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Abstract: Multidimensional single‐cell analysis requires approaches to visualize complex data in intuitive 2D graphs. In this regard, t‐distributed stochastic neighboring embedding (tSNE) is the most popular algorithm for single‐cell RNA sequencing and cytometry by time‐of‐flight (CyTOF), but its application to polychromatic flow cytometry, including the recently developed 30‐parameter platform, is still under investigation. We identified differential distribution of background values between samples, generated by either background calculation or spreading error (SE), as a major source of variability in polychromatic flow cytometry data representation by tSNE, ultimately resulting in the identification of erroneous heterogeneity among cell populations. Biexponential transformation of raw data and limiting SE during panel development dramatically improved data visualization. These aspects must be taken into consideration when using computational approaches as discovery tools in large sets of samples from independent experiments or immunomonitoring in clinical trials. [ABSTRACT FROM AUTHOR]

Circulating endothelial cells (CEC) and their progenitors (EPC) are restricted subpopulations of peripheral blood (PB), cord blood (CB), and bone marrow (BM) cells, involved in the endothelial homeostasis maintenance. Both CEC and EPC are thought to represent potential biomarkers in several clinical conditions involving endothelial turnover/remodeling. Although different flow cytometry methods for CEC and EPC characterization have been published so far, none of them have reached consistent conclusions, therefore consensus guidelines with respect to CEC and EPC identification and quantification need to be established. Here, we have carried out an in depth investigation of CEC and EPC phenotypes in healthy PB, CB and BM samples, by optimizing a reliable polychromatic flow cytometry (PFC) panel. Results showed that the brightness of CD34 expression on healthy PB and CB circulating cells represents a key benchmark for the identification of CEC (CD45neg/CD34bright/CD146pos) respect to the hematopoietic stem cell (HSC) compartment (CD45dim/CD34pos/CD146neg). This approach, combined with a dual-platform counting technique, allowed a sharp CEC enumeration in healthy PB ( n = 38), and resulting in consistent CEC counts with previously reported data (median = 11.7 cells/ml). In parallel, by using rigorous PFC conditions, CD34pos/CD45dim/CD133pos/VEGFR2pos EPC were not found in any healthy PB or CB sample, since VEGFR2 expression was never detectable on the surface of CD34pos/CD45dim/CD133pos cells. Notably, the putative EPC phenotype was observed in all analyzed BM samples ( n = 12), and the expression of CD146 and VEGFR2, on BM cells, was not restricted to the CD34bright compartment, but also appeared on the HSC surface. Altogether, our findings suggest that the previously reported EPC antigen profile, defined by the simultaneous expression of VEGFR2 and CD133 on the surface of CD45dim/CD34pos cells, should be carefully re-evaluated and further studies should be conducted to redefine EPC features in order to translate CEC and EPC characterization into clinical practice. © 2015 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Advances in flow cytometry bioinformatics have resulted in a wide variety of clustering, classification and visualization techniques. To objectively evaluate the performance of such methods, common benchmarks such as the FlowCAP initiative have proven to be of great value. In this work, we report on a novel method, FloReMi, which was developed to tackle the most recent FlowCAP IV challenge. This challenge was formulated as a survival modeling problem, where participants were expected to design a model to predict the time until progression to AIDS for HIV patients. It is known that variability in progression rate cannot be fully predicted by simple CD4+ T cell counts. However, it is hypothesized that the immunopathogenesis established early in HIV already indicates the course of future disease. Adequately estimating the progression rate of HIV patients is crucial in their treatment. Using an automated pipeline to preprocess the data, and subsequently identify and select informative cell subsets, a survival regression method based on random survival forests was built, which obtained the best results of all submitted approaches to the FlowCAP IV challenge. © 2015 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Analysis of high-dimensional flow cytometry datasets can reveal novel cell populations with poorly understood biology. Following discovery, characterization of these populations in terms of the critical markers involved is an important step, as this can help to both better understand the biology of these populations and aid in designing simpler marker panels to identify them on simpler instruments and with fewer reagents (i.e., in resource poor or highly regulated clinical settings). However, current tools to design panels based on the biological characteristics of the target cell populations work exclusively based on technical parameters (e.g., instrument configurations, spectral overlap, and reagent availability). To address this shortcoming, we developed RchyOptimyx (cellular hieraRCHY OPTIMization), a computational tool that constructs cellular hierarchies by combining automated gating with dynamic programming and graph theory to provide the best gating strategies to identify a target population to a desired level of purity or correlation with a clinical outcome, using the simplest possible marker panels. RchyOptimyx can assess and graphically present the trade-offs between marker choice and population specificity in high-dimensional flow or mass cytometry datasets. We present three proof-of-concept use cases for RchyOptimyx that involve 1) designing a panel of surface markers for identification of rare populations that are primarily characterized using their intracellular signature; 2) simplifying the gating strategy for identification of a target cell population; 3) identification of a non-redundant marker set to identify a target cell population. © 2012 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]