Your cells have to move. For example, immune cells must roam your body to locate sites of infection, and neurons must migrate to specific locations 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 smells, a cell figures out how to get to a destination by recognizing chemicals in its environment through receptors spread across the cell’s surface. For example, the site of an infection sheds certain molecules, and a white blood cell follows this trail of signals to find its source.
Understanding how cells migrate by reading signals in their environment is a fundamental part of understanding how living systems work, from immune cells in the human body to unicellular organisms that live in soil. New work from the lab of Caltech’s Matt Thomson, assistant professor of computational biology and investigator at the Heritage Medical Research Institute, provides new insights into how cells migrate and respond to information in their environment. The research is detailed in an article appearing in the journal cell systems on June 8th.
Biologists have traditionally understood the process of cell migration using 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) that gently decreases with increasing distance from the source. For example, imagine dropping a drop of dye into water. The water in close proximity to where the dye is placed would turn bright; With distance from this source, the intensity of the color would gradually decrease.
But this simple model doesn’t really depict what the chaotic, complex environment looks like in living tissues.
“If you want to manipulate cells to do a job in the body for biomedical applications — like killing tumors — that cell needs to know how to deal with real-world environments, not just the simplified environment of a lab bowl,” says the Graduate student Zitong Jerry Wang, the first author of the study.
In tissues, cells move through an intricate network of proteins called the extracellular matrix (ECM). Here, chemical signals don’t just float freely—they attach to the ECM itself, creating a signal environment that doesn’t look like a smooth gradient, but more like a blotchy, network-like jumble of clustered molecules.
How do cells localize the source of signaling molecules to navigate the real, chaotic environment within 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 although the cell can discover an area of relatively high signal concentration, it cannot move away from this local maximum to approach the actual signal source Find. In other words, the cell gets stuck in local spots with high concentrations but can’t really get where it needs to go. For example, imagine trying to climb a mountain by only moving uphill – you might get stuck on the top of a smaller intermediate hill because in a real mountainous environment you might have to descend in certain areas to get the highest one to reach summit.
To understand how cells cope, the team was motivated by experimental observations in yeast cells showing that when the cells sense pheromones, they rearrange the receptors on their surfaces so that more receptors are near areas of high signal concentration to be placed. The team was also intrigued by the fact that dynamic receptor rearrangement has 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 wave their antennae (which contain scent receptors) through space They move, which greatly enhances their ability to navigate to the source of spotty odor plumes.
With this in mind, the team developed a computational model in which cell 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. After this receptor optimization, cellular navigation was 30-fold more efficient, and the model closely matched the actual cellular behavior observed in the tissue. Although receptor rearrangement has been observed in myriad systems, this work is the first to show that it plays a crucial, functional role in cell navigation.
“In a forthcoming publication, we describe how the receptor redistribution mechanism we modeled precisely implements a so-called Bayesian filter, which is a well-known target-tracking algorithm that is actively used in robotics today,” explains Wang. “So cells in our body could actually use a similar algorithm to navigate as autonomous vehicles like self-driving cars do.”
The new model is crucial for understanding real cellular systems relevant to human health. “For a long time, people couldn’t really image tissue, so it was unknown what the tissue environment looked like,” says Wang. “Researchers would take cells from the body and study how they move in a laboratory dish using gently diffusing gradients of signals emitted by a pipette. But now we know that this is really not what is happening in the real environment, which is patchy. This work has inspired us to actually start a collaboration with doctors to image more tissue samples to better understand them in vivo Vicinity.”
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 structured in space and time, meaning that it varies over time and space due to statistical regularities inherent in natural stimuli,” says Wang. “Neuroscientists have found that neural sensory processing systems such as retinal processing and auditory processing have become adapted to the statistical property of the signals they are exposed to – the visual or auditory signal in the animal’s natural environment.”
“We know that a cell also lives in a spatially structured environment, so we first built statistical models of natural cell environments in both soil and tissue from image data and simulations, and then used information theory to ask how the sensory processing system works.” cell functions in case, distribution of receptors – is related to the statistical structure of the cell environment. We were surprised that this general principle from neuroscience also applies at the level of individual cells, in particular receptor distributions found on cells improve information uptake in of nature drastically environments. In addition, we show the same connectivity measures for cell navigation. Adaptive rearrangement of receptors observed on cells significantly improves cell navigation, but only in natural environments such as tissue. This raises the question of whether there are other aspects of cell biology that can also be better understood when placed in the context of a cell’s natural habitat, for example le strategies of cell-cell communication.”
The paper is titled “Localization of Signaling Receptors Maximizes Cellular Information Acquisition in Spatially Structured Natural Environments.” Funding came from the Heritage Medical Research Institute and the David and Lucile Packard Foundation.