*Article* **Choosing an Optimal Sample Preparation in** *Caulobacter crescentus* **for Untargeted Metabolomics Approaches**

**Julian Pezzatti 1, Matthieu Bergé 2, Julien Boccard 1,3, Santiago Codesido 1, Yoric Gagnebin 1, Patrick H. Viollier 2, Víctor González-Ruiz 1,3 and Serge Rudaz 1,3,\***

Received: 27 August 2019; Accepted: 17 September 2019; Published: 20 September 2019

**Abstract:** Untargeted metabolomics aims to provide a global picture of the metabolites present in the system under study. To this end, making a careful choice of sample preparation is mandatory to obtain reliable and reproducible biological information. In this study, eight different sample preparation techniques were evaluated using *Caulobacter crescentus* as a model for Gram-negative bacteria. Two cell retrieval systems, two quenching and extraction solvents, and two cell disruption procedures were combined in a full factorial experimental design. To fully exploit the multivariate structure of the generated data, the ANOVA multiblock orthogonal partial least squares (AMOPLS) algorithm was employed to decompose the contribution of each factor studied and their potential interactions for a set of annotated metabolites. All main effects of the factors studied were found to have a significant contribution on the total observed variability. Cell retrieval, quenching and extraction solvent, and cell disrupting mechanism accounted respectively for 27.6%, 8.4%, and 7.0% of the total variability. The reproducibility and metabolome coverage of the sample preparation procedures were then compared and evaluated in terms of relative standard deviation (RSD) on the area for the detected metabolites. The protocol showing the best performance in terms of recovery, versatility, and variability was centrifugation for cell retrieval, using MeOH:H2O (8:2) as quenching and extraction solvent, and freeze-thaw cycles as the cell disrupting mechanism.

**Keywords:** metabolomics; sample preparation; hydrophilic interaction liquid chromatography; ion mobility spectrometry; high resolution mass spectrometry; design of experiments; AMOPLS

#### **1. Introduction**

Untargeted metabolomic approaches aim to analyze the metabolic composition of biological samples in a holistic manner, with the goal of retrieving and storing as much chemical information as possible. To this end, adequate sample preparation is one of the most critical steps to obtain reliable biological insights [1]. Moreover, for an optimal interpretation of the information gathered from such systems, the annotation of metabolites of interest is a requirement. Indeed, the recent advances in liquid chromatography (LC) coupled with high resolution mass spectrometry (LC-HRMS) and the development of in-house and external databases have made possible the identification and relative quantification of an increasing number of metabolites in many types of biological samples [2,3].

When analyzing polar metabolites, techniques with separation mechanisms that are orthogonal to those of the classical reversed phase LC (RPLC) are needed [4]. Among these, hydrophilic liquid interaction chromatography (HILIC) has proven to be an attractive strategy for such classes of compounds and is well referenced in the literature [5–11]. Several groups have recently demonstrated that different HILIC stationary phases enable the analysis of many chemical classes of metabolites [12–14]. It has been shown that polymeric columns with zwitterionic stationary phases are interesting for the analysis of compounds related to the tricarboxylic acid cycle (TCA) [14].

To perform metabolite annotation in untargeted metabolomics, several criteria must be fulfilled to provide a certain level of confidence to the putatively identified compounds, as defined by the metabolomics standards initiative (MSI) [15]. To maximize the reliability of the identification of features, the retention time, exact mass, isotopic pattern, and MS/MS pattern must be used to match compound properties to those of standards measured under identical experimental conditions [16,17]. With the advent of the latest generation of quadrupole time-of-flight instruments (QTOF), coupled with ion mobility (IM), peak capacity and dynamic range have been increased, enabling to perform all ion fragmentation (AIF) approaches and including the measurement of collision cross section (CCS), which offers another structural information for identification [18,19]. Indeed, CCS values provide valuable physicochemical information about the ions such as three-dimensional conformations [20,21]. Furthermore, the IM technology allows to selectively isolate coeluting peaks resulting in cleaner MS and MS/MS spectra [22].

In untargeted metabolomics experiments, it has already been demonstrated that various sample preparation procedures result in very different metabolite recovery rates, regardless of the biological matrix studied, e.g., human plasma [23–27], mammalian cell cultures [28–31], and microbial cell cultures [32–35]. During sample preparation, subtle modifications of the conditions, such as slight changes in the composition of the extraction solvents, can have remarkable effects on the recovery of some metabolites [36]. In vitro metabolomic studies on cell cultures offer many advantages over more complex biological systems including an easily controlled environment, greater reproducibility, lower cost, and easier method transfer to external laboratories for validation [30]. However, when it comes to microbial cell cultures, sample preparation faces challenges such as fast metabolite turnover, degradation, leakage, and poor extraction reproducibility [30,37,38]. These cell cultures require harsher sample processing procedures than body fluids. Metabolite leakage must be prevented during the cell retrieval step [38]. While metabolite losses can be minimized by reducing the number of steps and simplifying the protocol, reproducible sample preparation procedures are necessary to ensure an accurate determination of small changes in metabolite levels [39]. As a rule of thumb, cell metabolism must be quenched quickly, and metabolites extracted rapidly, non-selectively, and reproducibly upon cell sampling [39]. Quenching and extraction steps are of upmost importance in the sample preparation process to obtain metabolic profiles which are representative of the physiological status at the time of sampling [30].

In this study, we aimed to perform a systematic evaluation of the influence of different sample preparation conditions on the nature and variability of the recovered metabolomic profile in Gram-negative bacterium *Caulobacter crescentus*. The latter was chosen as a model for Gram-negative bacteria because its central carbon metabolism, closely linked to cell cycle regulation and cell division, remains to be fully understood [40]. After a comprehensive evaluation of the literature on sample preparation for bacterial cell cultures [32–35], it was found that no systematic evaluation of different protocols supported by a design of experiment (DOE) approach was available. In the present study, two techniques of cell retrieval, two quenching and extraction solvents, and two cell disrupting mechanisms were investigated. Considering a full factorial design (FFD), eight different sample preparation protocols were tested for the relative quantification of polar metabolites from diverse chemical classes. The simultaneous investigation of several experimental factors constitutes a powerful approach to assess the impact of the metabolites extraction capabilities of each studied combination. Even if principal component analysis (PCA) constitutes often a potent tool to detect relevant variables and

patterns [41], an approach dedicated to fully exploit the data structure of complex omics data generated from DOE, i.e., analysis of variance multiblock orthogonal partial least squares (AMOPLS) [42–44], was used to assess the contribution of each studied factor on the annotated metabolites.

#### **2. Materials and Methods**

#### *2.1. Chemicals*

UPLC-MS grade acetonitrile (ACN), methanol (MeOH), and water (H2O) were obtained from Fisher Scientific ( Loughborough, UK). UPLC-MS grade formic acid (FA) was supplied by Biosolve (Valkenswaard, Netherlands). Chloroform (CHCl3) was provided by Acros Organics (Geel, Belgium). Ammonium hydroxide and ethylenediaminetetraacetic acid disodium salt dehydrate (EDTA) were obtained from Sigma-Aldrich (Buchs, Switzerland). The major mix IMS/T of calibration kit and leucine-encephalin were acquired from Waters (Milford, MA, USA).

#### *2.2. Bacterial Sample Preparation*

The eight evaluated sample preparation protocols are summarized in Table 1. Two cell retrieval procedures, two systems of quenching and extraction solvents, and two cell disrupting mechanisms were compared.

**Table 1.** Design of experiment (DOE) of the eight sample preparations, each investigated in triplicate, with differing cell retrieval systems (filter or centrifugation), quenching and extraction solvents (MeOH:H2O 8:2 or MeOH:H2O:CHCl3 7:2:1 + EDTA 1 mM), and cell disruption mechanisms (bead beating or freeze-thaw cycles).

#### 2.2.1. Bacterial Cell Cultures and Cell Retrieval

*C. crescentus* cells were grown overnight at 30 ◦C in a PYE rich medium (2 g/L bactopeptone, 1 g/L yeast extract, 1 mM MgSO4, and 0.5 mM CaCl2). Two cell retrieval methods were tested, the first one in a liquid medium, and the second as an adaptation of a filter culture-based method [40].

For the liquid culture, overnight cultures were diluted to reach OD600nm ~ 0.4 and 10 mL of cells were centrifuged at 8000 g for 5 min at 4 ◦C, then cells were resuspended in the desired precooled quenching and extracting solution. For metabolite extraction from filter cultures, overnight cultures were diluted at 30 ◦C in PYE medium until reaching OD600nm ~ 0.2. Then, 10 mL of culture were transferred onto 0.22 μm mixed cellulose ester (MCE) membrane filter (Millipore) by vacuum filtration. The filters were then deposited on the surface of PYE-agar made with the same growth medium, and the cells were allowed to continue growing at 30 ◦C for 3 h. Finally, filters were dropped directly into desired precooled quenching and extraction solution.

#### 2.2.2. Quenching and Extraction Methods

The two cold solvents (−20 ◦C) for quenching and extraction were respectively MeOH:H2O 8:2 and MeOH:H2O:CHCl3 7:2:1 + EDTA 1 mM. For each bacterial culture sample, 1 mL of solvent was added.

#### 2.2.3. Cell Disruption

Two lysis methods were compared, one by freeze and thaw (F/T) and another by bead beating. Cells were subjected either to lysis by 5 cycles of F/T (−80 ◦C/40 ◦C) or to mechanical lysis using 0.1 mm zirconia beads in a Fast prep-24 (MP) disruption instrument (5 cycles of 15" on, 1' off) maintained at 4 ◦C. In both cases, cellular debris were removed by centrifugation at 14,000 g, 20 min at 4 ◦C. Metabolite extracts were kept at −80 ◦C.

#### 2.2.4. Extract Preparation for LC-MS Analysis

The supernatants were collected, evaporated to dryness using a SpeedVac (ThermoFisher, Langenselbold, Germany) and reconstituted in 100 μL ACN:H2O 50:50. Quality control (QC) and diluted QC (dQC) samples were respectively prepared by pooling equivalent volumes of all reconstituted samples and by 1:1 dilution of a certain amount of the QC pool with ACN:H2O 50:50. QC and dQC were injected at regular intervals throughout the LC-MS analyses to assess analytical variability.

