Integrating intracellular and extracellular proteomic profiling for in-depth investigations of cellular communication in a model of prostate cancer

Abstract

Cellular communication is essential for cell-cell interactions, maintaining homeostasis and progression of certain disease states. While many studies examine extracellular proteins, the holistic extracellular proteome is often left uncaptured, leaving gaps in our understanding of how all extracellular proteins may impact communication and interaction. We used a cellular-based proteomics approach to more holistically profile both the intracellular and extracellular proteome of prostate cancer. Our workflow was generated in such a manner that multiple experimental conditions can be observed with the opportunity for high throughput integration. Additionally, this workflow is not limited to a proteomic aspect, as metabolomic and lipidomic studies can be integrated for a multi-omics workflow. Our analysis showed coverage of over 8000 proteins while also garnering insights into cellular communication in the context of prostate cancer development and progression. Identified proteins covered a variety of cellular processes and pathways, allowing for the investigation of multiple aspects into cellular biology. This workflow demonstrates advantages for integrating intra- and extracellular proteomic analyses as well as potential for multi-omics researchers. This approach possesses great value for future investigations into the systems biology aspects of disease development and progression.

Keywords: extracellular profiling; intracellular profiling; network analysis; prostate cancer; proteomic analysis.

Figure 1.

Brief outline depicting the general workflow for sample preparation. Cells were grown in serum-free media containing a defined serum supplement for 48 hours, at which point cells and media were harvested for preparation. Once proteins were collected and quantified for each sample type, a bottom-up sample preparation was implemented using trypsin as the enzyme of choice for digestion. Unlabeled peptides were analyzed in technical triplicate on a Q Exactive HF with database searching of raw spectra in Proteome Discoverer.

Figure 2.

Results detailing the number of proteins identified across sample types. A. Proteins identified across intracellular fractions with or without FBS supplementation as well as the extracellular proteome fraction. Error bars denote standard deviation of biological replicates. While there is a slight decrease in total number of proteins identified in the extracellular fraction, there is little change between the two intracellular fractions, indicating good proteomic coverage. B. Overlap of identified proteins across the three proteomic fractions. As expected, the majority of identified proteins are shared across all three groups, indicating that our proposed method provides adequate coverage and does not lead to a reduction of protein identifications.

Figure 3.

Examination of sample coverage and overlap within and across sample types. A. Principal component analysis (PCA) of all biological replicates for each of the three sample types. All biological replicates within a treatment group were found to cluster together, indicating reproducibility of our method. B. Box plots showing overall proteome coverage in terms of normalized protein expression levels. Looking across all three treatment groups, the majority of expression levels remain relatively constant against one another, demonstrating that serum replacement and analysis of secreted proteins does not substantially alter protein intensities.

Figure 4.

Functional annotation and enrichment analyses for the significantly upregulated proteins in the extracellular proteome fraction relative to their intracellular counterpart. A. Gene Ontology (GO) analysis of the significantly upregulated proteins within the extracellular proteome. The top 5 enriched cellular components are plotted above, with plot colors indicating the percentage of proteins mapped to a particular process out of the 647 total upregulated proteins. B. Visualization of enrichment analyses and how these various components interconnect to impact the entire system. All mapping was performed using Metascape, and proteins used to generate the protein interaction network pertain to those significantly enriched within the extracellular proteome samples. Listed processes and annotations are in order from most to least significant by Benjamini-Hochberg corrected p-value.

Figure 5.

The top five enriched pathways and processes for the 142 proteins identified exclusively within the extracellular proteome fraction. Enrichment analysis was performed using open source Metascape software.