**3. Systematic characterization of the phosphoproteome dynamics in GSCs**

The quantitative information on the phosphoproteome dynamics can provide us with systematic description of the key machinery for cellular signalling. In this section, we introduce two examples of global phosphoproteome analyses of GSCs using SILAC (stable isotope labelling by amino acids in cell culture)‐based quantitative technique [27, 28] (**Figure 2**). One was carried out using epidermal growth factor (EGF) to elucidate the mechanism for stemness maintenance of GSCs [29], whereas the other was conducted through serum‐induced differentiation of GSCs to unveil the key pathways responsible for disrupting stemness characteristics [30].

*3.1.1. IPA‐based network analysis*

*3.1.2. Upstream kinase prediction analysis*

data on EGF‐stimulated GSCs (**Figure 4(A)** and **(B)**).

IPA canonical pathway analysis was then performed using SILAC‐based quantitative phosphoproteome data on EGF‐stimulated GSCs [29] (**Figure 3**). Protein synthesis‐related pathways (EIF2 signalling, mTOR signalling) and cell cycle regulation‐related pathways (cyclins and cell cycle regulation, cell cycle: G1/S checkpoint regulation, cell cycle: G2/M DNA damage checkpoint regulation) were extracted with statistical significance (‐log (*p*‐value) > 5).

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Protein phosphorylation is known to be controlled by specific kinases depending on consensus sequence motifs of substrates [32]. The motif‐x algorithm [33, 34] is applicable to statistical extraction of significant consensus sequence motifs from the large‐scale phosphoproteome

NetworKIN [10, 11] is designed to predict upstream kinases based on the sequence motifs around the functionally regulated phosphorylation sites through construction of the related protein‐protein interaction (PPI) networks using STRING [35]. The NetworKIN algorithm enables further interpretation of the results obtained from the motif‐x analyses (**Figure 4 (C)**).

CSCs are regarded as one of the most clinically important cell populations in causing tumour heterogeneity, which is responsible for the resistance to chemotherapy [36]. As recent studies have demonstrated that non‐CSCs can also readily acquire CSC‐like characteristics [37], it is very important to figure out the detailed mechanisms underlying CSC differentiation and

**Figure 3.** IPA‐based pathway analysis of the quantitative phosphoproteome data on EGF‐stimulated GSCs. (A) The significant canonical pathways across the entire dataset (‐log (*p*‐value) > 5). (B) The mTOR signalling pathway is

representatively depicted with the predicted information on the biological activities related to this pathway.

**3.2. Global quantitative phosphoproteome analyses of serum‐induced GSCs**

**Figure 2.** Schematic workflow for quantitative proteome analysis using SILAC, a representative relative quantitation technique based on metabolic labelling of specific amino acids such as arginine. Two populations of GSCs were cultured in the media supplemented with 12C6 14N4 ‐Arg (light) or 13C6 15N4 ‐Arg (heavy), respectively. After one of the two cell populations was stimulated/perturbed, both of the cells were lysed, equally combined and enzymatically digested to perform nanoLC‐ MS/MS analyses. The intensity of each mass peak is used for relative quantitation of each peptide with high accuracy.

#### **3.1. Global quantitative phosphoproteome analyses of EGF‐stimulated GSCs**

EGF is known to be essential for maintenance and growth of GSCs [31]. The quantitative phosphoproteomic analysis of EGF‐stimulated GSCs was performed to acquire network‐wide information on the molecules related to stemness maintenance. As a result, a total of 6073 phosphopeptides from 2282 phosphorylated proteins were identified, leading to quantitative classification of 516 upregulated and 275 downregulated phosphorylation sites [29].

#### *3.1.1. IPA‐based network analysis*

IPA canonical pathway analysis was then performed using SILAC‐based quantitative phosphoproteome data on EGF‐stimulated GSCs [29] (**Figure 3**). Protein synthesis‐related pathways (EIF2 signalling, mTOR signalling) and cell cycle regulation‐related pathways (cyclins and cell cycle regulation, cell cycle: G1/S checkpoint regulation, cell cycle: G2/M DNA damage checkpoint regulation) were extracted with statistical significance (‐log (*p*‐value) > 5).

### *3.1.2. Upstream kinase prediction analysis*

**3.1. Global quantitative phosphoproteome analyses of EGF‐stimulated GSCs**

‐Arg (light) or 13C6

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the media supplemented with 12C6

14N4

classification of 516 upregulated and 275 downregulated phosphorylation sites [29].

