Projects

Algorithmic analysis and exploration of high-parameter flow cytometry data

Manual analysis of high-dimensional flow cytometry data is a subjective, unreproducible technique, and exhaustive manual analysis of high-dimensional flow data is not feasible.  We have developed a high-resolution, model-based flow cytometry data clustering program, SWIFT, that has higher resolution than most other gating or clustering algorithms, and detects sub-populations at levels as low as one part per million.

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High-resolution Analysis of T Cell Functions in Pregnancy

The developing fetus needs to be protected against two types of potential immune attack:  first, inflammatory responses against pathogens can indirectly damage the placenta; and second, the immune system could potentially attack paternal antigens on fetal cells.  A wide range of overlapping protective mechanisms, in aggregate, are so effective that pregnancies with wider MHC disparity actually have a slight advantage over less-disparate pregnancies.

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T Cell Signatures in Atopic Dermatitis, Cancer, HIV and Aging

Atopic Dermatitis is a potentially severe skin allergic reaction, and can be exacerbated by Staphylococcal aureus colonization.  As part of a Barrier study in the Atopic Dermatitis Research Network (ADRN) we compared the T cell responses to allergens and standard vaccine antigens in non-allergic subjects; AD patients without Staph, and AD with Staph.

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LAVA - Animating Stochastic Processes during T Cell Differentiation

Recent advances in understanding CD4+ T‐cell differentiation suggest that previous models of a few distinct, stable effector phenotypes were too simplistic. Although several well‐characterized phenotypes are still recognized, some states display plasticity, and intermediate phenotypes exist. As a framework for reexamining these concepts, we use Waddington's landscape paradigm, augmented with explicit consideration of stochastic variations.

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