Waddington’s famous drawing of the epigenetic landscape has been used for more than 60 years to visually describe the differentiation of cells. Now, a group has sought to move beyond a qualitative conceptualization of the process toward a more quantitative, and predictive, model.
To do that, a team led by Jonathan Weissman, PhD, professor of biology at the Massachusetts Institute of Technology (MIT), investigator of the Howard Hughes Medical Institute, and Whitehead Institute member, has built a machine learning framework that can define the mathematical equations describing a cell’s trajectory from one state to another—such as its development from a stem cell into one of several different types of mature cell.
The framework, called dynamo, can also be used to determine the underlying mechanisms driving changes in the cell. Dynamo moves beyond descriptive and statistical analyses of single-cell sequencing data to derive a predictive theory of cell fate transitions. The dynamo toolset can provide deep insights into how cells change over time, hopefully making cells’ trajectories predictable. Researchers could use these insights to manipulate cells into taking one path instead of another—a common goal in biomedical research and regenerative medicine.
The work is published in Cell in the paper, “Mapping Transcriptomic Vector Fields of Single Cells.”
“Our goal is to move towards a more quantitative version of single-cell biology,” Xiaojie Qiu, PhD, a postdoc in the Weissman lab, said. “We want to be able to map how a cell changes in relation to the interplay of regulatory genes as accurately as an astronomer can chart a planet’s movement in relation to gravity, and then we want to understand and be able to control those changes.”
Dynamouses data from many individual cells to derive its equations, relying on scRNA-seq data. Researchers use the starting levels of RNA, and how those RNA levels are changing, to predict the path of the cell. However, calculating changes in the amount of RNA from single-cell sequencing data is challenging, because sequencing only measures RNA once. Researchers must then use RNA velocity and metabolic labeling to estimate changing RNA levels.
The researchers’ next challenge was to move from observing cells at discrete points in time to a continuous picture of how cells change. The group used machine learning to reveal continuous functions that define these spaces.
“There have been tremendous advances in methods for broadly profiling transcriptomes and other ‘omic’ information with single-cell resolution. The analytical tools for exploring these data, however, to date have been descriptive instead of predictive. With a continuous function, you can start to do things that weren’t possible with just accurately sampled cells at different states. For example, you can ask: if I changed one transcription factor, how is it going to change the expression of the other genes?” said Weissman.
Dynamo can visualize these functions by turning them into math-based maps. The terrain of each map is determined by factors like the relative expression of key genes. A cell’s starting place on the map is determined by its current gene expression dynamics. Once you know where the cell starts, you can trace the path from that spot to find out where the cell will end up.
The researchers confirmed dynamo’s cell fate predictions by testing it against cloned cells—cells that share the same genetics and ancestry. One of two nearly-identical clones would be sequenced while the other clone differentiated. Dynamo’s predictions for what would have happened to each sequenced cell matched what happened to its clone.
With a continuous function for a cell’s path over time determined, dynamo can then gain insights into the underlying biological mechanisms. The researchers tested their tools on blood cells, which have a large and branching differentiation tree. They found that dynamoaccurately mapped blood cell differentiation and confirmed a recent finding that one type of blood cell, megakaryocytes, forms earlier than others. Dynamo also discovered the mechanism behind this early differentiation: the gene that drives megakaryocyte differentiation, FLI1, can self-activate, and because of this is present at relatively high levels early on in progenitor cells. This predisposes the progenitors to differentiate into megakaryocytes first.
The researchers hope that dynamowill not only help them understand how cells transition from one state to another, but also guide researchers in controlling this. To this end, dynamo includes tools to simulate how cells will change based on different manipulations, and a method to find the most efficient path from one cell state to another.
These tools provide a powerful framework for researchers to predict how to optimally reprogram any cell type to another, a fundamental challenge in stem cell biology and regenerative medicine, as well as to generate hypotheses of how other genetic changes will alter cells’ fate.