EGF is known to be essential for maintenance and growth of GSCs [31]. The quantitative phosphoproteomic analysis of EGF‐stimulated GSCs was performed to acquire network‐wide information on the molecules related to stemness maintenance. As a result, a total of 6073 phosphopeptides from 2282 phosphorylated proteins were identified, leading to quantitative

**Figure 2.** Schematic workflow for quantitative proteome analysis using SILAC, a representative relative quantitation technique based on metabolic labelling of specific amino acids such as arginine. Two populations of GSCs were cultured in

was stimulated/perturbed, both of the cells were lysed, equally combined and enzymatically digested to perform nanoLC‐ MS/MS analyses. The intensity of each mass peak is used for relative quantitation of each peptide with high accuracy.

‐Arg (heavy), respectively. After one of the two cell populations

15N4

Protein phosphorylation is known to be controlled by specific kinases depending on consensus sequence motifs of substrates [32]. The motif‐x algorithm [33, 34] is applicable to statistical extraction of significant consensus sequence motifs from the large‐scale phosphoproteome data on EGF‐stimulated GSCs (**Figure 4(A)** and **(B)**).

NetworKIN [10, 11] is designed to predict upstream kinases based on the sequence motifs around the functionally regulated phosphorylation sites through construction of the related protein‐protein interaction (PPI) networks using STRING [35]. The NetworKIN algorithm enables further interpretation of the results obtained from the motif‐x analyses (**Figure 4 (C)**).

### **3.2. Global quantitative phosphoproteome analyses of serum‐induced GSCs**

CSCs are regarded as one of the most clinically important cell populations in causing tumour heterogeneity, which is responsible for the resistance to chemotherapy [36]. As recent studies have demonstrated that non‐CSCs can also readily acquire CSC‐like characteristics [37], it is very important to figure out the detailed mechanisms underlying CSC differentiation and

**Figure 3.** IPA‐based pathway analysis of the quantitative phosphoproteome data on EGF‐stimulated GSCs. (A) The significant canonical pathways across the entire dataset (‐log (*p*‐value) > 5). (B) The mTOR signalling pathway is representatively depicted with the predicted information on the biological activities related to this pathway.

**Figure 4.** Phosphorylation site‐oriented network analysis of the quantitative phosphoproteome data on EGF‐stimulated GSCs. The consensus sequence motifs surrounding the quantitatively regulated phosphorylation sites regarding (A) downregulation and (B) upregulation can be described as a result of the motif‐x analyses. (C) The numerical distribution of the putative kinases predicted by NetworKIN. The colour of cells reflects the number of the predicted kinases for each consensus sequence as described in (A) and (B).

understand the principle of their heterogeneity. Serum‐induced phosphoproteome dynamics in GSCs was measured to systematically elucidate the regulatory nodes for stemness alteration over the entire signalling networks [30]. Among 2876 phosphorylation sites on 1584 proteins identified, 732 phosphorylation sites on 419 proteins were found to be regulated through serum‐induced differentiation. The integrative network analyses of the quantitative phosphoproteome data using various bioinformatical tools including IPA and NetworKIN indicated that transforming growth factor‐β receptor type‐2 (TGFBR2) might be one of the crucial upstream regulators concerning GSC alteration (**Figure 5**).

**Figure 5.** Upstream kinase/regulator analyses based on the regulated phosphoproteome data on serum‐induced GSCs. (A) Heatmap of the over‐representation *p*‐values calculated for each predicted kinase using PhosphoSiteAnalyzer, a bioinformatical platform for the NetworKIN prediction results from the phosphoproteome data [38]. The subset 'serum (−)' indicates SILAC ratio > 2.0, whereas 'serum (+)' shows SILAC ratio < 0.5. TGFBR2 and ACVR2A/B‐specific phosphorylation sites were predicted to be significantly enriched in the 'serum (−)' subset (adjusted *p*‐value < 0.05). (B) Upstream regulator analysis by IPA. The top 10 upstream regulators relevant to the regulated phosphoproteome are shown with the corresponding score (−log [*p*‐value]). (C) IPA‐based description of TGF‐β1 and the target molecules in the phosphoproteome

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data. Dashed lines represent indirect interactions caused by TGF‐β1, adapted from Ref. [30].

