Detall

Conferenciant: Prof. Paul Campagnola (University of Wisconsin-Madison, USA).

Resum: Many human diseases including all cancers, fibroses, cardiovascular disease and connective tissue disorders are characterized by alterations in the collagen organization relative to normal tissues. Collagen has a highly defined hierarchical structure from the triple helical α-chains molecular structure to crosslinked fibrils (~50-100 nm) diameters to crosslinked fibers (~0.5 – 1 µm diameter).  We have developed Second Harmonic Generation (SHG) microscope tools to selectively and specifically probe all these levels of organization. First we present results for human ovarian cancer, which has a poor 5 year survival rate (~25%).

Using a novel form of 3D machine learning, we successfully classified six types of ovarian tumors based on the observed collagen fiber morphology.

[1]  We also exploit the SHG coherence to extract sub-resolution fibril assembly (size and packing) by analyzing the emission directionality in conjunction with measured optical scattering properties and Monte Carlo simulations. [2]  This approach further classified a spectrum of ovarian tumors based on fibril organization. Lastly we developed polarization sensitive SHG methods to extract collagen macro/supramolecular structural aspects (α-chain pitch and chirality) and found significant differences between normal and malignant ovarian tissues. [3] Collectively, this structural information provides insight into disease etiology and suggests future diagnostic approaches.  We also used this set of SHG analyses to probe structural changes in idiopathic pulmonary fibroses (IPF) and found analogous changes.