Comparing cDNA and oligonucleotide array data: concordance of gene expression across platforms for the NCI-60 cancer cells

Microarray gene-expression profiles are generally validated one gene at a time by real-time RT-PCR. We describe here a different approach based on simultaneous mutual validation of large numbers of genes using two different expression-profiling platforms. The result described here for the NCI-60 cancer cell lines is a consensus set of genes that give similar profiles on spotted cDNA arrays and Affymetrix oligonucleotide chips. Global concordance is parameterized by a 'correlation of correlations' coefficient.     

Exploration,normalization, and summaries of high density oligonucleotide array probe level data

In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.     

Effect of cell-seeding density on the proliferation and gene expression profile of human umbilical vein endothelial cells within ex vivo culture

Background aimsCharacterization of endothelial cell–biomaterial interaction is crucial for the development of blood-contacting biomedical devices and implants. However, a crucial parameter that has largely been overlooked is the cell-seeding density.MethodsThis study investigated how varying cell-seeding density influences human umbilical vein endothelial cell (HUVEC) proliferation on three different substrata: gelatin, tissue culture polystyrene (TCPS) and poly-l-lactic acid (PLLA).ResultsThe fastest proliferation was seen on gelatin, followed by TCPS and PLLA, regardless of seeding density. On both TCPS and gelatin, maximal proliferation was attained at an initial seeding density of 1000 cells/cm². At seeding densities above and below 1000 cells/cm², the proliferation rate decreased sharply. On PLLA, there was a decrease in cell numbers over 7 days of culture, below a certain threshold seeding density (c. 2500–3000 cells/cm²), which meant that some of the cells were dying off rather than proliferating. Above this threshold seeding density, HUVEC displayed slow proliferation. Subsequently, quantitative real-time polymerase chain reaction (RT-qPCR) analysis of eight gene markers associated with adhesion and endothelial functionality (VEGF-A, integrin-α5, VWF, ICAM1, ICAM2, VE-cadherin, endoglin and PECAM1) was carried out on HUVEC seeded at varying densities on the three substrata. A significant downregulation of gene expression was observed at an ultralow cell-seeding density of 100 cells/cm². This was accompanied by an extremely slow proliferation rate, probably because of an acute lack of intercellular contacts and paracrine signaling.ConclusionHence, this study demonstrates that seeding density has a profound effect on the proliferation and gene expression profile of endothelial cells seeded on different biomaterial surfaces.     

Gene expression analysis following hypoxia-reoxygenation in rat gastric epithelial cells using a high-density oligonucleotide array

Recent investigations have demonstrated that the signaling of hypoxia-re-oxygenation is a major contributing pathway leading to gastric mucosal injury induced by stress, non-steroidal anti-inflammatory drugs, and Helicobacter pylori. The aim of the present study was to perform a gene expression analysis on the gastric mucosal cellular response to hypoxia-reoxygenation using a high-density oligonucleotide array. Cells were subjected to hypoxia with 95% N(2) and 5% CO(2) at 37 degrees C for 2 h. Reoxygenation was initiated by placing the cells in an environment of normoxia for 2 h. Total RNA was extracted, and differences in gene expression profiles between the normoxia and hypoxia-reoxygenation groups were investigated using a GeneChip of Rat Toxicology U34 array (Affymetrix). Hypoxia-reoxygenation up-regulated the stress-related genes (heat shock protein-70 [HSP-70], catalase). The enhanced expression of HSP-70 was confirmed by Western blot analysis. In conclusion, these results suggest that up-regulation of the HSP-70 gene after reoxygenation may play a role in maintaining cell survival and supporting cell function as a molecular chaperone.     

A comparison of normalization methods for high density oligonucleotide array data based on variance and bias

MOTIVATION: When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations. RESULTS: We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably. AVAILABILITY: Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html     

A high performance test of differential gene expression for oligonucleotide arrays

Logit-t employs a logit-transformation for normalization followed by statistical testing at the probe-level. Using four publicly-available datasets, together providing 2,710 known positive incidences of differential expression and 2,913,813 known negative incidences, performance of statistical tests were: Logit-t provided 75% positive-predictive value, compared with 5% for Affymetrix Microarray Suite 5, 6% for dChip perfect match (PM)-only, and 9% for Robust Multi-array Analysis at the p < 0.01 threshold. Logit-t provided 70% sensitivity, Microarray Suite 5 provided 46%, dChip provided 53% and Robust Multi-array Analysis provided 63%.     