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**Figure 5.** Upstream kinase/regulator analyses based on the regulated phosphoproteome data on serum‐induced GSCs. (A) Heatmap of the over‐representation *p*‐values calculated for each predicted kinase using PhosphoSiteAnalyzer, a bioinformatical platform for the NetworKIN prediction results from the phosphoproteome data [38]. The subset 'serum (−)' indicates SILAC ratio > 2.0, whereas 'serum (+)' shows SILAC ratio < 0.5. TGFBR2 and ACVR2A/B‐specific phosphorylation sites were predicted to be significantly enriched in the 'serum (−)' subset (adjusted *p*‐value < 0.05). (B) Upstream regulator analysis by IPA. The top 10 upstream regulators relevant to the regulated phosphoproteome are shown with the corresponding score (−log [*p*‐value]). (C) IPA‐based description of TGF‐β1 and the target molecules in the phosphoproteome data. Dashed lines represent indirect interactions caused by TGF‐β1, adapted from Ref. [30].

understand the principle of their heterogeneity. Serum‐induced phosphoproteome dynamics in GSCs was measured to systematically elucidate the regulatory nodes for stemness alteration over the entire signalling networks [30]. Among 2876 phosphorylation sites on 1584 proteins identified, 732 phosphorylation sites on 419 proteins were found to be regulated through serum‐induced differentiation. The integrative network analyses of the quantitative phosphoproteome data using various bioinformatical tools including IPA and NetworKIN indicated that transforming growth factor‐β receptor type‐2 (TGFBR2) might be one of the

**Figure 4.** Phosphorylation site‐oriented network analysis of the quantitative phosphoproteome data on EGF‐stimulated GSCs. The consensus sequence motifs surrounding the quantitatively regulated phosphorylation sites regarding (A) downregulation and (B) upregulation can be described as a result of the motif‐x analyses. (C) The numerical distribution of the putative kinases predicted by NetworKIN. The colour of cells reflects the number of the predicted kinases for each

crucial upstream regulators concerning GSC alteration (**Figure 5**).

270 Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

consensus sequence as described in (A) and (B).

**Figure 6.** Construction of phosphorylation‐oriented PPI networks via PTMapper. (A) Workflow for the visualization of kinase‐phosphorylation site relationships in PPI networks via PTMapper. Phosphorylation sites are connected with the parental protein nodes in PPI networks and the upstream kinases are then added to the phosphorylation sites. (B) Phosphorylation site‐oriented networks constructed from the phosphoproteome data on EGF‐stimulated glioblastoma stem cells. The solid arrows represent functionally directed protein‐protein interactions or kinase‐ substrate interactions, whereas the dotted lines show the linkages of proteins and their phosphorylation sites, adapted from Ref. [12].

**Figure 7.** Comparison of the sub‐networks extracted from EGF‐dependent phosphorylation dynamics of glioblastoma stem cells. (A) Schematic procedure for the evaluation of PTMapper‐based network construction. (B) The most significantly regulated sub‐networks extracted from the conventional protein interaction network. (C) The phosphorylation site‐ oriented network generated via PTMapper. The nodes surrounded by the border with the upper‐right numbers indicate the common molecules in the two types of the sub‐networks. The solid arrows represent functionally directed protein‐ protein interactions or kinase‐substrate interactions, whereas the dotted lines show the linkages of proteins and their

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phosphorylation sites. The dashed circles indicate p70S6K and Lyn, adapted from Ref. [12].

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**Figure 7.** Comparison of the sub‐networks extracted from EGF‐dependent phosphorylation dynamics of glioblastoma stem cells. (A) Schematic procedure for the evaluation of PTMapper‐based network construction. (B) The most significantly regulated sub‐networks extracted from the conventional protein interaction network. (C) The phosphorylation site‐ oriented network generated via PTMapper. The nodes surrounded by the border with the upper‐right numbers indicate the common molecules in the two types of the sub‐networks. The solid arrows represent functionally directed protein‐ protein interactions or kinase‐substrate interactions, whereas the dotted lines show the linkages of proteins and their phosphorylation sites. The dashed circles indicate p70S6K and Lyn, adapted from Ref. [12].

