3 Results
                A total of 72 spectra were obtained, which are included in the supplementary information. Because of the unsupervised nature of the technique, principal component analysis (PCA) was selected for data analysis that focuses on discovery aspects of unanticipated trends. PCA only reveals group structure when within-group variation is less than between-group variation.28 On the other hand, supervised techniques, such as partial least squares discriminate analysis (PLS-DA), are useful for many omics studies, including metabolic fingerprinting experiments, but there is concern that this type of analysis is prone to overfitting and providing false discoveries.29 Such supervised methods for data analysis could be employed in future studies once the discovery-focused PCA has revealed sufficient group structure to help unambiguously identify which specific features in the raw data contribute most to between-group variation. For this study, PCA was employed to reduce the chance of false identification of between-group variation. PCA identified subtle yet significant differences between the spectra and specific m/z values that contributed most to variance between data.27 These selected m/z values were then later used for comparison with high-performance liquid chromatography (HPLC) data to explore whether the spectral features identified via PCA corresponded to CQAs tentatively identified via HPLC.
Due to the small number of sample replicates at each time point, data from multiple time points was grouped to increase the robustness of PCA. The cells in the differentiated group are expected to have begun differentiation by time point 4 and completed by time point 5, while the cells in the undifferentiated group remained undifferentiated throughout the experiment. Time points 1 and 2 were grouped to represent cells in their initial state while time points 5 and 6 were grouped to represent cells in their final state. Subsequent staining (Fig. S7) of both the undifferentiated and differentiated cell lines confirmed that the cells had either remained in the undifferentiated state or had completed differentiation after time point 6, as expected. 
The first analysis compared cells in their final states from the undifferentiated and differentiated cultures, i.e., comparing time points 5 and 6 for both cultures. The resulting PCA cluster plots based on bulk samples, taken far from the cells, are shown in Figure 3A. The groupings in these bulk sampling plots are not as well segregated as the groupings for the localized samplings shown in Figure 3B. This suggests that localized sampling is important to being able to detect differences in cell differentiation state. Although both cell groups were given different media throughout the culture process, these differences are not revealed by the bulk samples which suggests that the differences observed in the local samples are due to secreted biomarkers captured near the cells, and not due differences in the cell culture itself.  
In order to observe if the same cell culture exhibited differences with time, and to remove the contribution of cell culture conditions to the variance in data, the differentiated cell culture was analyzed alone. For this PCA approach, time points 1 and 2 were grouped and compared to time points 5 and 6. These groupings compare the cells from the same culture in early time points, when they are still undifferentiated cells, to the cells in a fully differentiated state. Since all of the data was taken from the same cell culture in the same continuously performed cell growth and development experiment, this approach also removes the possibility that different culture conditions (e.g., seeding density, media type, etc.) contributed to the differences observed. Figure 4 shows the resulting PCA plots for these groupings. Once again, the spectra do not exhibit significant clustering for bulk sampling (Fig. 4A) but show a stronger clustering for the localized sampling (Fig. 4B). These PCA results (Fig. 3B and 4B) indicate that localized sampling provides an enhanced capability to detect differences between the cells due to secreted biomarkers, which are in highest concentration near the cell membrane.