Detecting antibodies with similar reactivity patterns in the HLDA8 blind panel of flow cytometry data

Salganik, M.P.; Hardie, Deborah; Swart, Bernadette; Dandie, GW; Zola, Heddy; Shaw, S; Shapiro, Howard M.; Tinckam, Kathryn; Milford, Edgar L.; Wand, M. P.

doi:10.1016/j.jim.2005.07.002

Cited by 2 publications

(1 citation statement)

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“…Because features are identified relative to an absolute calibration standard, pairwise comparisons used in many approaches (32)(33) are unnecessary. As a result, the database is cumulative; that is, new antibody data can be added to the database without the necessity of testing the entire antibody “panel.” A cumulative database is a particular advantage in nonhuman species because international antibody workshops have become increasingly impractical.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical clustering of monoclonal antibody reactivity patterns in nonhuman species

Pratt¹,

Zeng²,

Ravnic³

et al. 2009

Cytometry Pt A

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Monoclonal antibodies (Mab) are an important resource for defining molecular expression and probing molecular function. The characterization of Mab reactivity patterns, however, can be costly and inefficient in nonhuman experimental systems. To develop a computational approach to the pattern analysis of Mab reactivity, we analyzed a panel of 128 Mab recognizing sheep antigens. Quantitative single parameter flow cytometry histograms were obtained from five cell types isolated from normal animals. The resulting 640 histograms were smoothed using a Gaussian kernel over a range of bandwidths. Histogram features were selected by SiZer-an analytic tool that identifies statistically significant features. The extracted histogram features were compared and grouped using hierarchical clustering. The validity of the clustering was indicated by the accurate pairing of externally verified molecular reactivity. We conclude that our computational algorithm is a potentially useful tool for both Mab classification and molecular taxonomy in nonhuman experimental systems. ' 2009 International Society for Advancement of CytometryKey terms other animals; antibodies; antigens/epitopes THE focus of the early human leukocyte differentiation antigens (HLDA) workshops was the use of monoclonal antibodies (Mab) and flow cytometry to identify new molecules (1-7). The workshops characterized the reactivity patterns of large panels of antibodies against a panel of cell populations. For practical purposes, most of the expression analyses were performed with widely available cell lines. Because multipassaged cell lines generally represented uniform cell populations, the results of the analyses were typically binary; that is, the antibody either reacted with the cell line or it did not. Although the histograms derived from cell lines were typically amenable to parametric analyses, the antibody reactivity was effectively summarized as the percentage of cells with fluorescence intensity greater than the negative control. This ''percent positive'' data were subjected to hierarchical clustering to identify ''clusters of differentiation'' or CD specificities (1). In human biology, the ability to identify the gene corresponding to the target antigen has rendered this workshop approach to identify new molecules obsolete. Mab, however, continue to be an important resource for defining molecular expression and probing molecular function.Despite the utility of Mab in biologic investigations, attempts to develop antibody panels in other species have had limited success. A practical problem is that the HLDA approach requires widely available cell lines-a resource that is uncommon in most species. An alternative approach is to use isolated cell populations, such as peripheral blood, thymocytes, and splenocytes. These cell populations, however, are nonuniform. Flow cytometry analyses with most Mab produce histograms that cannot be effectively clustered using ''percent positive'' data. Furthermore, the visual inspection method of pairwise comparison ...

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Section: Discussionmentioning

confidence: 99%