Building a virtual 4D cell

Putting biology in place

At the core of creating the virtual cell is understanding not just how proteins are shaped, but where they are located within the cell. For a long time, scientists operated with a tacit assumption that a protein's shape has a one-to-one relationship with its job. This should mean that knowing a protein's structure is enough to tell us where and how it functions within the cell.

But it turns out that many proteins are more versatile than we first thought.

"One of the most exciting discoveries in our field is that more than half of human proteins are in multiple places in the cell," Lundberg explains. "If you think of the cell as a house, it would be like me moonlighting in both the kitchen and the laundry room at the same time, doing both of those different kinds of tasks. And if more than half of our proteins are capable of performing multiple functions, that really diversifies the functionality of proteome - and makes cells even more complex than we thought."

As if this 3D complexity weren't enough, especially when it comes to using virtual models to design and test better drugs, we also need to factor in a fourth dimension: time.

"A cell might react to a drug perturbation within a couple of minutes - or it could be days, or weeks, or a month," Lundberg says. "I'm so used to thinking about the spatial axis and how cells are spatially organized. But the temporal dimensions are even bigger than the spatial dimensions."

Bringing focus and clarity to cancer treatments

One especially pressing area for spatialomics is cancer drug development. Tumors are highly variable, not just from tumor to tumor but within each tumor itself. This variation in the tumor microenvironment leads to unpredictable clinical outcomes, including high failure rates during clinical trials.

The research team at the Danaher Beacon for Spatialomics is applying the latest in spatial biology with cutting-edge AI to create the next generation of smart microscopes, all with the aim of making more precise and more predictable cancer drugs. Lundberg, alongside microscopy experts at Leica Microsystems and using reagents from Abcam, is hoping to develop an analysis engine that can pick up on small but crucial changes in the tumor microenvironment, whether spatial, proteomic or metabolic.

"It's important to build these spatially resolved models of cells so we can understand where the functions are happening and which function is disrupted, ultimately so we can make more informed predictions about how those tumors will respond to potential therapies," says Lundberg. "It's an exciting and important application of spatial biology and structural cell modeling."

Data to AI and back again

The burgeoning field of AI-powered big data projects is prompting a fundamental shift in the relationship between AI and scientific data. Instead of collecting data and analyzing later - and perhaps realizing the data aren't suitable or are mismatched to the analysis tools - data collection and AI-driven analysis can inform one another.

"Maybe we start out doing in silico experiments and generate synthetic microscope images before we go out to the microscope and generate real images or predict how certain mutations would cause dysfunction of cells before we go and measure them," Lundberg offers. "It's closing the loop between data generation and data analysis so we can get meaningful results faster."

As the magnitude of our modeling challenges grows from folding proteins to creating virtual models of entire cells, so do the technical challenges - and the ways we think about bringing them together.

"Science is only becoming more complex in the spatial biology field," Lundberg says. "And I think this requires us to think outside of the box also in terms of how we do science."

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