The overarching goal of research in the Oldham lab is to understand the molecular basis of cellular identity in the human brain in health and disease, with a particular emphasis on glioma. In pursuit of this goal we are developing new ways of studying biological systems that combine standardized sampling strategies, multiomic data collection, and multivariate analytical methods.
The human brain is an extraordinarily complex, heterogeneous structure, comprised of diverse cell types whose molecular and functional identities are poorly understood. Because gene expression lies at the root of cellular identity, much of our work is focused on understanding the organization of the human brain transcriptome. Our work is motivated by a simple but powerful idea: by analyzing gene coexpression relationships over thousands of heterogeneous tissue samples, it is possible to isolate reproducible transcriptional signatures of distinct cell types and cellular processes in silico.
The engine of the lab is an in-house computational pipeline for detecting patterns in omics datasets that is implemented in the R computing environment. Insights from computational analyses are used to generate hypotheses that are tested at the bench. Most of our work is focused on the analysis of human brain tissue and cells, but we also perform work in model systems when appropriate.
Projects in the lab are organized around studies of glioma and other brain cancers, normal human brain, and developing human brain:
1) Understanding the most predictable transcriptional consequences of recurrent mutations that cause glioma (in particular, low-grade glioma) to identify novel therapeutic targets;
2) Developing quantitative definitions of cell types in the neurotypical adult human brain based on integrative gene coexpression analysis;
3) Determining the molecular characteristics of neural stem cells and their progeny in the developing human brain and how these compare with other species and the adult human brain.
Participation in these projects generally ensures that lab members are fluent (or develop fluency) in computational and experimental research strategies.