Rare-Event Analysis in Flow Cytometry
Section snippets
A survey of rare-event applications
The major histocompatibility complex (MHC)/tetramer assay has been developed to detect peptide-specific T cells [7]. Peptides are presented to T cells in the context of PE or allophycocyanine (APC)-labeled self-MHC tetramers. Tetramers bind specifically to T cells having the appropriate receptor. MHC class I tetramers, which detect peptide-specific CD8+ T cells, have been well characterized, whereas MHC class II tetramers, recognized by CD4+ T cells, now are coming into use. Tetramer
Sample concentration and flow rate
Because the event frequency is an inherent property of the sample, there is not much to be done about it, save preprocessing the sample to enrich the event of interest. Immunomagnetic separation sometimes is performed before flow cytometry for this purpose [50]. This is preferable for rare-event isolation, because it can greatly decrease time required to sort a rare population on a fluorescence-activated cell sorter, but the uncertainties of event recovery after preseparation may obscure the
Detection of cells expressing stem cell markers in normal human lung tissue
Performing rare-event flow cytometry on cells from solid organs presents several challenges. Tissue fragments must be digested and disaggregated mechanically before staining. Even the most meticulous preparation leaves cell clusters, dead cells, and cellular debris, all of which can interfere with analysis. In this example, human lung tissue was minced using a Becton Dickinson Medimachine, filtered though a 70-μ cell strainer, digested with collagenase, and separated on a Ficoll-Hypaque
Coda
Maximization of signal over noise, running appropriate controls, determining the lower limit of detection, and acquisition of an appropriate number of events are the elements of successful rare-event detection. When dealing with multiparameter datasets, it is useful to think of parameters as classifiers or outcomes. This facilitates a hierarchic analysis that avoids the all possible permutations problem (ie, 112 distinct parameter combinations in the last example). Having done this, it is
Acknowledgments
The authors would like to thank Kit Snow, Tom Franks, Erin McClelland, E. Michael Meyer, Darlene Monlish, Linda Moore, Peter Nobes, Melanie Pfeiffer, and David Roberts for their assistance with various aspects of the original data presented here.
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This work was supported by grants BC044784 and BC032981 from the Department of Defense, a grant from the National Institutes of Health National Institute of Arthritis and Musculoskeletal and Skin Diseases (NO1 AR9 2239), and the generous support of the Hillman Foundation.