Your cells have 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 the source of delicious scents, a cell figures out how to reach a target by detecting chemicals in its environment through receptors peppered 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 a fundamental part of understanding how living systems function, from immune cells in the human body to single-celled organisms living in soil. New work from the lab of Caltech’s Matt Thomson, assistant professor of computational biology and Heritage Medical Research Institute Investigator, offers new insights into how cells migrate and respond to information in their environment. The research is described in a paper appearing in the journal cell systems on June 8
Biologists have traditionally understood the process of cell migration with a simple model. In this model, a cell’s environment is represented as a gradient of signal concentrations, with a very high concentration coming from a source (such as the aforementioned example of infection) gradually decreasing further away from the source. For example, imagine dropping a drop of colored dye into water. The water in the immediate vicinity of where the dye is placed would become brightly colored; with distance from that source, the color would gradually decrease in intensity.
But this simple model doesn’t quite replicate what the messy, complex environment looks like in living tissues.
“If you want to develop cells to perform a task in the body for biomedical applications — such as killing tumors — that cell will need to know how to deal with real-world environments, not just the simplistic environment of a lab scale,” says graduate student Zitong Jerry Wang, the study’s lead author.
In tissues, cells move through a tangled web of proteins called the extracellular matrix (ECM). Here, chemical signals don’t just float freely — they stick to the ECM itself, creating a signaling environment that doesn’t look like a smooth gradient, but rather a patchy, network-like mess of clustered molecules.
How do cells locate the source of signaling molecules to navigate in the real, messy environment in tissues? The traditional gradient model of cell migration, where the cell smoothly follows its local signal concentration gradient, does not work in this realistic environment, because while the cell can detect a patch of relatively high signal concentration, it cannot deviate from that local maximum. to find the actual source of signals. In other words, the cell gets stuck in localized spots with high concentrations, but can’t really get where it needs to go. For example, imagine trying to climb a mountain just by going uphill – you might get stuck on the top of a smaller intermediate hill, because in a true mountainous environment you might have to descend in certain areas to reach the highest to reach the top.
To understand how cells cope with this, the team was motivated by experimental observations in yeast cells that showed that when the cells detect pheromones, they rearrange the receptors on their surface so that more receptors are placed near areas of 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 such as T cells and neurons can rearrange their receptors, and even grasshoppers actively sweep their antennae (which contain odor receptors) across space as they move. moving, greatly improving their ability to navigate to the source of patchy odor plumes.
With this in mind, the team developed a computer model in which cellular receptors can actively redistribute themselves 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 can find the real signal source. After this receptor optimization, cellular navigation was 30 times more efficient and the model closely matched the actual cellular behavior observed in tissue. Although receptor rearrangement has been observed in numerous systems, this work is the first to demonstrate that it plays a critical, functional role in cell navigation.
“In an upcoming paper, we’ll describe exactly how the receptor redistribution mechanism we modeled implements a so-called Bayesian filter, a well-known target-tracking algorithm actively used in robotics today,” explains Wang. “So cells in our bodies could use a similar algorithm for navigation as autonomous vehicles like self-driving cars.”
The new model is critical to understanding real cellular systems relevant to human health. “For a long time, people couldn’t really imagine themselves in tissues, so it was unknown what the tissue environment looked like,” Wang says. “Researchers would take cells out of the body and study how they move in a lab dish, with smooth diffuse gradients of signals released from a pipette. But now we know that’s not really what’s happening in the real environment, which is patchy. work has inspired us to actually partner with physicians to image more tissue samples to determine the in vivo surroundings.”
This research is particularly inspired by principles of neuroscience and how neurons process information about signals in their environment.
“The sensory information an organism receives in its natural environment is highly structured in time and space, meaning it varies in time and space due to statistical regularities inherent in natural stimuli,” Wang says. “Neurologists have found that neural sensory processing systems, such as retinal processing and auditory processing, are adapted to the statistical property of the signals they are exposed to — the visual or auditory cue in the animal’s natural environment.”
“We know that a cell also lives in a spatially structured environment, so we first constructed statistical models of natural cell environments in both soil and tissue based on both image data and simulation, and then used information theory to ask how the sensory A cell’s processing system – in this case, the distribution of receptors is related to the statistical structure of the cell’s environment.We were surprised to find that this general principle from neuroscience also applies to the scale of individual cells In particular, receptor distributions found on cells improve information acquisition in natural environments In addition, we show the same degree of association with cell navigation Adaptive rearrangement of receptors observed on cells significantly improves cell navigation, but only in natural environments such as tissue. the question of whether there are other aspects cts of cell biology that can also be better understood when placed in the context of a cell’s natural habitat, for example, and strategies of cell-cell communication.”
The article is titled “Localization of Signaling Receptors Maximizes Cellular Information Acquisition 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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