Identification of bacterial species at the single cell level using holographic optical technique.
Employing optical holographic techniques, researchers precise measurements of light scattering from individual bacteria from which ultrafast identification of bacterial species is realized.
Rapid identification of bacterial species is crucial in biomedical applications. One important case would be applications for identifying and curing of sepsis. However, conventional techniques based on cell culturing and administering successive biochemical treatments are arduous and time-consuming. New approaches are required to match the time requirement. Thus, scientific research studies during the past decades have adopted various approaches such as direct sequencing or optical scattering for rapid bacterial identification. However, still most of these schemes have technical limitations: they are not fast enough or require intensive biochemical labeling.
We have recently obtained single-shot measurements involving holographic light scattering from individual bacteria. In this manner, species of bacteria can be identified without using elaborate biochemical labeling agents or additional sample treatments. A research team led by Prof. YongKeun Park in the Department of Physics and also jointly affiliated at the Kaist Institutes (Center for Optics for Health Science) at KAIST recently developed a new technology by combining optical holography and machine learning techniques.
Specifically, a customized digital holographic microscopy was employed to measured the two-dimensional optical field map scattered from a sample. The optical field contains both the intensity and phase information, whereas conventional bright-field microscopy only addresses intensity information. The measurements of optical field information enables one to access light scattering information with both unprecedented precision and sensitivity. Employing this digital holographic microscopy technique, 2-D light scattering patterns from individual bacteria can now be measured for the first time.
The researchers established a method to maximally extract the species-specific light scattering information in the measured light scattering patterns to be exploited for single-bacterial identification via machine learning algorithms. Measured optical field maps were first decomposed into fundamental basis patterns for systematic recognition, and then analyzed employing the algorithms. The morphology of different species of rod-shapes bacteria look similar under conventional optical micrograph analysis. However, the measured 2-D light scattering patterns from individual bacterial contains subtle information corresponding to important differences between them: the distributions and molecular compositions of subcellular structures in bacterial in each species. To identify bacterial species based on the 2-D light scattering patterns, supervised machine learning algorithms were performed for the basis patterns of light scattering patterns. The results show that the accuracy of identification (both the sensitivity and specificity) is higher than 95%.
The research team of Park expects that the present method would will enable ultrafast bacterial species identification, reducing the time cost from days to seconds, when it is combined with an additional technique: high-speed flow cytometry. Utilizing other optical modalities, such as spectral or polarimetric measurements, might further enhance the accuracy of the technique and help generalize the identification of pathogens.
The first part this study on the holographic measurements of light scattering from individual bacteria was published in the journal Scientific Reports in May (2014). The second part on the bacterial identification based on light scattering measurements is now in preparation for submission.
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