Microarray-based gene expression profiling to elucidate cellular responses to nitric oxide--a review from an analytical and biomedical point of view

Nitric oxide (NO) produced by NO synthases (NOS) regulates a wide range of cellular functions. Analysis by gene arrays provides valuable information for identifying important elements of the cellular responses to NO. Such screening tools might be useful to elucidate NO-responsive regulators, which play a central role in mediating NO effects. Although the final importance of a particular gene is determined by the encoded protein and further protein modifications, measurements of RNA levels have proven to be partly valuable in identifying the molecular changes that occur in cells. Microarray technology permits large-scale and genome-wide analysis of gene expression from multiple samples. We review the current knowledge of the use of microarray gene expression screening in elucidating the effects of NO on various cells and tissues. We also point out the limitations of general microarray-based gene expression analyses and especially when investigating the effects of NO.     

Linking the growth inhibition response from the National Cancer Institute's anticancer screen to gene expression levels and other molecular target data

MOTIVATION: Data mining tools are proposed to establish mechanistic connections between chemotypes and specific cellular functions. Drawing on a previous study that classified the cellular response patterns of growth inhibition measurements log( GI(50)) from the National Cancer Institute's (NCI's) anticancer screen, we have examined additional data for mRNA expression, sets of known molecular targets and mutational status against these same tumor cell lines to relate chemosensitivity more precisely to biochemical pathways. RESULTS: Our analysis finds that gene expression levels do not, in general, correlate with log(GI(50)) measurements, instead they reflect a generic toxic condition. Within the remaining set of non-generic conditions, examples were found where a correlation suggesting a biochemical basis for cellular cytotoxicity could be supported. These included reconfirmation of previously observed associations between mutant and wild-type status of p53, and chemosensitivity to alkylating agents, while extending these results to reveal associations with gamma-induced expressions of MDM2, WAF1 and GADD45, signals that were not apparent in measurements of basal mRNA expression levels for any of these genes. Additional examinations revealed that mRNA expression levels directly correlated with paclitaxel chemosensitivity to mitosis, while also identifying additional chemotypes as P-glycoprotein substrates. Our analysis revealed well-known direct associations between p16 mutant status and chemotypes implicated in cell cycle control, and extended these results to include expression levels for three additional tyrosine kinase proteins (TEK, transgelin and hCdc4). Links were also found that suggested associations between chemosensitivity and the endocrine, paracrine ligand-receptor loops, via expression of the adrenergic receptor, calcium second messenger pathways via expression levels of carbonic anhydrase and cellular communication pathways via fibrillin.     

Mass spectrometric quantification of asparagine synthetase in circulating leukemia cells from acute lymphoblastic leukemia patients

The appearance of asparaginase-resistant acute lymphoblastic leukemia (ALL) in transformed cell lines has been correlated with increased expression of asparagine synthetase (ASNS). Recent measurements using mRNA-based assays have raised doubts, however, as to the importance of ASNS protein in the cellular mechanisms that confer drug resistance upon the leukemic cells. Studies aimed at determining the concentration of ASNS protein in human leukemias are therefore needed to resolve this issue. A mass spectrometry (MS)-based procedure is presented for the direct quantification of ASNS protein concentration in complex sample mixtures. This assay is able to distinguish samples from transformed cell lines that express ASNS over a wide dynamic range of concentration. Importantly, this method directly detects ASNS protein, the functional entity that may be synthesizing sufficient asparagine to render leukemia cells resistant to asparaginase-treatment. We also report the successful use of this MS method, which has lower limits of detection and quantification of 30 and 100 attomoles, respectively, for the first direct measurements of ASNS protein concentrations in four patient blast samples.     

