Purity and enrichment of laser-microdissected midbrain dopamine neurons

The ability to microdissect individual cells from the nervous system has enormous potential, as it can allow for the study of gene expression in phenotypically identified cells. However, if the resultant gene expression profiles are to be accurately ascribed, it is necessary to determine the extent of contamination by nontarget cells in the microdissected sample. Here, we show that midbrain dopamine neurons can be laser-microdissected to a high degree of enrichment and purity. The average enrichment for tyrosine hydroxylase (TH) gene expression in the microdissected sample relative to midbrain sections was approximately 200-fold. For the dopamine transporter (DAT) and the vesicular monoamine transporter type 2 (Vmat2), average enrichments were approximately 100- and 60-fold, respectively. Glutamic acid decarboxylase (Gad65) expression, a marker for GABAergic neurons, was several hundredfold lower than dopamine neuron-specific genes. Glial cell and glutamatergic neuron gene expression were not detected in microdissected samples. Additionally, SN and VTA dopamine neurons had significantly different expression levels of dopamine neuron-specific genes, which likely reflects functional differences between the two cell groups. This study demonstrates that it is possible to laser-microdissect dopamine neurons to a high degree of cell purity. Therefore gene expression profiles can be precisely attributed to the targeted microdissected cells.

Hidden Markov model neurons classification based on Mel-frequency cepstral coefficients

In neuroscience, the extracellular actions potentials of neurons are the most important signals, which are called spikes. However, a single extracellular electrode can capture spikes from more than one neuron. Spike sorting is an important task to diagnose various neural activities. The more we can understand neurons the more we can cure more neural diseases. The process of sorting these spikes is typically made in some steps which are detection, feature extraction and clustering. In this paper we propose to use the Mel-frequency cepstral coefficients (MFCC) to extract spike features associated with Hidden Markov model (HMM) in the clustering step. Our results show that using MFCC features can differentiate between spikes more clearly than the other feature extraction methods, and also using HMM as a clustering algorithm also yields a better sorting accuracy.

Neuron’s spikes noise level classification using hidden markov models

Considering that the uncertainty noise produced the decline in the quality of collected neural signal, this paper proposes a signal quality assessment method for neural signal. The method makes an automated measure to detect the noise levels in neural signal. Hidden Markov Models were used to build a classification model that classifies the neural spikes based on the noise level associated with the signal. This neural quality assessment measure will help doctors and researchers to focus on the patterns in the signal that have high signal to noise ratio and carry more information.

Oral treatment with Cu(II)(atsm) increases mutant SOD1 in vivo but protects motor neurons and improves the phenotype of a transgenic mouse model of amyotrophic lateral sclerosis

Mutations in the metallo-protein Cu/Zn-superoxide dismutase (SOD1) cause amyotrophic lateral sclerosis (ALS) in humans and an expression level-dependent phenotype in transgenic rodents. We show that oral treatment with the therapeutic agent diacetyl-bis(4-methylthiosemicarbazonato)copper(II) [Cu(II)(atsm)] increased the concentration of mutant SOD1 (SOD1G37R) in ALS model mice, but paradoxically improved locomotor function and survival of the mice. To determine why the mice with increased levels of mutant SOD1 had an improved phenotype, we analyzed tissues by mass spectrometry. These analyses revealed most SOD1 in the spinal cord tissue of the SOD1G37R mice was Cu deficient. Treating with Cu(II)(atsm) decreased the pool of Cu-deficient SOD1 and increased the pool of fully metallated (holo) SOD1. Tracking isotopically enriched (65)Cu(II)(atsm) confirmed the increase in holo-SOD1 involved transfer of Cu from Cu(II)(atsm) to SOD1, suggesting the improved locomotor function and survival of the Cu(II)(atsm)-treated SOD1G37R mice involved, at least in part, the ability of the compound to improve the Cu content of the mutant SOD1. This was supported by improved survival of SOD1G37R mice that expressed the human gene for the Cu uptake protein CTR1. Improving the metal content of mutant SOD1 in vivo with Cu(II)(atsm) did not decrease levels of misfolded SOD1. These outcomes indicate the metal content of SOD1 may be a greater determinant of the toxicity of the protein in mutant SOD1-associated forms of ALS than the mutations themselves. Improving the metal content of SOD1 therefore represents a valid therapeutic strategy for treating ALS caused by SOD1.

Chaotic synchronization of time-delay coupled Hindmarsh–Rose neurons via nonlinear control

Chaotic synchronization of two time-delay coupled Hindmarsh–Rose neurons via nonlinear control is investigated in this paper. Both the intrinsic slow current delay in a single Hindmarsh–Rose neuron and the coupling delay between the two neurons are considered. When there is no control, chaotic synchronization occurs for a limited range of the coupling strength and the time-delay values. To obtain complete chaotic synchronization irrespective of the time-delay or the coupling strength, we propose two nonlinear control schemes. The first uses adaptive control for chaotic synchronization of two electrically coupled delayed Hindmarsh–Rose neuron models. The second derives the sufficient conditions to ensure a complete synchronization between master and slave models through appropriate Lyapunov–Krasovskii functionals and the linear matrix inequality technique. Numerical simulations are carried out to show the effectiveness of the proposed methods.