Research

Main Research Questions

  1. How do patient-derived cellular models of brain disease differ from those of healthy individuals?
  2. How can patient-derived cellular models of brain disease be applied to identify novel treatments?
  3. How can large-scale electronic health records be used efficiently to identify novel treatments, risk genes, or predictors of treatment response?

Research Approaches

We are developing methods for modeling microglia-mediated synaptic pruning in vitro, and are using them to understand pruning abnormalities in schizophrenia and to identify novel treatments.

  • We developed one of the largest cellular biobanks in the world for the study of brain diseases, a key resource for understanding how disease-associated genes contribute to risk, as well as screening for new treatments.
  • We are now using our scalable human synaptic pruning in vitro model to investigate the potential effects of genes implicated by postmortem and genomic studies of schizophrenia risk genes
Figure 1. Synaptosome engulfment functional characterization of hiMGs in an in-vitro model of synaptic pruning. Overall schematic of pHrodo-labelled quantitative synaptosome phagocytosis assay by hiMGs. pHrodo is a pH sensitive dye that fluoresces brightly upon specific engulfment into the acidic post-phagocytic phagolysosome compartments.
Figure 2. Engulfment of synaptosomes in schizophrenia derived cultures show both neural and microglial-specific effects. (A) “Pure” disease models (Red), derived from subjects with schizophrenia (SZ), “mixed” models (Yellow) in which the same synaptic structures from SZ subjects were added to induced microglia-like cells (hiMGs) derived from matched healthy controls (HCs), as well as to matched “pure” HC models (Black). (B) Quantification of pHrodo (red)-labeled SYN uptake in hiMGs during live imaging sessions of 5.5 h

We are developing new tools for visualizing risk in clinical populations, allowing clinicians to make better use of emerging clinical and genomic predictors.

We are developing ways to make better use of health records and biobanks to drive genetic discovery – for example, understanding the effects of antidepressants on cardiac rhythms.

  • We helped lead the identification of the first risk genes for major depressive disorder
  • We developed machine learning approaches that apply electronic health records to predict suicide and other causes of mortality in individuals with psychiatric illness.