spatial

By combining imaging mass spectrometry and a new computational framework called uMAIA, we tracked more than 100 types of lipids in space and time in the developing zebrafish embryo, revealing how lipids form highly organized patterns that correspond to anatomical structures. Published in Nature Methods.

Our collaborative work on Spotiflow, a deep learning method for subpixel-accurate spot detection in fluorescence microscopy, has been published in Nature Methods. The method achieves state-of-the-art performance while being robust to different noise conditions.

Using single-cell lipidomics our lab and the lab of Giovanni D’Angelo discovered the existence of lipotypes, cell states characterized by their lipid composition. We describe how lipid composition can play a key role in determining functional transitions. Science paper here

Our lab has developed an algorithm that can work out the spatial pattern of gene expression inside the body without the need for microscopes and complicated equipment used currently. Nature Biotech paper here