational Biomedicine. "Ultimately, this computational direction will
help us understand the deepest mysteries of the human brain."
Anastassiou and his
team are next working to create computational models of human cells to study brain
function and disease in humans.
https://www.sciencedaily.com/releases/2022/08/220809141159.htm
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