Human CD4+ lymphocytes for antigen quantification: characterization using conventional flow cytometry and mass cytometry

To transform the linear fluorescence intensity scale obtained with fluorescent microspheres to an antibody bound per cell (ABC) scale, a biological cell reference material is needed. Optimally, this material should have a reproducible and tight ABC value for the expression of a known clinical reference biomarker. In this study, we characterized commercially available cryopreserved peripheral blood mononuclear cells (PBMCs) and two lyophilized PBMC preparations, Cyto-Trol and PBMC-National Institute for Biological Standard and Control (NIBSC) relative to freshly prepared PBMC and whole blood samples. It was found that the ABC values for CD4 expression on cryopreserved PBMC were consistent with those of freshly obtained PBMC and whole blood samples. By comparison, the ABC value for CD4 expression on Cyto-Trol is lower and the value on PBMC-NIBSC is much lower than those of freshly prepared cell samples using both conventional flow cytometry and CyTOF? mass cytometry. By performing simultaneous surface and intracellular staining measurements on these two cell samples, we found that both cell membranes are mostly intact. Moreover, CD4(+) cell diameters from both lyophilized cell preparations are smaller than those of PBMC and whole blood. This could result in steric interference in antibody binding to the lyophilized cells. Further investigation of the fixation effect on the detected CD4 expression suggests that the very low ABC value obtained for CD4(+) cells from lyophilized PBMC-NIBSC is largely due to paraformaldehyde fixation; this significantly decreases available antibody binding sites. This study provides confirmation that the results obtained from the newly developed mass cytometry are directly comparable to the results from conventional flow cytometry when both methods are standardized using the same ABC approach.     

Exploitation of expression profiles: examples in oncology

The analysis of biological processes has been revolutionized by the emergence of the DNA array technology. As cellular biological events are controlled by gene expression, their modulations are markers of the cellular activity. These modulations can be indicative of either a physiological process or a pathological one. Monitoring of the expression levels of thousands of genes simultaneously, the expression profiling method is based upon comparative studies where the identification of the differentially expressed genes in two samples is aimed. The two samples under study may be compared temporally or following drug treatment, they may also originate from different sources, e.g. normal versus pathological samples. In that case, gene expression profiling is conducted for diagnostics purposes or therapy monitoring, and offers an opportunity to identify new drug targets. Using different examples, we describe the potentialities of this approach in oncology.     

PERT: A Method for Expression Deconvolution of Human Blood Samples from Varied Microenvironmental and Developmental Conditions

The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT''s predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity.     

Computational exploration of the activated pathways associated with DNA damage response in breast cancer

Molecular signaling events regulate cellular activity. Cancer stimulating signals trigger cellular responses that evade the regulatory control of cell development. To understand the mechanism of signaling regulation in cancer, it is necessary to identify the activated pathways in cancer. We have developed RepairPATH, a computational algorithm that explores the activated signaling pathways in cancer. The RepairPATH integrates RepairNET, an assembled protein interaction network associated with DNA damage response, with the gene expression profiles derived from the microarray data. Based on the observation that cofunctional proteins often exhibit correlated gene expression profiles, it identifies the activated signaling pathways in cancer by systematically searching the RepairNET for proteins with significantly correlated gene expression profiles. Analyzing the gene expression profiles of breast cancer, we found distinct similarities and differences in the activated signaling pathways between the samples from the patients who developed metastases and the samples from the patients who were disease free within 5 years. The cellular pathways associated with the various DNA repair mechanisms and the cell-cycle checkpoint controls are found to be activated in both sample groups. One of the most intriguing findings is that the pathways associated with different cellular processes are functionally coordinated through BRCA1 in the disease-free sample group, whereas such functional coordination is absent in the samples from patients who developed metastases. Our analysis revealed the potential cellular pathways that regulate the signaling events in breast cancer.     

Spectra of intracellular Fura-2.

