Mapping cells in time and space: a new tool reveals a detailed history of tumor growth

All life is connected in a vast family tree. Every organism exists in relationship to its ancestors, descendants, and cousins, and the path between any two individuals can be traced. The same is true of cells within organisms—each of the trillions of cells in the human body is produced through successive divisions from a fertilized egg, and can all be related to one another through a cellular family tree. In simpler organisms such as the worm C. elegans, this cellular family tree has been fully mapped, but the cellular family tree of a human is many times larger and more complex.

In the past, Whitehead Institute Member Jonathan Weissman and other researchers have developed lineage tracing methods to track and reconstruct the family trees of cell divisions in model organisms in order to understand more about the relationships between cells and how they assemble into tissues, organs, and—in some cases—tumors. These methods could help to answer many questions about how organisms develop and diseases like cancer are initiated and progress.

Now, Weissman and colleagues have developed an advanced lineage tracing tool that not only captures an accurate family tree of cell divisions, but also combines that with spatial information: identifying where each cell ends up within a tissue. The researchers used their tool, PEtracer, to observe the growth of metastatic tumors in mice. Combining lineage tracing and spatial data provided the researchers with a detailed view of how elements intrinsic to the cancer cells and from their environments influenced tumor growth, as Weissman and postdocs in his lab Luke Koblan, Kathryn Yost, and Pu Zheng, and graduate student William Colgan share in a paper published in the journal Science on July 24.

“Developing this tool required combining diverse skillsets through the sort of ambitious interdisciplinary collaboration that’s only possible at a place like Whitehead Institute,” says Weissman, who is also a professor of biology at the Massachusetts Institute of Technology and an HHMI Investigator. “Luke came in with an expertise in genetic engineering, Pu in imaging, Katie in cancer biology, and William in computation but the real key to their success was their ability to work together to build PEtracer.”

“Understanding how cells move in time and space is an important way to look at biology, and here we were able to see both of those things in high resolution. The idea is that by understanding both a cell’s past and where it ends up, you can see how different factors throughout its life influenced its behaviors. In this study we use these approaches to look at tumor growth, though in principle we can now begin to apply these tools to study other biology of interest like embryonic development,” Koblan says.

Designing a tool to track cells in space and time

PEtracer tracks cells’ lineages by repeatedly adding short, predetermined codes to the DNA of cells over time. Each piece of code, called a lineage tracing mark, is made up of 5 bases, the building blocks of DNA. These marks are inserted using a gene editing technology called prime editing, which directly rewrites stretches of DNA with minimal undesired byproducts. Over time, each cell acquires more lineage tracing marks, while also maintaining the marks of its ancestors. The researchers can then compare cells’ combinations of marks to figure out relationships and reconstruct the family tree.

“We used computational modeling to design the tool from first principles, to make sure that it was highly accurate, and compatible with imaging technology. We ran many simulations to land on the optimal parameters for a new lineage tracing tool, and then engineered our system to fit those parameters,” Colgan says.

When the tissue—in this case, a tumor growing in the lung of a mouse—had sufficiently grown, the researchers collected these tissues and used advanced imaging approaches to look at each cell’s lineage relationship to other cells via the lineage tracing marks, along with its spatial position within the imaged tissue and its identity (as determined by the levels of different RNAs expressed in each cell). PEtracer is compatible with both imaging approaches and sequencing methods that capture genetic information from single cells. 

“Making it possible to collect and analyze all of this data from the imaging was a large challenge,” Zheng says. “What’s particularly exciting to me is not just that we were able to collect terabytes of data, but that we designed the project to collect data that we knew we could use to answer important questions and drive biological discovery.”

Reconstructing the history of a tumor

Combining the lineage tracing, gene expression, and spatial data let the researchers understand how the tumor grew. They could tell how closely related neighboring cells are and compare their traits. Using this approach, the researchers found that the tumors they were analyzing were made up of four distinct modules, or neighborhoods, of cells.

The tumor cells closest to the lung, the most nutrient-dense region, were the most fit, meaning their lineage history indicated the highest rate of cell division over time. Fitness in cancer cells tends to correlate to how aggressively tumors will grow.

The cells at the “leading edge” of the tumor, the far side from the lung, were more diverse and not as fit. Below the leading edge was a low-oxygen neighborhood of cells that might once have been leading edge cells, now trapped in a less desirable spot. Between these cells and the lung-adjacent cells was the tumor core, a region with both living and dead cells as well as cellular debris.

The researchers found that cancer cells across the family tree were equally likely to end up in most of the regions, with the exception of the lung adjacent region, where a few branches of the family tree dominated. This suggests that the cancer cells’ differing traits were heavily influenced by their environments, or the conditions in their local neighborhoods, rather than their family history. Further evidence of this point was that expression of certain fitness-related genes, such as Fgf1/Fgfbp1, correlated to a cell’s location rather than its ancestry. However, lung adjacent cells also had inherited traits that gave them an edge, including expression of the fitness-related gene Cldn4­—showing that family history influenced outcomes as well.

These findings demonstrate how cancer growth is influenced both by factors intrinsic to certain lineages of cancer cells and by environmental factors that shape the behavior of cancer cells exposed to them.

“By looking at so many dimensions of the tumor in concert, we could gain insights that would not have been possible with a more limited view,” Yost says. “Being able to characterize different populations of cells within a tumor will enable researchers to develop therapies that target the most aggressive populations more effectively.” 

“Now that we’ve done the hard work of designing the tool, we’re excited to apply it to look at all sorts of questions in health and disease, in embryonic development, and across other model species, with an eye toward understanding important problems in human health,” Koblan says. “The data we collect will also be useful for training AI models of cellular behavior. We’re excited to share this technology with other researchers and see what we all can discover.”