Fingerprints are one of the best-recognised examples of pattern formation by epithelial cells. The primary cells in the epithelium are the keratinocytes, and they are known to form patterns at the microscopic and macroscopic levels. While factors affecting this pattern formation have been reported, the exact mechanisms underlying the process are still not fully understood.

A team of researchers, led by Associate Professor Ken Natsuga at the Faculty of Medicine, Hokkaido University, have revealed that cell-cell adhesion governs pattern formation in keratinocytes. Their findings were published in the journal Life Science Alliance.

“In this study, we used an immortalized keratinocyte cell line, called HaCaT, which retains all the properties of normal keratinocytes,” Natsuga explained. “In order to ensure that our findings were accurate, we established single-cell cultures from this cell line.”

The team observed pattern formation in both the original heterogeneous cell line, as well as in single-cell-derived cultures. During culturing, the keratinocytes moved randomly and spontaneously formed high- and low-density regions, leading to pattern formation.

The pattern formation was markedly influenced by starvation. When the culture medium was renewed, patterns were obscured, but reappeared as the nutrients in the culture medium were consumed by the keratinocytes.

The team then examined the gene expression in the keratinocytes, which revealed that cell adhesion proteins and keratinocyte differentiation proteins were upregulated in high-density regions. “As cell adhesion is necessary for the development of high-cell-density regions, we specifically investigated the expression of adherens junction (AJ) molecules such as E-cadherin and actin,” Natsuga elaborated. “We found that these molecules were localized at the intercellular junctions of high-density regions.”

The authors then used a mathematical model to confirm that, under spatially uniform density and stress, strong cell adhesion leads to the formation of density patterns. They were also able to demonstrate that the keratinocyte patterns influenced cell proliferation and differentiation, and that serum starvation influences epidermal stratification (a type of differentiation) in skin cells from mice.

“Our study presents a novel and robust model of cell-cell adhesion-induced patterning (CAIP),” concludes Natsuga. “We have deepened our mechanistic insight into cellular organization and its consequences for cell fate decisions and epithelial stratification.” The team demonstrated that epithelial cell-cell adhesion is essential and sufficient for patterning. Future work will focus on adding more variables to the model to understand other processes that occur concurrently during development.

The work, led by scientists at the Department of Energy’s Pacific Northwest National Laboratory, published today in the journal Geophysical Research Letters.

Regulations put into effect in 2020 by the International Maritime Organization required a roughly 80 percent reduction in the sulfur content of shipping fuel used globally. That reduction meant fewer sulfur aerosols flowed into Earth’s atmosphere.

When ships burn fuel, sulfur dioxide flows into the atmosphere. Energized by sunlight, chemical intermingling in the atmosphere can spur the formation of sulfur aerosols. Sulfur emissions, a form of pollution, can cause acid rain. The change was made to improve air quality around ports.

In addition, water likes to condense on these tiny sulfate particles, ultimately forming linear clouds known as ship tracks, which tend to concentrate along maritime shipping routes. Sulfate can also contribute to forming other clouds after a ship has passed. Because of their brightness, these clouds are uniquely capable of cooling Earth’s surface by reflecting sunlight.

The authors used a machine learning approach to scan over a million satellite images and quantify the declining count of ship tracks, estimating a 25 to 50 percent reduction in visible tracks. Where the cloud count was down, the degree of warming was generally up.

Further work by the authors simulated the effects of the ship aerosols in three climate models and compared the cloud changes to observed cloud and temperature changes since 2020. Roughly half of the potential warming from the shipping emission changes materialized in just four years, according to the new work. In the near future, more warming is likely to follow as the climate response continues unfolding.

Many factors — from oscillating climate patterns to greenhouse gas concentrations — determine global temperature change. The authors note that changes in sulfur emissions aren’t the sole contributor to the record warming of 2023. The magnitude of warming is too significant to be attributed to the emissions change alone, according to their findings.

Due to their cooling properties, some aerosols mask a portion of the warming brought by greenhouse gas emissions. Though aerosols can travel great distances and impose a strong effect on Earth’s climate, they are much shorter-lived than greenhouse gasses.

When atmospheric aerosol concentrations suddenly dwindle, warming can spike. It’s difficult, however, to estimate just how much warming may come as a result. Aerosols are one of the most significant sources of uncertainty in climate projections.

“Cleaning up air quality faster than limiting greenhouse gas emissions may be accelerating climate change,” said Earth scientist Andrew Gettelman, who led the new work.

“As the world rapidly decarbonizes and dials down all anthropogenic emissions, sulfur included, it will become increasingly important to understand just what the magnitude of the climate response could be. Some changes could come quite quickly.”

The work also illustrates that real-world changes in temperature may result from changing ocean clouds, either incidentally with sulfur associated with ship exhaust, or with a deliberate climate intervention by adding aerosols back over the ocean. But lots of uncertainties remain. Better access to ship position and detailed emissions data, along with modeling that better captures potential feedback from the ocean, could help strengthen our understanding.

In addition to Gettelman, Earth scientist Matthew Christensen is also a PNNL author of the work. This work was funded in part by the National Oceanic and Atmospheric Administration.

The research team’s findings show that when decision makers quickly come to a conclusion, the decision is more influenced by their initial bias, or a tendency to err on the side of one of the choices presented. If decision makers wait to gather more information, the slower decision will be less biased. The work was published today in Physical Review E.

“The basic result might seem sort of intuitive, but the mathematics we had to employ to prove this was really non-trivial,” said co-author Bhargav Karamched, an assistant professor in the FSU Department of Mathematics and the Institute of Molecular Biophysics. “We saw that for the first decider in a group, the trajectory of their belief is almost a straight line. The last decider hovers around, going back and forth for a while before making a decision. Even though the underlying equation for each agent’s belief is the same except for their initial bias, the statistics and behavior of each individual is very different.”

The researchers built a mathematical model that represented a group of agents required to decide between two conclusions, one which was correct and one which was incorrect. The model assumed each actor within a group was acting rationally, that is, deciding based off their initial bias and the information they are presented, rather than being swayed by the decisions of individuals around them.

Even with evidence and assuming perfect rationality, bias toward a particular decision caused the earliest deciders in the model to make the wrong conclusion 50% of the time. The more information actors gathered, the more likely they were to behave as if they weren’t biased and to arrive at a correct conclusion.

Of course, in the real world, people are swayed by all sorts of inputs, such as their emotions, the decisions their friends made and other variables. This research offers a metric showing how individuals within a group should make decisions if they are acting rationally. Future research could compare real-world data against this metric to see where people are diverting from optimally rational choices and consider what might have caused their divergence.

The researchers’ model is known as a drift diffusion model, so called because it combines two concepts: individual actor’s tendency to “drift,” or move toward an outcome based on evidence, and the random “diffusion,” or variability of the information presented.

The work could be used, for example, to understand when people are being unduly swayed by early decisions or falling victim to groupthink. It even helps describe other complex scenarios with many individual actors, such as the immune system or the behavior of neurons.

“There is still a lot of work to do to understand decision making in more complicated situations, such as cases where more than two alternatives are presented as choices, but this is a good starting point,” Karamched said.

This research was a multi-institution collaboration involving doctoral candidate Samantha Linn and Associate Professor Sean D. Lawley of the University of Utah, Associate Professor Zachary P. Kilpatrick of the University of Colorado, and Professor Krešimir Josic of the University of Houston.

This research was supported by the National Science Foundation and the National Institutes of Health.