Classifying monoclonal antibodies, based on the similarity of their binding to the proteins (antigens) on the surface of blood cells, is essential for progress in immunology, hematology and clinical medicine. The collaborative efforts of researchers from many countries have led to the classification of thousands of antibodies into 247 clusters of differentiation (CD). Classification is based on flow cytometry and biochemical data. In preliminary classifications of antibodies based on flow cytometry data, the object requiring classification (an antibody) is described by a set of random samples from unknown densities of fluorescence intensity. An individual sample is collected in the experiment, where a population of cells of a certain type is stained by the identical fluorescently marked replicates of the antibody of interest. Samples are collected for multiple cell types. The classification problems of interest include identifying new CDs (class discovery or unsupervised learning) and assigning new antibodies to the known CD clusters (class prediction or supervised learning). These problems have attracted limited attention from statisticians. We recommend a novel approach to the classification process in which a computer algorithm suggests to the analyst the subset of the "most appropriate" classifications of an antibody in class prediction problems or the "most similar" pairs/ groups of antibodies in class discovery problems. The suggested algorithm speeds up the analysis of a flow cytometry data by a factor 10-20. This allows the analyst to focus on the interpretation of the automatically suggested preliminary classification solutions and on planning the subsequent biochemical experiments.

We recently reported the biological activity and some of the biochemical characteristics of a factor produced by a human T cell hybrid clone able to block hematopoietic progenitor cell proliferation. This 85-kD protein factor, which we have termed colony-inhibiting lymphokine (CIL), has growth regulatory activity on bone marrow precursors bearing Ia (class II) antigens of either granulocytic-monocytic (CFU-GM) or erythroid lineage (BFU-E and CFU-E). Experiments aimed to investigate the specificity of the inhibitory effect on hematopoietic progenitor cell growth suggested that the expression of HLA-DR surface antigens was required on the target cells. We describe in this communication how DR' cell lines ceased dividing after a few days of culture in the presence of CIL, whereas DR-cell lines were completely unaffected. The increased DR expression on the ML3 cell surface, mediated by the activity of the gamma interferon (IFNy), increases the sensitivity to the growth inhibition factor of the ML3 cell line. To verify the hypothesis that the DR antigens might serve as receptors for the factor, enabling it also to interfere in the immune response, we tested CIL in a mixed lymphocyte reaction (MLR), one of the best known in vitro Ia antigen-dependent T cell-mediated immune responses. CIL is able to block major histocompatibility comnplex-allogeneic MLR both in human and mouse systems. The data indicate that CIL recognizes a nonpolymorphic structure (presumably on all Ia molecules) presented by stimulator cells of either species, and thereby interferes with specific interactions between stimulator and responder cells. Blocking of the alloantigen stimulation stage is also indicated, since CIL is effective only if added to the culture medium during the first 48 h of the MLR. Finally, mouse monoclonal anti-DR antibodies are able to sharply reduce CIL activity on sensitive DR' cell lines. CIL may act physiologically as a multifunctional mediator in a complex network that links regulation of bone marrow differentiation and the generation of immune responses.

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