Neuroimmunology

 

We study the cellular gatekeepers at CNS barriers. The tissues that surround the brain, spinal cord, and eyes, such as the choroid plexus, meninges, and perivascular spaces, are critical checkpoints that regulate immune cell entry and activation. Our goal is to understand how immune dysfunction contributes to neurologic disease. Recent work in our lab maps the human choroid plexus at single-cell resolution and identifies disease-altered macrophage populations in Alzheimer’s disease. Building on this, we are investigating how border-associated macrophages sense mechanical and molecular signals, establish functional identity, and respond to aging and inflammatory triggers. By combining experimental neuroimmunology with human tissue profiling, we identify therapeutic vulnerabilities in diseases where immune dysregulation drives pathology.

Computational Biology

 

In partnership with the Yale Center for Neuroinflammation, we build machine learning and statistical methods that link CNS cell populations, their spatial arrangements, and their molecular signatures to disease behavior and treatment response. By connecting genomic and histologic phenotypes, we uncover hidden cell states and microenvironmental interactions that explain why some brains fail during aging, inflammation, and neurodegeneration. Our focus spans immune dysregulation, motor system diseases, and cancer, with the goal of discovering cell populations and pathways that represent new therapeutic entry points. We believe that computational pathology, grounded in clinically relevant disease models and human tissue, accelerates the translation from molecular discovery to clinical insight.

We develop software that analyzes genomic data in concert with histologic images taken from the kinds of slides produced in the routine clinical evaluation of tissue. By using statistical and machine learning techniques, these algorithms look for patterns in cell placement and morphology that correspond to the tissue genetic profiles. We believe this simultaneous genotypic and phenotypic characterization of tissue will provide a deeper understanding key microenvironments whose interactions advance our explanations and predictions of overall disease behavior and treatment response.

Digital Spatial Biology

 

The recent development of technologies for highly multiplexed assays of nucleic acid sequences, chromatin state, and proteins while preserving information about their native locations in tissue (‘spatial -omics’ technologies) offers the tantalizing promise of new insights into biological processes (e.g. development) and the ways in which they become dysfunctional (i.e. pathology). This promise has re-emphasized the importance of tissue context to deliver true insights into disease biology.

The data generated by these technologies is a skeleton; the assays themselves provide the relative locations of the analyzed molecules (and thus proximities to each other). These molecules exist in and around cells which are situated in tissue architecture, creating the networks, physiological units, microenvironments, barriers, and organs which keep us alive. This context, which is, essentially, the traditional histologic description of health and disease, has been the subject of intense study since the 17th century, providing an immense body of knowledge of the changes in cellular identities, cytomorphology, and microenvironment that accompany disease initiation and progression.

Deployment of spatial ‘-omics’ technologies productively to understand molecular pathways implicated in disease requires application to tissue in which these structural elements can be identified and related to disease phenotype. To that end, we are creating tools to do two things: 1) Accurately align the highly multiplexed data generated by a spatial ‘-omics’ experiment with images of the tissue from which it was derived, and 2) Capture expert and/or automated identification of important locations and features in tissue and integrate them into the analysis of the molecular data in a meaningful way. To do this, we develop tools which allows for localization and interpretation of the molecular data from highly multiplexed assays in the context of histologic images with annotations of pathologic features or morphologic characteristics such as lesions, breach of barriers, atrophy, or proximity to inflammation.