Your cells need to move. For example, immune cells must roam your body to locate sites of infection, and neurons must migrate to specific positions in the brain during development. But cells don’t have eyes to see where they’re going. Instead, like a dog sniffing out the source of some delicious smell, a cell learns how to reach a target by sensing chemicals in its environment through receptors dotted across the cell’s surface. For example, the site of an infection will emit certain molecules, and a white blood cell will follow this trail of signals to find their source.
Understanding how cells migrate by reading signals in their environment is fundamental to knowing how living systems work, from immune cells in the human body to single-celled organisms living in soils. New work from the lab of Matt Thomson at Caltech, assistant professor of computational biology and researcher at the Heritage Medical Research Institute, provides new insights into how cells migrate and respond to information in their environment. The research is described in an article published in the journal Cellular systems June 8.
Biologists have traditionally understood the process of cell migration with a simple model. In this model, the environment of a cell is represented as a gradient of signal concentrations, with a very high concentration emanating from a source (like the infection example mentioned earlier) gradually decreasing farther from the source. . For example, imagine that you release a drop of colored dye into the water. Water in close proximity to where the dye is placed would become brilliantly colored; with distance from this source, the color would gradually decrease in intensity.
But this simple model doesn’t actually replicate what the messy and complex environment looks like inside living tissue.
“If you wanted to design cells to perform a task in the body for biomedical applications, like killing tumors, that cell would have to know how to handle real environments, not just the simplistic environment of a lab dish,” explains the graduate. Zitong student Jerry Wang, first author of the study.
In tissues, cells move through a tangled protein network called the extracellular matrix (ECM). Here, the chemical signals don’t just float free, they adhere to the ECM itself, creating a signaling environment that doesn’t look like a smooth gradient, but rather an uneven, network-like mess of clustered molecules. .
How do cells locate the source of signaling molecules to navigate the real, messy tissue environment? The traditional gradient model of cell migration, in which the cell smoothly follows its local signaling concentration gradient, does not work in this realistic environment, because although the cell may discover a patch of relatively high signal concentration, it cannot deviate from this local maximum. to find the actual source of the signals. In other words, the cell gets stuck on local areas of high concentrations, but can’t actually get to where it needs to go. For example, imagine trying to reach the top of a mountain by moving only uphill. You might be stuck at the top of a smaller intermediate hill, because in a real mountainous environment you might have to descend in some areas to reach the highest peak. .
To understand how cells deal with this, the team was motivated by experimental observations made in yeast cells showing that when cells sense pheromones, they rearrange receptors on their surfaces so that more receptors are placed near areas at high signal concentration. The team was also intrigued by the fact that dynamic receptor rearrangement had been observed in a variety of systems – certain human cell types like T cells and neurons can rearrange their receptors, and even locusts actively sweep their antennae (containing odor receptors) through space as they move, which greatly improves their ability to navigate to the source of uneven odor plumes.
With this in mind, the team developed a computer model in which cellular receptors could actively redistribute in response to signals, based on known molecular mechanisms for receptor redistribution. In this dynamic model, cells do not get stuck in areas of local concentration and are able to find the true signal source. As a result of this receptor optimization, cell navigation was 30 times more efficient and the model accurately matched the actual cell behavior observed in the tissues. Although receptor rearrangement has been observed in a myriad of systems, this work is the first to show that it plays a crucial functional role in cellular navigation.
“In an upcoming paper, we describe how the receptor redistribution mechanism we modeled accurately implements what is known as a Bayesian filter, which is a well-known target tracking algorithm that is actively used in robotics today,” says Wang. “So the cells in our body could actually use a similar algorithm for navigation as self-driving vehicles like self-driving cars.”
The new model is essential for understanding real cell systems relevant to human health. “For a long time, people couldn’t actually imagine themselves in tissues, so we didn’t even know what the tissue environment looked like,” says Wang. “Researchers were taking cells out of the body and studying how they moved around in a lab dish, with smooth scattering gradients of signals from a pipette. But now we know that’s really not what’s happening. pass in the real environment, which is uneven. This work has inspired us to set up a collaboration with physicians to image more tissue samples to better understand the live environment.”
In particular, this research was inspired by principles of neuroscience and how neurons process information about signals in their environment.
“The sensory information that an organism receives in its natural environment is highly spatio-temporally structured, meaning that it varies over time and space due to statistical regularities inherent in natural stimuli,” says Wang. “Neuroscientists have discovered that neural sensory processing systems, such as retinal processing and auditory processing, have been tuned to the statistical property of the signals they are exposed to – the visual or auditory signal in the natural environment of the animal.”
“We know that a cell also lives in a spatially structured environment, so we first constructed statistical models of natural cell environments in soil and tissue from imaging and simulation data, and then used information theory to ask how a cell’s sensory processing system – in this case, the distribution of receptors – relates to the statistical structure of the cell’s environment We were surprised to find that this principle general neuroscience also applies at the scale of individual cells, in particular the distribution of receptors found on cells greatly enhances the acquisition of information in nature environments.Furthermore, we show the same extents of connection to the cellular navigation.The adaptive rearrangement of receptors observed on cells greatly improves cellular navigation, but s only in natural environments such as fabrics. This raises the question of whether there are other aspects of cell biology that may also be better. understood when put in the context of a cell’s natural habitat, eg cell-cell communication strategies.
The article is titled “Localization of signaling receptors maximizes the acquisition of cellular information in spatially structured natural environments”. Funding was provided by the Heritage Medical Research Institute and the David and Lucile Packard Foundation.
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