New Technology Revolutionizes Single-Cell Analysis

Researchers now have the ability to observe millions of cells as they develop into organs like the pancreas, thanks to an innovative technique called Moscot ("Multi-Omics Single-Cell Optimal Transport"). Developed by an international research team led by Helmholtz Munich, this technique was recently featured in the prestigious journal Nature.

For years, scientists have struggled to understand how cells grow and interact in their natural environment, such as during organ formation in embryos.

“Existing methods offered only isolated snapshots of a few cells or couldn’t link dynamic processes across space and time. This has significantly limited our understanding of the complex interactions during organ development and disease,” 

Dominik Klein, lead author of the study and a PhD candidate at Helmholtz Munich.

Moscot represents a significant advancement, allowing scientists to study the development of entire organs and organisms with remarkable precision. Created by Klein in collaboration with an interdisciplinary team—including Zoe Piran (Hebrew University of Jerusalem), Marius Lange (ETH Zurich), Michal Klein (Apple), and Giovanni Palla (Helmholtz Munich)—the technique is rooted in optimal transport theory, an 18th-century concept that models efficient movement from one location to another while minimizing time, energy, or cost.

While optimal transport was previously limited in its application to biomedical datasets, advances in artificial intelligence, heavily influenced by co-author Marco Cuturi (Apple), have overcome these challenges.

“We’ve adapted our mathematical models to accurately capture the molecular information and position of cells in the body during their development,” Klein said. “Optimal transport theory helps us understand how cells move, change, and transition from one state to another.”

The result? Moscot can now track millions of cells with previously unimaginable accuracy, providing a dynamic, multimodal map of cellular development in spatial tissues. Moscot enables biologists to simultaneously observe spatial and temporal changes in gene expression, helping to unravel the mechanisms behind cellular decisions. This technology also provides a user-friendly interface that integrates complex algorithms to analyze massive datasets, making it accessible to a broader range of researchers.

For the first time, Moscot allows scientists to trace intricate cellular processes across entire living organs and organisms, mapping molecular states and their evolution in both space and time.

A notable application of Moscot has been in pancreas research. The team successfully used multimodal measurements to track the development of hormone-producing cells in the pancreas. These insights open new possibilities for understanding the root causes of diabetes and developing targeted therapies that address the disease at its core rather than just managing symptoms.

“This new perspective on cellular processes enables targeted therapies that address the fundamental causes of diseases,”

Heiko Lickert, co-last author of the study and professor at Helmholtz Munich.

Fabian Theis, Director of the Institute of Computational Biology at Helmholtz Munich and professor at TUM, emphasized Moscot’s transformative potential for biomedical research.

“Moscot is changing how we understand and use biological data,” Theis said. “It allows us to capture the dynamics of cell development in unprecedented detail and make precise predictions about disease progression, paving the way for personalized therapies.”

The project exemplifies the power of interdisciplinary collaboration, Theis added. “This successful integration of mathematics and biology demonstrates how critical cross-disciplinary teamwork is for achieving meaningful scientific progress. Through close cooperation with Heiko Lickert’s team at the Helmholtz Diabetes Center, we validated Moscot’s predictions in laboratory experiments.”

Moscot offers a glimpse into a future where we can better understand organ development and disease processes at an unparalleled level of detail. By bridging biology and mathematics, it paves the way for deeper insights into the mechanisms of health and disease, with the potential to transform how we approach diagnosis and therapy.