During early development, tissues and organs begin to blossom through the transfer, division, and growth of thousands of cells.
A team of MIT engineers has now developed a way to predict minute-by-minute how individual cells will fold, divide and rearrange during the early stages of fruit fly development. The new method may one day be applied to predict the development of more complex tissues, organs and organisms. It could also help scientists identify cell patterns that match early diseases like asthma and cancer.
In a study published today in the journal nature methodsThe team presents a new deep-learning model that learns, then predicts, how certain geometric properties of individual cells will change as a fruit fly develops. The model records and tracks properties such as a cell’s position, and whether it is touching a neighboring cell at a given time.
The team applied the model to videos of developing fruit fly embryos, each of which starts out as a cluster of about 5,000 cells. They found that the model could predict with 90 percent accuracy how each of the 5,000 cells would bend, shift and rearrange minute by minute during the first hour of development, as the embryo transforms from a smooth, uniform shape into more defined structures and features.
“This early stage, known as gastrulation, lasts for just over an hour, when individual cells rearrange on a time scale of minutes,” says study author Ming Guo, associate professor of mechanical engineering at MIT. “By accurately modeling this early period, we can begin to uncover how local cell interactions give rise to global tissues and organisms.”
The researchers hope the model can be applied to predict cell-by-cell development in other species, such as zebrafish and mice. Then, they can begin to identify patterns that are common across species. The team also envisages that the method could be used to understand early patterns of disease such as asthma. Lung tissue in people with asthma looks markedly different from healthy lung tissue. How asthma-prone tissue initially develops is an unknown process that the team’s new method could potentially reveal.
“Asthma tissues show distinct cell dynamics when imaged live,” says co-author and MIT graduate student Haiqian Yang. “We envision that our model can capture these subtle dynamic differences and provide a more comprehensive representation of tissue behavior, potentially improving diagnostic or drug-screening assays.”
Study co-authors are Marcus Buehler, the McAfee Professor of Engineering in MIT’s Department of Civil and Environmental Engineering; George Roy and Tomer Stern of the University of Michigan; and Anh Nguyen and Dapeng Bi of Northeastern University.
points and foam
Scientists typically model how an embryo develops in one of two ways: one as a point cloud, where each point represents an individual cell as a point that moves over time; or as a “foam”, which represents individual cells as bubbles that shift and slide against each other, similar to the bubbles in shaving foam.
Rather than choose between the two approaches, Guo and Yang adopted both.
“There is debate about whether to model as a point cloud or as foam,” says Yang. “But these are both different ways of modeling essentially the same underlying graph, which is a great way to represent living tissues. By combining these as a graph, we can uncover more structural information, such as how cells are connected to each other as they rearrange over time.”
At the heart of the new model is a “dual-graph” structure that represents a developing embryo as both dynamic points and bubbles. Through this dual representation, the researchers hoped to capture more detailed geometric properties of individual cells, such as the location of the cell’s nucleus, whether a cell is touching a neighboring cell, and whether it is turning or dividing at a given time.
As a proof of principle, the team trained the new model to “learn” how individual cells change over time during fruit fly gastrulation.
“The overall shape of the fruit fly at this stage is roughly an ellipsoid, but there are huge dynamics going on at the surface during gastrulation,” says Guo. “It goes from being completely smooth to forming multiple folds at different angles. And we want to predict all those dynamics, moment by moment and cell by cell.”
where and when
For their new study, the researchers applied the new model to high-quality videos of fruit fly gastrulation taken by their colleagues at the University of Michigan. The videos are one-hour recordings of the development of fruit flies, taken at single-cell resolution. In addition, the videos contain labels of the edges and nuclei of individual cells – data that is incredibly detailed and difficult to obtain.
“These videos are extremely high quality,” Yang says. “This data is very rare, where you get submicron resolution of an entire 3D volume at a fairly fast frame rate.”
The team trained the new model with data from three of four fruit fly embryo videos, so that the model could “learn” how individual cells interact and change as the embryo develops. They then tested the model on an entirely new fruit fly video, and found that it was able to predict with high accuracy how most of the embryo’s 5,000 cells changed from minute to minute.
Specifically, the model can predict the properties of individual cells with about 90 percent accuracy, such as whether they will fold, divide, or continue to share an edge with a neighboring cell.
“We predict not only whether these things will happen, but also when they will happen,” Guo says. “For example, will this cell break away from this cell seven minutes from now, or eight minutes from now? We can tell when that will happen.”
The team believes that, in principle, the new model and dual-graph approach should be able to predict cell-by-cell development of other multicellular systems, such as more complex species and even some human tissues and organs. The limiting factor is the availability of high quality video data.
“From a model perspective, I think it’s ready,” Guo says. “The real hurdle is data. If we have good quality data of specific tissues, models can be directly applied to predict the development of many more structures.”
This work is supported, in part, by the US National Institutes of Health.