#### *2.3. Liquid Chromatography Conditions*

For the LC experiments, a Waters H-Class Acquity UPLC system composed of a quaternary pump, an autosampler including a 15 μL flow-through-needle injector for which temperature was set at 7 ◦C, and a two-way column manager (Waters, Milford, MA, USA) were used. The injected volume was 10 μL. HILIC separations were performed on a Merck SeQuant ZIC-pHILIC column (150 × 2.1 mm, 5 μm) with the appropriate guard kit (Merck KGaA, Darmstadt, Germany). Solvent A was ACN and solvent B was H2O containing 2.8 mM ammonium formate adjusted at pH 9.00. The pH of the solution was checked and found to be stable during one week at room temperature. Column temperature and flow rate were set at 40 ◦C and 300 μL min−1, respectively. The gradient elution was as follows: 5% B for 1 min, increasing to 51% B during 9 min, holding for 3 min at 51% B, and then returning back to 5% B in 0.1 min and re-equilibrating the column for 6.9 min.

#### *2.4. Mass Spectrometric Conditions*

HRMS was carried out on a Vion TWIMS-QTOF (Waters, Manchester, UK) equipped with an ESI source. Analyses were performed in negative ESI mode to acquire continuum data in the range of 50–1000 *m*/*z* with a scan time of 0.2 s. The source parameters were set as follows: capillary voltage was −2.0 kV, source and desolvation temperatures were set at 120 and 500 ◦C, respectively, cone and desolvation gas flow were 50 and 800 L/h, respectively. Velocity and height of StepWave1 and StepWave2 were set to 300 m/s and 5 V and to 200 m/s and 30 V, respectively. The high definition MSE (HDMSE, using ion mobility) settings consisted of trap wave velocity at 100 m/s; trap pulse height A at 10 V; trap pulse height B at 5 V; ion mobility spectrometry (IMS) wave velocity at 250 m/s; IMS pulse height at 45 V; wave delay set at 20 pushes, and gate delay at 0 ms. Gas flows of ion mobility instrument were set to 1.60 L/min for trap gas, and 25 mL/min for IMS gas. Buffer gas was nitrogen.

Fragmentation was performed in HDMSE mode. For the collision energy, 6.0 eV was used for low energy and high energy was a ramp from 10 to 60 eV. Nitrogen was used as collision gas. Leucine-encephalin served as a lock mass (554.2615 *m*/*z* for ESI-) infused at 5 min intervals. The CCS and mass calibration of the instrument were done with the calibration mix "Major mix IMS-TOF calibration" (Waters, Manchester, UK).

#### *2.5. Analysis of Raw Data*

Chromatogram alignment, peak picking, adduct deconvolution, and feature annotation were sequentially performed on Progenesis QI v2.3 (Nonlinear Dynamics, Waters, Newcastle upon Tyne, UK). The following tolerances were used for feature annotation with regard to a set of pure reference standards (MSMLS Library of Standards, Sigma-Aldrich) measured in the same instrument: 2.5 ppm for precursor and fragment mass, 10% for Rt, and 5% in the case of CCS. Data pretreatment was

performed with SUPreMe, an in-house software with capabilities for drift correction, noise filtering, and sample normalization. Finally, data were transferred to SIMCA-P 15.0 software (Umetrics, Umea, Sweden) to perform Principal Component Analysis (PCA). AMOPLS analysis was conducted after unit variance scaling as previously described [42] under the MATLAB® 8 environment (The MathWorks, Natick, MA, USA). A series of 104 random permutations was performed to validate the AMOPLS model and assess the statistical significance of the effects.

#### **3. Results and Discussion**

Bacterial cell cultures are an interesting resource to evaluate how a given system reacts upon the deletion of certain genes, modifications in the conditions of the growing media, or changes happening in the cell morphogenesis during the cell growth and proliferation processes. To increase our understanding of the endogenous metabolic changes appearing in such biological systems, untargeted metabolomics has already proven to be a valuable approach [39,40,45].

To study the effects of each sample preparation procedure on the extraction of polar metabolites, three parameters, namely cell retrieval, quenching and extraction solvent, and cell disruption, were systematically investigated by two methods. Medium filtering and centrifugation were compared for cell retrieval, cold MeOH:H2O 8:2 and cold MeOH:H2O:CHCl3 7:3:1 + EDTA 1 mM were compared as two quenching and extraction solvents and, finally, two cell disruption mechanisms (bead beating and freeze-thaw cycles) were tested. In total, the overall performance of eight different procedures was evaluated and compared against the others for the metabolomic analysis of *C. crescentus* cell cultures (Table 1).

The cell retrieval procedures were chosen on the basis of (i) their ability to separate the cells from media, (ii) their ease of application, and (iii) their recognized suitability for the determination of intracellular metabolites [46]. The quenching and extraction solvent mixtures were chosen due to their aptitude to (i) freeze the metabolic status after cell harvesting, (ii) inactivate intracellular enzymes and extract at the same time metabolites, and (iii) extract hydrophilic compounds as widely as possible with the best recoveries [46]. CHCl3 was studied because of its potential usefulness to enhance cell wall disruption and to inactivate enzymes, and EDTA due to its ability to chelate metal ions preventing metabolite oxidation and degradation [30,35,37]. Cell disrupting mechanisms were selected due to their capacity to (i) disrupt cell walls and facilitate metabolite extraction [30], (ii) complete the denaturation of all enzymes to avoid further metabolite interconversions, and (iii) prevent significant degradation and chemical conversion of the extracted metabolites [47]. Finally, metabolomic analyses were performed in HILIC mode coupled to a Vion TWIMS-QTOF operating in negative ESI polarity. The choice of this UHPLC-HRMS platform enables the investigation of highly polar metabolites such as those related to the energy metabolism with excellent performance, as described in our previous paper [14]. Of course, the use of other LC-MS platforms to analyze samples issued from the same experimental design would provide different results, since the performance of each method to retain and ionize different classes of metabolites will be different.

#### *3.1. Evaluation of the Extraction*

The first goal of the investigation was to assess the recovery and the overall variability on annotated metabolites. Analyses were performed in triplicate for each combination of conditions and evaluated with the help of various procedures such as relative standard deviation (RSD) and multivariate analysis (MVA). PCA was used to evaluate the similarity and dissimilarity of the different extraction conditions, while AMOPLS was used to estimate the contribution of experimental factors to variations annotated metabolites abundances.

The performance reached by each sample preparation protocol was first assessed on the overall set of features remaining after data pretreatment. From the raw dataset, more than 8500 features were found in the initial peak-picking step. Data pretreatment included filtering of the features based on QC-to-dQC peak area ratio and RSD in QC samples, LOESS drift correction by interpolation with the QC samples, and probabilistic quotient normalization (PQN) methods [48]. These respectively eliminate noisy features, alleviate the changes in the per-metabolite variations of the instrument's response factor, and fix any sample normalization issues remaining from the sample preparation. In particular, LOESS has the advantage of being nonparametric, so no external information needs to be introduced, which would unnecessarily increase variability. The only requirement is the presence of QC samples. PQN was chosen for similar reasons, since it is highly robust and does not depend either on external information that would introduce uncertainty into the correction. Its only drawback is the need for enough variables to derive the ratio distribution, but that need is more than fulfilled in untargeted analyses such as the present one. The whole pretreatment procedure reduces the analytical variability in the data, so as to present a dataset to the statistical analyses, where as much variability as possible is of biological origin. Altogether, a total of 904 filtered and corrected features were found to be analytically reliable across all the samples in the experimental design, and they were retained for the posterior analysis (Supplementary Materials, Table S1).

#### *3.2. Metabolites Annotation*

For the purpose of completing the LC-MS untargeted workflow of such experiments, feature annotation must be used to assess which metabolites are preferentially retrieved by each sample preparation procedure, thus enabling the study of certain metabolic pathways in future experiments with *C. crescentus*. In the present study, features were first annotated by using their *m*/*z* and retention time [15] and their identities were then confirmed by MS/MS or CCS data. MS/MS acquisitions were carried out in all ion fragmentation (AIF) mode by using an energy ramp (10–60 eV). The AIF mode presents the potential that a simultaneous acquisition of both precursor and product ions can be considered in the same analytical run. However, AIF suffers from inaccuracies, which can happen when LC coeluting peaks with similar peak shapes possess similar MS/MS spectra (common product ions). This limitation can be overcome by (i) computational deconvolution algorithms that link the precursor ions (at low energy) and the product ions (at high energy) [49] and (ii) the IMS technology, i.e., HDMSE, which allows the separation of LC coeluting precursor ions on the drift time dimension before fragmentation. Therefore precursor and product ions can be IMS-aligned, allowing to filter out fragments that do not match the precursor's drift time [18]. Moreover, IMS offers the potential to gain further structural information by providing CCS values, and therefore a gain in confidence in the metabolites annotation. An example of how both precursor and product ions can be cleared with the help of the IMS technology is presented in Figure 1 with the example of acetyl-CoA. Cleaner MS/MS also reduces the false positives rate during the annotation step. Among the 904 kept features, 133 of them were annotated with the help of *m*/*z* and retention time and 106 were confirmed by MS/MS or CCS values (Table S2). This result highlights the fact that false positive annotations (27 unconfirmed annotations) can occur when they are not validated by other orthogonal molecular descriptors. Most of the 106 identified metabolites (almost 55% of the confirmed compounds) belong to one of the following chemical groups: amino acids, nucleotides, and organic acids [50], as presented in Figure 2. This shows the ability of the developed analytical method to detect compounds related to fundamental metabolic pathways in biological samples, such as the energy metabolism.

**Figure 1.** Example of MS and MS/MS spectra without and with IMS filtration for the precursor ion of acetyl-CoA (M-H). Precursor ion and fragment ions are highlighted in filled bars. Cleaner MS and MS/MS spectra are obtained thanks to the IMS separation. Fragment ions were matched to the MS/MS spectra of the chemical standard analyzed under the same analytical conditions.

**Figure 2.** Annotated metabolites chemical groups. Percentage based on the total number of annotated metabolites (106).

#### *3.3. AMOPLS for Assessing the Contribution of Each Studied Factor to the Overall Variability*

To obtain an overview of the major sources of variability in the dataset, unsupervised data analysis was first performed on the 106 annotated metabolites, as measured from the eight sample preparation procedures. According to the PCA score plot presented in Figure 3, the sample preparation approaches that appear well clustered, indicating low intragroup variability, are: centrifugation-CHCl3-F/T, centrifugation-MeOH-F/T, and filter-CHCl3-F/T. An important difference in the sample preparation between centrifugation and filtering can also be highlighted. Both centrifugation-CHCl3-F/T and centrifugation-MeOH-F/T are separated, while filter procedures are regrouped at the top-left region of the PCA.

