Samples
In the following study using samples from dogs is in accordance with all relevant guidelines and regulations according to ARRIVE guidelines19. Furthermore, the animal Care Committee of the University of Prince Edward Island (#11–062) had approved the protocol and enrollment of dogs for the sample collection with written informed consent signed by animal owners 8,9. The sample size used in the current study was based on available number of samples from the original project that had two arms investigating serum and synovial fluid samples from client-owned dogs with OA secondary to naturally occurring degenerative (non-traumatic) cranial cruciate ligament (CrCL) tears in one or both knees and controls8,9. Presence of CrCL tears and OA changes in the OA group dogs had been confirmed intraoperatively by inspection of the joint via arthrotomy or arthroscopy. The controls were otherwise healthy, adult dogs with no orthopedic abnormalities with both knees free of gross abnormalities upon evaluation immediately after euthanasia for reasons unrelated to the study. Evaluation of the knee joint in control dogs was via opening of the knee joint and evaluation of the joint including cartilage surfaces, ligaments, menisci, synovium, and joint capsule). Briefly, venous blood samples had been collected and serum separated and saved in 0.5-1ml aliquots in cryovials (Nalgene Cryogenic tubes, VWR International, Batavia, IL, USA) and preserved at -80°C until batch analysis. Synovial fluid samples had been collected for the original study using aseptic technique from knees of dogs with a cranial cruciate ligament tear in the OA group under general anesthesia immediately prior to surgical intervention to treat knee instability (i.e., tibial plateau leveling osteotomy), and from healthy knees in the control group immediately after euthanasia. The synovial samples were also saved in 0.5 ml aliquots in cryovials and preserved at -80°C until batch analysis. After the completion of the original project, the unused aliquots of serum and synovial samples were maintained in -80°C storage for a minimum of five years.
The available sample inventory was reviewed for OA and Control samples with adequate volume left for analysis. For the OA group the serum and synovial fluid samples were those obtained prior to any surgical intervention. If more than one aliquoted sample was available, the clearest was selected (i.e., non-to minimal blood contaminated for synovial fluid and none to minimal hemolysis for serum samples). Only one serum sample per dog was selected. In the OA group, even if both knees were affected, only one sample from one knee per dog was included in the study. In the control group, both healthy knees of each dog had been sampled, but preferentially only one sample from each dog was included.
The age of samples at the time of first spectral analysis was calculated based on the date sample was obtained from the dog and the time when the sample was thawed, and the first spectral analysis was performed. Samples from this initial storage period (i.e., analyzed within a year after being acquired) are referred to as “short-term storage” samples. The age of samples at the time of second spectral analysis after being in storage for a minimum of 5 years was calculated based on date of sample acquisition from the dog and the time when the sample was run for the second time. Samples from the longer storage period are referred to as “long-term storage samples”. The interval between measurements for the short-term and long-term storage was calculated based on the dates the MIR spectra of each sample was obtained.
FTIR spectroscopy: At the time of first spectral acquisition (i.e., after short-term storage), serum and synovial fluid samples were thawed at 22°C and dried films were prepared as described previously 8,9,20. For each sample, an aliquot was drawn and diluted in a potassium thiocyanate (KSCN) (SigmaUltra, Sigma-Aldrich Inc, St Louis, MO) solution (4 g/L) at a 2:1 serum/SF–to–KSCN ratio (40:20 µL). KSCN was used as an internal control. For each sample, replicate (8 µL per replicate) dry films were made on a silicon microplate 5. After drying at room temperature (20–22°C), the microplate was mounted on a multi-sampler (HTS-XT, Autosampler, Bruker Optics, Milton, ON, Canada) interfaced to the infrared spectrometer (Tensor 37, Bruker Optics). Mid-infrared absorbance spectra in the range of 400 to 4,000 cm–1 was recorded with proprietary software (OPUS software, version 6.5, Bruker Optics, GmbH, Ettlingen, Germany). For each acquisition, 512 interferograms (scans) were accumulated and Fourier transformed to generate a spectrum with a nominal resolution of 4 cm− 1 5,6,21. Six (short-term storage) or three (long-term storage) replicates (due to limited volume of available samples) were prepared and analyzed for each sample and the corresponding spectra were averaged prior to the successive data processing. At each measurement time point, all the spectra were acquired within a short time span (~ 10 days).
