Statistical Bioinformatics Seminar: Scott Berry

Title: Learning consistent subcellular landmarks to quantify changes in multiplexed
protein maps 

Speaker: Dr Scott Berry (UNSW) 

Abstract: Highly multiplexed imaging holds enormous promise for understanding how
spatial context shapes the activity of the genome and its products at multiple length
scales.  We have recently developed a deep-learning framework called CAMPA (Conditional
Autoencoder for Multiplexed Pixel Analysis) to learn representations of molecular
pixel-profiles that are consistent across heterogeneous cell populations and
experimental perturbations.  CAMPA identifies consistent subcellular landmarks, which
can be quantitatively compared in terms of their sizes, shapes, molecular compositions,
and relative spatial organisation.  Using high-resolution multiplexed
immunofluorescence, this reveals how subcellular organisation changes upon perturbation
of RNA production, RNA processing, or cell size, and uncovers links between the
molecular composition of membraneless organelles and cell-to-cell variability in bulk
RNA synthesis rates.  By capturing interpretable cellular phenotypes, we anticipate that
CAMPA will accelerate the systematic mapping of multiscale atlases of biological
organisation to identify the rules by which context shapes physiology and disease.  

About the speaker: Scott has a background in Theoretical Physics and Molecular Biology.
He studied a PhD at the John Innes Centre in Norwich, UK, on mechanisms of epigenetic
memory in plants, before moving to the University of Zurich in Switzerland as an HFSP
and EMBO postdoctoral fellow.  In Zurich, Scott worked on mechanisms of mRNA
concentration homeostasis in mammalian cells and experimental and computational
approaches for acquiring and analysing highly multiplexed image data.  In 2021, Scott
started his group at Single Molecule Science, the EMBL Australia node at the University
of New South Wales in Sydney.  His group works on quantitative regulation of gene
expression at the single-cell level, primarily employing microscopy and systems biology
approaches – including mathematical modelling.