As a second step of data analysis, AMOPLS was used to get a deeper insight into the specific contribution of each experimental factor (i.e. main effects), and their potential interactions, to the total observed variability. This approach allows us to decompose, quantify, and evaluate the significance of each effect in large datasets issued from full factorial designs. The proposed experimental setup allows the simultaneous evaluation of the three experimental factors under study, i.e., the cell retrieval procedure, the quenching and extraction solvent, and cell disrupting mechanisms, as well as their interactions. The interpretation of the signal variations is performed using effect-specific scores and loadings.

**Figure 3.** Principal component analysis (PCA) score plot of the eight sample preparation procedures evaluated in triplicates.

The results of the AMOPLS model are presented in Table 2. All main effects had a significant contribution to the total observed variability, but no interaction between them was found to be statistically significant. The cell retrieval procedure accounted for 27.6% of the total variability, the quenching and extraction solvent for 8.4%, and the cell disrupting mechanism for 7.0%. Remarkably, the cell retrieval procedure (filter/centrifugation) is the most influential parameter. The residuals accounted for 57.0% of the total observed variability, suggesting an important contribution of other sources of variation not formally considered in the experimental design and possibly coming from the biological variability or the sample preparation itself.

**Table 2.** Relative variability and block contributions of the AMOPLS model of the data acquired from the investigated biological samples. RSR: residual structure ratio, tp1-3: predictive components, to: orthogonal component.

1 The highest contribution for each component is reported in bold.

The model was then interpreted with the help of the distribution of the observations (groupings) and the contribution of the variables to the specific predictive component associated with each main effect. The score plots are presented in Figure S1, generated with data coming from Table S3. Squared variable importance in the projection (VIP2) values were then calculated (Table S3) and taken as the quantitative measure of the contribution of all modelled effects coming from a single variable [44]. VIP2 values are helpful to rank compounds and spot the ones that play a major role in the model for each studied effect. Figure 4 presents the VIP2 values for each studied effect, for the 50 most relevant metabolites out of the 106 annotated ones. Larger VIP2 values indicate which effect(s) are the most important ones for each annotated metabolite. In Figure 4, metabolites are ranked according to the importance of the cell retrieval effect and compounds on the left side are those for which this procedure had the strongest impact (largest VIP2 values). On the contrary, this effect exerted little influence on metabolites appearing towards the right side of the plot (Figure S2). Some metabolites belonging to the groups of sugars (2-deoxy-glucose, glucose, and xylose) and nucleosides (cytidine, guanine, and xanthine) are among the most influenced ones. Besides the scores and corresponding loading plots (Figures S1 and S3, representing the data from Tables S4 and S5, respectively), these metabolites were found in higher abundance in the filtering procedure. Secondly, highly polar metabolites related to the amino acid family (L-histidine, L-glutamine, and L-asparagine), phosphorylated sugars (ribose-5-phosphate, glucose-6-phosphate, fructose-1, and 5-bisphosphate) and nucleotides (5'-CMP, IDP, IMP, and GTP) were highly influenced by the quenching and extraction solvent. Probably, the polarity of the used solvents accounts for these observed results, since MeOH:H2O 8:2 is more polar than the second system used, thus enhancing the recovery of more polar molecules. Indeed, by looking at the scores and corresponding loading plots (Figures S1 and S3), almost all metabolites given as examples were found in higher abundance in MeOH:H2O extracting solvent, except IDP, GTP, and fructose-1,5-bisphosphate which were found in higher abundance when CHCl3 was present, an observation that can be explained by an enhanced enzyme-denaturing process expected when this solvent was added. Finally, the effect of cells lysis was studied. Metabolites related to lipids (petroselinic acid, palmitic acid, and stearic acid) and nucleotides (ADP, CTP, GDP, CDP, GTP, and ATP) were the most influenced chemical classes. All of these metabolites were found in higher abundance in the F/T procedure except for palmitic acid and petroselinic acid (Figures S1 and S3). One hypothesis to explain these results would be the fact that both cell disrupting mechanisms are quite different in terms of the amount of energy applied to the sample. Indeed, beads beating is a harsher procedure compared to F/T cycles and metabolites amenable to degradation via hydrolysis, decarboxylation or oxidation could be more easily impacted.

**Figure 4.** Effect-specific variable importance in the projection (VIP)2 values for the 50 out of the 106 annotated metabolites ranked according to the impact of the cell retrieval effect.

#### *3.4. Assessing the Recovery and the Variability of the Annotated Metabolites*

A larger number of metabolites were found in higher abundance with the following combination of factors: cell filtering, MeOH:H2O, and F/T cycles (Figure S3). First, nucleosides (guanine, guanosine, cytidine, inosine, xanthosine, and xanthine) were more abundant in sample preparations starting with a filtering step, regardless of what other following procedures were used. One explanation could be that these metabolites leak from cells more easily during the longer centrifugation procedure, as it has already been found for *Escherichia coli* [51]. Second, the MeOH:H2O condition enables the retrieval of more metabolites in higher proportion. This can be explained by the medium's polarity, since this solvent enables a better extraction of polar compounds as compared to the CHCl3 system. However, it was expected that the addition of CHCl3 and EDTA would prevent metabolite turnover, and oxidation and degradation by metals for nucleotides, CoA, and CoA thioesters derivatives, as described in the literature [37]. This is confirmed for ATP, succinyl-CoA, and CoA which are found in higher proportion in the presence of CHCl3 (Figure S3). Interestingly, ADP was found in higher proportion in MeOH. This result supports that in these conditions ATP gets hydrolyzed into ADP, confirming that EDTA and CHCl3 prevents the degradation of ATP into ADP. For the cell disruption mechanisms, a large majority of metabolites were found in higher abundance in the F/T cycles procedure (Figure S3). Indeed, some nucleotides, CoA and CoA thioester derivatives, and some organic acids (citric acid), which are highly relevant indicators of the metabolic status of the cells, are found more abundantly when F/T was applied. This could be explained by the facilitation of non-enzymatic degradations in the case of bead beating, as previously discussed. Finally, acetyl-CoA, CoA, TMP, and DAMP, were more abundant in either centrifugation-CHCl3-F/T or centrifugation-MeOH:H2O-F/T. It can be seen that all four metabolites are more abundant after using centrifugation as cell retrieval, MeOH:H2O (except for CoA, found at the same abundance for both solvent systems used) and F/T cycles (except for TMP and DAMP, found at the same abundance for both cells disrupting mechanisms used). These results support that either centrifugation-CHCl3-F/T or centrifugation-MeOH:H2O-F/T conditions should be chosen in the case of interest towards these particular metabolites.

The recovery obtained using each sample preparation procedure was assessed on the 106 confirmed metabolites and results are shown in Figure S4. Metabolite integration was manually curated among the features of the preprocessed data and considered as undetected when an intensity value of zero was found (total absence). The recoveries of each sample preparation varied from 88% to 95%. It has been noticed that for each different sample preparation, more metabolites were recovered when MeOH:H2O solvent was used as compared to MeOH:H2O:CHCl3 + 1 mM EDTA. The sample preparation Filter-MeOH:H2O-F/T had the highest recovery percentage (95%) and centrifugation-CHCl3-F/T yielded the lowest recovery percentage (88%). Figure S4 also shows that more metabolites can be recovered when the filtering procedure is used, as opposed to centrifugation.

On the other hand, the RSD is crucial since the most repeatable procedures will guarantee data of higher quality in further metabolomics experiments. It was possible to calculate the RSD on the area of each metabolite obtained in triplicate. These results are presented in Figure 5. The RSD values (X) were classified into five categories: 0 < X ≤ 10%, 10 < X ≤ 20%, 20 < X ≤ 30%, 30 < X ≤ 40%, or >40%. The sample preparation extracting the largest number of metabolites, i.e., filter-MeOH:H2O-F/T, presents the worst RSD (>40%) results. On the other hand, we found sample preparation such as centrifugation-CHCl3-F/T and centrifugation-MeOH:H2O-F/T, encompassing RSDs of 0 < X ≤ 10%, 10 < X ≤ 20% and 20 < X ≤ 30% for the highest number of metabolites. Therefore, we would recommend use of the latter two sample preparation procedures when the metabolite extraction rate must be well balanced with repeatability.

**Figure 5.** Relative standard deviation (RSD) values calculated on the area reached by each detected metabolite for the eight sample preparations investigated. RSD (X) are classified into 5 categories: 0 < X ≤ 10%, 10 < X ≤ 20%, 20 < X ≤ 30%, 30 < X ≤ 40%, or >40%.

#### **4. Conclusions**

It is currently recognized that sample preparation is a crucial step in untargeted metabolomics workflows as biological outputs will only be drawn from retrieved metabolites. In this work, eight different sample preparation protocols for Gram-negative bacteria *C. crescentus*, combining two cell retrieval systems, two quenching and extraction solvents, and two cell disruption procedures were investigated. The use of a full factorial design to evaluate the contribution of various factors, with the help of an AMOPLS model, provided a potent way to examine their effect on the metabolite recovery rates. Moreover, annotated metabolite variability was assessed with the help of univariate data analysis via the RSD of the area of each metabolite.

The major conclusions about the studied experimental parameters are:

To summarize, even if filtering enables the retrieval of more metabolites with higher abundances, RSD evaluation demonstrated a detrimental extraction variability for any of the conditions, as compared to the centrifugation procedures. Therefore, centrifugation should be the selected method for cell retrieval. For the quenching and extraction solvent, the choice should be driven by the needs of the study regarding the specific nature of the metabolites to extract. If the target would be acetyl-CoA, an important metabolite for many biological pathways, MeOH should be used. The F/T cycles retrieve more metabolites at higher abundance and present much better RSD values as compared to bead beating for the cell disrupting mechanism. Finally, the combination of centrifugation-MeOH-F/T appears to perform the best among those evaluated in the present study.

**Supplementary Materials:** The following supplementary materials are available online at http://www.mdpi. com/2218-1989/9/10/193/s1, Table S1: Analytical variability of the 904 retained features after data filtration and processing. More than 90% of them presented an RSD below 30% in the QC samples; Table S2: 106 annotated metabolites' names, adapted name, observed Rt, m/z, CCS, adduct(s), CAS identifier, molecular properties confirmation steps (Rt, MS/MS, CCS, MS/MS, and CCS), and chemical class; Table S3: Ranking of the variables accounting for the cells retrieval effect. The VIP2 values of all metabolites were calculated for each variable (cells retrieval, solvent, and cells disruption); Figure S1: Score plots obtained from the AMOPLS model related to the predictive components bearing the variability coming from respectively (a) cells retrieval (tp1), (b) solvent (tp2), and (c) cells disruption (tp3) effects and the orthogonal component to. Plots were generated according to Table S4; Table S4: Data from the AMOPLS model for the predictive components tp1 (cells retrieval), tp2 (solvent), tp3 (cells disruption), and to (orthogonal component); Figure S2: Effect-specific VIP2 values for the 106 annotated metabolites ranked according to the impact of the cell retrieval effect; Figure S3: Loading plots generated from the AMOPLS predictive components related to the effect of (a) cells retrieval, (b) solvent, and (c) cells disruption on the metabolites extraction. Plots were generated according to Table S5; Table S5: Data from the AMOPLS model for the predictive components pp1 (cells retrieval), pp2 (solvent), pp3 (cells disruption), and po (orthogonal component); Figure S4: Metabolites overall recovery for each sample preparation evaluated. Percentage based on the number of metabolites detected for each condition on the total annotated metabolites (106).

