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Stripping flow cytometry: how many detectors do we need for bacterial identification?
Rubbens, P.; Props, R.; Garcia-Timermans, C.; Boon, N.; Waegeman, W. (2017). Stripping flow cytometry: how many detectors do we need for bacterial identification? Cytometry A 91(12): 1184-1191. https://hdl.handle.net/10.1002/cyto.a.23284
In: Cytometry Part A. Interscience/Wiley: Hoboken, N.J.. ISSN 1552-4922; e-ISSN 1552-4930, more
Peer reviewed article  

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  • Rubbens, P., more
  • Props, R., more
  • Garcia-Timermans, C., more
  • Boon, N., more
  • Waegeman, W.

Abstract
    Multicolor approaches are challenging for microbial flow cytometry; as flow cytometers are mainly developed for biomedical applications, modern instruments contain more detectors than needed. Some of these additional fluorescence detectors measure biological information due to spectral overlap, yet the extent to which this information is relevant for the identification of bacterial populations is ambiguous. In this paper we characterize the usefulness of these additional detectors. We propose a data‐driven detector selection method to select the smallest subset of detectors that will optimally discriminate between bacterial populations. Using a detector elimination strategy, we show that one or more detectors can be removed without loss of resolving power. A number of additional detectors are included in the final subset, which help to improve the identification of bacterial populations. Experimental data were retrieved from two types of modern cytometers with different configurations. The method reveals a clear ordering of detector importances, which depends on the instrument from which the data were retrieved. In addition, we were able to pinpoint unexpected behavior of SYBR Green I in the red spectrum. As the field of microbial flow cytometry is maturing, these results motivate the construction of a different kind of cytometric instruments for microbiologists, for which the number of detectors is reduced, but tailored toward the characteristics of microbial experiments.

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