How a cell decides its lineage fate is a long-standing and enigmatic question in biology. Our collaborative and multi-disciplinary research aims to decipher the cell autonomous gene regulatory mechanisms and non-autonomous cell communication mechanisms that drive cellular lineage decisions in development, and the dysregulation of these mechanisms in disease.
High throughput single-cell genomics technologies are enabling the study of lineage decisions at an unprecedented resolution. We develop machine learning methods to leverage these technologies and better characterize developmental and disease trajectories. We design our algorithms with a deep appreciation for the underlying biology to recapitulate developmental processes and derive novel mechanistic insights about the system. As an example, modeling of cell-fate choices as a continuous phenomenon enabled us to pin-point timing of lineage decisions and identify unexpected lineage transitions during mammalian endoderm development.
See Nature article: The emergent landscape of the mouse gut endoderm at single-cell resolution.
Cell autonomous gene regulatory networks are the primary drivers of lineage decisions in developmental trajectories. We are developing methods to integrate multiple single-cell modalities to infer the regulatory network reconfigurations that drive cell state transitions and cell-fate choices along trajectories. Gene regulatory networks invariably are downstream of cell communication and signaling mechanisms. We are developing algorithms to model cell-cell communication and how they shape developmental trajectories. Spatial organization of cells in tissues can often times be derived from single-cell RNA-seq but we also aim to employ the emerging and exciting advances in spatial transcriptomic technologies to understand how cell communication can shape lineage decisions.
Mutations in regulators have been demonstrated to be key drivers in many cancers. As with development, cell communication with the microenvironment also plays a central role in disease progression. We are interested in using the developmental / healthy system as a reference and develop methods to understand the disruption and dysregulation of these mechanisms in disease initiation, progression and transformation.
We are strongly committed to open and reproducible science. Our goal is to develop robust and generalizable algorithms that can be widely used by the scientific community. All of our algorithms are available as open-source software packages through the Resources page and are accompanied by clear documentation and use cases.