**Author Contributions:** Conceptualization, J.P., M.B., and V.G.-R.; methodology, J.P., M.B., J.B., S.C., Y.G., and V.G.-R.; software, J.P., J.B., and S.C.; validation, P.H.V. and S.R.; formal analysis, J.P., M.B., J.B., Y.G., and V.G.-R.; investigation, J.P., M.B., S.C., and V.G.-R.; resources, P.H.V. and S.R.; data curation, J.P., J.B., S.C., Y.G., and V.G.-R.; writing—original draft preparation, J.P., M.B., and V.G.-R.; writing—review and editing, J.P., M.B., J.B., S.C., Y.G., P.H.V., V.G.-R., and S.R.; visualization, J.P., J.B., and V.G.-R.; supervision, P.H.V. and S.R.; project administration, P.H.V. and S.R.; funding acquisition, P.H.V. and S.R.

**Funding:** This research was funded by the Swiss National Science Foundation through grant 31003A\_166658 for support through a fellowship to S.C. and by the Swiss Centre for Applied Human Toxicology through grants from the Research Programme 2017–2020 (Core Project 3: Neurotoxicity).

**Acknowledgments:** Julien Bourquin is warmly thanked for his valuable advice on the Vion TWIMS-QTOF instrument.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the conducted study; in the collection, analyses or interpretation of the data; in the writing of the manuscript or in the decision to publish the results.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**Author details**

Katia Manolova Sergieva Queen Giovanna University Hospital, Sofia Medical University, Sofia, Bulgaria

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**Author details**

Bradley A. Saville1,2\* and Sandra H. Saville2,3

1 Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada

2 Prenexus Health, Gilbert, AZ, United States

3 Saville Nutrition Consulting, Oakville, ON, Canada

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


*Article* **Acute and Chronic Dopaminergic Depletion Di**ff**erently A**ff**ect Motor Thalamic Function**

**Giuseppe Di Giovanni 1,2,\* , Laura Clara Grandi 3 , Ernesto Fedele 4,5 , Gergely Orban 3 , Agnese Salvadè 3 , Wei Song 3 , Eleonora Cuboni 1 , Alessandro Stefani 6 , Alain Kaelin-Lang 3,7,8 and Salvatore Galati 3,8,9,\***

Received: 9 March 2020; Accepted: 12 April 2020; Published: 15 April 2020

**Abstract:** The motor thalamus (MTh) plays a crucial role in the basal ganglia (BG)-cortical loop in motor information codification. Despite this, there is limited evidence of MTh functionality in normal and Parkinsonian conditions. To shed light on the functional properties of the MTh, we examined the effects of acute and chronic dopamine (DA) depletion on the neuronal firing of MTh neurons, cortical/MTh interplay and MTh extracellular concentrations of glutamate (GLU) and gamma-aminobutyric acid (GABA) in two states of DA depletion: acute depletion induced by the tetrodotoxin (TTX) and chronic denervation obtained by 6-hydroxydopamine (6-OHDA), both infused into the medial forebrain bundle (MFB) in anesthetized rats. The acute TTX DA depletion caused a clear-cut reduction in MTh neuronal activity without changes in burst content, whereas the chronic 6-OHDA depletion did not modify the firing rate but increased the burst firing. The phase correlation analysis underscored that the 6-OHDA chronic DA depletion affected the MTh-cortical activity coupling compared to the acute TTX-induced DA depletion state. The TTX acute DA depletion caused a clear-cut increase of the MTh GABA concentration and no change of GLU levels. On the other hand, the 6-OHDA-induced chronic DA depletion led to a significant reduction of local GABA and an increase of GLU levels in the MTh. These data show that MTh is affected by DA depletion and support the hypothesis that a rebalancing of MTh in the chronic condition counterbalances the profound alteration arising after acute DA depletion state.

**Keywords:** electrophysiology; microdialysis; immunohistochemistry; L-DOPA; deep brain stimulation

### **1. Introduction**

The motor thalamus (MTh) plays a pivotal role in motor information processing in both physiological and pathological conditions, being directly connected to both the basal ganglia (BG) and the cortex [1]. In particular, the MTh receives the BG input at the level of the ventral-anterior (VA) and the ventral-medial (VM) nuclei [2]. BG are assumed to inhibit the thalamus under basal conditions because substantia nigra pars reticulata (SNr) and internal globus pallidus (GPi) display high spontaneous spiking rates, releasing GABA in the MTh. However, when the BG network is activated by cortical input, BG output is transiently suppressed and downstream targets, including the MTh, are disinhibited [3,4]. Another source of GABA in the MTh comes from the nucleus reticular thalami (NRT) [5], a thin layer of GABAergic cells adjacent to the relay nuclei of the dorsal thalamus, that we have shown to be able to modulate the oscillatory activity of MTh in dopamine (DA)-depleted animals [6]. On the other hand, the excitatory input to the MTh is classically provided by the cerebellothalamic [7] and corticothalamic synapses [8,9]. Moreover, it has been recently shown that some substantia nigra (SN) neurons project to the NRT and the posterior thalamus, and co-release glutamate (GLU) and DA, while the VA and VM would receive preferential GABAergic SN fibers [10]. GABA, GLU and DA, therefore, may interact when modulating synapse and excitability within the MTh in normal and pathological conditions [11–14]. Although it is well established that the appearance of the cardinal features of Parkinson's disease (PD) is due to the degeneration of DA neurons, with the contextual dysfunction of different neurotransmitter systems and cortical and BG activity [15–17], the evidence of MTh alterations, both at the circuit and cellular level, is scanty and controversial [1,6]. According to the canonical BG schema [18,19], an increase in MTh GABA levels could be the consequence of the DA loss. This agrees with clinical evidence showing that clinical effective sub-thalamic nucleus (STN) deep brain stimulation (DBS) or even levodopa (L-DOPA) in PD patients reversibly reduced the extracellular MTh GABA [20,21]. Nevertheless, a decrease of GABA has been observed in the MTh of 6-OHDA-lesioned rats [22] and certain regions of the thalamus of post-mortem PD brains [23]. Therefore, these discrepancies cannot be only due to the difference in BG circuitry in rats compared to humans.

No changes in GLU concentrations have been described in the thalamuses of PD patients [23], or the striatum or SNr of 6-OHDA-lesioned rats [24]; there was a GLU increase in the entopeduncular nucleus (EPN) [25], in the SNr and the striatum in L-DOPA-induced dyskinesia (LID) 6-OHDA rat [24]. Indeed, AMPA, NMDA and metabotropic GLU receptor antagonists might be a useful target for PD, especially for LID (Cenci et al., 2014).

Finally, the role of the MTh in PD is questioned by the fact that a stereotactic MTh lesion neither worsens Parkinsonian bradykinesia in PD nor regularly causes bradykinesia in patients with essential tremor [26], clearly in contrast to the canonical *box and arrow* schema of BG [18,19].

Thus, the present study was aimed at elucidating the effects of acute and chronic DA depletion, induced respectively by TTX and 6-OHDA [27], on MTh functionality. We, therefore, investigated the electrophysiological changes in the MTh at the level of single neurons from acute and chronic DA-depleted rats. Our main hypothesis was that the MTh is altered differently during rapid changes of DA levels compared to chronic depleted states. This might account for the controversial findings on MTh. Since the BG activity changes in relation to the cortical pattern [28,29], all the recordings were performed during stable cortical slow-wave activity (SWA) induced by urethane anesthesia. This stable cortical pattern allowed us to investigate the coupling of SWA activity with the MTh neuronal activity in acute and chronic DA depletion states. In addition, we measured the extracellular levels of GABA and GLU by microdialysis in the same experimental conditions.

#### **2. Results**

#### *2.1. E*ff*ects of DA Depletion on the Activity of the Motor Thalamus Neurons*

We recorded a total of 82 neurons from the MTh (*n* = 23 control, *n* = 25 in 6-OHDA and *n* = 34 in TTX, Figure 1). We found that the mean firing rate of the recorded MTh neurons was lower after TTX infusion (TTX: 3.69 ± 0.18 spike/s, *p* < 0.05) compared to control group (CTL: 7.12 ± 0.15 spikes/s) and 6-OHDA-lesioned rats (6-OHDA: 8.02 ± 0.29 spikes/s; *p* < 0.05) (Figure 2a). The percentage of spikes in burst in chronic 6-OHDA DA-depleted rats (6-OHDA 76.72 ± 4.18%) was higher than in control

(CTL 59.89 ± 4.22%, *p* < 0.05) and TTX groups (60.53 ± 6.30%, *p* < 0.01) (Figure 2b). Burst length and interspike of burst (Figure 2c,d) were significantly higher in chronic than in acute TTX-mediated DA depletion but were not different from controls (length of burst: 6-OHDA 34.03 ± 3.01 ms, TTX 22.84 ± 2.61 ms, *p* < 0.05; burst interspike: 6-OHDA 15.33 ± 1.65 ms, TTX 10.5 ± 1.29 ms). interspike of burst (Figure 2c,d) were significantly higher in chronic than in acute TTX-mediated DA depletion but were not different from controls (length of burst: 6-OHDA 34.03 ± 3.01 ms, TTX 22.84 ± 2.61 ms, *p* < 0.05; burst interspike: 6-OHDA 15.33 ± 1.65 ms, TTX 10.5 ± 1.29 ms). *Int. J. Mol. Sci.* **2020**, *21*, x FOR PEER REVIEW 3 of 13 interspike of burst (Figure 2c,d) were significantly higher in chronic than in acute TTX-mediated DA depletion but were not different from controls (length of burst: 6-OHDA 34.03 ± 3.01 ms, TTX 22.84 ±

*Int. J. Mol. Sci.* **2020**, *21*, x FOR PEER REVIEW 3 of 13

**Figure 1.** (**A**) Representative electrophysiological recordings of the three groups of animals. From the Table 6. hydroxydopamine (6-OHDA)-lesioned rats, respectively. (**C**) Histological reconstruction of the MTh recording site in tetrodotoxin (TTX)-treated rats (in blue). The black circles represent the recorded neurons in MTh during the TTX-mediated blockade of MFB. **Figure 1.** (**A**) Representative electrophysiological recordings of the three groups of animals. From the Table 6. hydroxydopamine (6-OHDA)-lesioned rats, respectively. (**C**) Histological reconstruction of the MTh recording site in tetrodotoxin (TTX)-treated rats (in blue). The black circles represent the recorded neurons in MTh during the TTX-mediated blockade of MFB. **Figure 1.** (**A**) Representative electrophysiological recordings of the three groups of animals. From the Table 6. hydroxydopamine (6-OHDA)-lesioned rats, respectively. (**C**) Histological reconstruction of the MTh recording site in tetrodotoxin (TTX)-treated rats (in blue). The black circles represent the recorded neurons in MTh during the TTX-mediated blockade of MFB.

