Scientists simulate cell migration to understand how cancer cells move through tissues

During mesenchymal cell migration, cancer cells move
around the wall like geckos.

Physicist Nadir Kaplan explains that before choosing this mode of movement, the cell determines the size of
the surface it may adhere to.
If the surface is not too hard or too soft, and the forward path is not too tight, round cells will quickly grow protrusions, like temporary limbs, sticking forward and sticking to the surface
.
The cell then pulls itself forward and then retracts its posterior, repeating the process
.

This migration pattern is a way
for cancer cells to move through tissues during metastasis.
In one study, researchers explored mesenchymal migration
through cell simulation and mathematical modeling.
Their goal: to learn more about how cancer cells assess the hardness of surrounding tissue and adapt to their gecko-like movements in response
.

Wenya Shu says the model and its insights into mesenchymal migration are the first step
in understanding how cancer cells migrate as a whole.
Cell migration is complex: cells use multiple migration patterns, including single and colonies
.
"That's the advantage of coming up with computational models here," he said
.
"We can dissect the impact
of many ingredients at work.
"

Experiments have shown that during mesenchymal cell migration, cells adjust the way they navigate in tissues based on hardness: they are attracted to tissue surfaces or substrates that are neither too hard nor too soft
.
Cells can't grow and efficiently attach their protrusions to a substrate that is too hard, and if cells grab tissue that is too soft, they end up pulling it back into the body instead of using it to propel themselves forward
.
Cell mimicry by Shu and Kaplan supports these experimental findings
.

Their simulations confirmed to the researchers that cells distinguish soft and hard surfaces
by comparing the physical properties of their own soft tissues to soft surfaces.
The nature of the matrix material will affect the orientation of the cells and how efficiently
they move.

To ensure that the model accurately mimics the migration of cancer cells, Kaplan and Shu constructed not only how cells respond to tissue substrate mechanics, but also how cells adjust internal biochemical signals
.
As tissues navigate, cells may also chemically react
to secretions from nutrient sources in the body.
Shu said the researchers' model is the first to simulate how these two drivers of cell movement work
.

The researchers found that regardless of whether the overall movement was effective or not, cells preferred to move
in directions determined by their strong internal chemical signals.
But without a strong chemical signal to track, they focus on the nature of
the substrate.

By piecing together these elements of mesenchymal cell migration and reproducing them in a single model, Kaplan sees progress
in better understanding and determining how and where metastasis occurs.

Metastasis may also involve multiple patterns of cell migration
.
Mesenchymal migration tends to be the initial migration pattern through tissue and into blood vessels, but cells often turn to amoeba migration
.
The cells move like geckos in the former mode, while the latter makes them more like tank tracks
.
"They're just rolling forward," Kaplan said
.

Kaplan said chemotherapy is effective against cancer cells in mesenchymal cell migration, but not so well when cells turn to amoeba migration
.
In order for experimenters to understand this transition, they first need to have a better grasp of mesenchymal patterns
.

Next, Shu and Kaplan hope to use this model to study how cell-to-cell interactions affect migration, as individual cells collide with each other and trigger a change
in their orientation.
They also want to understand how cells move through more curved, narrower channels
in the microenvironment.

Each effort to more closely mimic cell migration brought the team closer to understanding how cancer cells invade the body
.
"We wanted to come up with a predictive model that could generate new types of qualitative behavior that would explain more measurements and inspire new experiments
," Kaplan said.

"The experiments are fairly comprehensive, but they clearly benefit from simulation
.
For example, when it comes to addressing very small timescales in the dynamics of these cell deformities
.
We're basically identifying all of these ingredients
.
”