**Figure 6.** Construction of phosphorylation‐oriented PPI networks via PTMapper. (A) Workflow for the visualization of kinase‐phosphorylation site relationships in PPI networks via PTMapper. Phosphorylation sites are connected with the parental protein nodes in PPI networks and the upstream kinases are then added to the phosphorylation sites. (B) Phosphorylation site‐oriented networks constructed from the phosphoproteome data on EGF‐stimulated glioblastoma stem cells. The solid arrows represent functionally directed protein‐protein interactions or kinase‐ substrate interactions, whereas the dotted lines show the linkages of proteins and their phosphorylation sites, adapted

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from Ref. [12].


*2.4. Post-Processing: Clustering and Reference Data*

Single-band orthomosaics were extracted from the multispectral orthomosaic and tested for band correlation. Green and red bands were de-correlated from the NIR band using the formula *Radj* = *Rini* − *RNIR* ∗ 0.8, where *Radj* is the corrected reflectance value, *Rini* is the initial value of the red or green bands and *RNIR* is the reflectance value in the NIR spectrum. This correction reduced the R2 correlation from 0.89 to 0.64 between the red and NIR band, and from 0.92 to 0.81 between the green and NIR band. Temperature and RGB values were not modified after the stitching.

**Figure 1.** Overall processing pipeline. In (**a**) processes are represented with a darker background, while data have a light background. The green steps represent data and processes, used for the unsupervised classification of multispectral and thermal data; the blue steps represent factors used for reference data estimation based on RGB clips; the yellow steps are common between the two processing pipelines. In (**b**) details of the segmentation process are depicted: starting with mask positioning along planting lines, proceeding with rectangular shape clipping and concluding with circular mask extraction. In (**c**) the classes derived by the expert assessment are shown for each vigour and plant health shift class. On the left, the four vigour classes are represented from highly vigorous and asymptomatic (V4) to dead (V1). On the right, the classification of the health shifts that occurred between 2017 and 2018 in plants that were initially highly vigorous and asymptomatic (V4) is reported: from those that remain highly vigorous and asymptomatic in the second year (S1), to the ones that in 2018 were weakened (S2), diseased (S3) or dead (S4). Abbreviations: ROI, region of interest; GeoRef, georeferenced.

Identification of the stump positions and determination of the canopy boundaries is very difficult to achieve when performing in-field UAV surveys over kiwi vines because, firstly, T-bar or pergola training systems create a flat and dense canopy preventing stump identification, and secondly, the canopies of two neighbouring plants usually overlap due to the disordered growth of shoots. For these reasons, a rough segmentation algorithm was applied to the RGB, thermal, and each single-band multispectral orthomosaic. Firstly, the orthomosaics were clipped along the planting lines with rectangular masks. These digital masks were 5 m wide and 1.5 m long and were placed with the longer side perpendicular to the direction of planting (Figure 1b). The rectangles were placed 1.5 m apart from each other to avoid overlapping. Secondly, a circular shape with a radius of 1 m was applied to the centre of each rectangle clip, retaining only the area within the circle. The shape resulting from the rectangle's and circle's intersection was selected to reduce the border effect, while the mask dimensions and the distances between the mask centres were set to minimise the interference from background pixels of the inter-row grass, and to reduce the canopy overlap within the same clip. Segmentation of RGB data

was stopped at the rectangular shape clipping, while multispectral and thermal data were subjected to the complete segmentation workflow. The RGB clips were used to estimate reference values for plant vigour and plant health shifts via expert assessment, while multispectral and thermal data were used for unsupervised clustering. Maintaining the entire rectangular clips for RGB data improved expert awareness of the canopy dimensions, and consequently the precision of plant vigour classification. On the other hand, multispectral and thermal data was reduced to the assumed canopy extent to reduce background bias, since the central portion of the canopy is usually flat and orthogonal to the acquisition of the images (Figure 1b).

Two types of reference data were evaluated from the rectangular RGB clips via expert assessment: plant vigour and plant health shifts (Figure 1c). Plant vigour is a good indicator to distinguish asymptomatic and symptomatic plants, but it is not directly associated with the status of the root system [2]. Therefore, the a posteriori evaluations of vigour shifts and the death rates between years were the only available means to perform realistic evaluations of plant health status in 2017 without uprooting plants.

Reference data for canopy vigour was estimated based on RGB clips derived from the segmentation process of the 2017 orthomosaics. Each clip was evaluated individually and independently by two experts and ranked in four classes based on canopy vigour, considering: (i) the presence/absence of symptoms on the leaves (yellowing, scorching or wilting), (ii) disease severity (percentage of canopy with wilting symptoms), and (iii) fractional vegetation coverage (Table 2 and Figure 1c).

