Probing the Mysteries of Pregnancy Loss — with Nanotubes

2024 Heising-Simons Faculty Fellow Markita Landry thinks nano-proteomics may predict miscarriage earlier than existing tests. 

Over 20% of all pregnancies end in miscarriages. Beyond basic factors like age and previous pregnancies, doctors cannot predict which patients are most likely to experience miscarriages, or which pregnancies are at highest risk of ending in the first few months. 

UC Berkeley scientist Markita Landry wants to change that. She is bringing a new kind of technology into the field of reproductive medicine: carbon nanotube-based sensors that can detect biochemical changes in the blood of IVF patients. With support from a Heising-Simons Faculty Fellows award, Landry is exploring how these carbon nanotube sensors could be used to identify biomarkers that predict whether a pregnancy will be sustained or lost — long before existing lab tests can provide any answers.

“There’s limited utility in simply confirming that a miscarriage is going to happen,” Landry says. “But if we can identify what’s causing that loss, or even predict it earlier in the IVF cycle, we could potentially intervene or avoid transferring an embryo that isn’t likely to succeed.”

Bringing Physics to Life Sciences

Landry grew up splitting time between Bolivia and Canada, speaking both Spanish and French. When she immigrated to the United States at age 14, she was drawn to science and engineering classes, where she could understand their universal vocabularies. 

Landry majored in physics and chemistry at the University of North Carolina, and then pursued a PhD in chemical physics at the University of Illinois. She initially studied the behavior of single molecules and carbon-based nanomaterials for energy storage applications. During a post-doctoral fellowship at MIT, Landry realized that some of what she was uncovering about these nanotubes — like their ability to change based on their surroundings— might make them more valuable as biological sensors than as batteries. 

“I discovered that how much light these nanotubes emitted depended on the molecules bound to their surfaces,” she explains. “That could make them really nice for imaging platforms and sensing.”

When she joined the faculty at Berkeley in 2016, Landry began building a lab that blended physics, chemistry, and engineering to tackle life science challenges. Her team has pioneered new ways to visualize neurotransmitters in the brain and deliver genetic material into plants. Much of her work relies on advanced microscopy and spectroscopy — tools that let researchers see how molecules behave at the nanoscale.

A New Tool for Reproductive Medicine

Landry’s preliminary findings when it comes to predicting miscarriage are already promising. Her group analyzed blood samples from nearly one thousand IVF patients — collected by collaborators at Cornell — using an array of carbon nanotube sensors. Landry’s lab has developed new spectrometry and artificial intelligence technologies to identify proteins on the surface of these nanotubes, and is implementing these technologies to identify new pregnancy loss biomarkers. 

The nanotubes themselves aren’t tuned to detect one specific molecule at a time. Instead, they act as a diverse sensor library: each probe has slightly different surface chemistry, and each responds differently to the surrounding mix of molecules in the blood. The nanotubes glow in near-infrared light, and the exact color or brightness of that glow shifts depending on which molecules they encounter. These subtle changes create distinct spectral “fingerprints” — like molecular barcodes — that can reveal the molecular makeup of a sample.

“To the naked eye, these spectra are indistinguishable,” says Landry. “But with enough samples, we can use proteomics to identify proteins on the sensors and use machine learning to identify the low abundance proteins likeliest to be predictive of pregnancy loss.”

Landry’s carbon nanotube-based sensors can predict miscarriage with greater than 80% accuracy, far surpassing current hormone-based measures like progesterone or estradiol levels. The current analysis is retrospective — using previously collected samples from IVF cycles — but the team hopes to eventually use it to guide clinical decisions in real time. 

The team has made good progress in figuring out what causes the differences in pregnancy outcomes. Why does blood from miscarriage-prone pregnancies react differently to the nanotubes? What biological molecules are interacting with the sensors? To this end, her lab has trained an AI program to identify relevant molecules based on lists of all proteins bound from blood samples of hundreds of women with known pregnancy outcomes.

With the support from the Heising-Simons Faculty Fellows program, Landry’s team is trying to pinpoint any proteins that are directly binding the nanotube surfaces. Then, they can carry out experiments to find out whether these proteins might underlie failed pregnancies. The ultimate goal is to identify a robust biomarker of early pregnancy loss or, even better, to test women’s blood samples before embryo transfer.

“It’s still a fundamental science project,” Landry says of the IVF work. “But there’s real clinical potential here. And what’s most rewarding is that students are excited about it — they’re learning how to navigate new fields, and contributing to something that could help people in a very tangible way.”