The beta-chemokines MIP-1alpha, MIP-1beta and RANTES inhibit infection of CD4+ T cells by primary, non-syncytium-inducing (NSI) HIV-1 strains at the virus entry stage, and also block env-mediated cell-cell membrane fusion. CD4+ T cells from some HIV-1-exposed uninfected individuals cannot fuse with NSI HIV-1 strains and secrete high levels of beta-chemokines. Expression of the beta-chemokine receptor CC-CKR-5 in CD4+, non-permissive human and non-human cells renders them susceptible to infection by NSI strains, and allows env-mediated membrane fusion. CC-CKR-5 is a second receptor for NSI primary viruses.

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer‐reviewed by leading experts in the field, making this an essential research companion.

International audienceThe classical model of hematopoiesis established in the mouse postulates that lymphoid cells originate from a founder population of common lymphoid progenitors. Here, using a modeling approach in humanized mice, we showed that human lymphoid development stemmed from distinct populations of CD127(-) and CD127(+) early lymphoid progenitors (ELPs). Combining molecular analyses with in vitro and in vivo functional assays, we demonstrated that CD127(-) and CD127(+) ELPs emerged independently from lympho-mono-dendritic progenitors, responded differently to Notch1 signals, underwent divergent modes of lineage restriction, and displayed both common and specific differentiation potentials. Whereas CD127(-) ELPs comprised precursors of T cells, marginal zone B cells, and natural killer (NK) and innate lymphoid cells (ILCs), CD127(+) ELPs supported production of all NK cell, ILC, and B cell populations but lacked T potential. On the basis of these results, we propose a "two-family" model of human lymphoid development that differs from the prevailing model of hematopoiesis

Sllmllllar~Although inhibition of natural killer (NK) cell-mediated lysis by the class I HLA molecules of target cells is an established phenomenon, knowledge of the features of class I molecules which induce this effect remains rudimentary. Using class I alleles HLA-B*1502 and B'1513 which differ only at residues 77-83 which define the Bw4 and Bw6 serological epitopes, we tested the hypothesis that the presence of the Bw4 epitope on class I molecules determines recognition by NKB1 + NK cells. HLA-B*1513 possesses the Bw4 epitope, whereas B'1502 has the Bw6 epitope. Lysis by NKB1 + NK cell clones of transfected target cells expressing B'1513 as the only HLA-A, -B, or -C molecule was inhibited, whereas killing of transfectants expressing B'1502 was not. Addition of an an anti-NKB1 monoclonal antibody reconstituted lysis of the targets expressing B'1513, but did not affect killing of targets bearing B'1502. The inhibitory effect of B'1513 could be similarly prevented by the addition of an anti-class I monoclonal antibody. These results show that the presence of the Bw4 epitope influences recognition of HLA-B molecules by NK cells that express NKB1, and suggest that the NKB1 molecule may act as a receptor for Bw4 + HLA-B alleles. Sequences outside of the Bw4 region must also affect recognition by NKB1 § NK cells, because lysis of transfectants expressing HLA-A*2403 or A'2501, which possess the Bw4 epitope but are in other ways substantially different from HLA-B molecules, was not increased by addition of the anti-NKB1 antibody. Asparagine 86, the single site of N-linked glycosylation on dass I molecules, is in dose proximity to the Bw4/Bw6 region. The glycosylation site of the Bw4-positive molecule B'5801 was mutated, and the mutant molecules tested for inhibition of NKB1 § NK cells. Inhibition that could be reversed by addition of the anti-NKB1 monoclonal antibody was observed, showing the presence of the carbohydrate moiety is not essential for class I recognition by NKB1 § NK cell clones.

(Table 1). Definitive quantitative assays include calibrators fit to a regression model to calculate absolute values and reference standards that are well characterized and fully representative of the endogenous measurand. Definitive quantitative assays can be both accurate and precise. Relative quantitative assays utilize responseconcentration calibration, however in this scenario the reference standards are not fully characterized or truly representative of the endogenous measurand. As such, imprecision can be demonstrated for a relative quantitative method, but accuracy can only be estimated. With quasi-quantitative assays there is a relationship between the response and the measurand but calibration standards are not used. Thus, quasi-quantitative methods can be validated for imprecision, but not accuracy. Qualitative methods generate categorical data. Flow cytometric methods largely fall in the two latter categories and are essentially therefore quasi-quantitative or qualitative.Multi-color flow cytometry is a unique technology, which enables the analysis of heterogeneous cellular systems and provides multiparametric information at a cellby-cell level. The strength of flow cytometry lies not only in the ability to simultaneously measure multiple parameters, but also in the flexibility to report them in different ways. The appropriate data output depends on the biology of the system being investigated, the analytical or scientific question being asked, and the intended use of the results. A wide variety of data outputs can be reported usually expressed in terms of several characteristics of cells, or cell subsets, in the sample tested for example, percentage of positive events, absolute counts, median fluorescence intensity, quantitative antigen expression levels, ratiometric indices, markers coexpression, or relative nucleic acid content.