September 2009
The immune response to vaccines and microbial pathogens is characterized by the spatial reorganization of leukocytes into microanatomical structures such as germinal centers and granulomas. Data on cellular organization is provided by immunofluorescence histology, in which antibodies against specific molecules are conjugated to fluorophores and used to stain thin sections of tissue for subsequent microscopic imaging. We have developed statistical tools to assist in the identification and quantitative characterization of cellular aggregates in immunofluorescent images. We model the spatial distribution of cells as a heterogeneous point process; the major inferential task then is the estimation of the Poisson intensity function underlying the point process. Note that this intensity function represents cellular density, not the fluorescence intensity itself. The intensity function is itself modeled as a flexible non-parametric Gaussian mixture model and provides the basis for the computation of statistics used to characterize the state of development of germinal centers and other cellular aggregates. We describe these methods and their efficient computational implementation and illustrate their use on high-resolution images of lymph node sections stained for CD4, IgM, B220 and GL7. We identify and quantitatively characterize some of the major structural components of post-immunization lymph nodes such as B-cell follicles and germinal centers.
Research was partially supported by grants to Duke University from the NSF (DMS-0342172) and the National Institutes of Health (grant P50-GM081883 and contract HHSN268200500019C). Aspects of the research were also partially supported by the NSF grant DMS-0635449 to the Statistical and Applied Mathematical Sciences Institute. Any opinions, findings and conclusions or recommendations expressed in this work are those of the authors and do not necessarily reflect the views of the NSF or NIH.