LFQ data analysis using FragPipe-Analyst

After processing data in FragPipe as mentioned in the last part of the tutorial, here we show how to further analyze the data using FragPipe-Analyst for downstream analysis and visualization (PCA, differential expression, etc.).

Just as a recap, a subset of a published gliomas dataset described by: https://pubmed.ncbi.nlm.nih.gov/36584682/ was used when running FragPipe.

J. Bader et al. “Proteomics separates adult-type diffuse high-grade gliomas in metabolic subgroups independent of 1p/19q codeletion and across IDH mutational status”, Cell Rep Med 2023 4(1):100877. doi: 10.1016/j.xcrm.2022.100877.

In the original study, authors studied high-grade adult-type diffuse gliomas are malignant neuroepithelial tumors with poor survival rates in combined chemoradiotherapy. They used MS1-based label-free quantification (LFQ) mass spectrometry to characterize 42 formalin-fixed, paraffin-embedded (FFPE) samples from IDH-wild-type (IDHwt) gliomas, IDH-mutant (IDHmut) gliomas, and non-neoplastic controls. Here, we just used 6 samples, 3 IDHmut and 3 IDHwt earlier when preparing the data and now we are about to further perform downstream analysis.

For the following steps, you can use the generated combined_protein.tsv and experiment_annotation.tsv or download them from here.

When we work on such project, one would typically start with explorative analyses such as Principal Component Analysis (PCA) to see if the protein data exhibit tumor/normal difference. Following that, one would look for known and potential diagnostic markers for various tumor subtypes with differential expression (DE) analysis, comparing the expression of each protein in tumor samples of one type to that of other types (or with that of normal samples). If for a protein A there is a significant difference between the expression of the two groups, A is seen as a potential marker. Since the comparison is done for many genes, multiple test adjustment is implemented to control the overall false discovery rate for differential expression.

And here are some findings reported in the paper by the authors related to IDHmut vs IDHwt comparisons:

The IDHwt gliomas in our study were most distinct from CNS ctrl and also segregated from IDHmut gliomas (Figures 1B and 1C). The IDHwt proteome was enriched with proteins linked to inflammation, MCM complex DNA polymerases, an integrin-, collagen-, and laminin-rich ‘‘basement membrane-like’’ extracellular matrix (ECM) profile, low in hyaluronic acid, which is associated with increased malignancy in gliomas (Figures 1C–1E). IDHwt/IDHmut differences aligned well with the CPTAC data and were largely unaffected by 1p/19q codeletion status in our data (Figures 1E, S1C, and S1D). In line with amore ‘‘aggressive’’ phenotype of IDHwt gliomas, many outlier proteins with high abundance in IDHwt are cancer drivers, several of them linked to invasion. Outlier proteins associated with IDHmut included tumor suppressors downregulated in IDHwt (Figures 1E andS1D; Table 2). Notably, these tumor suppressors include the histone proteins H1F0 and H2AFY2, which both maintain an epigenetic profile of differentiation and inhibit a return to a proliferative stem cell state through distinct mechanisms. Known proteome alterations driving progression of the IDHmut were apparent, such as the strong epigenetic downregulation of RBP1, as well as novel ones, such as AKR1C3 overexpression selectively in IDHmut (Figures 1E and S1E; Table 2).

Questions

Explore the data in FragPipe-Analyst to answer the following questions:

Notes