One of the most enduring goals in regenerative medicine is deceptively simple: replace a person’s damaged or dying cells with healthy new ones grown in the laboratory.
Researchers at Harvard Medical School and around the world have made striking progress toward that goal, learning how to guide stem cells to become muscle, nerve, and other specialized cell types. In principle, those lab-grown cells could one day be used to repair injured tissues or slow the progression of disease.
In practice, however, only a small fraction of these advances has moved beyond the lab.
The difficulty lies in how hard it is to control the process by which stem cells develop. As cells mature, they respond to a sequence of chemical signals that tell them what to become. Those signals must arrive at the right moment and in the right amount. Small missteps can cause cells to stray from their intended path, leaving them immature, inconsistent, or unsuitable for use as therapy. Even protocols that work well in one lab can be difficult to reproduce elsewhere.
Researchers in the Blavatnik Institute at HMS co-founded the company Cellular Intelligence to address that challenge.
Building on large experimental datasets from developmental, systems, and computational biology, the company aims to build a foundation model—a large machine-learning system trained on experimental data—to improve two requirements for turning cell replacement therapies into viable treatments: predictability and scalability.
“For many cell therapies, the biology works but not robustly enough,” said scientific co-founder Allon Klein, professor of systems biology at HMS. “You can get the right cells once, but reproducing that result reliably and at scale is a very different problem.”
Once trained and validated, the machine-learning tool could reveal underlying rules that guide cell development—rules that researchers can use to predict how cells will behave under new conditions.
“Developmental biology already has an internal logic,” Klein said. “What we’re trying to do is understand that logic well enough to guide it.”
Exploring translation, step by step
The origins of Cellular Intelligence, formerly known as Somite AI, lie in decades of basic research.
Much of the early work took place at HMS before the team ever envisioned a company. At that stage, the focus was on building tools and asking whether they could shed light on fundamental questions about how cells develop.
As the scientific picture sharpened, Klein and colleagues—including scientific co-founders Olivier Pourquié, the HMS Frank Burr Mallory Professor of Pathology at Brigham and Women’s Hospital and professor of genetics at HMS, and Clifford Tabin, the George Jacob and Jacqueline Hazel Leder Professor of Genetics and head of the Department of Genetics at HMS—began to consider what it would take to develop the work further.
Some questions, such as about reproducibility, scale, and integration of experimental and computational approaches, were difficult to address within academic labs.
The Blavatnik Harvard Life Lab Longwood offered a practical next step. The incubator provides shared laboratory space and infrastructure for early-stage, Harvard-connected life science startups, allowing teams to organize work outside the academic setting while remaining close to ongoing research.
For Cellular Intelligence, that proximity mattered. In its earliest phase, frequent interaction between the scientific founders and the growing team helped clarify which ideas could translate and which would need to be rethought.
“For something this complex, being able to easily move back and forth mattered,” Klein said. “It helped us test ideas quickly.”