## 3 Results
Figure 4: Six pie graphs depicting the protein composition of each sample, varying by coating (top axis) and serum type (left axis). Similar to the bar chart in Figure 3, the pie graphs in Figure 4 visually compare the abundance of each protein for all six nanoparticle-protein corona samples. The difference between the two figures is that the bar chart is used for quantitative analyses whereas the pie graph lends itself to qualitative analyses. The latter method best displays the different constituents of each sample so that trends in protein content with either serum-type or nanoparticle surface coating are noticeable. One trend demonstrated by the pie graphs is the increased abundance of protein MED22_CHICK in the samples incubated in ST1 compared to those incubated with ST2. A benefit of this method is the ability to easily see parts of the whole, i.e., the abundance of each protein in all samples. This method is limited by the sample size: if there are too many different proteins, it may be difficult to differentiate one protein from another. With a smaller sample size, this method works to compare individual samples. The Pie-of-Pie graph, shown in Figure 5, enables the visualization of proteins identified by their molecular function and subsequent protein composition. This Figure Supports interpretation as it relates to biological relevance and assist in the design of future toxicological studies by narrowing the scope of pathway analyses. The first pie graph illustrates four primary functions categorized as transcription, actin-binding, toxin activity, and other. Presenting data in this manner does not compromise space by expending excessive detail on smaller groups that may not be relevant to the study (i.e., the use of the 'other' category combines lesser functions into a single wedge in the entire pie). Pie-of-Pie is not limited to just two graphs; the data can be extended into many other pie graphs that detail the composition of other functional groups noted in the molecular function graph, i.e., actin-binding and toxin activity. The second pie graph depicts the individual proteins that comprise the most abundant transcription functional group proteins identified in the NH\({}_{3}\)-ST1 combination. In this sample, the MED22_CAEEL protein is the most abundant among the transcription proteins (73%). In some datasets, a figure needs to compare multiple parameters that are related in an experiment. In Figure 6, the abundance of protein is compared with the average NPPC complex diameter. Here,  
Figure 5: Pie-of-Pie graph illustrating the protein functional groups (**top**) among identified proteins (**bottom**) within most prevalent functional group in the AuNP-NH\({}_{3}\)-ST1 sample. the \(x\)-axis is the serum-type and the \(y\)-axis is the normalized abundance of protein. The color blocks within each column represent the different nanoparticle surface functionalization/charges. When comparing the columns, the gold nanoparticles coated with PEG seem to have absorbed more proteins from serum-type 2 when compared with serum-type 1. Conversely, the gold nanoparticles coated with amine and carboxyl functional grouped absorbed more proteins from serum-type 1 when compared with serum-type 2. In addition to assessing the amount of proteins absorbed onto particle surfaces, this plot indicates the differences in NPPC size relative to serum type (as indicated by white diamonds markers). On average, the particle diameter of NPPC complexes was larger when incubated in serum-type 2 (109 nm) compared to when particles were incubated in serum-type 1 (79 nm). Since the ST1-incubated particles contain more proteins but have a smaller diameter, we concluded that incubation with ST1 creates a more densely packed protein corona shell. TEM images shown in Figure S1 provide visual evidence of this phenomenon. In bottom-up proteomics methodology, the use of high-performance liquid chromatography (HPLC) is essential to separate peptides before analyzing with mass spectrometry.