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For years, scientists believed this organization depended mainly on chemical signals exchanged between cells. However, these signals lose strength over long distances, which is a problem for large structures like the developing brain.
Researchers at Cold Spring Harbor Laboratory have therefore explored a different lead. They wondered if the family history of cells, or their lineage, played a key role in their placement. The idea is based on the fact that cells from the same ancestor tend to remain neighbors through divisions, somewhat like family members often settle in the same region.
To test this hypothesis, the team first designed a mathematical model. This model shows how organized structures can emerge simply from kinship relationships between cells, without requiring long-range communication. Subsequently, they examined gene expression in developing mouse brains.
The observations on mice were complemented by experiments on zebrafish, confirming that the model applies to brains of different sizes. The results indicate that chemical signals and lineage-related mechanisms act together to guide cells to their correct location.
This discovery could have implications beyond the field of neuroscience. It could teach us about the growth of other tissues, such as certain tumors, where cells multiply in a disorganized manner. Furthermore, it inspires the design of artificial intelligence systems capable of self-replication and transmitting information from one generation to the next.
Neuroscientists tracked patterns of gene expression in two adjacent regions of the zebrafish brain, colored red and blue.
Credit: Zador lab/CSHL
The way such an elaborate structure as the brain builds itself from a single cell also offers research leads on the origin of intelligence.
The role of cell lineage in tissue organization
The notion of cell lineage refers to the kinship relationship between cells derived from the same original, or progenitor, cell. During development, each cell division creates daughter cells that inherit not only genetic material but also, according to this new theory, positional information linked to their ancestry.
Unlike chemical signals that must travel through tissue, this lineage-based positional information is intrinsic to the cells. It emerges from patterns of division and migration: cells from the same lineage have a high probability of remaining spatially close to each other as the organ develops. This mechanism allows for large-scale organization without requiring detailed central coordination.
This perspective transforms our understanding of morphogenesis, the process by which tissues take their shape. It indicates that simple rules of kinship and proximity can generate highly ordered and functional structures, like the brain, from a very simple initial state.
Biological inspirations for artificial intelligence
The principles discovered in brain development, such as information transmission through lineage, open research avenues for artificial intelligence. In particular, they interest the field of self-replicating and evolutionary systems, where artificial agents must multiply and organize autonomously.
In these systems, the idea is to incorporate a mechanism where one 'generation' of agents transmits not only data but also a context or functional 'position' to the next generation. This could allow networks of agents to develop detailed and specialized organization without explicit programming for each individual.
This approach, called bio-inspired, seeks to imitate efficient processes observed in nature. It contrasts with traditional AI methods that often rely on a fixed architecture and centralized decision-making.
By studying how the brain achieves this feat, computer scientists can design more robust and adaptive algorithms. These systems could, ultimately, better manage tasks in changing or unknown environments by inheriting and adapting the 'knowledge' of their predecessors in a distributed manner.