Data-Clustering Analysis of Scanning Ultrafast Acoustic Experiments: Revealing Acoustic and Structural Properties of a Motoneuron

Ultrafast acoustic imaging experiments are a powerful tool to investigate, at the nanometer scale, cell mechanical properties such as stiffness, viscosity, and adhesion, that play some roles in life death of cells. However, due complex structures, ultrafast signal analysis is quite intricate depends on multiple parameters. Complex data with poorly known parameters can be handled by clustering method already shown particle physics biology. In this work, tackled spectral followed hierarchical agglomerating method. Coupled conventional microscopy performed very same cell, clustered assigned inner-cell features nucleus, cytoplasm, cytoskeleton. The dependency thickness stiffness highlighted. Moreover, thanks improvement signal-to-noise ratio, nature adhesion also assessed through observation characterization polymerlike layer thin few nanometers.