Tool Development

Principal investigators

Kasper Lage

The Lage group develops algorithms, web platforms, and resources to interpret genetic datasets using protein network data. They have, for example, compiled a scored human protein-protein interaction network for genome interpretation (InWeb), a web platform for network-based interpretation of genetic data (GeNets), and a web platform to interactively integrate experimental proteomics data and results from exome sequencing analyses or genome-wide association studies (Genoppi).

Steve McCarroll and Evan Macosko

Single-cell biology
The brain contains hundreds of cell types, each with a different biological mission. Each expresses different genes in the pursuit of its own specialized biological functions. A longstanding scientific need is to understand how each cell type contributes to brain function and where each gene acts; this is also critical for understanding how genes shape risk of illness.

The McCarroll and Macosko labs recently developed a technology for profiling RNA expression genome-wide in thousands of individual cells at once. The method involves separating cells into millions of nanoliter-sized droplets, lysing the cells in droplets, and barcoding the contents of each droplet to mark the cell-of-origin of each RNA molecule. In a single sequencing reaction, they routinely profile gene expression genome-wide in thousands of individual cells. This technology is called Drop-Seq.

The teams are using Drop-Seq as to ascertain: the cell types that populate the brain; the pathophysiology involved in schizophrenia, autism, and other illnesses; and the ways in which genetic variation acts at the level of specific cell populations within complex tissues. The field now needs new kinds of algorithms and computational strategies to recognize all of the biologically meaningful information that is present in these vast novel data sets. More Drop-Seq information!