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<p>The accurate selection of living from dead pathogenic cells is crucial as exemplified in the context of detecting <i>Legionella</i> bacteria, which can be present in various water facilities and pose a threat to public health by causing severe respiratory problems. Traditional methods for <i>Legionella</i> detection, such as cultivation, are time-consuming, taking several days to yield valid results. Additionally, widely used bioanalytical methods like PCR lack the ability to distinguish between living and dead cells, leading to the potential for false-positive results. While dielectrophoresis has been proposed as a promising method for separating living and dead cells, our study contrasts with existing literature, revealing that the separation process and parameter characterization are non-trivial. In response to this challenge, our work introduces a novel, systematic approach of automated video analysis capable of quantifying the dielectrophoretic response of cells. By assigning a response coefficient to the dielectrophoretic effect at different conditions, our method identifies a narrow window for successful cell selection of viable <i>Legionella</i> cells from the non-pathogenic species <i>L. parisiensis</i> utilizing a microfluidic flow cell with top–bottom electrodes. These findings serve as a crucial pre-step in <i>Legionella</i> sensing, demonstrating applicability in experiments focused on the most relevant pathogenic species, <i>L. pneumophila</i>. Moreover, our method can be transferred to other cell types for quantitative detection of the dielectrophoretic response and identify optimal separation parameters.</p>

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Electronic selection of viable Legionella cells by a video-based, quantifiable dielectrophoresis approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月30日

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  • Madeline Altmann,
  • Anders Henriksson,
  • Peter Neubauer,
  • Mario Birkholz

摘要

The accurate selection of living from dead pathogenic cells is crucial as exemplified in the context of detecting Legionella bacteria, which can be present in various water facilities and pose a threat to public health by causing severe respiratory problems. Traditional methods for Legionella detection, such as cultivation, are time-consuming, taking several days to yield valid results. Additionally, widely used bioanalytical methods like PCR lack the ability to distinguish between living and dead cells, leading to the potential for false-positive results. While dielectrophoresis has been proposed as a promising method for separating living and dead cells, our study contrasts with existing literature, revealing that the separation process and parameter characterization are non-trivial. In response to this challenge, our work introduces a novel, systematic approach of automated video analysis capable of quantifying the dielectrophoretic response of cells. By assigning a response coefficient to the dielectrophoretic effect at different conditions, our method identifies a narrow window for successful cell selection of viable Legionella cells from the non-pathogenic species L. parisiensis utilizing a microfluidic flow cell with top–bottom electrodes. These findings serve as a crucial pre-step in Legionella sensing, demonstrating applicability in experiments focused on the most relevant pathogenic species, L. pneumophila. Moreover, our method can be transferred to other cell types for quantitative detection of the dielectrophoretic response and identify optimal separation parameters.