Authors:Ishwor Thapa1;Yohan Kim2;Fabrice Lucien2 andHesham Ali1
Affiliations:1College of Information Science and Technology, University of Nebraska at Omaha, Omaha, U.S.A.;2Department of Urology, Mayo Clinic, Rochester, U.S.A.
Keyword(s):Extracellular Vesicles, High Resolution Flow Cytometry, Automated Gating, Reproducibility and Robustness, Biological Signals, FCS.
Abstract:With the continuous advancements of biomedical technologies, we have access to instruments capable of producing new types of biological data or generating traditional data with higher degrees of quality. With the support of such data, researchers and practitioners continue to explore the possibilities of developing new approaches to obtain valuable data-driven signatures or biosignals to be used for diagnosis, classification, or assessment of treatments. However, with the emergence of new types of data, it is often the case that they are available in raw formats that are not suitable for extracting the needed biomarkers. Hence, much work is needed to process the raw data sets obtained from new medical instruments and transform the signals into products capable of capturing the desired knowledge. Next-generation biomarkers such as “liquid biopsies” are emerging tools to improve cancer diagnostics, disease stratification, and treatment monitoring. As potential cancer biomarkers, circulating Extracellular Vesicles (EV) levels may early-predict disease recurrence and resistance to treatment. High-resolution flow cytometry (hrFC) is a sensitive and high-throughput method for quantifying circulating levels of EVs with minimal sample processing. One of the benefits of using hrFC is that there is no need to isolate or purify the molecules of interest from the biological samples prior to running the flow. However, signals in hrFC data currently depend on manual and subjective approaches to gating the positive events. Such approaches are often time-consuming, error-prone, and lack the levels of robustness and reproducibility needed to trust the obtained information. This study proposes an automated quantitative technique to process flow cytometry data for EVs with a high degree of accuracy consistency. A publicly available Shiny web application is presented that performs quality check of flow cytometry files and automated gating of biosignals, viz. subpopulations of EVs that are of interest to next generation biomarker studies.(More)
With the continuous advancements of biomedical technologies, we have access to instruments capable of producing new types of biological data or generating traditional data with higher degrees of quality. With the support of such data, researchers and practitioners continue to explore the possibilities of developing new approaches to obtain valuable data-driven signatures or biosignals to be used for diagnosis, classification, or assessment of treatments. However, with the emergence of new types of data, it is often the case that they are available in raw formats that are not suitable for extracting the needed biomarkers. Hence, much work is needed to process the raw data sets obtained from new medical instruments and transform the signals into products capable of capturing the desired knowledge. Next-generation biomarkers such as “liquid biopsies” are emerging tools to improve cancer diagnostics, disease stratification, and treatment monitoring. As potential cancer biomarkers, circulating Extracellular Vesicles (EV) levels may early-predict disease recurrence and resistance to treatment. High-resolution flow cytometry (hrFC) is a sensitive and high-throughput method for quantifying circulating levels of EVs with minimal sample processing. One of the benefits of using hrFC is that there is no need to isolate or purify the molecules of interest from the biological samples prior to running the flow. However, signals in hrFC data currently depend on manual and subjective approaches to gating the positive events. Such approaches are often time-consuming, error-prone, and lack the levels of robustness and reproducibility needed to trust the obtained information. This study proposes an automated quantitative technique to process flow cytometry data for EVs with a high degree of accuracy consistency. A publicly available Shiny web application is presented that performs quality check of flow cytometry files and automated gating of biosignals, viz. subpopulations of EVs that are of interest to next generation biomarker studies.