The presence of abnormal cell populations in patient samples is diagnostic for a variety of human diseases, especially leukemias and lymphomas. One of the main technologies used for cell-based diagnostic evaluation is flow cytometry, which employs fluorescent reagents to measure molecular characteristics of cell populations in complex mixtures. While cytometry evaluation is routinely used for the diagnosis of blood-borne malignancies, it could be more widely applied to the diagnosis of other diseases (e.g. asthma, allergy and autoimmunity) if it could be reproducibly used to interpret higher complexity staining panels and recognize more subtle cell population differences. Flow cytometry analysis is also widely used for single cell phenotyping in translational research studies to explore the mechanisms of normal and abnormal biological processes. More recently, the development of mass cytometry promises to further increase the application of single cell cytometry evaluation to understand a wide range of physiological, pathological and therapeutic processes. The current practice for cytometry data analysis relies on ?manual gating? of two-dimensional data plots to identify cell subsets in complex mixtures. However, this process is subjective, labor intensive, and irreproducible making it difficult to deploy in multicenter translational research studies or clinical trials where protocol standardization and harmonization are essential. The goal of this project is to develop, validate and disseminate a user-friendly infrastructure for the computational analysis of cytometry data for both diagnostic and discovery applications that could help overcome the current limitations of manual analysis and provide for more efficient, objective and accurate analysis, through the following aims:
Specific Aim 1 ? Implement a novel computational infrastructure ? FlowGate ? for cytometry data analysis that includes visual analytics and machine learning;
Specific Aim 2 ? Assess the utility of FlowGate for cell population characterization in mechanistic translational research studies (T1);
Specific Aim 3 ? Assess the robustness and accuracy of FlowGate for clinical diagnostics in comparison with the current standard-of-care analysis of diagnostic cytometry data (T2);
Specific Aim 4 ? Develop training and educational resources and conduct directed outreach activities to stimulate adoption and use of the resulting FlowGate cyberinfrastructure. The project will have a major impact in advancing translational science by overcoming key hurdles for adoption of these computational methods by facilitating analysis pipeline optimization, providing intuitive user interfacing, and delivering directed training activities. The application of the developed computational infrastructure for improved diagnostics of AML and CLL will contribute to the new emphasis on precision medicine by more precisely quantifying the patient-specific characteristics of neoplastic and normal reactive cell populations. Although FlowGate will be developed by the UC San Diego, UC Irvine, and Stanford CTSAs, the resulting computational infrastructure will be made freely available to the entire research community.
Flow cytometry analysis is widely used for single cell phenotyping in the translational research lab to explore the mechanisms of normal and abnormal biological processes and in the clinical diagnostic lab for the identification and classification of blood-borne malignancies. However, the current practice for cytometry data analysis using ?manual gating? based on two-dimensional data plots is subjective, labor-intensive and unreliable, especially when using more complex high content staining panels. The goal of this project is to develop, validate and disseminate a user-friendly computational infrastructure for cytometry data analysis for both diagnostic and discovery applications that could help overcome the current limitations of manual analysis and provide for more efficient, objective, accurate, and reproducible analysis of cytometry data.
| Lee, Alexandra J; Chang, Ivan; Burel, Julie G et al. (2018) DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data. Cytometry A 93:597-610 |
| Scheuermann, Richard H; Bui, Jack; Wang, Huan-You et al. (2017) Automated Analysis of Clinical Flow Cytometry Data: A Chronic Lymphocytic Leukemia Illustration. Clin Lab Med 37:931-944 |
