Increasing the sensitivity of reverse phase protein arrays by antibody-mediated signal amplification

Background  

Reverse phase protein arrays (RPPA) emerged as a useful experimental platform to analyze biological samples in a high-throughput format. Different signal detection methods have been described to generate a quantitative readout on RPPA including the use of fluorescently labeled antibodies. Increasing the sensitivity of RPPA approaches is important since many signaling proteins or posttranslational modifications are present at a low level.     

HER-2 assessment in formalin-fixed paraffin-embedded breast cancer tissue by well-based reverse phase protein array

Background

The human epidermal growth factor receptor-2 (HER-2) expression level is a critical element for determining the prognosis and management of breast cancer. HER-2 targeted therapy in breast cancer depends on the reliable assessment of HER-2 expression status but current standard methods are lacking a rigorous quantitative assay. To address this challenge, we developed an assessment of HER-2 expression method by well-based reverse phase protein array (RPPA).

Results

Well-based RPPA is based on a robust protein isolation methodology paired with a novel electrochemiluminescence detection system. HER-2 value of well-based RPPA significantly correlated with dot blotting results (R² = 0.939). By well-based RPPA, we successfully detected HER-2 expression in 76 human breast formalin-fixed paraffin-embedded tissue samples. We observed 93.4% (71/76) concordance between well-based RPPA and current HER-2 immunohistochemical assessment guideline. When the cutoff level of HER-2 value was set to 0.689 (HER-2/GAPDH) on the basis of receiver-operating characteristic curve, the area under the curve was 0.975 (95% CI, 0.941-1.000). Sensitivity and specificity of well-based RPPA was 92.1% and 94.7%, respectively.

Conclusions

HER-2 value by well-based RPPA was correlated with the current HER-2 status guideline, suggesting that this normalized HER-2 assessment may offer advantages over unnormalized current immunohistochemical assessment methods.     

RPPAML/RIMS: A metadata format and an information management system for reverse phase protein arrays

Background  

Reverse Phase Protein Arrays (RPPA) are convenient assay platforms to investigate the presence of biomarkers in tissue lysates. As with other high-throughput technologies, substantial amounts of analytical data are generated. Over 1000 samples may be printed on a single nitrocellulose slide. Up to 100 different proteins may be assessed using immunoperoxidase or immunoflorescence techniques in order to determine relative amounts of protein expression in the samples of interest.     

Development of pan-specific antibody against trimethyllysine for protein research

Background  

Trimethylation of the Nε-lysine residues in a protein is one of the most important events of posttranslational modifications. Simple methods for rapid detection and isolation of the Nε-trimethylated protein species are needed. This report introduces a novel method to prepare the affinity purified antibody specific for the Nε-trimethylated lysine (tMeK). The applications of the purified antibody are also reported in this paper.     

A framework for personalized medicine: prediction of drug sensitivity in cancer by proteomic profiling

Background

The goal of personalized medicine is to provide patients optimal drug screening and treatment based on individual genomic or proteomic profiles. Reverse-Phase Protein Array (RPPA) technology offers proteomic information of cancer patients which may be directly related to drug sensitivity. For cancer patients with different drug sensitivity, the proteomic profiling reveals important pathophysiologic information which can be used to predict chemotherapy responses.

Results

The goal of this paper is to present a framework for personalized medicine using both RPPA and drug sensitivity (drug resistance or intolerance). In the proposed personalized medicine system, the prediction of drug sensitivity is obtained by a proposed augmented naive Bayesian classifier (ANBC) whose edges between attributes are augmented in the network structure of naive Bayesian classifier. For discriminative structure learning of ANBC, local classification rate (LCR) is used to score augmented edges, and greedy search algorithm is used to find the discriminative structure that maximizes classification rate (CR). Once a classifier is trained by RPPA and drug sensitivity using cancer patient samples, the classifier is able to predict the drug sensitivity given RPPA information from a patient.

Conclusion

In this paper we proposed a framework for personalized medicine where a patient is profiled by RPPA and drug sensitivity is predicted by ANBC and LCR. Experimental results with lung cancer data demonstrate that RPPA can be used to profile patients for drug sensitivity prediction by Bayesian network classifier, and the proposed ANBC for personalized cancer medicine achieves better prediction accuracy than naive Bayes classifier in small sample size data on average and outperforms other the state-of-the-art classifier methods in terms of classification accuracy.

