Select your localized edition:

Close ×

More Ways to Connect

Discover one of our 28 local entrepreneurial communities »

Be the first to know as we launch in new countries and markets around the globe.

Interested in bringing MIT Technology Review to your local market?

MIT Technology ReviewMIT Technology Review - logo


Unsupported browser: Your browser does not meet modern web standards. See how it scores »

{ action.text }

TR: When researchers talk about single-cell analysis, they often emphasize that the activities of individual cells within the same tissue are unique, and that averaging them together provides a blurry picture. How do you deal with this complexity and translate information about single cells into something that’s clinically useful?

LH: What has become increasingly clear with the advent of single-cell studies in higher organisms is that the responses are enormously heterogeneous. In fact, when we look at large populations of cells and average their signals together, we often miss some of the most definitive features of these cells’ responses.

One of the fundamental questions in doing single-cell studies is whether each cell is utterly individually unique–whether, whatever measurements you take, each will be uniquely different from one another. Or, in fact, whether the cells do fall into discrete populations, discrete states. My own firm conviction is, when we learn how to do these studies properly, there will be discrete states we can look at. Knowing those states, and then reconstituting them to see how populations work–that’s going to give us deep insights into developmental, physiologic, and disease mechanisms. If, on the other hand, there aren’t discrete states, if there is a continuous distribution of variability, that will represent an interesting challenge.

TR: You’ve said that solutions to biological complexity will be applied to complex problems in other fields. Can you explain what you mean by this?

LH: Evolution has had four billion years to figure out really clever solutions for new materials, new chemistries, new types of molecular machines, even new approaches to computing. I think by studying living organisms and deducing the mechanisms that underlie these evolutionarily sculpted solutions to complexity, those solutions can be applied to other fields. A classic example is materials science. The spectrum of different materials that organisms have evolved to make is enormous.

TR: For the past several years, researchers at your institute have talked about a diagnostic “nanochip” that would detect markers of disease from all over the body. Can you update me on that project?

LH: What we’re interested in doing is developing strategies that will let us identify proteins in the blood that will permit us to interrogate the state of individual organs: the liver, the heart, the muscle–whatever you’d like to look at.

The basic idea is that the organ-specific proteins from, say, the liver will reflect the operation of the networks in the liver. So they’ll be at one set of concentrations for normal liver, and a different set of concentrations for a liver that has cancer or hepatitis or cirrhosis and other diseases. These blood fingerprints, then, are not assays for a disease; they’re assays for all disease. We’ve looked at two organ systems: the brain and the liver. We’ve certainly verified in general ways these principles.

2 comments. Share your thoughts »

Credit: Institute for Systems Biology

Tagged: Biomedicine, diagnostics, synthetic biology, nanomedicine, Q&A, medical diagnostics, single-cell analysis, systems biology

Reprints and Permissions | Send feedback to the editor

From the Archives


Introducing MIT Technology Review Insider.

Already a Magazine subscriber?

You're automatically an Insider. It's easy to activate or upgrade your account.

Activate Your Account

Become an Insider

It's the new way to subscribe. Get even more of the tech news, research, and discoveries you crave.

Sign Up

Learn More

Find out why MIT Technology Review Insider is for you and explore your options.

Show Me