Periodic Reporting for period 1 - CELLMATES (Uncovering the Diversity of Cell-Cell Interactions that Impact Cell Fates)
Période du rapport: 2023-01-01 au 2025-06-30
Multicellular life relies on coordinated physical interactions and communication between cells. In these interactions, cells exchange information that determines their fate, physiological state and behavior. The substantial transcriptional heterogeneity that is evident even among cells considered the same type, however, raises questions about exactly which temporal and spatial interactions regulate development and homeostasis. During development, for instance, multipotent neural crest cells migrate along different pathways in which they encounter microenvironments that are thought to instruct their migration and fate choices. To ultimately reveal how cell communication shapes cell identities and cellular decisions, we need to know the transcriptional state of a cell and that of its neighbors. Current technologies either sequence single cells at high transcriptional resolution or localize cells in their spatial context but without capturing their precise transcriptional configuration. To overcome these limitations, we will establish a novel approach that provides high-resolution transcriptomic information on cellular microenvironments. We will use this approach to uncover the interactions and communication codes that neural crest cells are exposed to along their developmental path. In addition, we will develop strategies to systematically identify cell-cell interactions likely to determine migration and cell fate decisions, and elucidate their functional role in the developing embryo. Our overall goal is to uncover how the diversity of cell interactions orchestrates the development of a complex cell lineage. This approach and the insights obtained here will provide a foundation for studying how cells interact in other systems such as human tissues, and how these interactions are altered in disease.
We have developed a molecular label that reliably marks cells belonging to the same tissue neighborhood for subsequent single-cell transcriptional profiling. We demonstrated that this labeling is both stable and robust, enabling the unambiguous assignment of cells to their respective neighborhoods during downstream data analysis. We now aim to integrate this labeling into a targeted methodology that enables the specific isolation and profiling of neighborhoods containing neural crest cells along with their interaction partners.
A patent application has been filed. Further work is needed to achieve proof of concept.