Expression profiling by oligonucleotide microarrays spotted on coated polymer slides

We have developed a ready-to-spot polymer microarray slide, which is coated with a uniform layer of reactive electrophilic groups using anthraquinone-mediated photo-coupling chemistry. The slide coating reduces the hydrophobicity of the native polymer significantly, thereby enabling robust and efficient one-step coupling of spotted 5' amino-linked oligonucleotides onto the polymer slide. The utility of the coated polymer slide in gene expression profiling was assessed by fabrication of spotted oligonucleotide microarrays using a collection of 5' amino-linked 70-mer oligonucleotide probes representing 96 yeast genes from Operon. Two-colour hybridizations with labelled cDNA target pools derived from standard grown and heat-shocked wild type yeast cells could reproducibly measure heat shock induced expression of seven different heat shock protein (HSP) genes. Moreover, the observed fold changes were comparable to those reported previously using spotted cDNA arrays and high-density 25-mer oligonucleotide arrays from Affymetrix. The low hybridization signals obtained from the DeltaSSA4 mutant cDNA target, together with the high signal detected in two-colour hybridizations with heat-shocked wild type yeast relative to the DeltaSSA4 mutant strain implies that unspecific binding of cDNA target to the SSA4-specific 70-mer oligonucleotide probes is negligible. Combined, our results indicate that the coated polymer microarray slide represents a robust and cost-effective array platform for pre-spotted oligonucleotide arrays.     

Systematic intervention of transcription for identifying network response to disease and cellular phenotypes

MOTIVATION: A major challenge in post-genomic research has been to understand how physiological and pathological phenotypes arise from the networks of expressed genes. Here, we addressed this issue by developing an algorithm to mimic the behavior of regulatory networks in silico and to identify the dynamic response to disease and changing cellular conditions. RESULTS: With regulatory pathway and gene expression data as input, the algorithm provides quantitative assessments of a wide range of responses, including susceptibility to disease, potential usefulness of a given drug, or consequences to such external stimuli as pharmacological interventions or caloric restriction. The algorithm is particularly amenable to the analysis of systems that are difficult to recapitulate in vitro, yet they may have important clinical value. The hypotheses derived from the algorithm were biologically relevant and were successfully validated via independent experiments, as illustrated here in the analysis of the leukemia-associated BCR-ABL pathway and the insulin/IGF pathway related to longevity. The algorithm correctly identified the leukemia drug target and genes important for longevity, and also provided new insights into our understanding of these two processes. AVAILABILITY: The software package is available upon request to the authors.     

Secretome profiling with antibody microarrays

Following the advances in human genome sequencing, attention has shifted in part toward the elucidation of the encoded biological functions. Since proteins are the driving forces behind very many biological activities, large-scale examinations of their expression variations, their functional roles and regulation have moved to the central stage. A significant fraction of the human proteome consists of secreted proteins. Exploring this set of molecules offers unique opportunities for understanding molecular interactions between cells and fosters biomarker discovery that could advance the detection and monitoring of diseases. Antibody microarrays are among the relatively new proteomic methodologies that may advance the field significantly because of their relative simplicity, robust performance and high sensitivity down to single-molecule detection. In addition, several aspects such as variations in amount, structure and activity can be assayed at a time. Antibody microarrays are therefore likely to improve the analytical capabilities in proteomics and consequently permit the production of even more informative and reliable data. This review looks at recent applications of this novel platform technology in secretome analysis and reflects on the future.     

Systematic alteration of in vitro metabolic environments reveals empirical growth relationships in cancer cell phenotypes

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Predicting cell health phenotypes using image-based morphology profiling

Genetic and chemical perturbations impact diverse cellular phenotypes, including multiple indicators of cell health. These readouts reveal toxicity and antitumorigenic effects relevant to drug discovery and personalized medicine. We developed two customized microscopy assays, one using four targeted reagents and the other three targeted reagents, to collectively measure 70 specific cell health phenotypes including proliferation, apoptosis, reactive oxygen species, DNA damage, and cell cycle stage. We then tested an approach to predict multiple cell health phenotypes using Cell Painting, an inexpensive and scalable image-based morphology assay. In matched CRISPR perturbations of three cancer cell lines, we collected both Cell Painting and cell health data. We found that simple machine learning algorithms can predict many cell health readouts directly from Cell Painting images, at less than half the cost. We hypothesized that these models can be applied to accurately predict cell health assay outcomes for any future or existing Cell Painting dataset. For Cell Painting images from a set of 1500+ compound perturbations across multiple doses, we validated predictions by orthogonal assay readouts. We provide a web app to browse predictions: http://broad.io/cell-health-app. Our approach can be used to add cell health annotations to Cell Painting datasets.     

Analysis of repeatability in spotted cDNA microarrays

We report a strategy for analysis of data quality in cDNA microarrays based on the repeatability of repeatedly spotted clones. We describe how repeatability can be used to control data quality by developing adaptive filtering criteria for microarray data containing clones spotted in multiple spots. We have applied the method on five publicly available cDNA microarray data sets and one previously unpublished data set from our own laboratory. The results demonstrate the feasibility of the approach as a foundation for data filtering, and indicate a high degree of variation in data quality, both across the data sets and between arrays within data sets.     

