Latest developments in the microarray analysis of human brain mRNA

Microarrays are used to monitor the expression of thousands of gene transcripts. This technique requires high-quality RNA, which can be extracted from a variety sources, including autopsy brain tissue. Most nucleic acids and proteins are reasonably stable post mortem. However, their abundance and integrity can exhibit marked intraand inter-subject variability, so care must be taken when comparisons between case-groups are made. We will review issues associated with the sampling of RNA from autopsy brain tissue in relation to various ante- and post-mortem factors.

Microarray analysis of expression of cell death-associated genes in rat spinal cord cells exposed to cyclic tensile stresses in vitro

The application of mechanical insults to the spinal cord results in profound cellular and molecular changes, including the induction of neuronal cell death and altered gene expression profiles. Previous studies have described alterations in gene expression following spinal cord injury, but the specificity of this response to mechanical stimuli is difficult to investigate in vivo. Therefore, we have investigated the effect of cyclic tensile stresses on cultured spinal cord cells from E15 Sprague-Dawley rats, using the FX3000 Flexercell Strain Unit. We examined cell morphology and viability over a 72 hour time course. Microarray analysis of gene expression was performed using the Affymetrix GeneChip System, where categorization of identified genes was performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) systems. Changes in expression of 12 genes were validated with quantitative real-time reverse transcription polymerase chain reaction (RT-PCR).

Nonlinearity in all-night sleep EEG recorded with foramen ovale electrodes in a patient with temporal lobe epilepsy

Objective: It is investigated to which extent measures of nonlinearity derived from surrogate data analysis are capable to quantify the changes of epileptic activity related to varying vigilance levels. Methods: Surface and intracranial EEG from foramen ovale (FO-)electrodes was recorded from a patient with temporal lobe epilepsy under presurgical evaluation over one night. Different measures of nonlinearity were estimated for non-overlapping 30-s segments for selected channels from surface and intracranial EEG. Additionally spectral measures were calculated. Sleep stages were scored according to Rechtschaffen/Kales and epileptic transients were counted and classified by visual inspection. Results: In the intracranial recordings stronger nonlinearity was found ipsilateral to the epileptogenic focus, more pronounced in NREM sleep, weaker in REM sleep. The dynamics within the NREM episodes varied with the different nonlinearity measures. Some nonlinearity measures showed variations with the sleep cycle also in the intracranial recordings contralateral to the epileptic focus and in the surface EEG. It is shown that the nonlinearity is correlated with short-term fluctuations of the delta power. The higher frequency of occurrence of clinical relevant epileptic spikes in the first NREM episode was not clearly reflected in the nonlinearity measures. Conclusions: It was confirmed that epileptic activity renders the EEG nonlinear. However, it was shown that the sleep dynamics itself also effects the nonlinearity measures. Therefore, at the present stage it is not possible to establish a unique connection between the studied nonlinearity measures and specific types of epileptic activity in sleep EEG recordings.

MILVA:An interactive tool for the exploration of multidimensional microarray data

Clustering techniques such as k-means and hierarchical clustering are commonly used to analyze DNA microarray derived gene expression data. However, the interactions between processes underlying the cell activity suggest that the complexity of the microarray data structure may not be fully represented with discrete clustering methods.

Microarray-formatted clinical biomarker assay development using peptide aptamers to anterior gradient-2

Anterior gradient-2 protein was identified using proteomic technologies as a p53 inhibitor which is overexpressed in human cancers, and this protein presents a novel pro-oncogenic target with which to develop diagnostic assays for biomarker detection in clinical tissue. Combinatorial phage-peptide libraries were used to select 12 amino acid polypeptide aptamers toward anterior gradient-2 to determine whether methods can be developed to affinity purify the protein from clinical biopsies. Selecting phage aptamers through four rounds of screening on recombinant human anterior gradient-2 protein identified two classes of peptide ligand that bind to distinct epitopes on anterior gradient-2 protein in an immunoblot. Synthetic biotinylated peptide aptamers bound in an ELISA format to anterior gradient-2, and substitution mutagenesis further minimized one polypeptide aptamer to a hexapeptide core. Aptamers containing this latter consensus sequence could be used to affinity purify to homogeneity human anterior gradient-2 protein from a single clinical biopsy. The spotting of a panel of peptide aptamers onto a protein microarray matrix could be used to quantify anterior gradient-2 protein from crude clinical biopsy lysates, providing a format for quantitative screening. These data highlight the utility of peptide combinatorial libraries to acquire rapidly a high-affinity ligand that can selectively bind a target protein from a clinical biopsy and provide a technological approach for clinical biomarker assay development in an aptamer microarray format.

Microarray analysis of expression of cell death-associated genes in spinal cord cells with cyclic tensile strain

Previous studies have described alterations in gene expression following spinal cord injury, but this response to mechanical stimuli is difficult to investigate in vivo. Therefore, we have investigated the effect of cyclic tensile strain on cultured spinal cord cells from E15 Sprague-Dawley rats. Microarray analysis of gene expression and categorization of identified genes were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) systems. The application of cyclic tensile strain reduced the viability of cultured spinal cord cells significantly in a dose- and time-dependent manner. GO analysis identified candidate genes related to apoptosis (44) and to response to stimulus (17). KEGG analysis identified changes in the expression levels of 12 genes of the mitogen-activated protein kinase (MAPK) signaling pathway, which were confirmed to be upregulated and validated by RT-PCR analysis. Spinal cord cells undergo cell death in response to cyclic tensile strain, which were dose- and time-dependent, with upregulation of various genes, in particular of the MAPK pathway.

Muscle movement and electrodes motion artifact during vibration treatment

Vibration treatment by oscillating platforms is more and more employed in the fields of exercise physiology and bone research. The rationale of this treatment is based on the neuromuscular system response elicited by vibration loads. surface Electromyography (EMG) is largely utilized to assess muscular response elicited by vibrations and Root Mean Square of the electromyography signals is often used as a concise quantitative index of muscle activity; in general, EMG envelope or RMS is expected to increase during vibration. However, it is well known that during surface bio-potential recording, motion artifacts may arise from relative motion between electrodes and skin and between skin layers. Also the only skin stretch, modifying the internal charge distribution, results in a variation of electrode potential. The aim of this study is to highlight the movements of muscles, and the succeeding relevance of motion artifacts on electrodes, in subjects undergoing vibration treatments. EMGs from quadriceps of fifteen subjects were recorded during vibration at different frequencies (15-40 Hz); Triaxial accelerometers were placed onto quadriceps, as close as possible to muscle belly, to monitor motion. The computed muscle belly displacements showed a peculiar behavior reflecting the mechanical properties of the structures involved. Motion artifact related to the impressed vibration have been recognized and related to movement of the soft tissues. In fact large artifacts are visible on EMGs and patellar electrodes recordings during vibration. Signals spectra also revealed sharp peaks corresponding to vibration frequency and its harmonics, in accordance with accelerometers data. © 2008 Springer-Verlag.

Nanodiamond converted hollow graphene spheres as electrodes for supercapacitors

A porous composite formed of hollow graphene spheres with opens in them and amorphous carbon containing nitrogen and oxygenated groups has been fabricated by annealing the mixture of nanodiamond and polyacrylonitrile (PAN). Electrochemical tests on the electrode made of this material show that it may be a promising electrode material for supercapacitors. The relatively high capacitance is mainly attributed to the small inner electrical resistance, the huge specific surface area and the remaining nitrogen and oxygenated groups from the PAN.

A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.