The Rametrix™ LITE Toolbox v1.0 for MATLAB^®

Abstract

To contribute to the growing interest in using Raman spectroscopy to analyze biological samples and provide chemometric analysis, we have developed a Raman Chemometrics (Rametrix™) Toolbox for use with MATLAB®. The LITE version of the Rametrix™ Toolbox is free to academic users through GitHub (https://github.com/SengerLab/RametrixLITEToolbox) and provides a graphical user interface for application of the following to Raman spectra: baseline correction with the Goldindec algorithm, vector or specific band normalization, principal component analysis (PCA), discriminant analysis of principal components (DAPC), identification of wavenumber loadings for PCA and DAPC, and calculation of total canonical distance. Raman spectroscopy and analysis with the Rametrix™ LITE Toolbox were applied to generate calibration curves, monitor enzymatic reactions, and track Escherichia coli culture growth. Results were quantitatively consistent with traditional methods of analysis. Additionally, the ability to distinguish urine specimens from healthy individuals and from patients receiving treatment for chronic kidney disease through peritoneal dialysis was demonstrated using PCA and DAPC of Raman spectra, suggesting future applications to detect or monitor progression of the disease. Overall, the Rametrix™ LITE Toolbox provides a streamlined application of PCA and DAPC chemometric techniques, and total canonical distance offers an additional quantitative measure to interpret Raman spectra of biological samples.

Recent advances in linear and nonlinear Raman spectroscopy. Part XIII

“…Fisher and co‐workers described The Rametrix® LITE Toolbox v1.0 for MATLAB. Overall, this software provides a streamlined application of principal component analysis and discriminate analysis of principal components chemometric techniques, and total canonical distance offers an additional quantitative measure to interpret Raman spectra of biological samples . Hunter and Anis explored genetic support vector machines as powerful tools for the analysis of biomedical Raman spectra.…”

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

“…Raman data were collected/visualized using RSIQ TM software (Agiltron) and analyzed further with the Rametrix TM LITE (Fisher et al, 2018) and PRO (Senger & Robertson, 2020) Toolboxes for MATLAB R . All calculations were performed using MATLAB R r2018a (Mathworks; Natick, MA) and required the Statistics and Machine Learning Toolbox.…”

“…PCC6803 under the following growth conditions: (i) autotrophic, (ii) mixotrophic, (iii) photoautotrophic, (iv) photomixotrophic, (v) nitrogen deprivation, (vi) dark autotrophic (control), and (vii) dark heterotrophic (control). Cells were analyzed by Raman microscopy, and the resulting spectra were processed using the Rametrix TM LITE (Fisher et al, 2018) and PRO (Senger & Robertson, 2020) Toolboxes in MATLAB R . Averaged, truncated (400-1,800 cm −1 ), baselined, and vector normalized Raman spectra are shown in Fig.…”

“…Although acclimation of Synechocystis to the nutrient availability in the environment has been evaluated at the transcriptional and physiological levels (Hihara et al, 2001;Yang, Hua & Shimizu, 2002;Imamura et al, 2006;Takahashi, Uchimiya & Hihara, 2008;Beck et al, 2014;Saha et al, 2016), the approaches used were time and resource-demanding. In the effort to develop a method, by which Synechocystis (or any microorganism) can be rapidly and inexpensively identified/characterized, we chose to evaluate Synechocystis phenotypes of the circadian rhythm with Raman spectroscopy and Rametrix TM (Fisher et al, 2018). This powerful analytical technique has been applied to a wide range of biological samples (Das & Agrawal, 2011) including whole-cell bacteria (Jarvis & Goodacre, 2004;Gaus et al, 2006;Pahlow et al, 2015).…”

“…We also included (vi) an autotrophic culture grown in the dark (i.e., dark autotrophic) and (vii) a heterotrophic culture grown on 5 mM glucose and in the dark (i.e., dark heterotrophic) as a controls. This research tested the hypothesis that Raman spectroscopy and chemometric analysis using Rametrix TM LITE (Fisher et al, 2018) and PRO (Senger & Robertson, 2020) Toolboxes for MATLAB R and statistical analyses could distinguish between different Synechocystis phenotypes arising from the above experimental conditions. This included determining levels of some metabolites and macromolecules (glycogen, amino acids, fatty acids and chlorophyll) from Raman spectra and validating with accepted analytical methods.…”