Analysis conditions for proteomic profiling of mammalian tissue and cell extracts with antibody microarrays

Antibody microarrays are a developing tool for global proteomic profiling. A protocol was established that permits robust analyses of protein extracts from mammalian tissues and cells rather than body fluids. The factors optimized were buffer composition for surface blocking, blocking duration, protein handling and processing, labeling parameters like type of dye, molar ratio of label versus protein, and dye removal, as well as incubation parameters such as duration, temperature, buffer, and sample agitation.     

Epitomics: global profiling of immune response to disease using protein microarrays

The immune system retains memory of current and past infections and can sense the presence of cancer by elaborating autoantibodies to tumor proteins. In the presence of an autoimmune disease, the immune system is an efficient, natural biosensor. Therefore we exploit the immune system through a high-throughput process to isolate disease-specific epitopes for diagnostic and therapeutic purposes. These cloned disease-specific antigens are robotically spotted onto protein microarrays and interrogated with serum from the subjects under analyses. These arrays deliver personalized profiles of antigenic exposures and therapeutic targets for personalized immunotherapy. The immune system is the ultimate biosensor, superior to anything a human could create and ready to be exploited for biotechnology and biomedicine.     

Functional genomics reveals diverse cellular processes that modulate tumor cell response to oxaliplatin

Oxaliplatin is widely used to treat colorectal cancer, as both adjuvant therapy for resected disease and palliative treatment of metastatic disease. However, a significant number of patients experience serious side effects, including prolonged neurotoxicity, from oxaliplatin treatment creating an urgent need for biomarkers of oxaliplatin response or resistance to direct therapy to those most likely to benefit. As a first step to improve selection of patients for oxaliplatin-based chemotherapy, we have conducted an in vitro cell-based small interfering RNA (siRNA) screen of 500 genes aimed at identifying genes whose loss of expression alters tumor cell response to oxaliplatin. The siRNA screen identified twenty-seven genes, which when silenced, significantly altered colon tumor cell line sensitivity to oxaliplatin. Silencing of a group of putative resistance genes increased the extent of oxaliplatin-mediated DNA damage and inhibited cell-cycle progression in oxaliplatin-treated cells. The activity of several signaling nodes, including AKT1 and MEK1, was also altered. We used cDNA transfection to overexpress two genes (LTBR and TMEM30A) that were identified in the siRNA screen as mediators of oxaliplatin sensitivity. In both instances, overexpression conferred resistance to oxaliplatin. In summary, this study identified numerous putative predictive biomarkers of response to oxaliplatin that should be studied further in patient specimens for potential clinical application. Diverse gene networks seem to influence tumor survival in response to DNA damage by oxaliplatin. Finally, those genes whose loss of expression (or function) is related to oxaliplatin sensitivity may be promising therapeutic targets to increase patient response to oxaliplatin.     

Genetic heterogeneity of stably transfected cell lines revealed by expression profiling with oligonucleotide microarrays

Large-scale gene expression measurements with oligonucleotide microarrays have contributed tremendously to biological research. However, to distinguish between relevant expression changes and falsely identified positives, the source and magnitude of errors must be understood. Here, we report a source of biological variability in microarray experiments with stably transfected cell lines. Mouse embryonic fibroblast (MEF/3T3) and rat schwannoma (RT4) cell lines were generated to provide regulatable schwannomin expression. The expression levels of 29 samples from five different mouse embryonic fibroblast clonal cell lines and 18 samples from 3 RT4 cell lines were monitored with oligonucleotide microarrays. Using hierarchical clustering, we determined that the changes in gene expression induced by schwannomin overexpression were subtle when compared with those detected as a consequence of clonal selection during generation of the cell lines. The hierarchical clustering implies that significant alterations of gene expression were introduced during the transfection and selection processes. A total of 28 genes were identified by Kruskal-Wallis rank test that showed significant variation between clonal lines. Most of them were related to cytoskeletal function and signaling pathways. Based on these analyses, we recommend that replications of experiments with several selected cell lines are necessary to assess biological effects of induced gene expression.     

Systematic association of genes to phenotypes by genome and literature mining

One of the major challenges of functional genomics is to unravel the connection between genotype and phenotype. So far no global analysis has attempted to explore those connections in the light of the large phenotypic variability seen in nature. Here, we use an unsupervised, systematic approach for associating genes and phenotypic characteristics that combines literature mining with comparative genome analysis. We first mine the MEDLINE literature database for terms that reflect phenotypic similarities of species. Subsequently we predict the likely genomic determinants: genes specifically present in the respective genomes. In a global analysis involving 92 prokaryotic genomes we retrieve 323 clusters containing a total of 2,700 significant gene–phenotype associations. Some clusters contain mostly known relationships, such as genes involved in motility or plant degradation, often with additional hypothetical proteins associated with those phenotypes. Other clusters comprise unexpected associations; for example, a group of terms related to food and spoilage is linked to genes predicted to be involved in bacterial food poisoning. Among the clusters, we observe an enrichment of pathogenicity-related associations, suggesting that the approach reveals many novel genes likely to play a role in infectious diseases.     

Autoimmune profiling with protein microarrays in clinical applications

In recent years, knowledge about immune-related disorders has substantially increased, especially in the field of central nervous system (CNS) disorders. Recent innovations in protein-related microarray technology have enabled the analysis of interactions between numerous samples and up to 20,000 targets. Antibodies directed against ion channels, receptors and other synaptic proteins have been identified, and their causative roles in different disorders have been identified. Knowledge about immunological disorders is likely to expand further as more antibody targets are discovered. Therefore, protein microarrays may become an established tool for routine diagnostic procedures in the future.