In the theory of measurement of calcium ion activity by determination of Fura-2 fluorescence at two excitation wavelengths, the accuracy of the result depends upon the accuracy both of the sample measurements and of the calibration measurements which are made on calcium-bound and free dye. Two factors underlie adequate calibration and accuracy. The first is the elimination of systematic error due to spectral shifts arising from the intracellular environment felt by the dye. To this end, detailed comparisons between complete spectra of both calcium-bound and calcium-free Fura-2 can be used to help separate spectral effects due to light absorption by cellular constituents versus polarity and viscosity of the intracellular milieu. The second major factor which determines accuracy is the experimental uncertainty (in both sample and calibration measurements). For samples in which the ratio of bound to free dye is large, the uncertainty in the ratio is also large, even when it is expressed as a percentage of the ratio itself. The errors in calibration measurements impact on the accuracy of the method primarily through the measurements made at wavelengths which are off the spectral peaks of the bound or free dye, since these are the least accurate. In order to obtain a guide to the choice of wavelengths and estimation of the reliability of results, a mathematical expression is derived for the dependence of the accuracy of the method on the accuracy of both sample and calibration measurements.     

Approximate geodesic distances reveal biologically relevant structures in microarray data

MOTIVATION: Genome-wide gene expression measurements, as currently determined by the microarray technology, can be represented mathematically as points in a high-dimensional gene expression space. Genes interact with each other in regulatory networks, restricting the cellular gene expression profiles to a certain manifold, or surface, in gene expression space. To obtain knowledge about this manifold, various dimensionality reduction methods and distance metrics are used. For data points distributed on curved manifolds, a sensible distance measure would be the geodesic distance along the manifold. In this work, we examine whether an approximate geodesic distance measure captures biological similarities better than the traditionally used Euclidean distance. RESULTS: We computed approximate geodesic distances, determined by the Isomap algorithm, for one set of lymphoma and one set of lung cancer microarray samples. Compared with the ordinary Euclidean distance metric, this distance measure produced more instructive, biologically relevant, visualizations when applying multidimensional scaling. This suggests the Isomap algorithm as a promising tool for the interpretation of microarray data. Furthermore, the results demonstrate the benefit and importance of taking nonlinearities in gene expression data into account.     

Cytomorphometry of uveal melanoma. Comparison of fine needle aspiration biopsy samples with histologic sections.

A number of approaches are being investigated to increase the prognostic accuracy for uveal melanoma patients; the standard deviation of nucleolar area measurements and the DNA content appear to correlate better with survival than do classic histologic parameters. The utility of performing cytomorphometric measurements on fine needle aspiration (FNA) biopsy samples was prospectively analyzed for 24 eyes containing uveal melanomas that were examined with both 25-gauge FNA biopsy and standard histologic techniques. "Masked" analysis of the cellular composition of the 24 cases showed the presence or absence of epithelioid cells to be accurately predicted on the FNA samples in all cases. Image analysis cytomorphometric measurements of nucleolar area showed marked variability (with r less than 0.4) when FNA and histologic samples from the same case were compared. The relationship between these measurements was affected by cell type, sampling, specimen processing and investigator experience.     

Empirical model and in vivo characterization of the bacterial response to synthetic gene expression show that ribosome allocation limits growth rate

Synthetic biology uses modeling to facilitate the design of new genetic constructions. In particular, it is of utmost importance to model the reaction of the cellular chassis when expressing heterologous systems. We constructed a mathematical model for the response of a bacterial cell chassis under heterologous expression. For this, we relied on previous characterization of the growth-rate dependence on cellular resource availability (in this case, DNA and RNA polymerases and ribosomes). Accordingly, we estimated the maximum capacities of the cell for heterologous expression to be 46% of the total RNA and the 33% of the total protein. To experimentally validate our model, we engineered two genetic constructions that involved the constitutive expression of a fluorescent reporter in a vector with a tunable origin of replication. We performed fluorescent measurements using population and single-cell fluorescent measurements. Our model predicted cell growth for several heterologous constructions under five different culture conditions and various plasmid copy numbers with significant accuracy, and confirmed that ribosomes act as the limiting resource. Our study also confirmed that the bacterial response to synthetic gene expression could be understood in terms of the requirement for cellular resources and could be predicted from relevant cellular parameters.     

