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The rapid evolution of the flow cytometry field, currently allowing the measurement of 30-50 parameters per cell, has led to a marked increase in deep multivariate information. Manual gating is insufficient to extract all this information. Therefore, multivariate analysis (MVA) methods have been developed to extract information and efficiently analyze the high-density multicolour flow cytometry (MFC) data. To aid interpretation, MFC data are often logarithmically transformed before MVA. We studied the consequences of different transformations of flow cytometry data in datasets containing negative intensities caused by background subtractions and spreading error, as logarithmic transformation of negative data is impossible. Transformations such as logicle or hyperbolic arcsine transformations allow linearity around zero, whereas higher (positive and negative) intensities are logarithmically transformed. To define the linear range, a parameter (or cofactor) must be chosen. We show how the chosen transformation parameter has great impact on the MVA results. In some cases, peak splitting is observed, producing two distributions around zero in an actual homogeneous population. This may be misinterpreted as the presence of multiple cell populations. Moreover, when performing arbitrary transformation before MVA analysis, biologically relevant and statistically significant information might be missed. We present a new algorithm, Optimal Transformation for flow cytometry data (OTflow), which uses various statistical methods to optimally choose the parameter of the transformation and prevent artifacts such as peak splitting. Arbitrary or unconsidered transformation can lead to wrong conclusions for the MVA cluster methods, dimensionality reduction methods, and classification methods. We recommend transformation of flow cytometry data by using OTflow-defined parameters estimated per channel, in order to prevent peak splitting and other artifacts in the data., (© 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.)

Upon activation granulocytes upregulate several adhesion molecules (CD11b) and granule proteins (CD35, CD66b) and shed surface l-selectin (CD62L). These changes in expression, as assessed by flow cytometry, can be used as markers for activation. Whereas these markers are usually studied in fresh blood samples, a new method is required when samples are collected at a field site with no direct access to a flow cytometer. Therefore, we developed and tested a field-applicable method in which fixed leukocytes were cryopreserved. Using this method, the intensity of granulocyte activation markers was compared to samples that were either stained fresh, or fixed prior to staining but not cryopreserved. In addition, the response to an in vitro stimulation with fMLF was determined. While we observed differences in marker intensities when comparing fresh and fixed granulocytes, similar intensities were found between fixed cells that had been cryopreserved and fixed cells that did not undergo cryopreservation. Although fixation using FACS lysing solution might lead to membrane permeabilization, activation markers, and the responsiveness to fMLF or eotaxin could still be clearly measured. This method will, therefore, enable future studies of granulocyte activation in settings with limited resources and will allow simultaneous analysis of samples collected at different time points. © 2018 International Society for Advancement of Cytometry., (© 2018 International Society for Advancement of Cytometry.)