Literature DB >> 26041453

Destruction-free procedure for the isolation of bacteria from sputum samples for Raman spectroscopic analysis.

Sandra Kloß1,2, Björn Lorenz1,2, Stefan Dees2,3, Ines Labugger2,3, Petra Rösch1,2, Jürgen Popp4,5,6.   

Abstract

Lower respiratory tract infections are the fourth leading cause of death worldwide. Here, a timely identification of the causing pathogens is crucial to the success of the treatment. Raman spectroscopy allows for quick identification of bacterial cells without the need for time-consuming cultivation steps, which is the current gold standard to detect pathogens. However, before Raman spectroscopy can be used to identify pathogens, they have to be isolated from the sample matrix, i.e., sputum in case of lower respiratory tract infections. In this study, we report an isolation protocol for single bacterial cells from sputum samples for Raman spectroscopic identification. Prior to the isolation, a liquefaction step using the proteolytic enzyme mixture Pronase E is required in order to deal with the high viscosity of sputum. The extraction of the bacteria was subsequently performed via different filtration and centrifugation steps, whereby isolation ratios between 46 and 57 % were achieved for sputa spiked with 6·10(7) to 6·10(4) CFU/mL of Staphylococcus aureus. The compatibility of such a liquefaction and isolation procedure towards a Raman spectroscopic classification was shown for five different model species, namely S. aureus, Staphylococcus epidermidis, Streptococcus pneumoniae, Klebsiella pneumoniae, and Pseudomonas aeruginosa. A classification of single-cell Raman spectra of these five species with an accuracy of 98.5 % could be achieved on the basis of a principal component analysis (PCA) followed by a linear discriminant analysis (LDA). These classification results could be validated with an independent test dataset, where 97.4 % of all spectra were identified correctly. Graphical Abstract Development of an isolation protocol of bacterial cells out of sputum samples followed by Raman spectroscopic measurement and species identification using chemometrical models.

Entities:  

Keywords:  Isolation; Raman spectroscopy; Single-cell pathogen identification; Sputum

Mesh:

Year:  2015        PMID: 26041453     DOI: 10.1007/s00216-015-8743-x

Source DB:  PubMed          Journal:  Anal Bioanal Chem        ISSN: 1618-2642            Impact factor:   4.142


  8 in total

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Journal:  Molecules       Date:  2020-11-11       Impact factor: 4.411

Review 2.  Methodological tools to study species of the genus Burkholderia.

Authors:  Viola Camilla Scoffone; Gabriele Trespidi; Giulia Barbieri; Samuele Irudal; Aygun Israyilova; Silvia Buroni
Journal:  Appl Microbiol Biotechnol       Date:  2021-11-10       Impact factor: 4.813

Review 3.  Raman Spectroscopy-A Novel Method for Identification and Characterization of Microbes on a Single-Cell Level in Clinical Settings.

Authors:  Katarina Rebrosova; Ota Samek; Martin Kizovsky; Silvie Bernatova; Veronika Hola; Filip Ruzicka
Journal:  Front Cell Infect Microbiol       Date:  2022-04-22       Impact factor: 6.073

4.  Raman Spectroscopic Differentiation of Streptococcus pneumoniae From Other Streptococci Using Laboratory Strains and Clinical Isolates.

Authors:  Marcel Dahms; Simone Eiserloh; Jürgen Rödel; Oliwia Makarewicz; Thomas Bocklitz; Jürgen Popp; Ute Neugebauer
Journal:  Front Cell Infect Microbiol       Date:  2022-07-22       Impact factor: 6.073

5.  Multi-channel AgNWs-doped interdigitated organic electrochemical transistors enable sputum-based device towards noninvasive and portable diagnosis of lung cancer.

Authors:  Ru Zhang; Jing Zhang; Fei Tan; Deqi Yang; Bingfang Wang; Jing Dai; Yin Qi; Linyu Ran; Wenjuan He; Yingying Lv; Feilong Wang; Yin Fang
Journal:  Mater Today Bio       Date:  2022-08-05

6.  Label-free optical vibrational spectroscopy to detect the metabolic state of M. tuberculosis cells at the site of disease.

Authors:  Vincent O Baron; Mingzhou Chen; Simon O Clark; Ann Williams; Robert J H Hammond; Kishan Dholakia; Stephen H Gillespie
Journal:  Sci Rep       Date:  2017-08-29       Impact factor: 4.379

7.  A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species.

Authors:  Amira A Moawad; Anja Silge; Thomas Bocklitz; Katja Fischer; Petra Rösch; Uwe Roesler; Mandy C Elschner; Jürgen Popp; Heinrich Neubauer
Journal:  Molecules       Date:  2019-12-10       Impact factor: 4.411

8.  Isolation of bacteria from artificial bronchoalveolar lavage fluid using density gradient centrifugation and their accessibility by Raman spectroscopy.

Authors:  Christina Wichmann; Petra Rösch; Jürgen Popp
Journal:  Anal Bioanal Chem       Date:  2021-07-02       Impact factor: 4.142

  8 in total

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