833 Accelerating analysis of cytometry data sets leveraging a visualization tool built within CellEngine, a cloud-based cytometry analysis software

<h3>Background</h3> High-dimension cytometry panels have become a hallmark of clinical phase 1 and 2 trials. These panels can provide hundreds of readouts and need to be combined with subject metadata (e.g., health data, demographics, timepoint, and dose) to provide insights on the identification of biomarkers. Several bioinformatics tools are available to interpret these complex datasets; however, these applications are typically separate from where the raw data is analyzed and requires highly trained bioinformaticians. Here, we show how a new data visualization tool integrated within the CellEngine cytometry analysis software can accelerate data analysis and interpretation of samples without removing the scientist from the raw data. <h3>Methods</h3> The visualization tool, built-in CellEngine, displays both study- and cohort-level information in summary views as well as the staining profiles of both samples and controls. As a live link, any changes made to the data, such as gate adjustments, compensation modifications, or the addition of new samples, are automatically updated in the visualization dashboard. Additionally, the tool provides the ability to quickly go through large datasets, providing automatic generation of the chosen visual representation and stratification of samples based on selected parameters. <h3>Results</h3> Here, we show how CellEngine and its visualization tool were used to accelerate the data analysis/interpretation in the validation of a TBNK panel, an assay used to track lymphodepletion and B cell aplasia in the context of CAR-T clinical trials. During the validation of the assay, CellEngine and its visualization tool were leveraged to analyze and assess the performance of the assay including precision, stability, limit of detection and lower limit of quantification across different conditions and time points while consulting dot plots from samples and controls to guarantee quality of the results. <h3>Conclusions</h3> The use of CellEngine in combination with its visualization tool maximizes the utility and quality of cytometry data while greatly accelerating interpretation of immune monitoring samples. The ability to directly consult dot plots from trending graphs allows the scientist to remain close to the raw data. <h3>Ethics Approval</h3> The protocol CellCarta Biosciences, Inc. - G522, Blood Collection in Healthy Volunteers for Immunology and Proteomics Research (Pro00018464)used in the submitted study obtained ethics approval from Advarra IRB (registered with OHRP and FDA under IRB#00000971). Each donor completed an informed consent form.