A fundamental tenet of scientific research is that published results are open to independent validation and refutation. Minimum data standards aid data providers, users, and publishers by providing a specification of what is required to unambiguously interpret experimental findings. Here, we present the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard, stating the minimum information required to report flow cytometry (FCM) experiments. We brought together a crossdisciplinary international collaborative group of bioinformaticians, computational statisticians, software developers, instrument manufacturers, and clinical and basic research scientists to develop the standard. The standard was subsequently vetted by the International Society for Advancement of Cytometry (ISAC) Data Standards Task Force, Standards Committee, membership, and Council. The MIFlowCyt standard includes recommendations about descriptions of the specimens and reagents included in the FCM experiment, the configuration of the instrument used to perform the assays, and the data processing approaches used to interpret the primary output data. MIFlowCyt has been adopted as a standard by ISAC, representing the FCM scientific community including scientists as well as software and hardware manufacturers. Adoption of MIFlowCyt by the scientific and publishing communities will facilitate third-party understanding and reuse of FCM data. ' 2008 International Society for Advancement of Cytometry Key termsimmunology; fluorescence-activated cell sorting; knowledge representation FLOW cytometry (FCM) systems have been available to investigators for over 30 years, and the field continues to advance at a rapid rate. FCM has been responsible for major progress in basic and clinical research by enabling the phenotypic and functional characterization of individual cells in a high-throughput manner. Advances in the technology now allow for automated, multiparametric analyses of thousands of samples per day (1). Each data set can consist of multidimensional descriptions of millions of individual cells, producing data similar in size and complexity to gene expression microarrays. Like the microarray field, the ability to collect FCM data is outpacing the computational means for data handling and analysis. Furthermore, the lack of reporting standardization limits collaboration, independent validation/refutation, and meta-analysis, and thus minimizes the value of the wealth

Background Advances in multi-parameter flow cytometry (FCM) now allow for the independent detection of larger numbers of fluorochromes on individual cells, generating data with increasingly higher dimensionality. The increased complexity of these data has made it difficult to identify cell populations from high-dimensional FCM data using traditional manual gating strategies based on single-color or two-color displays. Methods To address this challenge, we developed a novel program, FLOCK (FLOw Clustering without K), that uses a density-based clustering approach to algorithmically identify biologically relevant cell populations from multiple samples in an unbiased fashion, thereby eliminating operator-dependent variability. Results FLOCK was used to objectively identify seventeen distinct B cell subsets in a human peripheral blood sample and to identify and quantify novel plasmablast subsets responding transiently to tetanus and other vaccinations in peripheral blood. FLOCK has been implemented in the publically available Immunology Database and Analysis Portal – ImmPort (http://www.immport.org) for open use by the immunology research community. Conclusions FLOCK is able to identify cell subsets in experiments that use multi-parameter flow cytometry through an objective, automated computational approach. The use of algorithms like FLOCK for FCM data analysis obviates the need for subjective and labor intensive manual gating to identify and quantify cell subsets. Novel populations identified by these computational approaches can serve as hypotheses for further experimental study.

CCAAT displacement protein (cux/CDP) is an atypical homeodomain protein that represses expression of several developmentally regulated lymphoid and myeloid genes in vitro, including gp91-phox, immunoglobulin heavy chain, the T-cell receptor ␤ and ␥ chains, and CD8. To determine how this activity affects cell development in vivo, a hypomorphic allele of cux/CDP was created by gene targeting. Homozygous mutant mice (cux/ CDP ⌬HD/⌬HD ) demonstrated a partial neonatal lethality phenotype. Surviving animals suffered from a wasting disease, which usually resulted in death between 2 and 3 weeks of age. Analysis of T lymphopoiesis demonstrated that cux/CDP ⌬HD/⌬HD mice had dramatically reduced thymic cellularity due to enhanced apoptosis, with a preferential loss of CD4 ؉ CD8 ؉ thymocytes. Ectopic CD25 expression was also observed in maturing thymocytes. B lymphopoiesis was also perturbed, with a 2-to 3-fold reduction in total bone marrow B-lineage cells and a preferential loss of cells in transition from pro-B/pre-BI to pre-BII stages due to enhanced apoptosis. These lymphoid abnormalities were independent of effects related to antigen receptor rearrangement. In contrast to the lymphoid demise, cux/CDP ⌬HD/⌬HD mice demonstrated myeloid hyperplasia. Bone marrow reconstitution experiments identified that many of the hematopoietic defects were linked to microenvironmental effects, suggesting that underexpression of survival factors or overexpression of death-inducing factors accounted for the phenotypes observed. Tumor necrosis factor (TNF) levels were elevated in several tissues, especially thymus, suggesting that TNF may be a target gene for cux/CDP-mediated repression. These data suggest that cux/CDP regulates normal hematopoiesis, in part, by modulating the levels of survival and/or apoptosis factors expressed by the microenvironment. (Blood. 2001;98:3658-3667)

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We examined the major patterns of changes in gene expression in mouse splenic B cells in response to stimulation with 33 single ligands for 0.5, 1, 2, and 4 h. We found that ligands known to directly induce or costimulate proliferation, namely, anti-IgM (anti-Ig), anti-CD40 (CD40L), LPS, and, to a lesser extent, IL-4 and CpG-oligodeoxynucleotide (CpG), induced significant expression changes in a large number of genes. The remaining 28 single ligands produced changes in relatively few genes, even though they elicited measurable elevations in intracellular Ca2+ and cAMP concentration and/or protein phosphorylation, including cytokines, chemokines, and other ligands that interact with G protein-coupled receptors. A detailed comparison of gene expression responses to anti-Ig, CD40L, LPS, IL-4, and CpG indicates that while many genes had similar temporal patterns of change in expression in response to these ligands, subsets of genes showed unique expression patterns in response to IL-4, anti-Ig, and CD40L.

Introduction The aim of this study was to investigate the magnetic resonance imaging (MRI) features of bone disease in the arthritis mutilans (AM) form of psoriatic arthritis (PsA).

http://www.geneontology.org.

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