Profiling B cell immune responses by microengraving

• Main Author: Papa, Eliseo
• Other Authors: Hidde L. Ploegh, J. Christopher Love and Robert Langer.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2009
• Subjects: Mechanical Engineering.
• Online Access: http://hdl.handle.net/1721.1/45203

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008. === Includes bibliographical references (leaves 83-87). === The ability to monitor an immune response in the course of vaccination or disease progression is highly desirable. Currently, no technique is able to generate a comprehensive profile of the individual cells involved and the antibodies they produce at a particular point during the immune response. The ability to obtain such detailed "snapshots" describing the immune response with a high level of resolution would have implications for diagnostics and biological discovery. Improvement in vaccination schemes, specific tailoring of anti-viral administrations, large-scale monitoring of complex latent infections in a population are all possibilities that would stem from a better understanding of the dynamics of immune responses. currently available methods for profiling of B cells that produce antigen-specific antibodies helped clarify humoral responses, but it remains a challenge to generate measurements capable of detailing the phenotypic changes and secretion patterns of individual lymphocytes. To address this need a soft lithographic approach termed microengraving ([mu]En) - previously used for the isolation and rapid selection of monoclonal antibodies[31] - was further developed and adapted to measure the affinity and isotype of secreted antibodies. The objective of this thesis was to employ microengraving in conjunction with bioinformatics analysis to obtain routinely state-based comprehensive profiles detailing cellular and humoral immune responses to antigens to the level of clonal B cells. Here I show how bioinformatics methods were employed to generate multidimensional datasets for large numbers of individual primary B cells (10² - 10⁴). These data include three characteristics of the antibodies secreted by each cell: antigenic specificity, isotype, and affinity. === (cont.) These data are sufficient to classify individual cells into distinct groups of related cells using algorithms for data clustering. In a series of mice immunizations designed to mimic a multipart vaccination, I apply this method to profile the resulting B cell response with single cell resolution. === by Eliseo Papa. === S.M.