     

Unique protein expression signatures of survival time in kidney renal clear cell carcinoma through a pan-cancer screening

Background

In 2016, it is estimated that there will be 62,700 new cases of kidney cancer in the United States, and 14,240 patients will die from the disease. Because the incidence of kidney renal clear cell carcinoma (KIRC), the most common type of kidney cancer, is expected to continue to increase in the US, there is an urgent need to find effective diagnostic biomarkers for KIRC that could help earlier detection of and customized treatment strategies for the disease. Accordingly, in this study we systematically investigated KIRC’s prognostic biomarkers for survival using the reverse phase protein array (RPPA) data and the high throughput sequencing data from The Cancer Genome Atlas (TCGA).

Results

With comprehensive data available in TCGA, we systematically screened protein expression based survival biomarkers in 10 major cancer types, among which KIRC presented many protein prognostic biomarkers of survival time. This is in agreement with a previous report that expression level changes (mRNAs, microRNA and protein) may have a better performance for prognosis of KIRC. In this study, we also identified 52 prognostic genes for KIRC, many of which are involved in cell-cycle and cancer signaling, as well as 15 tumor-stage-specific prognostic biomarkers. Notably, we found fewer prognostic biomarkers for early-stage than for late-stage KIRC. Four biomarkers (the RPPA protein IDs: FASN, ACC1, Cyclin_B1 and Rad51) were found to be prognostic for survival based on both protein and mRNA expression data.

Conclusions

Through pan-cancer screening, we found that many protein biomarkers were prognostic for patients’ survival in KIRC. Stage-specific survival biomarkers in KIRC were also identified. Our study indicated that these protein biomarkers might have potential clinical value in terms of predicting survival in KIRC patients and developing individualized treatment strategies. Importantly, we found many biomarkers in KIRC at both the mRNA expression level and the protein expression level. These biomarkers shared a significant overlap, indicating that they were technically replicable.

     

Simultaneous detection of mRNA and protein stem cell markers in live cells

Background  

Biological studies and medical application of stem cells often require the isolation of stem cells from a mixed cell population, including the detection of cancer stem cells in tumor tissue, and isolation of induced pluripotent stem cells after eliciting the expression of specific genes in adult cells. Here we report the detection of Oct-4 mRNA and SSEA-1 protein in live carcinoma stem cells using respectively molecular beacon and dye-labeled antibody, aiming to establish a new method for stem cells detection and isolation.     

A miniaturized sandwich immunoassay platform for the detection of protein-protein interactions

Background  

Analysis of protein-protein interactions (PPIs) is a valuable approach for the characterization of huge networks of protein complexes or proteins of unknown function. Co-immunoprecipitation (coIP) using affinity resins coupled to protein A/G is the most widely used method for PPI detection. However, this traditional large scale resin-based coIP is too laborious and time consuming. To overcome this problem, we developed a miniaturized sandwich immunoassay platform (MSIP) by combining antibody array technology and coIP methods.     

Characterization of protein expression levels with label-free detected reverse phase protein arrays

In reverse-phase protein arrays (RPPA), one immobilizes complex samples (e.g., cellular lysate, tissue lysate or serum etc.) on solid supports and performs parallel reactions of antibodies with immobilized protein targets from the complex samples. In this work, we describe a label-free detection of RPPA that enables quantification of RPPA data and thus facilitates comparison of studies performed on different samples and on different solid supports. We applied this detection platform to characterization of phosphoserine aminotransferase (PSAT) expression levels in Acanthamoeba lysates treated with artemether and the results were confirmed by Western blot studies.     

Sandwich ELISA for quantitative detection of human collagen prolyl 4-hydroxylase

Background  

We describe a method for specific, quantitative and quick detection of human collagen prolyl 4-hydroxylase (C-P4H), the key enzyme for collagen prolyl-4 hydroxylation, in crude samples based on a sandwich ELISA principle. The method is relevant to active C-P4H level monitoring during recombinant C-P4H and collagen production in different expression systems. The assay proves to be specific for the active C-P4H α₂β₂ tetramer due to the use of antibodies against its both subunits. Thus in keeping with the method C-P4H is captured by coupled to an anti-α subunit antibody magnetic beads and an anti-β subunit antibody binds to the PDI/β subunit of the protein. Then the following holoenzyme detection is accomplished by a goat anti-rabbit IgG labeled with alkaline phosphatase which AP catalyzes the reaction of a substrate transformation with fluorescent signal generation.     

