We develop and deploy cutting-edge molecular techniques to more deeply understand the function of cellular specialization in the nervous system. In particular, we seek clear, actionable explanations for how the cells of the brain go awry in major neuropsychiatric illnesses.
We see tremendous opportunity right now to develop new molecular technologies that redefine the scope and scale of what is possible to measure in biology. By combining molecular biology, microfluidics, and organic chemistry, we are trying to build better tools for detailed, information-rich molecular analyses of brain tissues. Recently, we invented Slide-seq (image at right) for generating high-resolution gene expression maps of intact tissue sections. Slide-seq is highly scalable, low-cost, and highly synergistic with single cell data, enabling cell types and states to be mapped into spatial coordinates in tissues. We currently are focused on technologies that can measure synaptic connectivity and plasticity, and methods that detect molecular interactions in situ. We see these new tools as a basis for a new field of tissue biology we call “Tissue Genomics,” which seeks to leverage the comprehensiveness and scale of modern genomics methods to more deeply understand how tissues operate.
Spatial and single cell genomics technologies provide exciting opportunities to more clearly characterize how tissues respond to disease states. Specific projects in the lab include characterizing cell states uniquely induced in animal models of schizophrenia, and molecularly characterizing the cells that are vulnerable to cell death in Parkinson’s disease.
Cell type atlases of the mammalian brain
The mouse brain is a crucial guide for understanding the human brain, because of how much functional work has been done to functionally and physiologically annotate cell types. We are using single cell and spatial tools to construct a full atlas of the adult mouse brain, mapping cell types into specific neuroanatomical nuclei. We want to know things like: how many cell types are shared across brain structures, and how many are specific? Are there structural principles that underlie cell type organization in the brain? The balance of excitatory and inhibitory neurons in cortex is an example of such a principle; we suspect there are many, many more.
Our studies of cell type diversity in the mouse brain have so far been staggering. Particularly in midbrain and hindbrain, we identify many specialized neuronal populations, many of which are largely uncharacterized by modern neuroscience.
Novel Computation, software and tools
We’ve developed LIGER (Linked Inference of Genomic Experimental Relationships), a new pipeline and R package for comparing and contrasting single-cell datasets across individuals, species, modalities, and other experimental contexts. Learn more about the method and how to use the package here.
Drop-seq was our foray into tissue genomics methods development in 2015, and embodies how we think about inventing technologies in the lab today: first, we build an assay that clearly informs if the method is working. Second, using the assay as a guide, we explore ideas widely, aiming to maximize technical simplicity. Third, when we’re successful, we try to disseminate it as widely as possible, to maximize its impact, and get critical feedback on its utility from other scientists.
Here is some (now archival) footage introducing Drop-seq: