A closed droplet based lab-on-a-chip (LOC) device has been developed for the differentiation of six species of mycobacteria, i.e., both Mycobacterium tuberculosis complex (MTC) and nontuberculous mycobacteria (NTM), using surface-enhanced Raman spectroscopy (SERS). The combination of a fast and simple bead-beating module for the disruption of the bacterial cell with the LOC-SERS device enables the application of an easy and reliable system for bacteria discrimination. Without extraction or further treatment of the sample, the obtained SERS spectra are dominated by the cell-wall component mycolic acid. For the differentiation, a robust data set was recorded using a droplet based LOC-SERS device. Thus, more than 2100 individual SERS spectra of the bacteria suspension were obtained in 1 h. The differentiation of bacteria using LOC-SERS provides helpful information for physicians to define the conditions for the treatment of individual patients.

The detection of M. tuberculosis trDNA from urine specimen is a promising method for the diagnosis tuberculosis. The assay may be a candidate diagnostic tool for patients with paucibacillary and extrapulmonary disease, as method to assess treatment responses and could be helpful to diagnose tuberculosis in children.

In this study, Raman microspectroscopy has been utilized to identify mycobacteria to the species level. Because of the slow growth of mycobacteria, the per se cultivation-independent Raman microspectroscopy emerges as a perfect tool for a rapid on-the-spot mycobacterial diagnostic test. Special focus was laid upon the identification of Mycobacterium tuberculosis complex (MTC) strains, as the main causative agent of pulmonary tuberculosis worldwide, and the differentiation between pathogenic and commensal nontuberculous mycobacteria (NTM). Overall the proposed model considers 26 different mycobacteria species as well as antibiotic susceptible and resistant strains. More than 8800 Raman spectra of single bacterial cells constituted a spectral library, which was the foundation for a two-level classification system including three support vector machines. Our model allowed the discrimination of MTC samples in an independent validation dataset with an accuracy of 94% and could serve as a basis to further improve Raman microscopy as a first-line diagnostic point-of-care tool for the confirmation of tuberculosis disease.

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.

Tuberculosis (TB) diagnosis among sputum-scarce patients is time consuming. Thus, a nonsputum diagnostic alternative is urgently needed. The Mycobacterium tuberculosis-specific transrenal (Tr) DNA from urine is a potential target for TB diagnostics. In this study, a new urine-based Tr-DNA molecular assay was evaluated for diagnosis of pulmonary tuberculosis among 428 adults suspected of having pulmonary TB (164 HIV positive, 263 HIV negative) from Cape Town, South Africa. Tr-DNA was isolated from 4 mL of EDTA urine, and a rapid, double-stranded, primer-based PCR method was performed targeting the Mycobacterium tuberculosis-specific direct repeat region. Each Tr-DNA eluate was tested in triplicate using an automated molecular analyzer with controls included in each test. With liquid culture used as the gold standard, the Tr-DNA assay showed sensitivity of 42.9% (n = 75/175; 95% CI, 35.4%-50.5%) and specificity of 88.6% (n = 210/237; 95% CI, 83.9%-92.4%). Among HIV-infected patients with TB, sensitivity and specificity were 45.2% and 89.0%, respectively. The combination of smear microscopy and Tr-DNA increased the sensitivity to 83.8% (smear microscopy alone, 75.1%), with 96.6% specificity. This study indicates that Tr-DNA has a moderate specificity with low sensitivity for diagnosis of pulmonary TB. Despite low sensitivity, this diagnostic test may have potential in combination with smear microscopy to support TB diagnosis in HIV-endemic regions, where sputum-scarce patients are common.