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UBC Theses and Dissertations

Single-cell analytics for phospho flow cytometry reveals dynamic interactions between molecular pathways Huang, Yue


Quantitative analysis of large single-cell measures acquired by phospho flow cytometry typically involves establishing inclusion gate thresholds and combining measures from accepted cells into a single median metric. Though this analysis method is simple, it overlooks the heterogeneity of cell populations and there could be information missing from the single-cell level. Here, we have formulated approaches that can recognize the heterogeneity and extract additional information involving dose-response and interactions between multiple molecules from phospho flow cytometry datasets. Using phospho flow multiplexed sampling of cell physical features, and primary antibodies against protein markers, including GAPDH as a protein expression control, HA tag as an exogenous gene/variant transfection measurement, and 8 antibodies detecting the activation (phosphorylation) states of 8 proteins within conserved molecular pathways, two panels of phospho-specific antibodies were used simultaneously for multiplexed measures in the same cells. Our approach involves single-cell standardization, fitting loess regression, identifying linear domains in dose-response plots, building linear mixed-effects models, and multi-dimensional analyses to detect interactions between phosphorylated protein markers. We demonstrate the utility of this approach by expressing wild-type and 5 variants (4A, D268E, Y138L, P38H, G129E) of PTEN on 8 markers of molecular pathways downstream of PTEN, and we also expressed RHEB WT testing its impact on markers in the shared associated pathways. We succeeded in differentiating subtypes of PTEN loss-of-function variants and were able to predict that PTEN P38H is a loss-of-lipid-phosphatase-function variant. We were also able to infer that pAKT, p4EBP1, pS6, and pCREB are all downstream targets of PTEN regulation while pAKT is between PTEN and p4EBP1, pS6, or pCREB. In conclusion, our results demonstrate dose response and molecular pathway interactions unavailable from reducing population data to single values, and our approach manifests strong promise in variant function measurement and molecular signaling pathway inference.

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