| Title: | Rapid in vitro differentiation of bacteria by ion mobility spectrometry |
| Author(s): | Steppert I; Schonfelder J; Schultz C; Kuhlmeier D; |
| Address: | "MicroDiagnostics, Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany. Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany. MicroDiagnostics, Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany. jessy.schoenfelder@izi.fraunhofer.de. Project Hub Microelectronic and Optical Systems for Biomedicine MEOS, Fraunhofer Institute for Cell Therapy and Immunology IZI, Erfurt, Germany. jessy.schoenfelder@izi.fraunhofer.de. Project Hub Microelectronic and Optical Systems for Biomedicine MEOS, Fraunhofer Institute for Cell Therapy and Immunology IZI, Erfurt, Germany" |
| Journal Title: | Appl Microbiol Biotechnol |
| DOI: | 10.1007/s00253-021-11315-w |
| ISSN/ISBN: | 1432-0614 (Electronic) 0175-7598 (Print) 0175-7598 (Linking) |
| Abstract: | "Rapid screening of infected people plays a crucial role in interrupting infection chains. However, the current methods for identification of bacteria are very tedious and labor intense. Fast on-site screening for pathogens based on volatile organic compounds (VOCs) by ion mobility spectrometry (IMS) could help to differentiate between healthy and potentially infected subjects. As a first step towards this, the feasibility of differentiating between seven different bacteria including resistant strains was assessed using IMS coupled to multicapillary columns (MCC-IMS). The headspace above bacterial cultures was directly drawn and analyzed by MCC-IMS after 90 min of incubation. A cluster analysis software and statistical methods were applied to select discriminative VOC clusters. As a result, 63 VOC clusters were identified, enabling the differentiation between all investigated bacterial strains using canonical discriminant analysis. These 63 clusters were reduced to 7 discriminative VOC clusters by constructing a hierarchical classification tree. Using this tree, all bacteria including resistant strains could be classified with an AUC of 1.0 by receiver-operating characteristic analysis. In conclusion, MCC-IMS is able to differentiate the tested bacterial species, even the non-resistant and their corresponding resistant strains, based on VOC patterns after 90 min of cultivation. Although this result is very promising, in vivo studies need to be performed to investigate if this technology is able to also classify clinical samples. With a short analysis time of 5 min, MCC-IMS is quite attractive for a rapid screening for possible infections in various locations from hospitals to airports.Key Points* Differentiation of bacteria by MCC-IMS is shown after 90-min cultivation.* Non-resistant and resistant strains can be distinguished.* Classification of bacteria is possible based on metabolic features" |
| Keywords: | Bacteria Humans *Ion Mobility Spectrometry *Volatile Organic Compounds Antibiotic resistance Bacteria identification Diagnostic Infection Ion mobility spectrometry (IMS) Volatile organic compounds (VOC); |
| Notes: | "MedlineSteppert, Isabel Schonfelder, Jessy Schultz, Carolyn Kuhlmeier, Dirk eng 03ZZ0812B/BMBF/ Germany 2021/05/12 Appl Microbiol Biotechnol. 2021 May; 105(10):4297-4307. doi: 10.1007/s00253-021-11315-w. Epub 2021 May 11" |
