Biomedical subjects

Susan M Varnum

Publications and source records attributed to Susan M Varnum.

10 recordsLinked to original sources

PubMed2006

High dynamic range characterization of the trauma patient plasma proteome.

Although human plasma represents an attractive sample for disease biomarker discovery, the extreme complexity and large dynamic range in protein concentrations present significant challenges for characterization, candidate biomarker discovery, and validation. Herein we describe a strategy that combines immunoaffinity subtraction and subsequent chemical fractionation based on cysteinyl peptide and N-glycopeptide captures with two-dimensional LC-MS/MS to increase the dynamic range of analysis for plasma. Application of this "divide-and-conquer" strategy to trauma patient plasma significantly improved the overall dynamic range of detection and resulted in confident identification of 22,267 unique peptides from four different peptide populations (cysteinyl peptides, non-cysteinyl peptides, N-glycopeptides, and non-glycopeptides) that covered 3,654 different proteins with 1,494 proteins identified by multiple peptides. Numerous low abundance proteins were identified, exemplified by 78 "classic" cytokines and cytokine receptors and by 136 human cell differentiation molecules. Additionally a total of 2,910 different N-glycopeptides that correspond to 662 N-glycoproteins and 1,553 N-glycosylation sites were identified. A panel of the proteins identified in this study is known to be involved in inflammation and immune responses. This study established an extensive reference protein database for trauma patients that provides a foundation for future high throughput quantitative plasma proteomic studies designed to elucidate the mechanisms that underlie systemic inflammatory responses.

Blood Proteins↗

PubMed2006

ProMAT: protein microarray analysis tool.

SUMMARY: ProMAT is a software tool for statistically analyzing data from enzyme-linked immunosorbent assay microarray experiments. The software estimates standard curves, sample protein concentrations and their uncertainties for multiple assays. ProMAT generates a set of comprehensive figures for assessing results and diagnosing process quality. The tool is available for Windows or Mac, and is distributed as open-source Java and R code. AVAILABILITY: ProMAT is available at http://www.pnl.gov/statistics/ProMAT. ProMAT requires Java version 1.5.0 and R version 1.9.1 (or more recent versions). ProMAT requires either Windows XP or Mac OS 10.4 or newer versions.

Algorithms↗

PubMed2005

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.

Algorithms↗

PubMed2005

Studying cellular processes and detecting disease with protein microarrays.

Protein microarrays are a rapidly developing analytic tool with diverse applications in biomedical research. These applications include profiling of disease markers or autoimmune responses, understanding molecular pathways, protein modifications, and protein activities. One factor that is driving this expanding usage is the wide variety of experimental formats that protein microarrays can take. In this review, we provide a short, conceptual overview of the different approaches for protein microarray. We then examine some of the most significant applications of these microarrays to date, with an emphasis on how global protein analyses can be used to facilitate biomedical research.

Cell Physiological Phenomena↗

PubMed2004

Identification of proteins in human cytomegalovirus (HCMV) particles: the HCMV proteome.

Human cytomegalovirus (HCMV), a member of the herpesvirus family, is a large complex enveloped virus composed of both viral and cellular gene products. While the sequence of the HCMV genome has been known for over a decade, the full set of viral and cellular proteins that compose the HCMV virion are unknown. To approach this problem we have utilized gel-free two-dimensional capillary liquid chromatography-tandem mass spectrometry (MS/MS) and Fourier transform ion cyclotron resonance MS to identify and determine the relative abundances of viral and cellular proteins in purified HCMV AD169 virions and dense bodies. Analysis of the proteins from purified HCMV virion preparations has indicated that the particle contains significantly more viral proteins than previously known. In this study, we identified 71 HCMV-encoded proteins that included 12 proteins encoded by known viral open reading frames (ORFs) previously not associated with virions and 12 proteins from novel viral ORFs. Analysis of the relative abundance of HCMV proteins indicated that the predominant virion protein was the pp65 tegument protein and that gM rather than gB was the most abundant glycoprotein. We have also identified over 70 host cellular proteins in HCMV virions, which include cellular structural proteins, enzymes, and chaperones. In addition, analysis of HCMV dense bodies indicated that these viral particles are composed of 29 viral proteins with a reduced quantity of cellular proteins in comparison to HCMV virions. This study provides the first comprehensive quantitative analysis of the viral and cellular proteins that compose infectious particles of a large complex virus.

Cells, Cultured↗

PubMed2004

A rational approach for discovering and validating cancer markers in very small samples using mass spectrometry and ELISA microarrays.

