Evaluating concentration estimation errors in ELISA microarray experiments

BACKGROUND: Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to estimate a protein's concentration in a sample. Deploying ELISA in a microarray format permits simultaneous estimation of the concentrations of numerous proteins in a small sample. These estimates, however, are uncertain due to processing error and biological variability. Evaluating estimation error is critical to interpreting biological significance and improving the ELISA microarray process. Estimation error evaluation must be automated to realize a reliable high-throughput ELISA microarray system. In this paper, we present a statistical method based on propagation of error to evaluate concentration estimation errors in the ELISA microarray process. Although propagation of error is central to this method and the focus of this paper, it is most effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization, and statistical diagnostics when evaluating ELISA microarray concentration estimation errors. RESULTS: We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of concentration estimation errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error. We summarize the results with a simple, three-panel diagnostic visualization featuring a scatterplot of the standard data with logistic standard curve and 95% confidence intervals, an annotated histogram of sample measurements, and a plot of the 95% concentration coefficient of variation, or relative error, as a function of concentration. CONCLUSIONS: This statistical method should be of value in the rapid evaluation and quality control of high-throughput ELISA microarray analyses. Applying propagation of error to a variety of ELISA microarray concentration estimation models is straightforward. Displaying the results in the three-panel layout succinctly summarizes both the standard and sample data while providing an informative critique of applicability of the fitted model, the uncertainty in concentration estimates, and the quality of both the experiment and the ELISA microarray process

Protein secretion in human mammary epithelial cells following HER1 receptor activation: influence of HER2 and HER3 expression

<p>Abstract</p> <p>Background</p> <p>Protein secretion by mammary cells results in autocrine and paracrine signaling that defines cell growth, migration and the extracellular environment. Even so, we have a limited understanding of the cellular processes that regulate protein secretion.</p> <p>Methods</p> <p>In this study, we utilize human epithelial mammary cell (HMEC) lines that were engineered to express different levels of HER1, HER2 and HER3. Using an ELISA microarray platform, we evaluate the effects of epidermal growth factor family receptor (HER) expression on protein secretion in the HMEC lines upon initiation of HER1 receptor activation. The secreted proteins include three HER1 ligands, interleukins 1α and 18, RANTES, vascular-endothelial and platelet-derived growth factors, matrix metalloproteases 1, 2 and 9, and the extracellular portion of the HER1 and HER2 proteins. In addition, we investigate whether MAPK/Erk and PI3K/Akt signaling regulate protein secretion in these cell lines and if so, whether the involvement of HER2 or HER3 receptor alters their response to MAPK/Erk and PI3K/Akt signal pathway inhibition in terms of protein secretion.</p> <p>Results</p> <p>Differential expression of HER2 and HER3 receptors alters the secretion of a variety of growth factors, cytokines, and proteases. Some alterations in protein secretion are still observed when MAPK/Erk or PI3K/Akt signaling is inhibited.</p> <p>Conclusion</p> <p>This study suggests that HER overexpression orchestrates broad changes in the tumor microenvironment by altering the secretion of a diverse variety of biologically active proteins.</p

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. Results: We applied an experimental design approach for the optimization of the antibody concentrations used in the sandwich ELISA. The assay sensitivity was 0.1 ng of C-P4H. The method was utilized for the analysis of C-P4H accumulation in crude cell extracts of E. coli overexpressing C-P4H. The sandwich ELISA signals obtained demonstrated a very good correlation with the detected protein activity levels measured with the standard radioactive assay. The developed assay was applied to optimize C-P4H production in E. coli Origami in a system where the C-P4H subunits expression acted under control by different promoters. The experiments performed in a shake flask fed-batch system (EnBase®) verified earlier observations that cell density and oxygen supply are critical factors for the use of the inducer anhydrotetracycline and thus for the soluble C-P4H yield. Conclusions: Here we show an example of sandwich ELISA usage for quantifying multimeric proteins. The method was developed for monitoring the amount of recombinant C-P4H tetramer in crude E. coli extracts. Due to the specificity of the antibodies used in the assay against the different C-P4H subunits, the method detects the entire holoenzyme, and the signal is not disturbed by background expression of the separate subunits

Microfluidics-Based Single-Cell Functional Proteomics Microchip for Portraying Protein Signal Transduction Networks within the Framework of Physicochemical Principles, with Applications in Fundamental and Translational Cancer Research