Cell behavior observation and gene expression analysis of melanoma associated with stromal fibroblasts in a three‐dimensional magnetic cell culture array

A three‐dimensional (3D) multicellular tumor spheroid culture array has been fabricated using a magnetic force‐based cell patterning method, analyzing the effect of stromal fibroblast on the invasive capacity of melanoma. Formation of spheroids was observed when array‐like multicellular patterns of melanoma were developed using a pin‐holder device made of magnetic soft iron and an external magnet, which enables the assembly of the magnetically labeled cells on the collagen gel‐coated surface as array‐like cell patterns. The interaction of fibroblast on the invasion of melanoma was investigated using three types of cell interaction models: (i) fibroblasts were magnetically labeled and patterned together in array with melanoma spheroids (direct‐interaction model), (ii) fibroblasts coexisting in the upper collagen gel (indirect‐interaction model) of melanoma spheroids, and (iii) fibroblast‐sheets coexisting under melanoma spheroids (fibroblast‐sheet model). The fibroblast‐sheet model has largely increased the invasive capacity of melanoma, and the promotion of adhesion, migration, and invasion were also observed. In the fibroblast‐sheet model, the expression of IL‐8 and MMP‐2 increased by 24‐fold and 2‐fold, respectively, in real time RT‐PCR compared to the absence of fibroblasts. The results presented in this study demonstrate the importance of fibroblast interaction to invasive capacity of melanoma in the 3D in vitro bioengineered tumor microenvironment. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2013     

Performance assessment of kernel density clustering for gene expression profile data

Kernel density smoothing techniques have been used in classification or supervised learning of gene expression profile (GEP) data, but their applications to clustering or unsupervised learning of those data have not been explored and assessed. Here we report a kernel density clustering method for analysing GEP data and compare its performance with the three most widely-used clustering methods: hierarchical clustering, K-means clustering, and multivariate mixture model-based clustering. Using several methods to measure agreement, between-cluster isolation, and withincluster coherence, such as the Adjusted Rand Index, the Pseudo F test, the r(2) test, and the profile plot, we have assessed the effectiveness of kernel density clustering for recovering clusters, and its robustness against noise on clustering both simulated and real GEP data. Our results show that the kernel density clustering method has excellent performance in recovering clusters from simulated data and in grouping large real expression profile data sets into compact and well-isolated clusters, and that it is the most robust clustering method for analysing noisy expression profile data compared to the other three methods assessed.     

Cell culture density affects the proliferation activity of human adipose tissue stem cells

In this study, we investigated the effect of cell density on the proliferation activity of human mesenchymal stem cells (MSCs) derived from adipose tissue (AT‐MSCs) over time in culture. Passage #4 (P4) and #12 (P12) AT‐MSCs from two donors were plated at a density of 200 (culture condition 1, CC1) or 5000 (culture condition 2, CC2) cells cm^?2. After 7 days of incubation, P4 and P12 AT‐MSCs cultured in CC1 were thin and spindle‐shaped, whereas those cultured in CC2 had extensive cell‐to‐cell contacts and an expanded cell volume. In addition, P4 and P12 AT‐MSCs in CC1 divided more than three times, while those in CC2 divided less than once on average. Flow cytometric analysis using 5(6)‐carboxyfluorescein diacetate N‐succinimidyl ester dye showed that the fluorescence intensity of AT‐MSCs was lower in CC1 than in CC2. Furthermore, expression of proliferation‐associated genes, such as CDC45L, CDC20A and KIF20A, in P4 AT‐MSCs was higher in CC1 than in CC2, and this difference was also observed in P12 AT‐MSCs. These data demonstrated that cell culture density affects the proliferation activity of MSCs, suggesting that it is feasible to design a strategy to prepare suitable MSCs using specific culture conditions. Copyright © 2016 John Wiley & Sons, Ltd.     

Decay of hormone responsiveness in mouse melanoma cells in culture as a function of cell density

Cloudman S91 mouse melanoma cells lose their ability to demonstrate an MSH-induced increase in tyrosinase activity as cell density increases. This loss in hormone responsiveness occurs before confluency is reached and cannot be reversed by exposure of cells to increasing concentrations of MSH. The failure of high-density cultures to respond to MSH is apparently not the result of an inability of MSH to stimulate cAMP production, since either low- or high-density cultures exposed to MSH demonstrate equivalent increases in intracellular levels of cAMP. Further, neither theophylline (1mM), dibutyryl cyclic AMP (10(-4)M), or prostaglandin E1 (10(-6)M) is effective in stimulating tyrosinase activity in melanoma cells cultured at densities exceeding 6 X 10(4) cells/cm2. This finding suggests that the decay of hormone responsiveness occurs at a cellular site distal to cAMP production. The decrease in tyrosinase stimulation by MSH as cell density increases is also apparently not the result of an increase in activity of any soluble inhibitor of the enzyme, for cytosol preparations from high-density cultures (10(5) cells/cm2) fail to inhibit tyrosinase activity in cell homogenates from low-density cultures treated with MSH.     

Sonic vibration affects the metabolism of yeast cells growing in liquid culture: a metabolomic study

It is undeniable that music and sounds can affect our emotions and mood, but so far the study of physical stimuli provoked by sound on living organisms has been mostly focused on brain and sensorimotor structures rather than cellular metabolism. Using metabolomics, we compared the physiology of yeast cells growing in defined liquid medium exposed to music, high and low frequency sonic vibration and silence. All sonic stimuli tested not only increased the growth rate of the yeast cells by 12% but they also reduced biomass production by 14%. The intra- and extracellular metabolite profiles differed significantly depending on the sonic stimulus applied showing that different metabolic pathways are affected differently by different sound frequency. Therefore, our results clearly demonstrate that sound does affect microbial cell metabolism when growing in liquid culture, opening an entirely new perspective for scientific investigation interfacing acoustics, biophysics and biochemistry.