New approaches for investigating the causes and consequences of cellular heterogeneity - ABSTRACT My lab has focused on developing methods to understand the causes and consequences of biological heterogeneity. Examples of heterogeneity include differences between the traits of individuals in a population, between cells in an organism or between proteins in a cell. Heterogeneity defines the phenotypic landscape at the organismal, cellular and molecular levels, shaping how biological systems respond to short-term perturbations and how species and tumors evolve. Heterogeneity can be encoded by genetic variants or it can arise from environmental perturbations or stochastically. My lab’s past successes include a plethora of new approaches to understand phenotypic heterogeneity arising from genetic variation. In particular, I developed deep mutational scanning, which can measure the consequences of tens or hundreds of thousands of genetic variants on different aspects of protein or cell function. Using deep mutational scanning, my lab has gained fundamental insights into protein structure and function, enhanced computational variant effect prediction and reshaped how human genetic variants are interpreted. Moreover, hundreds of labs around the world have taken up my methods and, collectively, the methods have been used to measure the effects of millions of genetic variants in hundreds of genes. Now, I am investigating the causes and consequences of non-genetic biological heterogeneity. For example, I am fascinated by questions like “how do isogenic cells in a population, each with a different transcriptomic, proteomic and metabolomic state, respond differently to a perturbation? How does the initial state of a cell determine its eventual fate? What, if anything, does cellular morphologic heterogeneity mean either in terms of cell state or fate?” However, answering these questions requires developing new methods. Thus, in this MIRA application, I am seeking funding to extend my work probing the basic biology of non-genetic cellular heterogeneity by developing a slate of new methods and using them to answer fundamental questions like those highlighted above.