Single-cell functional proteomics assays can connect genomic information to biological function through quantitative and multiplex protein measurements. Tools for single-cell proteomics have developed rapidly over the past 5 years and are providing unique opportunities. This thesis describes an emerging microfluidics-based toolkit for single cell functional proteomics, focusing on the development of the single cell barcode chips (SCBCs) with applications in fundamental and translational cancer research. The microchip designed to simultaneously quantify a panel of secreted, cytoplasmic and membrane proteins from single cells will be discussed at the beginning, which is the prototype for subsequent proteomic microchips with more sophisticated design in preclinical cancer research or clinical applications. The SCBCs are a highly versatile and information rich tool for single-cell functional proteomics. They are based upon isolating individual cells, or defined number of cells, within microchambers, each of which is equipped with a large antibody microarray (the barcode), with between a few hundred to ten thousand microchambers included within a single microchip. Functional proteomics assays at single-cell resolution yield unique pieces of information that significantly shape the way of thinking on cancer research. An in-depth discussion about analysis and interpretation of the unique information such as functional protein fluctuations and protein-protein correlative interactions will follow. The SCBC is a powerful tool to resolve the functional heterogeneity of cancer cells. It has the capacity to extract a comprehensive picture of the signal transduction network from single tumor cells and thus provides insight into the effect of targeted therapies on protein signaling networks. We will demonstrate this point through applying the SCBCs to investigate three isogenic cell lines of glioblastoma multiforme (GBM). The cancer cell population is highly heterogeneous with high-amplitude fluctuation at the single cell level, which in turn grants the robustness of the entire population. The concept that a stable population existing in the presence of random fluctuations is reminiscent of many physical systems that are successfully understood using statistical physics. Thus, tools derived from that field can probably be applied to using fluctuations to determine the nature of signaling networks. In the second part of the thesis, we will focus on such a case to use thermodynamics-motivated principles to understand cancer cell hypoxia, where single cell proteomics assays coupled with a quantitative version of Le Chatelier's principle derived from statistical mechanics yield detailed and surprising predictions, which were found to be correct in both cell line and primary tumor model. The third part of the thesis demonstrates the application of this technology in the preclinical cancer research to study the GBM cancer cell resistance to molecular targeted therapy. Physical approaches to anticipate therapy resistance and to identify effective therapy combinations will be discussed in detail. Our approach is based upon elucidating the signaling coordination within the phosphoprotein signaling pathways that are hyperactivated in human GBMs, and interrogating how that coordination responds to the perturbation of targeted inhibitor. Strongly coupled protein-protein interactions constitute most signaling cascades. A physical analogy of such a system is the strongly coupled atom-atom interactions in a crystal lattice. Similar to decomposing the atomic interactions into a series of independent normal vibrational modes, a simplified picture of signaling network coordination can also be achieved by diagonalizing protein-protein correlation or covariance matrices to decompose the pairwise correlative interactions into a set of distinct linear combinations of signaling proteins (i.e. independent signaling modes). By doing so, two independent signaling modes – one associated with mTOR signaling and a second associated with ERK/Src signaling have been resolved, which in turn allow us to anticipate resistance, and to design combination therapies that are effective, as well as identify those therapies and therapy combinations that will be ineffective. We validated our predictions in mouse tumor models and all predictions were borne out. In the last part, some preliminary results about the clinical translation of single-cell proteomics chips will be presented. The successful demonstration of our work on human-derived xenografts provides the rationale to extend our current work into the clinic. It will enable us to interrogate GBM tumor samples in a way that could potentially yield a straightforward, rapid interpretation so that we can give therapeutic guidance to the attending physicians within a clinical relevant time scale. The technical challenges of the clinical translation will be presented and our solutions to address the challenges will be discussed as well. A clinical case study will then follow, where some preliminary data collected from a pediatric GBM patient bearing an EGFR amplified tumor will be presented to demonstrate the general protocol and the workflow of the proposed clinical studies.</p

Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is the most common type of leukemia. The Cancer Genome Atlas Research Network has demonstrated the increasing genomic complexity of acute myeloid leukemia (AML). In addition, the network has facilitated our understanding of the molecular events leading to this deadly form of malignancy for which the prognosis has not improved over past decades. AML is a highly heterogeneous disease, and cytogenetics and molecular analysis of the various chromosome aberrations including deletions, duplications, aneuploidy, balanced reciprocal translocations and fusion of transcription factor genes and tyrosine kinases has led to better understanding and identification of subgroups of AML with different prognoses. Furthermore, molecular classification based on mRNA expression profiling has facilitated identification of novel subclasses and defined high-, poor-risk AML based on specific molecular signatures. However, despite increased understanding of AML genetics, the outcome for AML patients whose number is likely to rise as the population ages, has not changed significantly. Until it does, further investigation of the genomic complexity of the disease and advances in drug development are needed. In this review, leading AML clinicians and research investigators provide an up-to-date understanding of the molecular biology of the disease addressing advances in diagnosis, classification, prognostication and therapeutic strategies that may have significant promise and impact on overall patient survival

Analysis of striatal transcriptome in mice overexpressing human wild-type alpha-synuclein supports synaptic dysfunction and suggests mechanisms of neuroprotection for striatal neurons

Abstract Background Alpha synuclein (SNCA) has been linked to neurodegenerative diseases (synucleinopathies) that include Parkinson's disease (PD). Although the primary neurodegeneration in PD involves nigrostriatal dopaminergic neurons, more extensive yet regionally selective neurodegeneration is observed in other synucleinopathies. Furthermore, SNCA is ubiquitously expressed in neurons and numerous neuronal systems are dysfunctional in PD. Therefore it is of interest to understand how overexpression of SNCA affects neuronal function in regions not directly targeted for neurodegeneration in PD. Results The present study investigated the consequences of SNCA overexpression on cellular processes and functions in the striatum of mice overexpressing wild-type, human SNCA under the Thy1 promoter (Thy1-aSyn mice) by transcriptome analysis. The analysis revealed alterations in multiple biological processes in the striatum of Thy1-aSyn mice, including synaptic plasticity, signaling, transcription, apoptosis, and neurogenesis. Conclusion The results support a key role for SNCA in synaptic function and revealed an apoptotic signature in Thy1-aSyn mice, which together with specific alterations of neuroprotective genes suggest the activation of adaptive compensatory mechanisms that may protect striatal neurons in conditions of neuronal overexpression of SNCA

Protein Detection

The book explores distinct aspects of protein purification and characterization steps. It discusses solubility problems, resin selection tricks, and essential credentials in a purification process. In addition, the book examines aggregation and proteinopathy-related protein detection methods and reviews several essential protein detection and characterization methods in cancer for diagnosis, prognosis, and therapy, such as enzyme-linked immunosorbent assay (ELISA), immunohistochemistry, flow cytometry, western blot, mass spectrometry, and others

High throughput toxicity screening and intracellular detection of nanomaterials

EC FP7 NANoREG (Grant Agreement NMP4-LA-2013-310584)Free PMC Article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5215403/With the growing numbers of nanomaterials (NMs), there is a great demand for rapid and reliable ways of testing NM safety—preferably using in vitro approaches, to avoid the ethical dilemmas associated with animal research. Data are needed for developing intelligent testing strategies for risk assessment of NMs, based on grouping and read-across approaches. The adoption of high throughput screening (HTS) and high content analysis (HCA) for NM toxicity testing allows the testing of numerous materials at different concentrations and on different types of cells, reduces the effect of inter-experimental variation, and makes substantial savings in time and cost.info:eu-repo/semantics/publishedVersio

A Cell-Surface Membrane Protein Signature for Glioblastoma.

We present a systems strategy that facilitated the development of a molecular signature for glioblastoma (GBM), composed of 33 cell-surface transmembrane proteins. This molecular signature, GBMSig, was developed through the integration of cell-surface proteomics and transcriptomics from patient tumors in the REMBRANDT (n = 228) and TCGA datasets (n = 547) and can separate GBM patients from control individuals with a Matthew\u27s correlation coefficient value of 0.87 in a lock-down test. Functionally, 17/33 GBMSig proteins are associated with transforming growth factor β signaling pathways, including CD47, SLC16A1, HMOX1, and MRC2. Knockdown of these genes impaired GBM invasion, reflecting their role in disease-perturbed changes in GBM. ELISA assays for a subset of GBMSig (CD44, VCAM1, HMOX1, and BIGH3) on 84 plasma specimens from multiple clinical sites revealed a high degree of separation of GBM patients from healthy control individuals (area under the curve is 0.98 in receiver operating characteristic). In addition, a classifier based on these four proteins differentiated the blood of pre- and post-tumor resections, demonstrating potential clinical value as biomarkers