Nonrandom RNAseq gene expression associated with RNAlater and flash freezing storage methods

RNA sequencing is a popular next‐generation sequencing technique for assaying genome‐wide gene expression profiles. Nonetheless, it is susceptible to biases that are introduced by sample handling prior gene expression measurements. Two of the most common methods for preserving samples in both field‐based and laboratory conditions are submersion in RNAlater and flash freezing in liquid nitrogen. Flash freezing in liquid nitrogen can be impractical, particularly for field collections. RNAlater is a solution for stabilizing tissue for longer‐term storage as it rapidly permeates tissue to protect cellular RNA. In this study, we assessed genome‐wide expression patterns in 30‐day‐old fry collected from the same brood at the same time point that were flash‐frozen in liquid nitrogen and stored at ?80°C or submerged and stored in RNAlater at room temperature, simulating conditions of fieldwork. We show that sample storage is a significant factor influencing observed differential gene expression. In particular, genes with elevated GC content exhibit higher observed expression levels in liquid nitrogen flash‐freezing relative to RNAlater storage. Further, genes with higher expression in RNAlater relative to liquid nitrogen experience disproportionate enrichment for functional categories, many of which are involved in RNA processing. This suggests that RNAlater may elicit a physiological response that has the potential to bias biological interpretations of expression studies. The biases introduced to observed gene expression arising from mimicking many field‐based studies are substantial and should not be ignored.     

Resolving Cell Population Heterogeneity: Real-Time PCR for Simultaneous Multiplexed Gene Detection in Multiple Single-Cell Samples

Decoding the complexity of multicellular organisms requires analytical procedures to overcome the limitations of averaged measurements of cell populations, which obscure inherent cell-cell heterogeneity and restrict the ability to distinguish between the responses of individual cells within a sample. For example, defining the timing, magnitude and the coordination of cytokine responses in single cells is critical for understanding the development of effective immunity. While approaches to measure gene expression from single cells have been reported, the absolute performance of these techniques has been difficult to assess, which likely has limited their wider application. We describe a straightforward method for simultaneously measuring the expression of multiple genes in a multitude of single-cell samples using flow cytometry, parallel cDNA synthesis, and quantification by real-time PCR. We thoroughly assess the performance of the technique using mRNA and DNA standards and cell samples, and demonstrate a detection sensitivity of ∼30 mRNA molecules per cell, and a fractional error of 15%. Using this method, we expose unexpected heterogeneity in the expression of 5 immune-related genes in sets of single macrophages activated by different microbial stimuli. Further, our analyses reveal that the expression of one ‘pro-inflammatory’ cytokine is not predictive of the expression of another ‘pro-inflammatory’ cytokine within the same cell. These findings demonstrate that single-cell approaches are essential for studying coordinated gene expression in cell populations, and this generic and easy-to-use quantitative method is applicable in other areas in biology aimed at understanding the regulation of cellular responses.     

Integrating gene expression and metabolic profiles

Recent advances in high throughput technologies have generated an abundance of biological information, such as gene expression, protein-protein interaction, and metabolic data. These various types of data capture different aspects of the cellular response to environmental factors. Integrating data from different measurements enhances the ability of modeling frameworks to predict cellular function more accurately and can lead to a more coherent reconstruction of the underlying regulatory network structure. Different techniques, newly developed and borrowed, have been applied for the purpose of extracting this information from experimental data. In this study, we developed a framework to integrate metabolic and gene expression profiles for a hepatocellular system. Specifically, we applied genetic algorithm and partial least square analysis to identify important genes relevant to a specific cellular function. We identified genes 1) whose expression levels quantitatively predict a metabolic function and 2) that play a part in regulating a hepatocellular function and reconstructed their role in the metabolic network. The framework 1) preprocesses the gene expression data using statistical techniques, 2) selects genes using a genetic algorithm and couples them to a partial least squares analysis to predict cellular function, and 3) reconstructs, with the assistance of a literature search, the pathways that regulate cellular function, namely intracellular triglyceride and urea synthesis. This provides a framework for identifying cellular pathways that are active as a function of the environment and in turn helps to uncover the interplay between gene and metabolic networks.