Research

Condensates, membrane-less subcellular assemblies including nucleoli, nuclear speckles, and Cajal bodies, provide a fundamental mechanism of subcellular organization; they are involved in RNP complex assembly, gene regulation, stress response, and viral replication. Aberrant condensate formation is linked to neurodegenerative diseases and cancer. Understanding the rules of condensate formation will enable novel ways to modulate cell behavior and disease progression. A primary protein or RNA sequence must somehow encode its ability to form condensates – what specific sequence features confer this property? We recently developed CondenSeq, a high-throughput pooled imaging-based approach to efficiently characterize the propensities of thousands of protein sequences to form condensates in a single experiment. We are leveraging and building on this approach to systematically decipher the condensate code. Combining the resulting large-scale datasets with computational approaches, we are developing predictive models of condensates.

The structures of RNA molecules and their interactions with proteins are critical to processes such as gene regulation, splicing, and translation, and aberrant changes in these properties underpin many diseases. For most biological questions, we need to understand structure — spanning multiple length scales — in the cellular environment, which can deviate substantially from in vitro structure. Even within a cell, structures often vary between different subcellular compartments. We are developing new methods to predict and characterize RNA and RNP structure and function in cells, across subcellular compartments.