Spectra pre-processing by atmospheric compensation was conducted with OPUS® software, version 6.5 (Bruker, Ettlingen, Germany), while remaining spectra pre-processing and processing analysis were conducted with Orange3 version 3.35.0 (Bioinformatics Lab., University of Ljubljana, Ljubljana, Slovenia). Spectra baseline correction based on the Rubber Band method, unit vector normalization, and with second derivative spectra, based on a Savitzky–Golay filter with a 2nd polynomial degree, was evaluated. Dimensionality reduction techniques, such as the t-distributed stochastic neighbor embedding method (t-SNE), heatmaps, dot matrix, and hierarchical cluster analysis (HCA), were performed. Feature selection was conducted by an information gain algorithm. Supervised Naïve Bayes models were developed. The Leave-One-Out Cross-Validation (LOOCV) procedure was applied. The models’ performances were assessed by the area under the receiver operating characteristics curve (AUC). The classification accuracy, F-1 score, precision, sensitivity, and specificity corresponded, on the ROC curve, to the minimum distance from the upper-left corner of the unit square, representing the optimal point on the ROC curve where these metrics are maximized.
Opus software version 6
Manufactured by Bruker
Sourced in Germany
OPUS software, version 6.5, is a software package developed by Bruker for the operation and analysis of spectroscopy data. The core function of the software is to provide a comprehensive platform for the acquisition, processing, and interpretation of spectroscopic measurements.
Automatically generated - may contain errors
Lab products found in correlation
Unscrambler x version 10, by CAMO Software (2 mentions)
Tensor 27, by Bruker (2 mentions)
Xy microtiter plate accessory, by PIKE Technologies (2 mentions)
Tensor 27 ftir spectrometer, by Bruker (2 mentions)
Hts xt microplate adapter, by Bruker (2 mentions)
Tryptic soy agar, by Thermo Fisher Scientific (1 mentions)
Tensor 37, by Bruker (1 mentions)
Sigmaultra, by Merck Group (1 mentions)
11 protocols using opus software version 6
1
Spectroscopic Analysis of Biological Samples
2
Spectral Pre-processing and Statistical Analysis
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Spectra were pre-processed by atmospheric correction, baseline correction, unit vector normalization, standard normal variate (SNV), multiplicate scatter correction (MSC), and its extended version (EMSC). First and second derivative spectra were computed from raw spectra pre-processed only with atmospheric correction, using a Savitzky–Golay filter, and a second order polynomial over a 15-point window. Atmospheric and baseline corrections were conducted with OPUS software, version 6.5 (Bruker, Ettlingen, Germany), and all remaining pre-processing work, PCA, HCA (based on Spearman’s rank correction and hierarchical average-linkage), and partial least squares regression associated with discriminant analysis (PLS-DA) were conducted with The Unscrambler X, version 10.5 (CAMO software AS, Oslo, Norway). The statistical analysis by Student’s t-test was conducted on the basis of peak height of the second derivative spectra with vector unit normalization, between 400 and 1800 and 2700 and 4000 cm−1. The Student’s t-test was based on a paired test with two tails, and was performed on Microsoft Excel.
3
Infrared Spectroscopy Analysis of Serum Samples
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Serum samples were thawed at room temperature and replicate (x 6) dry films made for each sample on a silicon 96-well microplate [17 (link),38 (link),39 (link)]. The microplate was mounted on a multi-sampler accessory (XY Microtiter Plate Accessory, PIKE Technologies, Madison, WI, USA) interfaced with an IR spectrometer (Tensor 27, Bruker Optics, Preston, Victoria, Australia). Infrared absorbance spectra in the range of 400 to 4000 cm−1 were generated and recorded with proprietary software (OPUS software, version 6.5, Bruker Optics, Ettlingen, Germany). For each acquisition, 512 interferograms were averaged, and Fourier transformed to obtain a spectrum with a resolution of 4 cm−1.
4
Spectral Analysis of Biomedical Samples
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The spectra data obtained were processed using Opus software, version 6.5 (Bruker Optics, Reinstetten, Germany). Measurements were performed in the mid-infrared region (4000–400 cm−1). For the generation of average spectra and band areas, the spectra were vector normalized and baseline corrected by the Rubberband method to avoid errors during sample preparation and spectra analysis. Band positions were measured using the frequency corresponding to the weight center of each band. Band areas were calculated from normalized and baseline-corrected spectra using OPUS software [16 (link)].
The Kolmogorov–Smirnov test was applied to test the normality of the variables. The band area data were analyzed using the Student’s t-test. Sensitivity and specificity values were calculated from the dataset by applying the ROC curve (Receiver operating characteristic) analysis. All these analyzes were performed using GraphPad Prism software (GraphPad Prism version 7.00 for Windows, GraphPad Software, San Diego, CA, USA). Only p values < 0.05 were considered significant and results were expressed as mean ± S.D.
The Kolmogorov–Smirnov test was applied to test the normality of the variables. The band area data were analyzed using the Student’s t-test. Sensitivity and specificity values were calculated from the dataset by applying the ROC curve (Receiver operating characteristic) analysis. All these analyzes were performed using GraphPad Prism software (GraphPad Prism version 7.00 for Windows, GraphPad Software, San Diego, CA, USA). Only p values < 0.05 were considered significant and results were expressed as mean ± S.D.
