We use a variety of microscopy methods, among them coherent Raman and nonlinear fluorescence, to study fundamental processes in soft matter systems: from force transduction in cells and materials, to subtle biochemical modifications in metabolic disorders, such as:
1) LIPID CHEMISTRY IN OXIDATIVE AND ADIPOSE TISSUES AS RELATED TO OBESITY
2) GRANULE FORMATION IN RELATION TO NEURODEGENERATION
3) MECHANO-CHEMICAL CONTROL OF CELLS AND BIOMATERIALS IN CANCER
Mechano-chemistry in biomaterial networks
Biomaterials can be used to mimic the way the extracellular matrix (ECM) hosts cells in the body. Cells and the ECM have a reciprocal relationship via biochemical and biomechanical pathways that influence their respective behaviors.
In this project, we investigate how mechanical inputs in protein-dense ECMs and related networks are coupled with molecular transitions in constituent biomolecules.
Lipid chemistry in health and disease
Lipids are critical component of cells and tissues, and are the basic molecules in barriers for cellular entry and into many intracellular compartments. These molecules are often packaged into lipid droplets (LDs) that are incredibly dense energy stores. Until recently have we begun to study the metabolic function of these LDs; however, significant research in the last decade has hinted toward a substantial impact of LDs in pathological transformation in diabetes, liver disease, and neurodegeneration.
We are developing tools to quantify lipid chemsitry and morphology in situ.
Liquid-liquid phase separation in cells
Liquid-liquid phase separation (LLPS) is a classical thermodynamic phenomenon that can be easily recreated watching oil and vinegar separate in salad dressing, and is ubiquitous in biology -for both normal and pathological conditions.
The goal of this topic is to explore how the dynamic molecular interactions between proteins and nuclei acids underlie LLPS in cells.
Microscopy instrumentation and image processing
Super resolution microscopy (and the chemistry that made it possible) is arguably the most fundamental breakthrough in microscopy technology of our era. It allows scientists to overcome the Abbe resolution limit, making it possible to resolve smaller objects.
Our efforts involve improving contrast and resolution of chemical microscopy and trying to find ways to focus on what is "important" using machine learning.