Multiplexing T- and B-Cell FLUOROSPOT Assays: Experimental Validation of the Multi-Color ImmunoSpot® Software Based on Center of Mass Distance Algorithm.

Over the past decade, ELISPOT has become a highly implemented mainstream assay in immunological research, immune monitoring, and vaccine development. Unique single cell resolution along with high throughput potential sets ELISPOT apart from flow cytometry, ELISA, microarray- and bead-based multiplex assays. The necessity to unambiguously identify individual T and B cells that do, or do not co-express certain analytes, including polyfunctional cytokine producing T cells has stimulated the development of multi-color ELISPOT assays. The success of these assays has also been driven by limited sample/cell availability and resource constraints with reagents and labor. There are few commercially available test kits and instruments available at present for multi-color FLUOROSPOT. Beyond commercial descriptions of competing systems, little is known about their accuracy in experimental settings detecting individual cells that secrete multiple analytes vs. random overlays of spots. Here, we present a theoretical and experimental validation study for three and four color T- and B-cell FLUOROSPOT data analysis. The ImmunoSpot® Fluoro-X™ analysis system we used includes an automatic image acquisition unit that generates individual color images free of spectral overlaps and multi-color spot counting software based on the maximal allowed distance between centers of spots of different colors or Center of Mass Distance (COMD). Using four color B-cell FLUOROSPOT for IgM, IgA, IgG1, IgG3; and three/four color T-cell FLUOROSPOT for IL-2, IFN-γ, TNF-α, and GzB, in serial dilution experiments, we demonstrate the validity and accuracy of Fluoro-X™ multi-color spot counting algorithms. Statistical predictions based on the Poisson spatial distribution, coupled with scrambled image counting, permit objective correction of true multi-color spot counts to exclude randomly overlaid spots.

Assessment of Humoral, Innate, and T-Cell Immune Responses to Adeno-Associated Virus Vectors.

Adeno-associated virus (AAV)-based gene therapy is being applied to treat a wide array of diseases. Preexisting host immune responses to AAV and immune responses elicited by AAV vector administration remain a problem that needs to be further studied. Here we present a series of protocols to assess immune responses before and after AAV vector administration that are applicable to multiple animal models and phase 1 clinical trials. More specifically, they may be use to evaluate (1) the humoral immune response, through levels of AAV-neutralizing and binding antibodies; (2) the innate immune response, through the acute induction of inflammatory cytokines; and (3) the T-cell immune response, through the activation of transgene- and vector-specific CD8+ and CD4+ T cells.

Statistical methods for the assessment of EQAPOL proficiency testing: ELISpot, Luminex, and Flow Cytometry.

In September 2011 Duke University was awarded a contract to develop the National Institutes of Health/National Institute of Allergy and Infectious Diseases (NIH/NIAID) External Quality Assurance Program Oversight Laboratory (EQAPOL). Through EQAPOL, proficiency testing programs are administered for Interferon-γ (IFN-γ) Enzyme-linked immunosorbent spot (ELISpot), Intracellular Cytokine Staining Flow Cytometry (ICS) and Luminex-based cytokine assays. One of the charges of the EQAPOL program was to apply statistical methods to determine overall site performance. We utilized various statistical methods for each program to find the most appropriate for assessing laboratory performance using the consensus average as the target value. Accuracy ranges were calculated based on Wald-type confidence intervals, exact Poisson confidence intervals, or via simulations. Given the nature of proficiency testing data, which has repeated measures within donor/sample made across several laboratories; the use of mixed effects models with alpha adjustments for multiple comparisons was also explored. Mixed effects models were found to be the most useful method to assess laboratory performance with respect to accuracy to the consensus. Model based approaches to the proficiency testing data in EQAPOL will continue to be utilized. Mixed effects models also provided a means of performing more complex analyses that would address secondary research questions regarding within and between laboratory variability as well as longitudinal analyses.