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

Autoři: Ryan S. Senger aff001;  Varun Kavuru aff004;  Meaghan Sullivan aff001;  Austin Gouldin aff001;  Stephanie Lundgren aff001;  Kristen Merrifield aff001;  Caitlin Steen aff001;  Emily Baker aff001;  Tommy Vu aff002;  Ben Agnor aff001;  Gabrielle Martinez aff001;  Hana Coogan aff001;  William Carswell aff001;  Lampros Karageorge aff004;  Devasmita Dev aff004;  Pang Du aff005;  Allan Sklar aff006;  Giuseppe Orlando aff007;  James Pirkle, Jr aff008;  John L. Robertson aff003
Působiště autorů: Department of Biological Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States of America aff001;  Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia, United States of America aff002;  DialySenors, Inc., Blacksburg, Virginia, United States of America aff003;  Veteran Affairs Medical Center, Salem, Virginia, United States of America aff004;  Department of Statistics, Virginia Tech, Blacksburg, Virginia, United States of America aff005;  Lewis-Gale Medical Center, Salem, Virginia, United States of America aff006;  Department of Surgical Sciences – Transplant, Wake Forest University Baptist Medical Center, Winston-Salem, North Carolina, United States of America aff007;  Department of Internal Medicine – Nephrology, Wake Forest University Baptist Medical Center, Winston-Salem, North Carolina, United States of America aff008;  Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, Virginia, United States of America aff009;  Virginia Tech-Carilion School of Medicine and Research Institute, Blacksburg, Virginia, United States of America aff010
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222115


Raman chemometric urinalysis (Rametrix) was used to analyze 235 urine specimens from healthy individuals. The purpose of this study was to establish the “range of normal” for Raman spectra of urine specimens from healthy individuals. Ultimately, spectra falling outside of this range will be correlated with kidney and urinary tract disease. Rametrix analysis includes direct comparisons of Raman spectra but also principal component analysis (PCA), discriminant analysis of principal components (DAPC) models, multivariate statistics, and it is available through GitHub as the Rametrix LITE Toolbox for MATLAB®. Results showed consistently overlapping Raman spectra of urine specimens with significantly larger variances in Raman shifts, found by PCA, corresponding to urea, creatinine, and glucose concentrations. A 2-way ANOVA test found that age of the urine specimen donor was statistically significant (p < 0.001) and donor sex (female or male identification) was less so (p = 0.0526). With DAPC models and blind leave-one-out build/test routines using the Rametrix PRO Toolbox (also available through GitHub), an accuracy of 71% (sensitivity = 72%; specificity = 70%) was obtained when predicting whether a urine specimen from a healthy unknown individual was from a female or male donor. Finally, from female and male donors (n = 4) who contributed first morning void urine specimens each day for 30 days, the co-occurrence of menstruation was found statistically insignificant to Rametrix results (p = 0.695). In addition, Rametrix PRO was able to link urine specimens with the individual donor with an average of 78% accuracy. Taken together, this study established the range of Raman spectra that could be expected when obtaining urine specimens from healthy individuals and analyzed by Rametrix and provides the methodology for linking results with donor characteristics.

Klíčová slova:

Biomarkers – Creatinine – Glucose – Chronic kidney disease – Kidneys – principal component analysis – Urine – Urea


1. Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, et al. The Human Urine Metabolome. PLOS ONE. 2013;8: e73076. doi: 10.1371/journal.pone.0073076 24023812

2. Christians U, McCrery S, Klawitter J, Klawitter J. Chapter 4—The Role of Proteomics in the Study of Kidney Diseases and in the Development of Diagnostic Tools. In: Edelstein CL, editor. Biomarkers of Kidney Disease. San Diego: Academic Press; 2011. pp. 101–176.

3. Bennett MR, Devarajan P. Chapter One—Characteristics of an Ideal Biomarker of Kidney Diseases. In: Edelstein CL, editor. Biomarkers of Kidney Disease (Second Edition). Academic Press; 2017. pp. 1–20.

4. Edelstein C. Biomarkers of Acute Kidney Injury. Biomarkers of Kidney Disease. 2nd ed. Academic Press; 2017.

5. Reichel RR. Acute Kidney Injury: Quoi de Neuf? Ochsner J. 2014;14: 359–368. 25249802

6. Shardlow A, McIntyre NJ, Fraser SDS, Roderick P, Raftery J, Fluck RJ, et al. The Clinical Utility and Cost Impact of Cystatin C Measurement in the Diagnosis and Management of Chronic Kidney Disease: A Primary Care Cohort Study. PLoS Med. 2017;14: e1002400. doi: 10.1371/journal.pmed.1002400 29016597

7. Prakoura N, Chatziantoniou C. Periostin in Kidney Diseases. Cell Mol Life Sci CMLS. 2017;74: 4315–4320. doi: 10.1007/s00018-017-2650-6 28884334

8. Senger R, Du P, DeLaTorre Campos D, Carswell W, Webster K, Sullivan M, et al. Assessment of Urine Specimen Storage Conditions using Raman Chemometric Urinalysis (Rametrix™). Submitted to PeerJ. 2019;

9. Fisher AK, Carswell WF, Athamneh AIM, Sullivan MC, Robertson JL, Bevan DR, et al. The Rametrix™ LITE Toolbox v1.0 for MATLAB®. J Raman Spectrosc. 2018;49: 885–896. doi: 10.1002/jrs.5348

10. Senger R, Robertson J. The Rametrix™ PRO Toolbox v1.0 for MATLAB®. Submitted to PeerJ. 2019;

11. Gardiner D. Practical Raman spectroscopy. Springer-Verlag; 1989.

12. Athamneh AIM, Senger RS. Peptide-Guided Surface-Enhanced Raman Scattering Probes for Localized Cell Composition Analysis. Appl Env Microbiol. 2012;78: 7805–7808. doi: 10.1128/AEM.02000-12 22923413

13. Athamneh AIM, Alajlouni RA, Wallace RS, Seleem MN, Senger RS. Phenotypic Profiling of Antibiotic Response Signatures in Escherichia coli Using Raman Spectroscopy. Antimicrob Agents Chemother. 2014;58: 1302–1314. doi: 10.1128/AAC.02098-13 24295982

14. Zu TNK, Athamneh AIM, Collakova E, Robertson J, Hawken T, Aardema C, et al. Assessment of ex vivo Perfused Liver Health by Raman Spectroscopy. J Raman Spectrosc. 2015;46: 551–558. doi: 10.1002/jrs.4688

15. Zu TNK, Athamneh AIM, Wallace RS, Collakova E, Senger RS. Near-Real-Time Analysis of the Phenotypic Responses of Escherichia coli to 1-Butanol Exposure Using Raman Spectroscopy. J Bacteriol. 2014;196: 3983–3991. doi: 10.1128/JB.01590-14 25157078

16. Movasaghi Z, Rehman S, Rehman DIU. Raman Spectroscopy of Biological Tissues. Appl Spectrosc Rev. 2007;42: 493–541. doi: 10.1080/05704920701551530

17. Liu J, Sun J, Huang X, Li G, Liu B. Goldindec: A Novel Algorithm for Raman Spectrum Baseline Correction. Appl Spectrosc. 2015;69: 834–42. doi: 10.1366/14-07798 26037638

Článek vyšel v časopise


2019 Číslo 9

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…

Kurzy Doporučená témata