Skip to Content

Monitoring HIV on a Cheap Chip

A microfluidic chip could measure effectiveness of patient treatments in resource-poor countries.

Measuring viral load, or the concentration of HIV in the bloodstream, is one of the techniques that physicians use to monitor the effectiveness of HIV treatments. A spike in viral load can be a warning of drug failure or drug resistance, possibly indicating that the patient should be switched to a different drug. But in resource-poor settings, such monitoring is prohibitively expensive and equipment-heavy. A new microfluidic chip designed by the lab of Rustem Ismagilov at Caltech may make it possible to monitor viral load in HIV and other viral infections more cheaply and easily, and the technique could also be useful for other kinds of genetic tests.

Slip and slide: The microfluidic device shown here, called a SlipChip, contains two slides imprinted with wells of varying volumes, making it possible to measure molecules at a wide range of concentrations. Here, one chip is used to analyze five different samples, represented by different colors.

Viral load is often measured with PCR, a standard laboratory tool that copies the DNA or RNA in a sample many times. A newer approach, called digital PCR, makes it possible to get much more precise counts. Using microfluidics, the sample is first divided among a multitude of tiny wells, so that each well is likely to hold no more than one molecule. When the molecules are then amplified, the result is a simple yes-or-no signal for each well.

“The bottleneck of those methods comes when you need a measurement with a large dynamic range,” Ismagilov says. HIV viral load, for example, can range from 50 to a million molecules per milliliter. A test to measure it must be able to handle large numbers of molecules, yet be sensitive enough count rare molecules. Normally, achieving such sensitivity requires diluting a sample and spreading it out over more and more wells in order to ensure that no more than one molecule is in each well. Ismagilov says that such large numbers of wells can be cumbersome to analyze. At the same time, the sample can’t be spread so thin that scarce molecules will be missed.

Ismagilov and his lab members came up with a trick to handle this dilemma: divide the sample into a series of different-sized wells calibrated to detect molecules at different concentrations, which can be calculated together. “Each volume is sensitive to a particular concentration range,” he says. “Together these volumes provide more information than any one volume individually.”

The technique relies on the SlipChip, a simple microfluidic device developed by Ismagilov. Two overlapping glass or plastic slides can be injected with a fluid sample and then rotated slightly to separate the fluid into the wells. The rotation can also bring certain wells into contact so that chemical reactions can be performed.

In two recent papers in Analytical Chemistry and the Journal of the American Chemical Society, Ismagilov and his colleagues describe the mathematics of the design and its application in testing viral load in both HIV and hepatitis C. The chips can be designed to perform multiple tests or measure multiple samples, which Ismagilov says adds to their flexibility. Currently, other devices are needed for other stages of PCR preparation and analysis, but the researchers’ ultimate goal is for one chip to handle all these steps.

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

Google’s Gemini is now in everything. Here’s how you can try it out.

Gmail, Docs, and more will now come with Gemini baked in. But Europeans will have to wait before they can download the app.

This baby with a head camera helped teach an AI how kids learn language

A neural network trained on the experiences of a single young child managed to learn one of the core components of language: how to match words to the objects they represent.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.