Evaluation of Signaling Pathways Profiling in Human Dermal Endothelial Cells Treated by Snake Venom Cysteine-Rich Secretory Proteins (svCRiSPs) from North American Snakes Using Reverse Phase Protein Array (RPPA)

Cysteine-Rich Secretory Proteins (CRiSPs) are typically found in many snake venoms; however, the role that these toxins play in the pathophysiology of snakebites is still unclear. Herein, we compared the effects of snake venom CRiSPs (svCRiSPs) from the most medically important species of North American snakes on endothelial cell permeability and vascular permeability. We used reverse phase protein array (RPPA) to identify key signaling molecules on human dermal lymphatic (HDLECs) and blood (HDBECs) endothelial cells treated with svCRiSPs. The results showed that Css-CRiSP isolated from Crotalus scutulatus scutulatus and App-CRiSP from Agkistrodon piscivorus piscivorus are the most potent causes of increase vascular and endothelial permeability in comparison with other svCRiSPs used in this study. We examined the protein expression levels and their activated phosphorylation states in HDLECs and HDBECs induced by App-CRiSP and Css-CRiSP using RPPA. Interestingly, both App-CRiSP and Css-CRiSP induced caveolin-1 expression in HDBECs. We also found that stimulating HDBECs with Css-CRiSP and App-CRiSP significantly induced the phosphorylation of mTOR and Src, respectively. In HDLECs, Css-CRiSP significantly downregulated the expression of N-Cadherin and phospholipase C-gamma, while App-CRiSP significantly enhanced Akt and JNK phosphorylation. These results suggest that the increased endothelial permeability in HDLECs and HDBECs by Css-CRiSP and App-CRiSP may occur through different pathways.

Multicenter reverse-phase protein array data integration

Among the technologies available for protein biomarker discovery and validation, reverse-phase protein array (RPPA) benefits from unequalled sample throughput. Panels of high-quality antibodies enable the quantification by RPPA of protein abundance and posttranslational modifications in biological specimens with high precision and sensitivity. Incorporation of RPPA technology into clinical and drug development pipelines requires robust assays that generate reproducible results across multiple laboratories. We implemented the first international multicenter pilot study to investigate RPPA workflow variability. We characterized the proteomic responses of a series of breast cancer cells to two cancer drugs. This analysis quantified 86,832 sample spots, representing 108 biological samples, arrayed at three independent RPPA platforms. This unique integrated set of data is publicly available as a resource to the proteomic and cancer research communities to catalyse further analysis and investigation. We anticipate that this dataset will form a reference for the comparison of RPPA workflows and reagents, which can be expanded in the future, and will aid the identification of platform-robust treatment-marker antigens in breast cancer cells.

A Reverse Phase Protein Array Based Phospho-antibody Characterization Approach and Its Applicability for Clinical Derived Tissue Specimens

Background: Systematic quantification of phosphoprotein within cell signaling networks in solid tissues remains challenging and precise quantification in large scale samples has great potential for biomarker identification and validation. Methods:We developed a reverse phase protein array (RPPA) based phosphor-antibody characterization workflow by taking advantage of the lysis buffer compatible with alkaline phosphatase (AP) treatment and here termed it as a bottom-up antibody screening that differs from the conventional RPPA antibody validation procedure and applied it onto fresh frozen and formalin-fixed and paraffin-embedded tissue (FFPE) to test its applicability.Results:We tested the feasibility of this method by screening 106 phospho-antibodies with RPPA first followed by western blots on a panel of cell lines and demonstrated that AP treatment could serve as an independent factor that can be adopted for rapid RPPA phospho-antibody selection. We also performed studies on different clinical materials. For fresh frozen (FF) samples, pre-selected highly-specific antibodies showed a desirable data reproducibility and antibody specificity based on AP treatment indicating a potential for fresh tissue-based phospho-protein RPPA profiling. Of further clinical significance, using the same approach, based on two sets of FFPE samples from 63 melanoma and 40 lung cancer patients, we showed great interexperimental reproducibility and significant correlation with pathological markers MelanA for melanoma as well as a panel of lung cancer biomarkers for subtyping (EGFR, Napsin A, p63/p40, TTF1 and CK7) generating meaningful data that match clinical features. Conclusions:Our findings establish a highly efficient approach for phospho-antibody characterization by taking advantage of RPPA whereby the same methodology can be applied for tissue-based proteomics and phosphoproteomics in clinical assay development and application.

Integrative analysis of multi-platform reverse-phase protein array data for the pharmacodynamic assessment of response to targeted therapies

Reverse-phase protein array (RPPA) technology uses panels of high-specificity antibodies to measure proteins and protein post-translational modifications in cells and tissues. The approach offers sensitive and precise quantification of large numbers of samples and has thus found applications in the analysis of clinical and pre-clinical samples. For effective integration into drug development and clinical practice, robust assays with consistent results are essential. Leveraging a collaborative RPPA model, we set out to assess the variability between three different RPPA platforms using distinct instrument set-ups and workflows. Employing multiple RPPA-based approaches operated across distinct laboratories, we characterised a range of human breast cancer cells and their protein-level responses to two clinically relevant cancer drugs. We integrated multi-platform RPPA data and used unsupervised learning to identify protein expression and phosphorylation signatures that were not dependent on RPPA platform and analysis workflow. Our findings indicate that proteomic analyses of cancer cell lines using different RPPA platforms can identify concordant profiles of response to pharmacological inhibition, including when using different antibodies to measure the same target antigens. These results highlight the robustness and the reproducibility of RPPA technology and its capacity to identify protein markers of disease or response to therapy.