Background and Objectives Flow cytometry (FCM) is widely used in clinical research and offers rapid and quantitative characterisation at single cell level. Traditional analysis is a semiautomated, time-consuming process of gating and successive 2-D projections, influenced by investigator-specific settings. With an increasing number of parameters for multiplexing, the manual analysis step is most limiting and impedes high throughput analysis in FCM. Therefore, we developed a new algorithm for automated and standardised analysis of multiplex FCM data.
Materials and Methods Automation included asinh-transformation of data, cell grouping, population detection and population feature extraction. For grouping of cells, an unbiased unsupervised model based t-mixture approach with Expectation Maximisation (EM)-iteration was applied. Populations were detected and identified by meta-clustering of several experiments according to position and extension of cell-clusters in multi-dimensional space and by including a General Procrustes Analysis (GPA) step. For validation, peripheral leukocytes from healthy donors and patients with rheumatoid arthritis (RA) were prepared by hypoosmotic erythrocyte lysis and stained with different sets of lineage-specific antibodies, including CD3, CD4, CD8, CD56, CD19, CD14 and CD15. In parallel, different leukocyte samples were depleted of one of these populations by magnetic beads. Qualitative and quantitative characteristics of major populations were compared with conventional manual analysis.
Results Whole blood leukocytes stained simultaneously with up to 7 markers were correctly distinguished in all major populations including granulocytes (CD15+), T-cells and their subpopulations (CD3+, CD4+, CD8+), monocytes (CD14+), B-cells (CD19+), and NK-cells (CD56+). The result was comparable to the “gold standard” of manual evaluation by an expert. The new technology is able to detect subclusters and to characterise so far neglected smaller populations based on the new parameters generated. Automated clustering did not require fluorescence compensation of data. Cell-grouping is applicable even for large FCM datasets of at least 10 parameters and more than 1 million events. Comparing the cell-clusters between RA and healthy controls, differences were detectable in several cell (sub-)populations, stable enough to perform correct classification into controls and disease.
Conclusions Our approach reveals first promising results for the analysis of large datasets as generated by multiplex FCM analysis in an automated and time-saving way. Defined clustering algorithms avoid operator-induced bias. In addition, our unsupervised procedure is able to detect unexpected sub-clusters and to characterise so far neglected smaller populations, which may help not only to distinguish normal from disease but also to develop markers for disease activity and therapeutic stratification.
Acknowledgement BTCure IMI grant agreement no. 115142.
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