25: AI/ML enabled automatic Flow Cytometry data gating and analysis

Music City Center 

Flow cytometry profiles immune cells by detecting scattered light and fluorescent signals. Development in flow cytometry assays has enabled the capability to monitor 15 or more fluorescent probes at the same time, allowing characterizing immune cells at greater details. However, the standard practice of analyzing flow cytometry data involves a manual step called "gating", which requires the scientists to manually define the boundaries of positive/negative cells. To overcome the limitation of manual gating and allow objective analysis of clinical flow data, we developed an AI/ML gating pipeline that can identify thresholds separating positive/negative population based on cell distributions and closely follow a predefined gating hierarchy, allowing incorporation of biological information. To reliably identify cell populations that expressed at low frequency, we leveraged either negative or positive controls. In comparing with manual gating counts, the Pearson correlation coefficient surpassed 0.9 for all three abundance subgroups across the three validation datasets. In the more challenging rare event gating, 12 out of 14 immune cell subpopulations had Pearson correlation coefficient

Flow Cytometry

Auto Gating

AI/ML

Clinical trial 

Biopharmaceutical Section