**Figure 2.** (**A**–**D**) Firing rate and pattern of the MTh in CTL, during tetrodotoxin (TTX) infusion and in 6-hydroxydopamine (6-OHDA)-lesioned rats. \* *p* < 0.05. (**E**,**F**). Phase correlation analysis. (**E**) MTh circular plots of the phase-locked firing of the MTh neurons in control, acute and chronic DA depletion states. The vectors of the preferred firing of individual neurons are shown as lines radiating from the center. The last smaller circular plots show the mean vector for the preferred phases of **Figure 2.** (**A**–**D**) Firing rate and pattern of the MTh in CTL, during tetrodotoxin (TTX) infusion and in 6-hydroxydopamine (6-OHDA)-lesioned rats. \* *p* < 0.05. (**E**,**F**). Phase correlation analysis. (**E**) MTh circular plots of the phase-locked firing of the MTh neurons in control, acute and chronic DA depletion states. The vectors of the preferred firing of individual neurons are shown as lines radiating from the center. The last smaller circular plots show the mean vector for the preferred phases of neurons in each group. (**F**) The histograms on the right show the vector lengths and phase angle comparisons between MTh (blue) in control (dark color), acute (dashed color) and chronic (light color) DA depletion states. On the left, the representation of hypothetical cortical slow oscillation comparing **Figure 2.** (**A**–**D**) Firing rate and pattern of the MTh in CTL, during tetrodotoxin (TTX) infusion and in 6-hydroxydopamine (6-OHDA)-lesioned rats. \* *p* < 0.05. (**E**,**F**). Phase correlation analysis. (**E**) MTh circular plots of the phase-locked firing of the MTh neurons in control, acute and chronic DA depletion states. The vectors of the preferred firing of individual neurons are shown as lines radiating from the center. The last smaller circular plots show the mean vector for the preferred phases of neurons in each group. (**F**) The histograms on the right show the vector lengths and phase angle comparisons between MTh (blue) in control (dark color), acute (dashed color) and chronic (light color) DA depletion states. On the left, the representation of hypothetical cortical slow oscillation comparing the phase angle and

neurons in each group. (**F**) The histograms on the right show the vector lengths and phase angle comparisons between MTh (blue) in control (dark color), acute (dashed color) and chronic (light color) DA depletion states. On the left, the representation of hypothetical cortical slow oscillation comparing vector length between MTh (blue) control (dark color), TTX (dashed color) and 6-OHDA (light color) rats. \* *p* < 0.05, Kruskal–Wallis test. MTh: motor thalamus; CTL: control; TTX: tetrodotoxin; 6-OHDA: 6-hydroxydopamine. The 0◦ represents the mid-ascending phase of cortical slow-wave activity (SWA), the 90◦ represents the cortical SWA peak and the 180◦ represents the mid-descending phase of cortical SWA.

### *2.2. E*ff*ects of DA Depletion on the Cortical–Thalamic Coupling*

We compared the preferred phase angles and the vector lengths in control, acute and chronic DA depletion states. In control rats, the MTh neurons fired preferentially at 42.77 ± 12.36◦ (*n* = 21, Figure 2e), with a vector length of 0.73 ± 0.06. In acute DA-depleted animals (*n* = 34), the phase was 22.54 ± 6.96◦ with a vector length of 0.83 ± 0.03, while in the chronic DA depletion state (*n* = 22), it was 79.89 ± 15.98◦ with a vector length of 0.56 ± 0.07. The vector length, i.e., the strength of corticothalamic coupling, was significantly lower in chronic DA depletion state than in control and in acute DA depletion state (*p* < 0.05). The phase angle was significantly lower in control and acute DA depletion state than that in chronic DA depletion state (*p* < 0.05) (Figure 2e).

#### *2.3. E*ff*ects of DA Depletion on the Extracellular Levels of GABA and GLU in the Motor Thalamus*

ANOVA analysis followed by Dunnet's multiple comparison test showed a significant effect of TTX infused in the medial forebrain bundle induced an increase in the endogenous GABA extracellular level (215 ± 67.5; *n* = 14; *p* < 0.05) compared to the control (100 ± 7.90, *n* = 14) and post-infusion period (159.2 ± 50.2, *n* = 14) (Figure 3a). No changes in the GLU levels were observed in the MTh after the infusion of TTX (PRE: 100 ± 2.69; TTX: 122.7 ± 24.8, *n* = 7; POST: 123.7 ± 53.9, *n* = 14) (Figure 3b). The extracellular GABA levels in the MTh were significantly lower in 6-OHDA animals than in sham-lesioned animals (100 ± 11.09 vs. 43.54 ± 6.65, *n* = 9; \*\* *p* < 0.01) (Figure 3c). GLU levels were significantly augmented in the MTh of 6-OHDA rats when compared to those measured in sham-lesioned rats by unpaired student t-test (100 ± 13.75 vs. 276.69 ± 43.52, *n* = 9; \*\* *p* < 0.01) (Figure 3d) levels.

**Figure 3.** Effect of acute (TTX) and chronic (6-OHDA) dopamine (DA)-depletion on the extracellular levels of endogenous GABA and glutamate (GLU) in the motor thalamus (MTh). TTX infused in the medial forebrain bundle induced an increase in the endogenous GABA extracellular levels (**A**) while did not change the GLU levels in the MTh. Data in (**A**) and (**B**) are expressed as mean ± SEM normalized to the baseline levels (pre TTX infusion) from *n* = 14 experiments per group; \* *p* < 0.05. One-and two-way ANOVA for repeated measures, followed by Dunnet's multiple comparison test. 6-OHDA-lesion infused in the medial forebrain bundle produced, after 2 weeks, a decrease in GABA (**C**) and an increase in GLU extracellular levels in the MTh (**D**). Data in (**C**) and (**D**) are expressed as **Figure 3.** Effect of acute (TTX) and chronic (6-OHDA) dopamine (DA)-depletion on the extracellular levels of endogenous GABA and glutamate (GLU) in the motor thalamus (MTh). TTX infused in the medial forebrain bundle induced an increase in the endogenous GABA extracellular levels (**A**) while did not change the GLU levels in the MTh. Data in (**A**) and (**B**) are expressed as mean ± SEM normalized to the baseline levels (pre TTX infusion) from *n* = 14 experiments per group; \* *p* < 0.05. One-and two-way ANOVA for repeated measures, followed by Dunnet's multiple comparison test. 6-OHDA-lesion infused in the medial forebrain bundle produced, after 2 weeks, a decrease in GABA (**C**) and an increase in GLU extracellular levels in the MTh (**D**). Data in (**C**) and (**D**) are expressed as means ± SEMs normalized to sham-lesioned (*n* = 9) neurotransmitter levels from *n* = 9 experiments per group; \*\* *p* < 0.01, unpaired student *t*-test.

#### means ± SEMs normalized to sham-lesioned (*n* = 9) neurotransmitter levels from *n* = 9 experiments **3. Discussion**

per group; \*\* *p* < 0.01, unpaired student *t*-test. **3. Discussion**  We demonstrated that MTh is deeply affected by both acute and chronic DA depletion both in terms of electrophysiological and neurochemical alterations. The main findings of the present study are threefold. Firstly, the results emphasize the different effects of DA deafferentation on MTh, with a decrease in firing rate in TTX-treated animals and an increase of burst firing in 6-OHDA-lesioned rats. Secondly, the results show that DA depletions change, in an opposite fashion, MTh-cortical coupling with the MTh neurons fired preferentially at the beginning of the UP-state in TTX-treated rats compared to the 6-OHDA-lesioned rats, which showed a shift towards the peak of the SWA. We demonstrated that MTh is deeply affected by both acute and chronic DA depletion both in terms of electrophysiological and neurochemical alterations. The main findings of the present study are threefold. Firstly, the results emphasize the different effects of DA deafferentation on MTh, with a decrease in firing rate in TTX-treated animals and an increase of burst firing in 6-OHDA-lesioned rats. Secondly, the results show that DA depletions change, in an opposite fashion, MTh-cortical coupling with the MTh neurons fired preferentially at the beginning of the UP-state in TTX-treated rats compared to the 6-OHDA-lesioned rats, which showed a shift towards the peak of the SWA. Finally, the neurochemical results demonstrated that the extracellular levels of GABA increase and those of GLU do not change in the MTh of TTX-treated animals; in the 6-OHDA-lesioned rats, GABA levels decreased and those of GLU increased.

Finally, the neurochemical results demonstrated that the extracellular levels of GABA increase and those of GLU do not change in the MTh of TTX-treated animals; in the 6-OHDA-lesioned rats, GABA levels decreased and those of GLU increased. Despite representing a crucial point in the cortico-BG circuit, only a few studies have investigated the role of the MTh and its properties in PD, and controversial data are present in the literature [1,6]. Furthermore, controversial data are present in the literature which regard MTh electrophysiology and properties in the Parkinsonian state. While some studies show a slight MTh firing rate reduction in

awake Parkinsonian monkeys [30–34], others revealed increased [1] or unchanged firing rates [35]. We recently showed that all the EEG frequency bands are altered in TTX and 6-OHDA-depleted states; MTh oscillation changes occur preferentially in acute DA depletion, but not in the chronic state [6].

