Towards Raman spectroscopy of urine as screening tool

Žukovskaja, Olga; Ryabchykov, Oleg; Straßburger, Maria; Heinekamp, Thorsten; Brakhage, Axel A.; Hennings, Christopher J; Hübner, Christian A.; Wegmann, Michael; Cialla‐May, Dana; Bocklitz, Thomas; Weber, Karina; Popp, Jürgen

doi:10.1002/jbio.201900143

Cited by 20 publications

(17 citation statements)

References 39 publications

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“…Within this study, we investigate the potential of Raman microspectroscopy for the rapid detection of TAFC from urine samples. Raman spectroscopy provides a very high specificity due to the chemical information concealed in the fingerprint region of the spectra which allows the unambiguous identification of various analytes and biomarkers [14]. Acquiring spectra is usually achieved within seconds and nowadays cost-efficient portable handheld devices are available, enabling the use of Raman spectroscopy for point-of-care applications [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Rapid detection of the aspergillosis biomarker triacetylfusarinine C using interference-enhanced Raman spectroscopy

et al. 2020

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Triacetylfusarinine C (TAFC) is a siderophore produced by certain fungal species and might serve as a highly useful biomarker for the fast diagnosis of invasive aspergillosis. Due to its renal elimination, the biomarker is found in urine samples of patients suffering from Aspergillus infections. Accordingly, non-invasive diagnosis from this easily obtainable body fluid is possible. Within our contribution, we demonstrate how Raman microspectroscopy enables a sensitive and specific detection of TAFC. We characterized the TAFC iron complex and its iron-free form using conventional and interference-enhanced Raman spectroscopy (IERS) and compared the spectra with the related compound ferrioxamine B, which is produced by bacterial species. Even though IERS only offers a moderate enhancement of the Raman signal, the employment of respective substrates allowed lowering the detection limit to reach the clinically relevant range. The achieved limit of detection using IERS was 0.5 ng of TAFC, which is already well within the clinically relevant range. By using an extraction protocol, we were able to detect 1.4 μg/mL TAFC via IERS from urine within less than 3 h including sample preparation and data analysis. We could further show that TAFC and ferrioxamine B can be clearly distinguished by means of their Raman spectra even in very low concentrations. Electronic supplementary material The online version of this article (10.1007/s00216-020-02571-2) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Rapid detection of the aspergillosis biomarker triacetylfusarinine C using interference-enhanced Raman spectroscopy

et al. 2020

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“…The spectrum exhibits a well-established pattern that is typical for bacterial spectra, where the peaks at 786, 1242, 1335, 1362, 1485, 1530, and 1575 cm −1 originate primarily from nucleic acid residues and the peaks at 762, 831, 855, 1014, 1176, 1212, and 1617 cm −1 from aromatic amino acids [28,39,[44][45][46][47][48]. The peaks at 1242 and 1335 cm −1 represent a mixture of signals from nucleic acids and aromatic amino acids [44].…”

Section: Comparison Of Raman Spectra Of Resistant and Sensitive Bacteriamentioning

confidence: 81%

Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy

et al. 2022

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In recent years, we have seen a steady rise in the prevalence of antibiotic-resistant bacteria. This creates many challenges in treating patients who carry these infections, as well as stopping and preventing outbreaks. Identifying these resistant bacteria is critical for treatment decisions and epidemiological studies. However, current methods for identification of resistance either require long cultivation steps or expensive reagents. Raman spectroscopy has been shown in the past to enable the rapid identification of bacterial strains from single cells and cultures. In this study, Raman spectroscopy was applied for the differentiation of resistant and sensitive strains of Escherichia coli. Our focus was on clinical multi-resistant (extended-spectrum β-lactam and carbapenem-resistant) bacteria from hospital patients. The spectra were collected using both UV resonance Raman spectroscopy in bulk and single-cell Raman microspectroscopy, without exposure to antibiotics. We found resistant strains have a higher nucleic acid/protein ratio, and used the spectra to train a machine learning model that differentiates resistant and sensitive strains. In addition, we applied a majority of voting system to both improve the accuracy of our models and make them more applicable for a clinical setting. This method could allow rapid and accurate identification of antibiotic resistant bacteria, and thus improve public health. Graphical abstract

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“…[9][10][11][12][13][14] Raman spectroscopy is a well-documented spectrographic method of examining the molecular composition of both solids and solutions. 3,9,13,15,16 When examining even dilute and complex aqueous solutions (such as urine), 3,[9][10][11][12][13][15][16][17][18][19][20] Raman spectroscopy may take on the order of seconds to generate spectral data, with minimal spectral interference from water. Analysis of aqueous solution spectra involves data transformation in the form of baseline correction, truncation, normalization, and statistical processing.…”

Section: Introductionmentioning

confidence: 99%

Raman Spectroscopic Detection and Quantification of Macro- and Microhematuria in Human Urine

2022

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Hematuria refers to the presence of blood in urine. Even in small amounts, it may be indicative of disease, ranging from urinary tract infection to cancer. Here, Raman spectroscopy was used to detect and quantify macro- and microhematuria in human urine samples. Anticoagulated whole blood was mixed with freshly collected urine to achieve concentrations of 0, 0.25, 0.5, 1, 2, 6, 10, and 20% blood/urine (v/v). Raman spectra were obtained at 785 nm and data analyzed using chemometric methods and statistical tests with the Rametrix toolboxes for Matlab. Goldindec and iterative smoothing splines with root error adjustment (ISREA) baselining algorithms were used in processing and normalization of Raman spectra. Rametrix was used to apply principal component analysis (PCA), develop discriminate analysis of principal component (DAPC) models, and to validate these models using external leave-one-out cross-validation (LOOCV). Discriminate analysis of principal component models were capable of detecting various levels of microhematuria in unknown urine samples, with prediction accuracies of 91% (using Goldindec spectral baselining) and 94% (using ISREA baselining). Partial least squares regression (PLSR) was then used to estimate/quantify the amount of blood (v/v) in a urine sample, based on its Raman spectrum. Comparing actual and predicted (from Raman spectral computations) hematuria levels, a coefficient of determination (R2) of 0.91 was obtained over all hematuria levels (0–20% v/v), and an R2 of 0.92 was obtained for microhematuria (0–1% v/v) specifically. Overall, the results of this preliminary study suggest that Raman spectroscopy and chemometric analyses can be used to detect and quantify macro- and microhematuria in unprocessed, clinically relevant urine specimens.

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Towards Raman spectroscopy of urine as screening tool

Cited by 20 publications

References 39 publications

Rapid detection of the aspergillosis biomarker triacetylfusarinine C using interference-enhanced Raman spectroscopy

Rapid detection of the aspergillosis biomarker triacetylfusarinine C using interference-enhanced Raman spectroscopy

Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy

Raman Spectroscopic Detection and Quantification of Macro- and Microhematuria in Human Urine

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