Multiple Protein Analysis of Formalin-fixed and Paraffin-embedded Tissue Samples with Reverse phase Protein Arrays

Reverse-phase protein arrays (RPPAs) have become an important tool for the sensitive and high-throughput detection of proteins from minute amounts of lysates from cell lines and cryopreserved tissue. The current standard method for tissue preservation in almost all hospitals worldwide is formalin fixation and paraffin embedding, and it would be highly desirable if RPPA could also be applied to formalin-fixed and paraffin embedded (FFPE) tissue. We investigated whether the analysis of FFPE tissue lysates with RPPA would result in biologically meaningful data in two independent studies. In the first study on breast cancer samples, we assessed whether a human epidermal growth factor receptor (HER) 2 score based on immunohistochemistry (IHC) could be reproduced with RPPA. The results showed very good concordance between the IHC and RPPA classifications of HER2 expression. In the second study, we profiled FFPE tumor specimens from patients with adenocarcinoma and squamous cell carcinoma in order to find new markers for differentiating these two subtypes of non-small cell lung cancer. p21-activated kinase 2 could be identified as a new differentiation marker for squamous cell carcinoma. Overall, the results demonstrate the technical feasibility and the merits of RPPA for protein expression profiling in FFPE tissue lysates.Many diseases are characterized by the expression of specific proteins and the activation status of distinct signaling pathways (1). Thus, protein expression profiling and activation patterns are instrumental for understanding disease, the development of effective treatments, and the identification of patients who will respond to particular therapies. Traditional ways of analyzing protein expression (e.g. Western blot) can be used for these purposes but often are labor intensive, have low throughput, and consume high sample volumes. Reverse-phase protein array (RPPA)¹ technology is a very promising method that circumvents these issues (2–4). For RPPA, minute amounts of whole protein lysates from a multitude of samples are spotted onto slides, and individual proteins are detected via protein-specific antibodies. This enables medium- to high-throughput analysis of precious low-volume sample material.Lysates for RPPA have so far been generated mainly from cell lines or fresh frozen tissue. However, because of the high amount of effort involved in the use of liquid nitrogen for sample preservation, in almost all hospitals worldwide formalin fixation and paraffin embedding is the preferred method for tissue preservation. Therefore, it would be highly desirable if protein-specific epitopes could be quantitatively extracted and analyzed from formalin-fixed and paraffin embedded (FFPE) tissue, as this would make the majority of clinical specimens accessible for mechanistic protein-based research.In recent years, several research groups have established protocols for protein extraction from FFPE tissue. Common to all of them is the use of high concentrations of ionic detergents, such as sodium dodecyl sulfate, and high temperature. It was shown that these methods even make it possible to extract full-length proteins from FFPE tissue (5–12). The coefficient of variation of the relative extraction efficiency based on Western blot and densitometric assessment of actin typically is below 20% (13). To assess whether the analysis of FFPE tissue lysates would result in biologically meaningful data, we analyzed FFPE breast cancer tissue samples by RPPA for the expression of human epidermal growth factor receptor 2 (HER2) and compared it to HER2 assessment by the gold standard used in clinical practice, which is based on immunohistochemistry (IHC). Successful recovery of HER2 from FFPE tissue should result in concordant HER2 classification between RPPA and IHC.In the second part of the study, FFPE samples of non-small cell lung cancer (NSCLC) were examined via RPPA. Samples from two subtypes of NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SCC), were analyzed for more than 150 proteins, including two proteins that are known to be differentially expressed between the two subtypes. The objectives of this analysis were to further assess the validity of the approach by confirming the two positive controls and to identify new markers for the differentiation of the two subtypes of NSCLC.     

Fluorescent labeling in semi-solid medium for selection of mammalian cells secreting high-levels of recombinant proteins

Background  

Despite the powerful impact in recent years of gene expression markers like the green fluorescent protein (GFP) to link the expression of recombinant protein for selection of high producers, there is a strong incentive to develop rapid and efficient methods for isolating mammalian cell clones secreting high levels of marker-free recombinant proteins. Recently, a method combining cell colony growth in methylcellulose-based medium with detection by a fluorescently labeled secondary antibody or antigen has shown promise for the selection of Chinese Hamster Ovary (CHO) cell lines secreting recombinant antibodies. Here we report an extension of this method referred to as fluorescent labeling in semi-solid medium (FLSSM) to detect recombinant proteins significantly smaller than antibodies, such as IGF-E5, a 25 kDa insulin-like growth factor derivative.     

