Spectral characteristics of urine specimens from healthy human volunteers analyzed using Raman chemometric urinalysis (Rametrix)

Senger, Ryan S.; Kavuru, Varun; Sullivan, Meaghan; Gouldin, Austin; Lundgren, Stephanie; Merrifield, Kristen; Steen, Caitlin; Baker, Emily; Vu, Tommy; Agnor, Ben; Martinez, Gabrielle; Coogan, Hannah; Carswell, William; Karageorge, Lampros; Dev, Devasmita; Du, Pang; Sklar, Allan H.; Orlando, Giuseppe; Pirkle, James L.; Robertson, John L.

doi:10.1371/journal.pone.0222115

Cited by 18 publications

(56 citation statements)

References 16 publications

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“…(1), (2) and (3)) were calculated and used in ANOVA and pairwise comparison tests. As observed here, and shown previously (Senger et al, 2019), statistical calculations with TSD and TPD are very similar, so only TPD and TCD are reported here. All distance data (both raw and mean values) are given in Appendix S1.…”

Section: Glucose-induced Phenotypessupporting

confidence: 83%

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Characterizing glucose, illumination, and nitrogen-deprivation phenotypes ofSynechocystisPCC6803 with Raman spectroscopy

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Denbow

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Background Synechocystis sp. PCC6803 is a model cyanobacterium that has been studied widely and is considered for metabolic engineering applications. Here, Raman spectroscopy and Raman chemometrics (Rametrix™) were used to (i) study broad phenotypic changes in response to growth conditions, (ii) identify phenotypic changes associated with its circadian rhythm, and (iii) correlate individual Raman bands with biomolecules and verify these with more accepted analytical methods. Methods Synechocystis cultures were grown under various conditions, exploring dependencies on light and/or external carbon and nitrogen sources. The Rametrix™ LITE Toolbox for MATLAB® was used to process Raman spectra and perform principal component analysis (PCA) and discriminant analysis of principal components (DAPC). The Rametrix™ PRO Toolbox was used to validate these models through leave-one-out routines that classified a Raman spectrum when growth conditions were withheld from the model. Performance was measured by classification accuracy, sensitivity, and specificity. Raman spectra were also subjected to statistical tests (ANOVA and pairwise comparisons) to identify statistically relevant changes in Synechocystis phenotypes. Finally, experimental methods, including widely used analytical and spectroscopic assays were used to quantify the levels of glycogen, fatty acids, amino acids, and chlorophyll a for correlations with Raman data. Results PCA and DAPC models produced distinct clustering of Raman spectra, representing multiple Synechocystis phenotypes, based on (i) growth in the presence of 5 mM glucose, (ii) illumination (dark, light/dark [12 h/12 h], and continuous light at 20 µE), (iii) nitrogen deprivation (0–100% NaNO3 of native BG-11 medium in continuous light), and (iv) throughout a 24 h light/dark (12 h/12 h) circadian rhythm growth cycle. Rametrix™ PRO was successful in identifying glucose-induced phenotypes with 95.3% accuracy, 93.4% sensitivity, and 96.9% specificity. Prediction accuracy was above random chance values for all other studies. Circadian rhythm analysis showed a return to the initial phenotype after 24 hours for cultures grown in light/dark (12 h/12 h) cycles; this did not occur for cultures grown in the dark. Finally, correlation coefficients (R > 0.7) were found for glycogen, all amino acids, and chlorophyll a when comparing specific Raman bands to other experimental results.

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Section: Glucose-induced Phenotypessupporting

confidence: 83%

“…Here, P x,i is the value of the ith principal component of spectrum x. A more detailed description of the TPD calculation and its use in ANOVA and pairwise comparisons has been published (Senger et al, 2019).…”

Section: Rametrix Tm and Statistical Analysesmentioning

confidence: 99%

Characterizing glucose, illumination, and nitrogen-deprivation phenotypes ofSynechocystisPCC6803 with Raman spectroscopy

Tanniche

Collakova

Denbow

et al. 2020

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“…These are identified for each in Appendix S1 in the Raw Distances and Mean Distances sheets. More details about TPD and its value in statistical calculations have been published (Senger et al, 2019). Similarly, the distance calculation was applied to each spectrum to calculate the Total Spectral Distance (TSD), based on normalized intensity values across all Raman shifts, and the Total Canonical Distance (TCD), based on the first five canonicals in the DAPC model.…”

Section: Methodsmentioning

confidence: 99%

Characterizing metabolic stress-induced phenotypes ofSynechocystisPCC6803 with Raman spectroscopy