In the present study, we demonstrated that the TTX-inhibition of the medial forebrain bundle (MFB) (for technical details, see our review [27]) determined an acute increase in GABA content with the reduction of the MTh activity. In rats, the SNr activity is reduced after acute DA depletion caused by TTX [36], in accordance with electrophysiological and biochemical data obtained in PD patients [37]. Thus, the changes in MTh activity have to be due to other GABAergic afferents of the network. For instance, we occasionally recorded NRT neurons during TTX infusion and observed a consistent increment of their activity (unpublished observation). Considering that NRT is a powerful modulator of different high-order thalamic nuclei [38], it is likely that this nucleus tightly controls the MTh as well. Conversely, we observed that the MTh neuronal firing rate returns to control condition in the chronic depletion state induced by 6-OHDA. This could be due to the aberrant decrease in GABA and an increase in GLU levels in the MTh of 6-OHDA-lesioned rats that might compensate for the marked inhibition observed in the acute depletion state. The shift of the excitation/inhibition (E/I) balance toward a net excitation in the MTh of the 6-OHDA lesioned rats might indicate that the plastic changes observed in the striatum and cortex of the PD animal model [39] could also occur at the level of the thalamic area. Surprisingly, GABA levels decrease in the chronic DA-depleted MTh, a finding that cannot be explained by the canonical BG/thalamus/cortex loop [19]. Nevertheless, our data, although surprising, are in line with the findings of the only available in vivo study showing a decrease of GABA in the MTh of rats lesioned by striatal infusion of 6-OHDA [22]. Instead, we are the first to report a sustained GLU increase in the MTh of animal models of PD. The decrease in GABA levels is not trivial since the administration of the vasoactive intestinal peptide (VIP) was effective at reversing the motor deficits and contextually increased thalamic GABA contents [22]. Other areas may be involved in rebalancing the MTh activity in chronic DA depletion, such as hyperactivity of the cortex, likely leading the increased release of GLU that we observed within the MTh of 6-OHDA-lesioned rats. The increase of burst activity in the MTh that we recorded in 6-OHDA conditions might depend on the cortex, which, by acting as the main driver, produces a burst input, leading to a coincident burst of activity in MTh neurons, without necessarily causing LTS bursts [1]. The fact that we did not observe a strong effect on burst activity may depend on the canceling effects of different inputs to the MTh, such as synchronized bursts from the BG. The contextual increase in GLU may also cause the production of nitric oxide and contribute to PD development processes [15,40,41].

However, it is also necessary to underline that, since the lesion in our study was unilateral, we cannot exclude that the contralateral hemisphere could compensate, at least partially, for these changes [42]. Furthermore, a recent tractography study in healthy subjects showed that MTh may receive DAergic projections from the ventral tegmental area that are directed towards the supplementary motor area and primary motor cortex (44).

An interesting finding of this study is the interplay between motor thalamus and cortical activity. The DA depletion influenced mainly the MTh-cortical coupling. The divergence of MTh-SWA coupling in the chronic state of DA depletion further suggests that the MTh has undergone the effect of compensatory mechanisms, changing the cross-talk between MTh and the cortex. In accordance with the SWA pattern, the MTh neurons fired preferentially at the beginning of the UP-state in TTX-treated rats compared to the 6-OHDA-lesioned rats, which showed a shift towards the peak of the SWA. Thus, in 6-OHDA rats, there is a delay in the coupling between neuronal activity and the beginning of cortical SWA

We showed that, following the acute reduction of DA, the MTh immediately changes the activity, but the chronic lack of DA leads other structures to rebalance the MTh activity. It is possible to hypothesize that, inside the complex circuit involved in PD, rapid clinical transitions of the motor and non-motor symptoms could be directly related to rapid changes in MTh activity, as observed during

DBS surgery [20,21]. These hypotheses need further investigation of the MTh activity in PD animal models and PD patients.

Our study presents a number of limitations. For instance, we should keep in mind that our recordings and the position of the microdialysis probe were at ranges of depths in the MTh that correspond to both basal ganglia and cerebellar recipient zones. Therefore, our electrophysiological and neurochemical data may have included values obtained from the two different MTh territories. Moreover, we did not distinguish the two types of MTh neurons during cortical activation by performing the tail-pinch (Nakamura et al., Cerebral cortex, 2014). Further experiments will be required to identify the tonic and burst firing cells in MTh in response to cortical activation, and the eventual changes of their firing characteristics following DA depletion by TTX and 6-OHDA treatment. Another limitation of this study is the use of toxin-induced PD animal models and anesthetized preparations. Despite the usefulness of the animal models, they have limitations that need to be considered when interpreting our findings and translating them to humans [27]. Nevertheless, although rodent models cannot recapitulate all features of PD, they remain a crucial approximation in order to open new avenues.

In conclusion, our data show that in chronic conditions, the firing changes observed in the MTh are relatively mild, considering the profound changes observed in other BG nuclei. This could be due to remodeling and adaptation developed as a consequence of the DA depletion. Indeed, acute TTX induced a strong decrease in the firing rate of MTh neurons. On the other hand, the modification of GLU and GABA are highly significant in 6-OHDA DA-depleted rats and are likely a consequence of changes in the MTh inputs, difficult to explain with the current knowledge of PD pathology. The roles of the MTh in the mechanisms of information processing under physiological and pathological conditions need to be further investigated and clarified.

#### **4. Materials and Methods**

#### *4.1. Animals*

Most of the electrophysiological, neurochemical and histological procedures were carried out on adult male Sprague-Dawley rats in compliance with Swiss laws on animal experimentation, and all procedures were approved by the Animal Research Committee and the Veterinary Office of the Canton of Ticino, Switzerland (TI-08-2015).

A subset of neurochemical experiments was performed on male Sprague-Dawley rats at the Department of Physiology and Biochemistry at the University of Malta in accordance with the European Union (EU) Directive 2010/63/EU and the Institutional Animal Use and Care Committee (IAUCC) of the University of Malta.

Utmost care was taken to limit the number of rats used and their suffering.

#### *4.2. Pharmacological Blockade of the Medial Forebrain Bundle*

The pharmacological blockade of the MFB was performed on adult male Sprague–Dawley rats weighing 399 ± 68.4 g according to our previously described method [6,27]. Briefly, TTX was infused via reverse microdialysis using a probe with a 1 mm dialytic membrane (CMA/11 microdialysis probe, CMA Microdialysis, Stockholm, Sweden), which was positioned according to the following coordinates: 2.5 mm posterior to the bregma, 2 mm lateral to the midline and 8.6 mm below the cortical surface [43]. The probe was perfused with saline solution for 1 h and then with TTX (1 µM) using a syringe pump (CMA/400, CMA Microdialysis) at a flow rate of 1 µL/min. The MTh neuronal activity was recorded and analyzed for at least the next 10 min after the TTX infusion in 27 animals, while the GABA and GLU MTh levels were measured in 14 rats.

#### *4.3. Unilateral 6-OHDA Lesion*

We performed standard unilateral 6-OHDA denervation in the left hemisphere [6,27] to obtain chronic DA-depleted animals. The animals (399 ± 36.3 g) were anesthetized with 1.5%–2.5% isoflurane in oxygen and then injected with desipramine (25 mg/kg, i.p.) and pargyline (50 mg/kg, i.p.) to minimize 6-OHDA uptake by noradrenergic neurons and metabolism by MAO, respectively. Then, the animals were mounted on a stereotaxic instrument (Stoelting Co., Wheat Lane, Wood Dale, IL, USA) for injection of the neurotoxin (6-OHDA; Sigma; 8 µg/4 µL of saline solution containing 0.1% of ascorbic acid) in the medial forebrain bundle (MFB; stereotaxic coordinates: 2.5 mm posterior to the bregma, 2 mm lateral to the midline, and 8.6 mm below the cortical surface) [44]. The MTh neuronal activity was recorded an analyzed in 6-OHDA-lesioned rats (*n* = 8) and control animals (*n* = 6). The microdialysis experiments were performed in sham animals (*n* = 9), receiving instead, 4 µL of saline solution containing 0.1% of ascorbic acid infused into the MFB and 6-OHDA-lesioned animals (*n* = 9). Electrophysiological and microdialysis experiments were performed 21–29 days after the administration of 6-OHDA

#### *4.4. Prerecording Surgery*

Rats were anesthetized with urethane (1.4 g/kg, i.p.) (Sigma Chemical Co., St Louis, MO, USA) and mounted on a stereotaxic instrument (Stoelting Co.) according to our standard operating procedure [6,27]. Body temperature was maintained at 37–38 ◦C with a heating pad (Stoelting Co.). After a local anaesthetic injection (lidocaine 0.5%), a midline scalp incision was made, and the skull was almost completely drilled on the left side. The dura were then spread out to expose the cortical surface.

#### *4.5. Electrophysiological Recordings*

We recorded single neurons for the entire dorsoventral track in the same medio-lateral site in the MTh. In particular, we recorded a total of 82 neurons from the MTh (*n* = 23 control, *n* = 25 in 6-OHDA and *n* = 34 in TTX) from 5000 µm from the cortex to a maximum of 5770 µm.

We recorded multiple MTh neurons from TTX infused rats over the 10 min after infusion The MTh neurons (*n* = 34) recorded after the TTX-mediated blockade of the MFB were localized from 5250 µm to 5870 µm from the first neuronal detection of the cortex.

The MTh neurons recorded were localized at the following coordinates: 1.8 mm posterior and 2.2 medial to bregma. Single unit activity was off-line analyzed using Spike 2 software (Cambridge Electronic Design, Cambridge, UK) and principal component analysis for spike sorting.

The Electrocorticogram (ECoG) was recorded through a screw electrode (Dentorama, 8 mm of total length, 3 mm tip length) placed on the cortical surface above the frontal cortex (3.0 mm anterior of bregma and 2.0 mm lateral to the midline) and referenced against an indifferent screw electrode placed above the cerebellum. Raw ECoG was bandpass filtered (0.1–300 Hz) and amplified (×2000; Neurolog). The ECoG was on-line digitalized at a sample rate of 600 kHz through an analogical/digital interface (Micro1401 mk II, Cambridge Electronic Design, Cambridge, UK) and stored on a computer to identify off-line robust SWA, which will be the object of further analysis. Simultaneously, we collected the single-unit activity from the MTh. The acquisition of the recording was performed after the stabilization of neuronal activity. The recordings were performed using a tungsten electrode (WPI, TM33B01; impedance 0.9–1.2 MΩ). The trace containing the neuron action potentials was amplified (×10,000; Neurolog), bandpass filtered (300–1000 Hz) and sampled at 60 kHz for digital storage on a computer connected to the CED 1401 interface [36]. The neuronal activity was recorded for at least 10 min after the TTX infusion.

Figure 1A shows an example of the recordings with the EEG traces, i.e., SWA (top panel); the discriminated single neurons (middle panel); and the neuronal traces (bottom panel).