**Table 2.** Criteria used by experts to evaluate plant vigour and health shift classes. Plant vigour was estimated from RGB clips for 2017 and 2018 surveys. Plant health shifts between the two years were then derived only for plants that were highly vigorous (V4) in 2017.

Reference data for plant health shifts were derived by comparing plant vigour classes in 2017 with those observed in 2018. Vigour classes for 2018 were evaluated via the same procedure described above. Shifts that occurred in highly vigorous plants from 2017 (V4) were the only ones considered due to their relevance for aetiological studies (Table 2 and Figure 1c). Indeed, plants with canopies resembling the diseased classes (V2 and V1) usually possessed a heavily deteriorated root system that may have already been colonised by secondary invaders, while the vigour reduction of plants in class V3 could have been caused by several aspects unrelated to the disease being studied.

Data reduction was applied to multispectral and thermal data by averaging the pixel values within each masked area and storing the results in a matrix associated with the coordinates of the masking centres. K-means [48] and Ward's hierarchical [49] clustering were then performed separately for multispectral (NIR and red bands) and thermal data, using two, three and four clusters. The highest number of clusters was selected based on the capability of the experts to correctly classify the reference

data, while lower numbers were tested to better understand the clustering prediction capability (see below for details).


**5. Conclusions**

While the importance of ecosystem services is today widely acknowledged, our results also show that there is still a gap in assessment methods on urban local scales [14,19,27]. The aim of our review was to raise awareness of this issue and provide a framework for further research. Previous reviews on the assessment of urban ES have mainly focused on larger spatial scales within cities. Our findings follow on from here, giving an update as well as checking whether any additional studies have examined even smaller spatial structures in the context of urban ES assessments (such as "green roofs", small garden patches, individual trees, etc.). Our results show that a research gap still exists at this point.

The significance of urban ES is readily acknowledged by scientists, and first steps have already been undertaken to adapt this concept for policymaking [17,62]. The high numbers of studies in the last years indicate that this topic is still evolving, especially in Europe. Several published reports have acknowledged that Europe, North America and northeast Asia are the main centers of research into the interrelationship between human well-being and green spaces [34,63,64].

In recent years, multiple ES classes have been investigated in cities as well as in their local spatial structures. Since the review by Haase et al. [14], more studies have been published on ES on the level of small urban structures; yet, the majority of work still focuses on larger spatial structures, mostly applying generalizing methods that provide results with a poor fit to reality [54]. Our results have shown a distinction between the numbers of ES assessments at city- and site-dimension. For a more rigorous implementation of the ES concept in urban planning as well as to develop adapted measures and design clearly additional research is needed, especially in evaluation methods on local urban scales. Improving our understanding of the ecosystem services provided by site-scaled green infrastructure types will constitute an important step towards setting policy objectives and creating suitable measures for sustainable urban development [9,19].

Approaches are needed that require data gathering [19], as these lead to credible and more realistic assessments of urban ES [54]. Furthermore, Beichler et al. [25] have stressed the importance of spatial scale in ES assessments, arguing that the exclusion of settlements and built-up areas from investigations can cause us to overlook ES provided by small ecosystems within such structures. In the urban context, results from spatially comprehensive (natural and built-up areas) approaches ultimately form more

convincing arguments for the ecological and sustainable design of future cities with their small green structures and, in this way, could usefully influence the decision-making processes.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2073-445X/9/5/150/s1, **S1.** Overview of the studies assessed in this review sorted according to reading order; **S2.** Short descriptions of method categories; **S3.** Overview of assessed ES sections and classes according to CICES V5.1 in regard to urban green infrastructures.

**Author Contributions:** P.B. designed the study and conceptualized the methodology. Both P.B. and A.S. conducted the literature search and reviewed the articles. P.B. performed the analysis, visualized the data and wrote the manuscript in consultation with A.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was mainly funded by the Federal Ministry of Education and Research (BMBF) in the joint project "HeatResilientCity" (subproject grant number: 01LR1724A). The promoter of this project is the DLR project management agency (DLR-PT).

**Acknowledgments:** The authors would like to thank Derek Henderson for language editing and proofreading. We also thank the three anonymous reviewers for their very constructive comments and valuable hints.

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