Since the distinction was made between immunoglobulin-bearing (B) lymphocytes that give rise to antibody forming cells and thymus derived, Thy-l-bearing (T) lymphocytes responsible for a host of other immune functions, substantial effort has been directed toward finding individual cell surface markers that subdivide these populations. In the mid-1970s, the Lyt-1, Lyt-2, and Lyt-3 antigens were shown (with the assays then available) to be represented exclusively on T cells (1, 2) and to identify functionally distinct T cell subpopulations. Lyt-1 appeared to be restricted to the helper-amplifier subset, and Lyt-2 and Lyt-3 defined the suppressor-cytotoxic subset (3-6).The development of monoclonal anti-Lyt reagents increased the sensitivity with which these antigens could be measured and significantly changed the status of the Lyt-1 (Ly-1) 1 marker: quantitative immunofluorescence studies with the fluorescenceactivated cell sorter (FACS) z revealed that all Thy-l-bearing cells have some Ly-1 (8); that lower levels of Ly-1 on cytotoxic-suppressor cells explains the previous failure to detect this antigen on the Lyt-2,3 subset with cytotoxic depletion assays; and that the frequency of Ly-1 + cells in normal spleen, in fact, is usually slightly greater than the frequency of Thy-1 + cells in the same organ (9).Recent studies (10, 11) demonstrating the existence of Ly-l-bearing B cells (Ly-1 B) indicate that such cells account for at least part of this excess. These findings, documented by FACS analyses on cell populations from normal animals, are consistent with a variety of previous observations: small numbers of Ly-1 + cells are present in B cell areas of stained tissue sections in normal spleen (8); certain mouse B cell lymphomas synthesize and coexpress Ly-1 and IgM (7); old NZB mice tend to have increased numbers of Ly-1 bearing cells (12) that we have now shown to be IgMpositive and Thy-1-negative (unpublished observations); human B cell chronic lymphocytic leukemias tend to carry IgM and Leu-1 (13), a human cell surface molecule whose properties are homologous to mouse Ly-1 (14). Furthermore, a subpopulation * This work was supported in part by grants GM-17367, HD-01287, and CA-04681 from the National Institutes of Health.:~ Fellow of the American Cancer Society. t Several years after its initial description as the first in a series of lymphocyte differentiation antigens, Ly-1 was renamed Lyt-1 to reflect its apparently exclusive expression on T cells. We return here to the original usage since this antigen was recently demonstrated on B cell tumors (7) and we now report its presence on a subpopulation of normal B ceils. No other cell surface antigen is presently called Ly-l.2 Abbreviations used m this paper: Bi, biotin; FACS, fluorescence-activated cell sorter; FCS, fetal calf serum; Fl, fluorescein; Ly-1 B, Ly-l-bearing B cell; PFC, plaque-forming cells; PI, propidium iodide; R1A, radioimmunoassay; TR, Texas red. 202J. Exp. MEn.

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The Fluorescence Activated Cell Sorter (FACS) was invented in the late 1960s by Bonner, Sweet, Hulett, Herzenberg, and others to do flow cytometry and cell sorting of viable cells. Becton Dickinson Immunocytometry Systems introduced the commercial machines in the early 1970s, using the Stanford patent and expertise supplied by the Herzenberg Laboratory and a Becton Dickinson engineering group under Bernie Shoor. Over the years, we have increased the number of measured FACS dimensions (parameters) and the speed of sorting to where we now simultaneously measure 12 fluorescent colors plus 2 scatter parameters. In this history, I illustrate the great utility of this state-of-the-art instrument, which allows us to simultaneously stain, analyze, and then sort cells from small samples of human blood cells from AIDS patients, infants, stem cell transplant patients, and others. I also illustrate analysis and sorting of multiple subpopulations of lymphocytes by use of 8–12 colors. In addition, I review single cell sorting used to clone and analyze hybridomas and discuss other applications of FACS developed over the past 30 years, as well as give our ideas on the future of FACS. These ideas are currently being implemented in new programs using the internet for data storage and analysis as well as developing new fluorochromes, e.g., green fluorescent protein and tandem dyes, with applications in such areas as apoptosis, gene expression, cytokine expression, cell biochemistry, redox regulation, and AIDS. Finally, I describe new FACS methods for measuring activated kinases and phosphatases and redox active enzymes in individual cells simultaneously with cell surface phenotyping. Thus, key functions can be studied in various subsets of cells without the need for prior sorting.

Background: In immunofluorescence measurements and most other flow cytometry applications, fluorescence signals of interest can range down to essentially zero. After fluorescence compensation, some cell populations will have low means and include events with negative data values. Logarithmic presentation has been very useful in providing informative displays of wide-ranging flow cytometry data, but it fails to adequately display cell populations with low means and high variances and, in particular, offers no way to include negative data values. This has led to a great deal of difficulty in interpreting and understanding flow cytometry data, has often resulted in incorrect delineation of cell populations, and has led many people to question the correctness of compensation computations that were, in fact, correct. Results: We identified a set of criteria for creating data visualization methods that accommodate the scaling difficulties presented by flow cytometry data. On the basis of these, we developed a new data visualization method that provides important advantages over linear or logarithmic scaling for display of flow cytometry data, a scaling we refer to as ''Logicle'' scaling. Logicle functions represent a particular generalization of the hyperbolic sine function with one more adjustable parameter than linear or logarithmic functions. Finally, we developed methods for objectively and automatically selecting an appropriate value for this parameter. Conclusions: The Logicle display method provides more complete, appropriate, and readily interpretable representations of data that includes populations with low-to-zero means, including distributions resulting from fluorescence compensation procedures, than can be produced using either logarithmic or linear displays. The method includes a specific algorithm for evaluating actual data distributions and deriving parameters of the Logicle scaling function appropriate for optimal display of that data. It is critical to note that Logicle visualization does not change the data values or the descriptive statistics computed from them.