Our Research

Our Research:

We strive to better understand the underlying mechanisms that drive autoimmunity and neuroinflammation and to translate that knowledge into meaningful medical advances.

Our research comprises of both basic and translational programs focused on chronic inflammation underlying neurologic disorders such as autoimmune and degenerative disorders, metabolic diseases, and cancer. In particular, we study the role of the immune system and the glial network in neurological disorders such as Multiple Sclerosisbrain tumorsAlzheimer’s disease, and ischemic brain injury (Stroke).

Current projects in the lab include:

Elucidating the role of the glial cells in brain malignancies, autoimmune diseases, and neurologic disorders.

Studying the crosstalk between the innate and adaptive immune systems in the central nervous system.

Developing novel strategies for manipulating glial activation and autoimmune responses.

Investigating the immunometabolic response in the brain.

Approach and Techniques

The central nervous system (CNS) can be targeted by a broad set of insults ranging from genetic, autoimmune, infectious, or neurodegenerative diseases to cancer. Both the peripheral immune cells (e.g. T-cells) as well as the resident glial cells (including astrocytes, microglial cells, and NG2-glia cells) contribute to the pathogenesis of many of these diseases.

We take a transdisciplinary approach in our research endeavors using immunologic, genomic, proteomic and metabolomic approaches to study neuroinflammation.

Our research combines state of the art technologies such as genome-wide sequencing, genomic editing, in-vivo imaging, targeted and un-targeted mass-spectrometry, in-situ molecular imaging and real-time metabolic analysis.

Elucidating inflammation

To understand the complicity of an immune reaction, one must be able to study it both on the cellular and molecular levels. One of the basic tools in the lab involves the use of our own Fluorescence Activated Cell Sorting (FACS) sorter. We employ different FACS analysis strategies to analyze cell activation and preform immune profiling, including multi-dimensional data reduction analysis (e.g. ViSNE and SPADE). We also employ FACS sorting methodology to isolate desired cell types and study them in-vitro, or analyze their activity using advanced transcriptomic, proteomic, and metabolomics approaches (qRT, RNA-Sequencing, and mass spectrometry).


Mapping the transcriptional regulation of neuroinflammation.

We employ high-throughput transcriptional analysis platforms (e.g. DNA microarray and RNA-sequencing) to analyze cellular phenotypes and to identify regulatory mechanisms. However, the transcriptional programs of cells, even within a seemingly coherent cell population may differ dramatically with regards to the cell spatial distribution in the tissue and their interactions with neighboring cells. Much of this complexity could be lost in traditional ensemble-based methods such as bulk RNA-seq. Thus, we also employ single-cell genomics methods to characterize cell identity and function. Primarily, we utilize the novel  Drop-seq technology, a method which allows us to analyze genome-wide gene expression in thousands of individual cells in a single experiment.


Immunometabolism is an emerging field of investigation at the interface between the historically distinct disciplines of immunology and metabolism. Recent evidence suggests that the coupling between immunologic and metabolic pathways play a critical role in regulating leukocyte activity and shaping their pathogenic potential. Likewise, systemic metabolism and metabolic diseases are very much influenced by immunity. Our lab uses advanced techniques including metabolic profiling using mass spectrometry, and real-time bioenergetic analysis (Seahorse XF Analyzers).

Imaging biological processes in live animals.

To gain greater understanding of disease progression we use in-vivo imaging solutions, such as IVIS imagining system or MRI imaging, to investigate cellular activity, tissue inflammation, and tumor growth in a spectrum of preclinical models. These techniques allow us to study the underlying mechanisms of disease progression and to analyze the effects of potential therapeutics.