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.     

Searching for functional gene modules with interaction component models

Background  

Functional gene modules and protein complexes are being sought from combinations of gene expression and protein-protein interaction data with various clustering-type methods. Central features missing from most of these methods are handling of uncertainty in both protein interaction and gene expression measurements, and in particular capability of modeling overlapping clusters. It would make sense to assume that proteins may play different roles in different functional modules, and the roles are evidenced in their interactions.     

Considerations when using the significance analysis of microarrays (SAM) algorithm

Background  

Users of microarray technology typically strive to use universally acceptable data analysis strategies to determine significant expression changes in their experiments. One of the most frequently utilised methods for gene expression data analysis is SAM (significance analysis of microarrays). The impact of selection thresholds, on the output from SAM, may critically alter the conclusion of a study, yet this consideration has not been systematically evaluated in any publication.     

High quality protein microarray using in situ protein purification

Background  

In the postgenomic era, high throughput protein expression and protein microarray technologies have progressed markedly permitting screening of therapeutic reagents and discovery of novel protein functions. Hexa-histidine is one of the most commonly used fusion tags for protein expression due to its small size and convenient purification via immobilized metal ion affinity chromatography (IMAC). This purification process has been adapted to the protein microarray format, but the quality of in situ His-tagged protein purification on slides has not been systematically evaluated. We established methods to determine the level of purification of such proteins on metal chelate-modified slide surfaces. Optimized in situ purification of His-tagged recombinant proteins has the potential to become the new gold standard for cost-effective generation of high-quality and high-density protein microarrays.     

Using expression arrays for copy number detection: an example from E. coli

Background  

The sequencing of many genomes and tiling arrays consisting of millions of DNA segments spanning entire genomes have made high-resolution copy number analysis possible. Microarray-based comparative genomic hybridization (array CGH) has enabled the high-resolution detection of DNA copy number aberrations. While many of the methods and algorithms developed for the analysis microarrays have focused on expression analysis, the same technology can be used to detect genetic alterations, using for example standard commercial Affymetrix arrays. Due to the nature of the resultant data, standard techniques for processing GeneChip expression experiments are inapplicable.     

Feature selection using Haar wavelet power spectrum

Background  

Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical methods are utilized more in this domain. Most of them do not fit for a wide range of datasets. The transform oriented signal processing domains are not probed much when other fields like image and video processing utilize them well. Wavelets, one of such techniques, have the potential to be utilized in feature selection method. The aim of this paper is to assess the capability of Haar wavelet power spectrum in the problem of clustering and gene selection based on expression data in the context of disease classification and to propose a method based on Haar wavelet power spectrum.     

Frozen protein arrays: a new method for arraying and detecting recombinant and native tissue proteins

DNA microarrays are powerful tools for high throughput analysis of gene expression; however, they do not measure protein expression. Current methods for producing protein arrays require sophisticated equipment or extensive protein modification. We developed a low overhead, customizable assay platform called frozen protein arrays that can detect native proteins in protein lysates. Frozen protein arrays were formed from a block of frozen histologic embedding compound containing an array of wells. The wells were filled with samples, which freeze and bond to the block. Cryosections were cut and transferred to nitrocellulose-coated slides. The reproducibility, linearity, and sensitivity was confirmed using frozen protein arrays filled with prostate specific antigen. Frozen protein arrays could detect native tissue proteins. The alpha1 subunit of NaK-ATPase was detected in rat kidneys with a coefficient of variation of 4.3-6.6%. Frozen protein array analysis indicated that the protein abundance decreased by 48.7% following renal ischemia, similar to the 40% decrease by Western blotting. We conclude that frozen protein arrays are a low cost, moderate size platform for arraying samples including protein lysates. Production of many identical frozen protein arrays is easy, inexpensive, and requires only small sample volumes. The method is gentle on proteins as they remain frozen during production.     

Spatiotemporal expression profiling of proteins in rat sciatic nerve regeneration using reverse phase protein arrays

Background  

Protein expression profiles throughout 28 days of peripheral nerve regeneration were characterized using an established rat sciatic nerve transection injury model. Reverse phase protein microarrays were used to identify the spatial and temporal expression profile of multiple proteins implicated in peripheral nerve regeneration including growth factors, extracellular matrix proteins, and proteins involved in adhesion and migration. This high-throughput approach enabled the simultaneous analysis of 3,360 samples on a nitrocellulose-coated slide.     

Accelerated search for biomolecular network models to interpret high-throughput experimental data

Background  

The functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein expression. Models of biomolecular network structure and dynamics can be inferred from high-throughput measurements of gene and protein expression. We build on our previously developed fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays. We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy biomolecular network models consistent with a biological data set. We also develop a method to estimate the probability of a potential network model fitting a set of data by chance. The resulting metric provides an estimate of both model quality and dataset quality, identifying data that are too noisy to identify meaningful correlations between the measured variables.     

High-throughput protein arrays: prospects for molecular diagnostics

High-throughput protein arrays allow the miniaturized and parallel analysis of large numbers of diagnostic markers in complex samples. Using automated colony picking and gridding, cDNA or antibody libraries can be expressed and screened as clone arrays. Protein microarrays are constructed from recombinantly expressed, purified, and yet functional proteins, entailing a range of optimized expression systems. Antibody microarrays are becoming a robust format for expression profiling of whole genomes. Alternative systems, such as aptamer, PROfusion, nano- and microfluidic arrays are all at proof-of-concept stage. Differential protein profiles have been used as molecular diagnostics for cancer and autoimmune diseases and might ultimately be applied to screening of high-risk and general populations.     

New miRNA labeling method for bead-based quantification

Background  

microRNAs (miRNAs) are small single-stranded non-coding RNAs that act as crucial regulators of gene expression. Different methods have been developed for miRNA expression profiling in order to better understand gene regulation in normal and pathological conditions. miRNAs expression values obtained from large scale methodologies such as microarrays still need a validation step with alternative technologies.     

TAMEE: data management and analysis for tissue microarrays

Background  

With the introduction of tissue microarrays (TMAs) researchers can investigate gene and protein expression in tissues on a high-throughput scale. TMAs generate a wealth of data calling for extended, high level data management. Enhanced data analysis and systematic data management are required for traceability and reproducibility of experiments and provision of results in a timely and reliable fashion. Robust and scalable applications have to be utilized, which allow secure data access, manipulation and evaluation for researchers from different laboratories.