#### *4.6. Microdialysis Analysis*

An additional series of experiments were conducted on adult rats (312 ± 25 g) to measure GABA and GLU before, during and after the TTX-mediated blockade of MFB and in sham and 6-OHDA-lesioned rats [28]. Microdialysis was performed using concentric microdialysis probes (CMA11 with 1 mm PES membrane, outside diameter 0.6 mm, cut-off 35 kDa, Microbiotech) which were positioned into the right MTh (1.8 mm posterior to the bregma, 2 mm lateral to the midline and 6.2 mm below the cortical surface).

Probes were then connected to a high precision micro-injection pump (CMA/100, CMA 159 Microdialysis) and infused at a flow rate of 5 µL/min with an artificial cerebrospinal fluid containing 160 (in mM): NaCl 145, KCl 3, MgCl2 1, CaCl2 1.26, buffered at pH 7.4 with 2 mM phosphate buffer.

After stabilization (4 h), consecutive samples were collected every 10 min. In the acute depleted animals, TTX was infused in the MFB (see above) at the end of the 3rd sample. All samples were immediately frozen at −80 ◦C until analyzed. At the end of the experiment, rats were sacrificed, and the correct position of the probe was histologically verified (36).

For the experiments in 6-OHDA and sham-lesioned animals, the microdialysis experiments were performed 21–29 days after the lesion.

The concentrations of GABA and GLU in the samples were determined by HPLC analysis following precolumn derivatization with o-phthalaldehyde and resolution through a C18-reverse phase chromatographic column (Chrompack, Middleburg, The Netherlands) connected to a fluorometric detector (excitation 350 nm; emission 450 nm). Buffers were prepared as follows: solvent A, 0.1 M sodium acetate (pH 5.8): methanol, 80:20; solvent B, 0.1 M sodium acetate (pH 5.8): methanol, 20:80; solvent C, 0.1 M sodium acetate (pH 6.0): methanol, 80:20. Gradient programme: 100% C for 4 min from the initiation of the programme; 90% A and 10% B in 1 min; isocratic step for 2 min; 78% A and 22% B in 2 min; isocratic step 172 for 6.50 min; 66% A and 34% B in 1.10 min; isocratic step for 1.50 min; 42% A and 58% B in 1.10 min; isocratic step for 3.50 min; flow rate 0.9 mL/min (Waters 600MS gradient system). Homoserine was used as an internal standard [21].

#### *4.7. Histological and Microscopic Analysis*

At the end of the experiment, the positions of the electrodes (Figure 1b,c) were histologically checked, and the unilateral dopaminergic lesion in each PD rat was confirmed by quantification of tyrosine hydroxylase (TH) immunoreactivity, as described and analyzed in our previous publication [28].

#### *4.8. Statistical Analysis*

Concerning the single-unit data, statistical comparisons of firing rates and burst parameters (control vs. 6-OHDA and post-TTX) were conducted using the Kruskal–Wallis test with the Man–Whitney test for post hoc comparisons (asymptotic significance (2-tailed) *p*-value).

The firing rate comparison was performed taking into account the mean of the first 5 min of recording. The comparison of the burst activity was performed in selected 100-s increments in the presence of stable cortical urethane-induced SWA with a frequency below 3 Hz, as shown elsewhere (45.) SWA is not affected by DA denervation and DAergic treatment [45].

To investigate the correlation between cortex and MTh, we performed the phase correlation analysis, with the calculation of descriptive circular statistics being performed using the CircStat toolbox for MATLAB (2). For the burst analysis, the phase correlation analysis was performed in the same selected 100 s in the presence of stable cortical SWA. For circular analyses and statistical comparisons, we included all neurons from the previous analyses.

For each neuron, the linear phase histograms were analyzed, taking into account the up phase of the SWA, and the spike phase values were used for circular analysis. Then, each neuron was tested for significant SWA phase-locked firing (comparison of degree; Rayleigh's uniformity test). The selected neurons were then used for the analysis of the preferred phase angle, i.e., the mean resultant vector in degree (with 90◦ corresponding to the peak of the SWA phase), and the vector length (corresponding to the level of dispersion of the firing). The mean resultant vector length of the phase distribution, bound between zero and one, was used to quantify the level of phase-locking around the mean angle (the closer to one, the more grouped the firing; 2).

The differences between the mean of the resultant vector (degree) and the vector lengths of the three groups (Control, TTX and 6-OHDA) of MTh were tested using the Kruskal-Wallis test.

All circular analyses were performed using the degree and the upper peak in SWA oscillations corresponding to a 90◦ phase and the down peak to a 270◦ phase.

In all experimental conditions, all neurons were tested for significantly phase-locked firing; i.e., not randomly distributed in relation to the SWA (comparison of degree; Rayleigh's uniformity test) according to the method described by Nakamura and colleagues [2]. Firstly, we analyzed the activity by using the CircStat toolbox for MATLAB. As reported in the text, for each neuron, the linear phase histograms were analyzed, taking into account the up phase of the SWA, and the spike phase values were used for circular analysis. Then, each neuron was tested for significant SWA phase-locked firing; i.e., not-randomly distributed in relation to the SWA (comparison of degree; Rayleigh's uniformity test). To exclude this hypothesis, we performed chi-square tests on the percentage of discarded MTh neurons in the three conditions; i.e., control, acute and chronic DA depletion states. The significance threshold was set at *p* < 0.05 for all analyses.

**Author Contributions:** Conceptualization, S.G and G.D.G.; methodology G.O., L.C.G., E.F., A.S. (Alessandro Stefani), S.G. and G.D.G.; formal analysis, L.C.G., S.G. and G.D.G.; investigation, G.O., L.C.G., E.F., A.S. (Agnese Salvadè), W.S., E.C., L.C.G., S.G. and G.D.G; resources, S.G. and G.D.G.; data curation, G.O., L.C.G., E.F., A.S. (Agnese Salvadè), S.G. and G.D.G.; writing—original draft preparation, S.G., L.C.G. and G.D.G.; writing—review and editing, E.F., A.S. (Alessandro Stefani), A.K.-L. and G.D.G.; supervision, S.G. and G.D.G.; project administration, S.G. and G.D.G.; funding acquisition, S.G. and G.D.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Parkinson Svizzera and the Fondazione per lo studio delle malattie neurodegenerative delle persone adulte e dell'anziano del Ticino to SG and by the University of Malta, Internal Research Grant Scheme to G.D.G.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **Abbreviations**

#### **References**

1. Bosch-Bouju, C.; Hyland, B.I.; Parr-Brownlie, L.C. Motor thalamus integration of cortical, cerebellar and basal ganglia information: Implications for normal and parkinsonian conditions. *Front. Comput. Neurosci.* **2013**, *7*, 163. [CrossRef]

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Ioannis Efthimiou1 and Kostadinos Skrepetis2

1 Department of Urology, University Hospital of Alexandroupolis, Greece

2 Department of Urology, General Hospital of Kalamata, Greece


*Edited by Michael T. Aide*

*Rare Earth Elements - Emerging Advances, Technology Utilization, and Resource Procurement* illustrates the global interest in rare earth elements. The section on "Emerging Advances and Technology" presents research on the usage of rare earth elements in emerging technologies involving medicine, digital technology, mineral processing, and chemistry. The section on "Utilization and Resource Procurement" focuses on the global issues of pollution because of rare earth mining. The research presented underlies the rapid development of emerging technologies that require rare earth elements and the social issues that arise because of these technologies.

Published in London, UK © 2023 IntechOpen © DrPAS / iStock

Rare Earth Elements - Emerging Advances, Technology Utilization, and Resource Procurement

Rare Earth Elements

Emerging Advances, Technology Utilization,

and Resource Procurement

*Edited by Michael T. Aide*


**13.2.2 Legg-Calve Perthe disease**

Legg-Calve Perthes disease is an idiopathic ischemic necrosis of the femoral head that occurs characteristically in children between 5 to 8 years. Bone scintigraphy is more sensitive than radiography for early diagnosis, and comparable to MRI (Shammas, 2009; as cited in Ma et al., 2007). Studies performed early after the onset of clinical symptoms show absence of activity in the capital femoral epiphysis and it may precede radiographic changes (Shammas, 2009; as cited Connolly & Treves, 1998). Later scans may demonstrate increased activity due to revascularization and remodeling. Bone scintigraphy has also a pronostic value and can be used in routine management to identify patients at high risk for a poor outcome (Shammas, 2009; as cited in Comte et al., 2003): Persistent absence of bone uptake in the proximal femoral epiphysis after 5 months or metaphyseal hyperactivity is highly correlated with more severe disease and a poorer prognosis. The early formation of a lateral column of tracer uptake in the capital femoral epiphysis, even before radiography, is associated with a good prognosis due to early revascularization (Shammas, 2009; as cited in Conway, 1993 and Tsao et al., 1997).

#### **13.2.3 Slipped capital femoral epiphysis**

Slipped capital femoral epiphysis is characterized by a displacement of the capital femoral epiphysis from the femoral neck through the physeal plate with medial and posterior rotation of the epiphysismost commonly in the adolescence. Bone scintigraphy is useful for

Nuclear Medicine in Musculoskeletal Disorders: Clinical Approach 129

to other radiologic investigations. Its major advantage is the increased sensitivity in detecting evidence of soft-tissue and bone trauma (25%to 50%), and in the documentation of specific and characteristic sites of abuse, such as in the ribs or the diaphyses of the extremities (Nadel &

Benign bone tumors are by far the most common type of tumors that grow within the skeleton. Nonossifying fibromas, osteochondromas and simple bone cysts are the types most often found in children and teenagers. Children between the ages of 6 and 12 are the most likely to develop benign bone tumors, although the tumors sometimes show up in children as young as age 2. Exostosis tumors are slightly more common in boys than girls. Nonossifying fibroma usually is in the actively growing sections of long bones such as the thighbone (femur). Exostosis (osteochondroma) contains both bone and cartilage and usually grows in the thighbone, the shinbone (tibia) or the bone in the upper arm (humerus). Unicameral (simple) bone cysts are holes in the bone that fill with fluid and tissue. They usually occur in the bone in the upper arm or in the upper part of the thighbone. In some cases, a benign bone tumor can cause problems while it grows. It can weaken the child's bone and make it more likely that the bone will break. Tumors also can press on nerves and cause pain. In cases like these, surgery may be necessary. Malignant primary bone tumors make up 5% of childhood malignancies, and osteosarcoma is the most commonly isolated malignant bone tumor in children, followed by Ewing's sarcoma. These bone neoplasms usually begin during childhood and adolescence, when bones are growing quickly and they often are taking part in sports and other physical activities. Langerhans cell histiocytosis is also more common in the pediatric population. A smaller number of patients have other diagnosis such as malignant fibrous histiocytoma, angiosarcoma and chondrosarcoma, but these conditions are very rare in paediatric population. The use of nuclear medicine techniques in the diagnosis, staging and monitoring of the different bone tumors has been detailed in a previous section of this chapter. FDG-PET has become to be one of the best tools for the baseline evaluation and follow-up, although some benign bone lesions may show high 18F-FDG accumulation, equivalent to osteosarcomas. Despite this, 18F-FDG-PET has a high specificity for excluding malignant bone tumors (Cook et al., 2010). FDG- PET has been shown to help determine the presence and extent of sarcomas and even may allow the noninvasive estimation of the histologic grade of some tumors, although the biopsy remains necessary. This technique allows targeted biopsies, which can reduce the likelihood of underestimation of tumor grade and inadequate therapy. Furthermore, 18F-FDG-PET is useful in the pediatric population for detection of skip metastases in cases of

Stilwell, 2001; as cited in Conway et al., 1993, Sty & Wells, 1994).