A Targeted Proteomics Approach for Biomarker Discovery Using Bilateral Matched Nipple Aspiration Fluids

Introduction  

The objective of this study was to identify cancer-associated protein expression patterns in bilateral matched nipple aspiration fluids using nanoscale reciprocal Cy3/Cy5 labeling and high-content antibody microarrays. This novel platform allows the pair-wise comparisons of the relative abundance of 512 different antigens using minimal NAF sample containing 1 μg of total protein.     

Co-expression of Skp and FkpA chaperones improves cell viability and alters the global expression of stress response genes during scFvD1.3 production

Background  

The overexpression of scFv antibody fragments in the periplasmic space of Escherichia coli frequently results in extensive protein misfolding and loss of cell viability. Although protein folding factors such as Skp and FkpA are often exploited to restore the solubility and functionality of recombinant protein products, their exact impact on cellular metabolism during periplasmic antibody fragment expression is not clearly understood. In this study, we expressed the scFvD1.3 antibody fragment in E. coli BL21 and evaluated the overall physiological and global gene expression changes upon Skp or FkpA co-expression.     

LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise

Background  

Mass spectrometry protein profiling is a promising tool for biomarker discovery in clinical proteomics. However, the development of a reliable approach for the separation of protein signals from noise is required. In this paper, LIMPIC, a computational method for the detection of protein peaks from linear-mode MALDI-TOF data is proposed. LIMPIC is based on novel techniques for background noise reduction and baseline removal. Peak detection is performed considering the presence of a non-homogeneous noise level in the mass spectrum. A comparison of the peaks collected from multiple spectra is used to classify them on the basis of a detection rate parameter, and hence to separate the protein signals from other disturbances.     

Spatial Normalization of Reverse Phase Protein Array Data

Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples. The quality of RPPA data may be affected by various sources of error. One of these, spatial variation, is caused by uneven exposure of different parts of an RPPA slide to the reagents used in protein detection. We present a method for the determination and correction of systematic spatial variation in RPPA slides using positive control spots printed on each slide. The method uses a simple bi-linear interpolation technique to obtain a surface representing the spatial variation occurring across the dimensions of a slide. This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide. The adoption of the method results in increased agreement between technical and biological replicates of various tumor and cell-line derived samples. Further, in data from a study of the melanoma cell-line SKMEL-133, several slides that had previously been rejected because they had a coefficient of variation (CV) greater than 15%, are rescued by reduction of CV below this threshold in each case. The method is implemented in the R statistical programing language. It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis. The method is made available, along with suggestions for implementation, at http://bitbucket.org/rppa_preprocess/rppa_preprocess/src.     

Silica nanoparticles for the layer-by-layer assembly of fully electro-active cytochrome c multilayers

Background

Gold nanoparticles (AuNPs) scatter light intensely at or near their surface plasmon wavelength region. Using AuNPs coupled with dynamic light scattering (DLS) detection, we developed a facile nanoparticle immunoassay for serum protein biomarker detection and analysis. A serum sample was first mixed with a citrate-protected AuNP solution. Proteins from the serum were adsorbed to the AuNPs to form a protein corona on the nanoparticle surface. An antibody solution was then added to the assay solution to analyze the target proteins of interest that are present in the protein corona. The protein corona formation and the subsequent binding of antibody to the target proteins in the protein corona were detected by DLS.

Results

Using this simple assay, we discovered multiple molecular aberrations associated with prostate cancer from both mice and human blood serum samples. From the mice serum study, we observed difference in the size of the protein corona and mouse IgG level between different mice groups (i.e., mice with aggressive or less aggressive prostate cancer, and normal healthy controls). Furthermore, it was found from both the mice model and the human serum sample study that the level of vascular endothelial growth factor (VEGF, a protein that is associated with tumor angiogenesis) adsorbed to the AuNPs is decreased in cancer samples compared to non-cancerous or less malignant cancer samples.

Conclusion

The molecular aberrations observed from this study may become new biomarkers for prostate cancer detection. The nanoparticle immunoassay reported here can be used as a convenient and general tool to screen and analyze serum proteins and to discover new biomarkers associated with cancer and other human diseases.     

Hidden Markov model speed heuristic and iterative HMM search procedure

Background  

Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases.