Feline Lindhout

2 56 Bioinformatic analysis proteomics All mass spectrometry data were analyzed using R statistical software (R Core Team, 2017). To infer protein dynamics upon differentiation, TMT reporter intensity values of hiPSC neurons at time point day 3 and day 7 were normalized to their correspondent day 1 or alternatively day 7 was normalized to day 3. TMT generated ratios (previously normalized to the median) were then log2-transformed. A log2-transformed mean of the TMT-ratios of the individual replicates was calculated. Proteins with less than 3 peptides used for TMT quantification or with a reporter ion variability > 100% in at least one TMT-ratio (high reporter ions variability) or with a median log2 fold-change > 0.4 between the replicates in at least one TMT-ratio (high replicate variability) were excluded from the analyses. Good correlation of replicates was assessed by comparing TMT ratios of all quantified proteins at different time points using Pearson correlation. Proteins with an absolute log2 fold-change > 0.3 between day 3 and day 1 or between day 7 and day 3 or between day 7 and day 1 were considered significantly regulated. Only significant regulated proteins were subjected to cluster analysis by using K-means clustering in R. Functional enrichment analysis within different clusters of expression profiles was performed using gProfilerR package in R (Raudvere et al., 2019). Network analyses were performed using the GeneMania plugin (Montojo et al., 2010) within Cytoscape (Shannon et al., 2003). Heatmaps in the Figures were prepared applying hierarchical clustering using Euclidean distance. Statistical Analysis Data processing and statistical analyses were performed using Prism GraphPad (version 8.0) software. All statistical tests are described in the corresponding Figure legends. Differences were considered significant when P < 0.05, and P-values are represented as: * P < 0.05, ** P < 0.01, and *** P < 0.001.