**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 12C<sup>6</sup> 14N<sup>4</sup> ‐Arg (light) or 13C<sup>6</sup> 15N<sup>4</sup> ‐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 13C<sup>6</sup>

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the media supplemented with 12C<sup>6</sup>

14N<sup>4</sup>

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

15N<sup>4</sup>

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**).

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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].
