With the help of the new computer models of individual brain cells, neuroscientists can ‘knock out’ each gene and see what happens.
Cedars-Sinai neuroscientists construct the most accurate and intricate computer models of individual brain cells to date, paving the door for lab research.
Researchers at Cedars-Sinai have constructed the most bio-realistic and intricate computer models of individual brain cells to date and in an unprecedented number.
Their research, which was published today in the peer-reviewed journal Cell Reports, explains how these models could one day provide answers to issues regarding brain illnesses and even human intelligence that cannot be investigated through biological trials.
“These models,” according to Costas Anastassiou, PhD, a research scientist in the Department of Neurosurgery at Cedars-Sinai and the study’s senior author, “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.”
This enables the replication of single-cell brain activity.
For the first time, the models provide a comprehensive picture of the electrical, genetic, and biological activity of single neurons by combining data sets from several types of laboratory research. According to Anastassiou, the models can be used to evaluate theories that would involve conducting dozens of tests in the lab.
“Imagine that you wanted to investigate how 50 different genes affect a cell’s biological processes,” Anastassiou adds. “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.”
The models also give researchers complete control over the parameters of the experiment. This creates the opportunity to prove that a single factor, such a protein expressed by a neuron, influences a change in the cell or a medical condition, like epileptic seizures, according to Anastassiou. In the lab, researchers may frequently demonstrate a relationship but find it challenging to establish a cause.
“In laboratory experiments, the researcher doesn’t control everything,” adds Anastassiou. “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.”
Using two different sets of data on the mouse primary visual cortex, the region of the brain that processes information from the eyes, Anastassiou and his team from the Anastassiou Lab (@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—created their models.
Tens of thousands of single cells’ whole genomic profiles were revealed in the first data collection. The second experiment linked the electrical responses and physical properties of 230 brain cells from the same area. The researchers combined these two datasets using machine learning to produce bio-realistic models of 9,200 single neurons and their electrical activity.
According to Keith L. Black, MD, the Ruth and Lawrence Harvey Chair in Neuroscience at Cedars-Sinai, and chair of the department of neurosurgery, “This work represents a significant advancement in high-performance computing.” Additionally, it enables researchers to look for connections within and between different cell types and to gain a better knowledge of how various cell types in the brain function.
The study was carried out in partnership with Seattle’s Allen Institute for Brain Science, which also donated 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,” adds 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.”
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