Identifying useful markers of cancer can be problematic due to limited amounts of sample. Some samples such as nipple aspirate fluid (NAF) or early-stage tumors are inherently small. Other samples such as serum are collected in larger volumes but archives of these samples are very valuable and only small amounts of each sample may be available for a single study. Also, given the diverse nature of cancer and the inherent variability in individual protein levels, it seems likely that the best approach to screen for cancer will be to determine the profile of a battery of proteins. As a result, a major challenge in identifying protein markers of disease is the ability to screen many proteins using very small amounts of sample. In this review, we outline some technological advances in proteomics that greatly advance this capability. Specifically, we propose a strategy for identifying markers of breast cancer in NAF that utilizes mass spectrometry (MS) to simultaneously screen hundreds or thousands of proteins in each sample. The best potential markers identified by the MS analysis can then be extensively characterized using an ELISA microarray assay. Because the microarray analysis is quantitative and large numbers of samples can be efficiently analyzed, this approach offers the ability to rapidly assess a battery of selected proteins in a manner that is directly relevant to traditional clinical assays.

Bacterial Proteins↗

PubMed2004

A protein microarray ELISA for screening biological fluids.

Protein microarrays permit the simultaneous measurement of many proteins in a small sample volume and therefore provide an attractive approach for the quantitative measurement of proteins in biological fluids, including serum. This chapter describes a microarray enzyme-linked immunosorbent assay (ELISA). Capture antibodies are immobilized onto a glass surface; the covalently attached antibodies bind a specific antigen from a sample overlaying the array. A second, biotinylated antibody that recognizes the same antigen as the first antibody, but at a different epitope, is then used for detection. Detection is based on an enzymatic signal-enhancement method known as tyramide signal amplification (TSA). By coupling a microarray-ELISA format with the signal amplification of tyramide deposition, the assay sensitivity is as low as sub-pg/mL.

Amides↗

PubMed2003

Proteomic characterization of nipple aspirate fluid: identification of potential biomarkers of breast cancer.

Mammary ductal cells are the origin for 70-80% of breast cancers. Nipple aspirate fluid (NAF) contains proteins directly secreted by the ductal and lobular epithelium in non-lactating women. Proteomic approaches offer a largely unbiased way to evaluate NAF as a source of biomarkers and are sufficiently sensitive for analysis of small NAF volumes (10-50 microl). In this study, we initially evaluated a new process for obtaining NAF and discovered that this process resulted in a volume of NAF that was suitable for analysis in approximately 90% of subjects. Proteomic characterization of NAF identified 64 proteins. Although this list primarily includes abundant and moderately abundant NAF proteins, very few of these proteins have previously been reported in NAF. At least 15 of the NAF proteins identified have previously been reported to be altered in serum or tumor tissue from women with breast cancer, including cathepsin D and osteopontin. In summary, this study provides the first characterization of the NAF proteome and identifies several candidate proteins for future studies on breast cancer markers in NAF.

Adult↗

PubMed2002

Toward a human blood serum proteome: analysis by multidimensional separation coupled with mass spectrometry.

Blood serum is a complex body fluid that contains various proteins ranging in concentration over at least 9 orders of magnitude. Using a combination of mass spectrometry technologies with improvements in sample preparation, we have performed a proteomic analysis with submilliliter quantities of serum and increased the measurable concentration range for proteins in blood serum beyond previous reports. We have detected 490 proteins in serum by on-line reversed-phase microcapillary liquid chromatography coupled with ion trap mass spectrometry. To perform this analysis, immunoglobulins were removed from serum using protein A/G, and the remaining proteins were digested with trypsin. Resulting peptides were separated by strong cation exchange chromatography into distinct fractions prior to analysis. This separation resulted in a 3-5-fold increase in the number of proteins detected in an individual serum sample. With this increase in the number of proteins identified we have detected some lower abundance serum proteins (ng/ml range) including human growth hormone, interleukin-12, and prostate-specific antigen. We also used SEQUEST to compare different protein databases with and without filtering. This comparison is plotted to allow for a quick visual assessment of different databases as a subjective measure of analytical quality. With this study, we have performed the most extensive analysis of serum proteins to date and laid the foundation for future refinements in the identification of novel protein biomarkers of disease.

Blood Proteins↗

PubMedDate not supplied

Elevated HGF levels in sera from breast cancer patients detected using a protein microarray ELISA.

We developed an ELISA in high-density microarray format to detect hepatocyte growth factor (HGF) in human serum. The microassay can detect HGF at sub-pg/mL concentrations in sample volumes of 100 microL or less. The microassay is also quantitative and was used to detect elevated HGF levels in sera from recurrent breast cancer patients. The microarray format provides the potential for high-throughput quantitation of multiple biomarkers in parallel, as demonstrated with a multiplex analysis of five biomarker proteins.

Biomarkers, Tumor↗