5
FTIR Spectroscopy Analysis of Mesenchymal Stem Cells
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FTIR spectra were recovered from the OPUS software version 6.5 (Bruker Optics GmbH, Ettlingen, Germany) and transferred to MS Excel. Principal Component Analysis (PCA) and Significant Wavelengths Analysis (SWA) were performed in an R environment. SWA was employed to select the FTIR spectral regions with statistically significant differences in the comparison between the spectra of parental and MSCs cultures from the different experimental conditions tested [35 (link)]. In addition, pairs of spectra, each with three replicates, were compared using the Student’s t-test for each wavelength separately. For each wave number, the calculated p-value was recorded. Significant wavelengths were selected based on p < 0.01. Hierarchical cluster analysis was performed with MetaboAnalyst 5.0 [36 (link)]. Data were filtered based on interquartile range, normalized to the sample median, and scaled by Pareto scaling. Hierarchical cluster analysis (HCA) was employed to highlight the metabolic differences under stress between MSCs and PS cultures, using the Euclidean correlation method and the ward.D clustering algorithm. Significant wavelengths were selected based on these criteria: t-test (p adjusted < 0.05) and one-way ANOVA (p-value < 0.05).
6
FTIR Spectroscopy of Bacterial Samples
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Samples were harvested by centrifugation (8000× g, 20 min, RT) from 6 to 18 h in two-hour intervals (Supplementary Figure S1 ). The pellet was washed once with 1 mL PBS (5000× g, 2 min, RT) and resuspended in 100 μL of sterile deionised water. Subsequently, 30 μL each of the bacterial suspensions were spotted on a zinc selenite (ZnSe) optical plate, dried at 40 °C for 40 min, and measured with an HTS-XT microplate adapter coupled to a Tensor 27 FTIR spectrometer (Bruker Optics GmbH, Ettlingen, Germany). OPUS software (version 6.5; Bruker Optics GmbH) was used to process the measured FTIR spectra and perform chemometric analysis. Subtraction spectra were generated from second derivative, vector-normalised, average FTIR spectra by subtracting HA spectra from IN spectra for each time point. For a hierarchical cluster analysis (HCA), the spectral region that offers information about carbohydrate constituents (1200–800 cm−1) was selected, which was shown to be highly discriminatory, based on the expression of CP and/or other cell surface glycans, such as wall teichoic acid [16 (link),56 (link)]. Bacterial strains were grown three times independently, each with three technical replicate measurements.
7
Spectral Data Analysis Protocol
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Spectra were preprocessed by atmospheric and baseline correction, and second derivative using a Savitzky–Golay filter, with a 2nd order polynomial over a 15-point window with unit vector normalization. Atmospheric correction was conducted with OPUS® software, version 6.5 (Bruker, Bremen, Germany; Billerica, MA, USA), whereas the second derivative, unit vector normalization and processing methods, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted with The Unscrambler® X, version 10.5 (CAMO software AS, version 10.4, Oslo, Norway). HCA was based on Spearman’s rank correction (distance measure) and hierarchical average-linkage (clustering method). The t-student analysis of spectral bands between T0 and T90 was performed on Microsoft Excel™.
8
FTIR Spectroscopy Characterization of Bacterial Isolates
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All isolates were streaked onto tryptic soy agar (Oxoid, Basingstoke, United Kingdom) with a drigalski spatula and incubated at 30°C for 24 ± 0.5 hours. A loop-full of cells were suspended in 100 μl distilled water and vortexed. An aliquot of 30 μl was transferred to a ZnSe sample carrier (BrukerOptics GmbH, Ettlingen, Germany) and dried for 40 minutes at 40°C. The FTIR measurements were performed using an HTS-XT microplate adapter coupled to a Tensor 27 FTIR spectrometer (both BrukerOptics GmbH, Ettlingen, Germany). All spectra were recorded between 4000 and 500 cm-1 in transmission mode, with a resolution of 6 cm-1 and an aperture of 6.0 mm. For each spectrum, 32 scans were averaged. The analysis of data was carried out using the OPUS software (version 6.5; BrukerOptics GmbH, Ettlingen, Germany).
9
FTIR Spectroscopic Analysis of Protein Samples
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FTIR (Bruker
Optics, Esslingen, Germany) equipped with a single bounce diamond
ATR crystal was used to determine functional group changes of the
powdered protein and prepared films. The analysis for each sample
was performed in the wavelength range of 410–4000 cm–1. All sample spectra were collected using 16 scans at a resolution
of 4 cm–1 and averaged using OPUS software version
6.5 provided by Bruker. A background spectrum of the clean ATR crystal
was taken before running the sample spectrum. Spectral examination,
measurements, and processing were done using Nicolet OMNIC spectra
software.
Optics, Esslingen, Germany) equipped with a single bounce diamond
ATR crystal was used to determine functional group changes of the
powdered protein and prepared films. The analysis for each sample
was performed in the wavelength range of 410–4000 cm–1. All sample spectra were collected using 16 scans at a resolution
of 4 cm–1 and averaged using OPUS software version
6.5 provided by Bruker. A background spectrum of the clean ATR crystal
was taken before running the sample spectrum. Spectral examination,
measurements, and processing were done using Nicolet OMNIC spectra
software.
10
IR Spectroscopy of Synovial Fluid
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Synovial fluid samples were thawed at 20 °C, and replicate (×6) dry films were made for each aliquot on a silicon 96-well microplate [16 (link),17 (link),33 (link),36 (link)]. The microplate was mounted on a multi-sampler accessory (XY Microtiter Plate Accessory, PIKE Technologies, Madison, WI, USA) interfaced with an IR spectrometer (Tensor 27, Bruker Optics, Preston, Victoria, Australia). Infrared absorbance spectra of were generated and recorded with proprietary software (OPUS software, version 6.5, Bruker Optics, Ettlingen, Germany). For each sample, 512 IR interferograms were averaged, and Fourier transformed to obtain a spectrum with a resolution of 4 cm–1 over the 400 to 4000 cm–1 wave number (WN) range.
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