Cancer cells migrate through the body for many reasons; some are simply following the flow of a fluid, while others are actively following specific chemical trails. So how do you determine which cells are moving and why? Purdue University researchers reverse-engineered a cellular signal processing system and used it as a logic gate – a simple computer – to better understand what causes specific cells to migrate.
For many years, mechanical engineering professor Bumsoo Han and his research group have studied cancer cells. He builds microfluidic structures to simulate their biological environment; he even used these structures to build a “time machine” to reverse the growth of pancreatic cancer cells.
“In our experiments, we observe and study how these cancer cells migrate, because it’s an important aspect of cancer metastasis,” said Hye-ran Moon, a postdoctoral researcher on Han’s team. “But this is different. We’re trying to address the fundamental mechanisms behind these behaviors. And it’s very challenging because cells are very complex systems of molecules and are exposed to many signals that make them move.”
One such clue involves chemical trails, which many cells are inherently attracted to (much like ants following a scent trail). Another is fluid flow; if fluids are flowing around cells in a certain direction, many cells will simply follow the path. So if a cell is moving, how can you tell if it’s driven by chemicals, fluid movement, or both?
The team adopted a ternary gate model to analyze these cues and predict how cells would move in different environments. His research was published in Lab on a chipa journal of the Royal Society of Chemistry.
Their experiments took place in a microfluidic platform with a central cell chamber and two lateral platforms. Using this device, they could replicate fluidic flows in one direction, the opposite direction, or no flow at all. They could also introduce a chemical known to cause cells to migrate. Again, they had the option of chemotaxis in one direction, the opposite direction, or none at all. Would these two clues multiply or cancel each other out?
“With two clues and three options each, we had enough observable data to build a ternary logic gate model,” Moon said.
Logic gates are a construct of computing, where transistors take a 1 or 0 input and return a 1 or 0 output. Binary logic gates take a combination of two 1s and 0s and produce different results based on the type of gate. Ternary logic gates do the same thing, except with three possible inputs and outputs: 1, 0, and -1.
Moon assigned values in which direction the cells moved under the two different stimuli. “If the cells moved in the direction of flow, it’s 1,” Moon said. “If they have no directionality, it’s 0. If they move in the opposite direction to the flow, it’s -1.”
When cells encounter chemicals or fluid flow individually, they move in the positive direction (the “1”). When both were present in the same direction, the effect was additive (still “1”). However, when the two flowed in opposite directions, the cells moved in the direction of the chemicals (the “-1”) rather than the flow of the fluid.
Based on these observations, Moon extrapolated a 3×3 grid to simplify the results. The tracks from these cancer cells could now be diagrammed the way an electrical engineer would diagram a circuit.
Of course, the real world is never that simple. “Actually, the chemical stimulus is a gradient, not an on/off switch,” Moon said. “The cells will only move when a certain flow limit is introduced; and if you introduce too much, the cell will short-circuit and not move. The accuracy with which we can predict that movement is not a linear relationship.”
Moon also emphasized that this particular experiment is very simple: two stimuli, in strictly opposite directions, in a single dimension. The next step would be to build a similar experiment, but on a two-dimensional plane; and then another into a three-dimensional volume. And that’s just for starters; after adding multiple stimuli and factoring time as the 4th dimension, the calculations become incredibly complex. “Now you understand why biologists need to use supercomputers!” said Moon.
This study was in collaboration with the Purdue Institute for Cancer Research; the Weldon School of Biomedical Engineering; the Purdue Department of Physics and Astronomy; and Andrew Mugler and Soutick Saha of the Department of Physics and Astronomy at the University of Pittsburgh.
“This is a perfect example of how microfluidic devices can be used in cancer research,” Moon said. “Doing this experiment in a biological environment would be extremely difficult. But with these devices, we can go straight to individual cells and study their behavior in a controlled environment.”
“This model can apply to much more than just physical cancer cells,” continued Moon. “Any cell can be affected by different cues, and this provides a framework for researchers to study these influences and determine why they happen. Genetic engineers have also adopted the logic gate model, treating genes as processors that give different results when you gives them certain instructions. There are many branches we can go with this concept.”