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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

Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks – mammalian cell population identification to determine if automated algorithms can reproduce expert manual gating, and sample classification to determine if analysis pipelines can identify characteristics that correlate with external variables (e.g., clinical outcome). This analysis presents the results of the first of these challenges. Several methods performed well compared to manual gating or external variables using statistical performance measures, suggesting that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

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