**13.2.7 Primary bone tumors in childhood**

equivocal MRI findings (Even-Sapir, 2007).

**14. Therapeutic alternatives with radionuclides: Radiosynoviorthesis**

In the treatment of inflammatory rheumatic diseases with chronic course, operative and respectively arthroscopic synovectomy on the one hand and synoviorthesis on the other hand come into question. Synoviorthesis by corticosteroids has a very large indication if the corresponding measures of precaution are heeded. Chemical synoviorthesis, mainly by osmium tetroxide, is applied above all in exudative inflammatory diseases, whereas radiosynoviorthesis with the nuclides used at present is mainly applied in proliferative diseases. The cytotoxic effects intrinsic to the beta radiation emission from some

assessing the vascularity of the femoral head. In the absence of avascular necrosis, the bone scan findings in slipped capital femoral epiphysis are nonspecific and consist of mildly increased activity with widening and blurring of the growth plate activity (Shammas, 2009; as cited in Connolly et al., 2006, 2007).


*2.4. Spectral Variability*

VHR images show the required details of the mangrove ecosystem. Therefore, the Worldview-2 image was used to select the eight targeted land cover classes: (1) closed canopy mangrove, (2) open canopy mangrove, (3) individual mangrove trees, (4) mudflats, (5) aerial roots, (6) tidal zone, (7) shallow water, and (8) deep water. In order to better separate them and distinguish between the spectral

signatures, based on the field survey and image interpretation, reflectance values of the target classes (by 100 points) were extracted from Worldview-2 image bands. The boxplots in Figure 3 show that the two classes closed canopy mangrove and open canopy mangrove are clearly distinguished by the blue band and the yellow band. Moreover, it shows that the aerial roots are clearly distinguished from the mudflats in the green, yellow and red bands. Figure 4 shows the reflectance values of the eight land cover classes for the Sentinel-2 bands.

**Figure 3.** Reflectance values of the eight land cover classes for each Worldview-2 band: (**B2**) (Blue: 450–510 nm), (**B3**) (Green: 510–580 nm), (**B4**) (Yellow: 585–625 nm), (**B5**) (Red: 630–690 nm), (**B6**) (Red edge: 705–745 nm), (**B7**) (Near-infrared 1: 770–895 nm), and (**B8**) (Near-infrared 2: 860–1040 nm). The letters A to H show the land cover classes namely closed canopy mangrove, open canopy mangrove class, individual mangrove trees, mudflats, aerial roots, tidal zone, shallow water, and deep water, respectively.

**Figure 4.** Reflectance values of the eight land cover classes for Sentinel-2: (**B2**) (Blue band 490 nm), (**B3**) (Green band 560 nm), (**B4**) (Red band 665 nm), (**B5**) (Vegetation Red Edge band 705 nm), (**B6**) (Vegetation Red Edge band 740 nm), (**B7**) (Vegetation Red Edge band 783 nm), (**B8**) (Near-infrared band 842 nm), and (**B8A**) (Vegetation Red Edge band 865 nm). The letters A to H show the land cover classes namely closed canopy mangrove, open canopy mangrove class, individual mangrove trees, mudflats, aerial roots, tidal zone, shallow water, and deep water, respectively.

**Table 2.** Sensor specifications of the Worldview-2 and Sentinel-2 imagery.

## *2.5. Reference Data*

The sampling of reference data used Object-Based Image Analysis (OBIA), which is based on segmentation [34,47]. The multi-resolution segmentation algorithm from eCognition 9.2 software (Trimble Inc., Munich, Germany) [48] was used, which classifies homogeneous image objects by using attributes of image objects rather than the attributes of individual pixels or a hierarchical object-oriented approach using a knowledge base. In the present study, a series of scale parameters, shape and compactness (from low to high) were tested to control the size of segmentation. In order to generate reliable reference samples, information from the Normalized Difference Vegetation Index (NDVI) layer and the Moran Index using the Worldview-2 bands was additionally included for image segmentation. In previous studies, NDVI has been successfully applied to display and quantify mangrove forest changes [12,49,50]. NDVI values were computed as:

$$\text{NDVI} = \frac{\text{NIR} - \text{Red}}{\text{NIR} + \text{Red}} \tag{1}$$

where NIR is band 8 and Red is band 5.

The Moran index provides the correlation between attributes at each location in a study area and the statistical mean of the values from neighboring locations. The Moran index has successfully been applied in almost all studies dealing with spatial autocorrelation (for a review see [51]). It evaluates the magnitude of homogeneity of a target image object to other objects surrounding it. If targets are attracted to (or repelled from) each other, the observations are dependent [52]. In addition, the Moran Index is similar to correlation coefficients and its value ranges from −1 to 1 [53]. Moreover, the Moran index provides quantitative clustering information that is used to select homogeneous regions. The Moran index measures the degree of spatial auto-correlation at each particular location [54]. Information and photos from the field observations, as well as a visual interpretation of Worldview-2 images, were used to develop the rule sets to select segmentations for each class as reference data (Ground Truth or OBIA training). In order to use spectral features (mean and standard deviation of blue, yellow, red edge bands and NDVI), additional geometric features such as shape and extent were used. The total number of variables selected was based on visual inspection of the reflectance values of the eight classes. The feature selection process was completed with the eCognition feature optimization tool using 100-point datasets.

#### *2.6. Upscaling by Reference Data*

After the generation of the reference data, RF was used to classify Worldview-2 and Sentinel-2 images. In this step, Sentinel-2 imagery was preliminarily mapped over the same extent as the Worldview-2 image with 70% of the reference data. The accuracy of the map of the RF algorithm was then checked, and the reference data were used for mapping mangrove classes to a larger extent. A layer stack was created from the NDVI, blue, green, red and near-infrared bands. Sentinel-2 data (10 m spatial resolution) served as input for the RF classification. RF was performed using the *Ranger* Package in the R statistical software [55]. Figure 5 shows the main steps of the classification approach applied in this study.

**Figure 5.** Flow chart of the upscaling approach for mapping land cover in mangrove ecosystems.


*Article* **Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete**

**Xiongzhou Yuan 1, Yuze Tian 2,\*, Waqas Ahmad 3,\*, Ayaz Ahmad 3,4, Kseniia Iurevna Usanova 5, Abdeliazim Mustafa Mohamed 6,7 and Rana Khallaf 8**

**Abstract:** Compressive and flexural strength are the crucial properties of a material. The strength of recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate concrete. Several factors, including the recycled aggregate replacement ratio, parent concrete strength, water– cement ratio, water absorption, density of the recycled aggregate, etc., affect the RAC's strength. Several studies have been performed to study the impact of these factors individually. However, it is challenging to examine their combined impact on the strength of RAC through experimental investigations. Experimental studies involve casting, curing, and testing samples, for which substantial effort, price, and time are needed. For rapid and cost-effective research, it is critical to apply new methods to the stated purpose. In this research, the compressive and flexural strengths of RAC were predicted using ensemble machine learning methods, including gradient boosting and random forest. Twelve input factors were used in the dataset, and their influence on the strength of RAC was analyzed. The models were validated and compared using correlation coefficients (R2), variance between predicted and experimental results, statistical tests, and k-fold analysis. The random forest approach outperformed gradient boosting in anticipating the strength of RAC, with an R2 of 0.91 and 0.86 for compressive and flexural strength, respectively. The models' decreased error values, such as mean absolute error (MAE) and root-mean-square error (RMSE), confirmed the higher precision of the random forest models. The MAE values for the random forest models were 4.19 MPa and 0.56 MPa, whereas the MAE values for the gradient boosting models were 4.78 MPa and 0.64 MPa, for compressive and flexural strengths, respectively. Machine learning technologies will benefit the construction sector by facilitating the evaluation of material properties in a quick and cost-effective manner.

**Keywords:** recycled aggregate concrete; sustainable aggregate; compressive strength; flexural strength; gradient boosting; random forest


2.2.2. Construction of the Ideal TTT Diagram from (*t*s,cct(*T*s,cct), *T*s,cct) type CCT Diagram

The numerical value of the isothermal start time can also be obtained from the (*t*s,cct(*T*s,cct), *T*s,cct) type constant cooling rate CCT diagram, where *t*s,cct and *T*s,cct are the time and temperature coordinates of the CCT curve and *T*start is the cooling start temperature temperature, by using the difference approximation described in Section 2.2.1. Since the constant cooling rate is ˙ *θ*c = (*T*start − *T*s,cct)/*t*s,cct(*T*s,cct), Equation (8) describes the ideal isothermal start time as a function of temperature.

$$\begin{split} \tau \left( T\_{\text{s,cct}} \right) &\approx -\frac{\Delta T\_{\text{s,cct}}}{\Delta \theta\_{\text{c}} \left( T\_{\text{s,cct}} \right)} \\ &= \frac{h}{\frac{T\_{\text{start}} - \left( T\_{\text{s,cct}} - h/2 \right)}{t\_{\text{s,cct}} \left( T\_{\text{s,cct}} - h/2 \right)} - \frac{T\_{\text{start}} - \left( T\_{\text{s,cct}} + h/2 \right)}{t\_{\text{s,cct}} \left( T\_{\text{s,cct}} + h/2 \right)} \end{split} \tag{8}$$

where *t*s,cct(*T*s,cct ± *h*/2) represent the function values of *t*s,cct(*T*) at *T* = *T*s,cct ± *h*/2. Equation (8) can be used to calculate the ideal isothermal transformation start, *τ*(*T*), when the constant cooling CCT curve (*t*s,cct(*T*s,cct), *T*s,cct) is known.