Statistical analysis
Analyses of non-spectral data were performed using SPSS software (IBM SPSS Statistics, v. 25). Variables without a normal distribution were described by their median and interquartile range (IQR). All acquired serum and synovial fluid spectra files from both time-points were imported in MATLAB® (R2015b (8.6.0.267246); The Mathworks, Natick, MA) for the successive data processing which was carried out utilizing in-house written scripts. For each sample, at each time point, the average of the replicate spectra was used for analysis. Prior to any modeling, the data were preprocessed by first derivative (using Savitzky-Golay algorithm 22 with 19 points window and second order polynomial) followed by mean centering.
Modeling was conducted in two different stages. At first, multilevel simultaneous component analysis (MSCA) was used to verify whether there could be any significant difference between the spectra of the same samples measured after short- and long-term storage. Indeed, MSCA can be considered as a multivariate generalization of repeated measurements ANOVA 23,24. In particular, the overall (preprocessed) spectral matrix \(\varvec{X}\) is partitioned into the individual contributions of between-sample (\({\varvec{X}}_{b}\)) and within-sample (\({\varvec{X}}_{w})\)systematic sources of variability plus the residuals (\({\varvec{X}}_{res}\), i.e., the variation not accounted for by the model), according to:
$$\varvec{X}={\varvec{X}}_{b}+{\varvec{X}}_{w}+{\varvec{X}}_{res}$$
In particular, the effect of sample aging on the spectroscopic signal is related to the within-sample variation described by \({\varvec{X}}_{w}\) and can be quantified as the sum of squares of the elements in that matrix. Significance of the contribution is evaluated by comparing such effect with its distribution under the null hypothesis, which is estimated by means of a permutation test. On the other hand, if the effect is found to be significant, its impact on the spectroscopic signal can be interpreted by PCA of the associated matrix \({\varvec{X}}_{w}\). In the present study, MSCA analysis was conducted independently on the serum and synovial fluid samples.
In a second stage, to verify whether MIR analysis of synovial fluid or serum samples could provide the basis for a reliable discrimination between OA and control even after long-term storage, and if the same spectroscopic markers could be found as when analysis was carried out after short-term storage, supervised pattern recognition (classification) models were built and validated 25. Due to its ability to deal with highly collinear predictors (e.g., spectral variables), partial least squares discriminant analysis (PLS-DA) was selected 26,27. The PLS-DA method is based on projecting the data onto a low-dimensional sub-space of latent variables, which are relevant to highlight differences between the groups. Accordingly, model building is required to identify the optimal dimensionality of such subspace. On the other hand, the quality of the predictive models, which can be summarized by different figures of merit, such as sensitivity, specificity, overall classification accuracy and the area under the ROC curve (AUC), needs to be evaluated on samples not used for model development, to avoid overoptimistic results. Accordingly, to build the models and validate the prediction results and the identified spectroscopic markers, a repeated double cross-validation (rDCV) procedure coupled with permutation tests was adopted 28,29. In rDCV the available samples are split according to two nested loops of cross-validation: the outer loop samples are treated as external validation samples, which do not take part either in model building or in model selection; indeed, optimization of the model parameter is carried out on the inner loop samples. In the present study, for each data set, the available samples were split into 10 cancelation groups in the outer loop and 8 cancelation groups in the inner loop, and the whole procedure was repeated 50 times. At each repetition the distribution of the spectra in the different groups were changed, so to be able to calculate confidence intervals for all the classification figures of merit and the model parameters. Furthermore, to rule out any possibility of obtaining good results just due to chance correlations, permutation tests were used to non-parametrically evaluate the null distributions of the classification figures of merit providing P values to estimate the significance of the observed discrimination 28. Statistical significance was set at P < 0.05. In The analyses was conducted independently to report model performance based on whether serum or synovial fluid samples were used. Our previous studies on serum and synovial fluid sample of this cohort of dogs had also evaluated the impact of confounding variables such as age, weight, gender and breed differences between OA and control groups and had not found them to be significant contributors to the model performance8,9.
In conclusion, storing serum and synovial fluid samples of dogs with knee OA and controls in -80°C results in changes in the spectral patterns after ~ 5 years. However, these changes have minimal impact on the ability to use the spectral fingerprints of these samples after long-term storage for discriminating between OA and control samples. Future studies can evaluate measured MIR spectra of samples in storage prospectively at shorter intervals to establish trends of sample degradation over longer follow up times and set maximal limits on sample viability for biobanking purposes.