UCLA-Led Consortium Launches Billion Cell×Cell Project to Decode Cellular Communication, Published in Nature Biotechnology
核心洞察
The Billion Cell×Cell Project, published in Nature Biotechnology, aims to generate nearly 1 billion measurements of controlled interactions between pairs of human cells to map cellular communication systematically.
Led by Dr. Dino Di Carlo at UCLA with collaborators from USC and Caltech, the initiative will unfold in three stages: mapping gene expression changes, identifying molecular drivers, and connecting interactions to functional outcomes.
The project leverages nanovial technology—a "lab-on-a-particle" platform—enabling researchers to capture defined cell pairs and profile their interactions using widely available tools like flow cytometry and single-cell sequencing.
A consortium led by UCLA, in collaboration with USC and Caltech, has issued a call to the scientific community to join the Billion Cell×Cell Project—an ambitious initiative to systematically map how individual pairs of human cells influence one another. The perspective, published in Nature Biotechnology, outlines a framework for generating nearly 1 billion measurements of controlled cell-cell interactions, with the goal of creating a foundational resource for understanding cellular communication in health and disease.
The project is spearheaded by Dr. Dino Di Carlo, the Armond and Elena Hairapetian Professor and Chair of Bioengineering at the UCLA Samueli School of Engineering and member of the Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at UCLA.
"You have many different cells playing different parts," Di Carlo said. "A healthy tissue emerges when those parts are coordinated—when cells listen and respond to one another in the right way."
The Gap in Current Single-Cell Science
Over the past decade, single-cell technologies and spatial biology have transformed researchers' ability to catalog cell types and map their locations within tissues. However, these approaches leave a critical gap: they cannot reveal which cell caused another to change, when that influence occurred, or whether the interaction produced a meaningful biological outcome.
"You can look at a tissue and identify all the players that are involved," Di Carlo explained. "But that's only a snapshot."
The Billion Cell×Cell Project addresses this limitation by focusing on the cellular dyad—a controlled interaction between two individual cells. Unlike standard co-cultures that blur individual encounters, the dyad approach allows scientists to determine which cell influenced which partner, when the interaction began, and what changed as a result.
Nanovial Technology Enables Scalable Mapping
A key enabling technology for the project is the nanovial, a "lab-on-a-particle" platform developed in part by Di Carlo's lab at UCLA. These tiny hydrogel particles can capture individual cells—or defined pairs of interacting cells—in self-contained microenvironments. Researchers can bring two cells together, control when their interaction begins, measure what they secrete, and profile how gene activity changes, all while remaining compatible with widely used tools such as flow cytometry, cell sorting, and single-cell sequencing.
"If we want this to be scalable, we want any lab with standard tools to be able to contribute," Di Carlo said. "The goal is to make this something the broader scientific community can build together."
Three-Stage Implementation
The initiative is designed to unfold in three stages. The first will map how defined cell pairs alter one another's gene expression. The second will add genetic and biochemical perturbations to identify the molecules and pathways that drive those changes. The third will connect molecular interactions to functional outcomes.
Together, these layers could help scientists understand cell-cell communication in sufficient detail to engineer it more precisely.
Therapeutic Implications
The knowledge generated by the project could directly inform the design of emerging therapies that already work by altering how cells recognize, influence, or attack one another—including CAR T cells (搜索), bispecific antibodies (搜索), engineered T cell receptors, and checkpoint inhibitors (搜索).
"Many of today's most exciting therapeutic strategies act at the interface between cells," said Dr. Owen Witte, founding director emeritus of the Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at UCLA and co-director of the UCLA Parker Institute for Cancer (搜索) Immunotherapy. "A deeper map of those interactions could help us design therapies that redirect cellular communication with far greater precision."
Disease Relevance
When cellular signals are misheard or fall out of sync, the consequences can be severe. In fibrosis (搜索), misfiring messages drive cells into scar-producing overdrive, stiffening lungs, hearts, and kidneys. In cancer (搜索), tumor cells can distort molecular signals to suppress or misdirect immune attack.
A Community-Driven Vision
The long-term vision for the project extends to training computational models—sometimes described as virtual cells or virtual tissues—that simulate how cells behave together. Such models could eventually allow scientists to test therapeutic ideas in silico before moving into laboratory experiments, potentially making drug discovery faster and less expensive while reducing reliance on animal models.
The team has launched cellxcell.org as a hub for updates and opportunities for others to get involved. The project will require collaboration among biologists, engineers, computational scientists, clinicians, and drug developers.
"For years we have been listening to cells play their lone melodies," Di Carlo said. "Now we want to understand how cells play off one another to create the whole symphony."
The work was supported by the Chan Zuckerberg Initiative (搜索). The perspective was shaped in part by joint activities, discussions, and technology demonstrations enabled through the CZI-supported consortium and the Cell-Cell Symposium convened on April 16, 2025, at UCLA. Additional authors include Heather J. Wright, Mohamad Abedi, Sean Yamada-Hunter, Jason Zhang, Keriann Backus, Thomas Rando, and John K. Lee from UCLA; Leonardo Morsut, Megan L. McCain, Eunji Chung, and Yingxiao Wang from USC; and Long Cai, Matt Thomson, and Michael Elowitz from Caltech.