Identification of B-cell subsets: an exposition of 11-color (Hi-D) FACS methods

JW Tung, DR Parks, WA Moore, LA Herzenberg… - B cell protocols, 2004 - Springer
JW Tung, DR Parks, WA Moore, LA Herzenberg, LA Herzenberg
B cell protocols, 2004Springer
In the last few years, the effectiveness of developmental and functional studies of individual
subsets of cells has increased dramatically owing to the identification of additional subset
markers and the extension of fluorescence-activated cell sorter (FACS) capabilities to
simultaneously measure the expression of more markers on individual cells. For example,
introduction of a 6–8 multiparameter FACS instrument resulted in significant advances in
understanding B-cell development. In this chapter, we describe 11-color high-dimensional …
Abstract
In the last few years, the effectiveness of developmental and functional studies of individual subsets of cells has increased dramatically owing to the identification of additional subset markers and the extension of fluorescence-activated cell sorter (FACS) capabilities to simultaneously measure the expression of more markers on individual cells. For example, introduction of a 6–8 multiparameter FACS instrument resulted in significant advances in understanding B-cell development. In this chapter, we describe 11-color high-dimensional (Hi-D) FACS staining and data analysis methods that provide greater clarity in identifying the B-cell subsets in bone marrow, spleen, and peritoneal cavity. Further, we show how a single Hi-D FACS antibody reagent combination is sufficient to unambiguously identify most of the currently defined B-cell developmental subsets in the bone marrow (Hardy fractions A–F) and the functional B-cell subsets (B-1a, B-1b, B-2, and marginal zone [MZ] B cells) in the periphery.
Although we focus on murine B-cell subsets, the methods we discuss are relevant to FACS studies conducted with all types of cells and other FACS instruments. We introduce a new method for scaling axes for histograms or contour plots of FACS data. This method, which we refer to as Logicle visualization, is particularly useful in promoting correct interpretations of fluorescence-compensated FACS data and visual confirmation of correct compensation values. In addition, it facilitates discrimination of valid subsets. Application of Logicle visualization tools in the Hi-D FACS studies discussed here creates a strong new base for in-depth analysis of B-cell development and function.
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