Infinicyt™ Software
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Infinicyt™ Software Automatic Population Separator (APS) The APS feature is based on principal component analysis (PCA) and generates automatic n-dimensional separation of sample clusters for further characterization. APS analyzes all parameter combinations at one time to display the best separation of cell clusters. It also provides information on which of these paramaters are the most important for optimal separation, helping indicate if new analysis panels should be developed or existing ones improved. Figure 1: APS diagram displays all events separated by PCA and allows for the efficient identification of different populations. File Merge This tool allows the user to link information from different FCS files into one single file for a more comprehensive analysis. A parameter band dot plot can be used to evaluate all of the parameters at the same time. Infinicyt™ also provides a level of quality control that confirms the selected population is equally represented in all of the files for the common parameters. Merge Files Gate using common markers Figure 2: A parameter dot plot displays the complete results of an experiment composed of multiple single tubes. Reference Image Reference images can be displayed as dot plots or density lines with corresponding statistical data and allows the user to compare previously stored events with the current sample being analyzed. This tool is useful for comparing normal and abnormal marker expression, assessing maturation patterns, evaluating minimal residual disease (MRD) and comparing control samples with test samples in research laboratories. For MRD evaluations, several images can be saved at different time points for monitoring purposes. Figure 3: A reference image of an MRD sample at day 0 (red dots) compared with follow up (blue dots).

Maturation Tool The Maturation tool introduces a brand new approach for studying maturation patterns and offers an easy solution for assessing the evolution of multiple markers in a single population. It is especially useful in the study of maturation defects such as myelodysplastic syndromes (MDS). Since these syndromes comprise a heterogeneous group of clonal hematological disorders that can affect one or more bone marrow myeloid cell lineages, a tool for correctly identifying the stage and cell lineage involved is beneficial. When using a maturation database, the user can quantitatively...