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Collakova

Denbow

et al. 2020

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Background During their long evolution, Synechocystis sp. PCC6803 developed a remarkable capacity to acclimate to diverse environmental conditions. In this study, Raman spectroscopy and Raman chemometrics tools (RametrixTM) were employed to investigate the phenotypic changes in response to external stressors and correlate specific Raman bands with their corresponding biomolecules determined with widely used analytical methods. Methods Synechocystis cells were grown in the presence of (i) acetate (7.5–30 mM), (ii) NaCl (50–150 mM) and (iii) limiting levels of MgSO4 (0–62.5 mM) in BG-11 media. Principal component analysis (PCA) and discriminant analysis of PCs (DAPC) were performed with the RametrixTM LITE Toolbox for MATLABⓇ. Next, validation of these models was realized via RametrixTM PRO Toolbox where prediction of accuracy, sensitivity, and specificity for an unknown Raman spectrum was calculated. These analyses were coupled with statistical tests (ANOVA and pairwise comparison) to determine statistically significant changes in the phenotypic responses. Finally, amino acid and fatty acid levels were measured with well-established analytical methods. The obtained data were correlated with previously established Raman bands assigned to these biomolecules. Results Distinguishable clusters representative of phenotypic responses were observed based on the external stimuli (i.e., acetate, NaCl, MgSO4, and controls grown on BG-11 medium) or its concentration when analyzing separately. For all these cases, RametrixTM PRO was able to predict efficiently the corresponding concentration in the culture media for an unknown Raman spectra with accuracy, sensitivity and specificity exceeding random chance. Finally, correlations (R > 0.7) were observed for all amino acids and fatty acids between well-established analytical methods and Raman bands.

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“…The chemical composition, physical characteristics, and types/amounts of suspended materials in urine change when kidney (and systemic) disease is present [1][2][3][4]. In this study, Raman Chemometric Urinalysis (Rametrix TM ) [5][6][7][8] was used to determine if differences in molecular spectra could be detected in the following specimen types: (i) urine from healthy human volunteers, (ii) urine from patients undergoing peritoneal dialysis (PD) therapy for end-stage kidney disease (ESKD) and (iii) spent dialysate from these patients receiving PD therapy. Rametrix TM relies on Raman spectroscopy and a biological region of the spectrum (400-1,800 cm -1 ) that is composed of spectral signatures of the thousands of molecules known to the urine metabolome [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…To achieve useful results, minimal technician training is required, and Rametrix TM software automates analysis. This has enabled studies, such as this and others [5][6][7][8][24][25][26][27], using very large datasets, to discover spectral differences between normal and abnormal (e.g., diseased, stressed, decayed, chemically treated, etc.) samples.…”

Section: Introductionmentioning

confidence: 99%

Spectral characteristics of urine from patients with end-stage kidney disease analyzed using Raman Chemometric Urinalysis (Rametrix)

et al. 2020

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Raman Chemometric Urinalysis (Rametrix TM) was used to discern differences in Raman spectra from (i) 362 urine specimens from patients receiving peritoneal dialysis (PD) therapy for end-stage kidney disease (ESKD), (ii) 395 spent dialysate specimens from those PD therapies, and (iii) 235 urine specimens from healthy human volunteers. Rametrix TM analysis includes spectral processing (e.g., truncation, baselining, and vector normalization); principal component analysis (PCA); statistical analyses (ANOVA and pairwise comparisons); discriminant analysis of principal components (DAPC); and testing DAPC models using a leave-one-out build/test validation procedure. Results showed distinct and statistically significant differences between the three types of specimens mentioned above. Further, when introducing "unknown" specimens, Rametrix TM was able to identify the type of specimen (as PD patient urine or spent dialysate) with better than 98% accuracy, sensitivity, and specificity. Rametrix TM was able to identify "unknown" urine specimens as from PD patients or healthy human volunteers with better than 96% accuracy (with better than 97% sensitivity and 94% specificity). This demonstrates that an entire Raman spectrum of a urine or spent dialysate specimen can be used to determine its identity or the presence of ESKD by the donor.

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Spectral characteristics of urine specimens from healthy human volunteers analyzed using Raman chemometric urinalysis (Rametrix)

Cited by 18 publications

References 16 publications

Characterizing glucose, illumination, and nitrogen-deprivation phenotypes ofSynechocystisPCC6803 with Raman spectroscopy

Characterizing glucose, illumination, and nitrogen-deprivation phenotypes ofSynechocystisPCC6803 with Raman spectroscopy

Characterizing metabolic stress-induced phenotypes ofSynechocystisPCC6803 with Raman spectroscopy

Spectral characteristics of urine from patients with end-stage kidney disease analyzed using Raman Chemometric Urinalysis (Rametrix)

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