WIS‐neuromath enables versatile high throughput analyses of neuronal processes

Automated analyses of neuronal morphology are important for quantifying connectivity and circuitry in vivo, as well as in high content imaging of primary neuron cultures. The currently available tools for quantification of neuronal morphology either are highly expensive commercial packages or cannot provide automated image quantifications at single cell resolution. Here, we describe a new software package called WIS‐NeuroMath, which fills this gap and provides solutions for automated measurement of neuronal processes in both in vivo and in vitro preparations. Diverse image types can be analyzed without any preprocessing, enabling automated and accurate detection of neurites followed by their quantification in a number of application modules. A cell morphology module detects cell bodies and attached neurites, providing information on neurite length, number of branches, cell body area, and other parameters for each cell. A neurite length module provides a solution for images lacking cell bodies, such as tissue sections. Finally, a ganglion explant module quantifies outgrowth by identifying neurites at different distances from the ganglion. Quantification of a diverse series of preparations with WIS‐NeuroMath provided data that were well matched with parallel analyses of the same preparations in established software packages such as MetaXpress or NeuronJ. The capabilities of WIS‐NeuroMath are demonstrated in a range of applications, including in dissociated and explant cultures and histological analyses on thin and whole‐mount sections. WIS‐NeuroMath is freely available to academic users, providing a versatile and cost‐effective range of solutions for quantifying neurite growth, branching, regeneration, or degeneration under different experimental paradigms. © 2012 Wiley Periodicals, Inc. Develop Neurobiol, 2013     

Testosterone-mediated sex differences in the face shape during adolescence: subjective impressions and objective features

Sex identification of a face is essential for social cognition. Still, perceptual cues indicating the sex of a face, and mechanisms underlying their development, remain poorly understood. Previously, our group described objective age- and sex-related differences in faces of healthy male and female adolescents (12–18 years of age), as derived from magnetic resonance images (MRIs) of the adolescents' heads. In this study, we presented these adolescent faces to 60 female raters to determine which facial features most reliably predicted subjective sex identification. Identification accuracy correlated highly with specific MRI-derived facial features (e.g. broader forehead, chin, jaw, and nose). Facial features that most reliably cued male identity were associated with plasma levels of testosterone (above and beyond age). Perceptible sex differences in face shape are thus associated with specific facial features whose emergence may be, in part, driven by testosterone.     

Efficient calculation of interval scores for DNA copy number data analysis.

DNA amplifications and deletions characterize cancer genome and are often related to disease evolution. Microarray-based techniques for measuring these DNA copy-number changes use fluorescence ratios at arrayed DNA elements (BACs, cDNA, or oligonucleotides) to provide signals at high resolution, in terms of genomic locations. These data are then further analyzed to map aberrations and boundaries and identify biologically significant structures. We develop a statistical framework that enables the casting of several DNA copy number data analysis questions as optimization problems over real-valued vectors of signals. The simplest form of the optimization problem seeks to maximize phi(I) = Sigmanu(i)/radical|I| over all subintervals I in the input vector. We present and prove a linear time approximation scheme for this problem, namely, a process with time complexity O (nepsilon(-2)) that outputs an interval for which phi(I) is at least Opt/alpha(epsilon), where Opt is the actual optimum and alpha(epsilon) --> 1 as epsilon --> 0. We further develop practical implementations that improve the performance of the naive quadratic approach by orders of magnitude. We discuss properties of optimal intervals and how they apply to the algorithm performance. We benchmark our algorithms on synthetic as well as publicly available DNA copy number data. We demonstrate the use of these methods for identifying aberrations in single samples as well as common alterations in fixed sets and subsets of breast cancer samples.     

A CT-based high-order finite element analysis of the human proximal femur compared to in-vitro experiments

The prediction of patient-specific proximal femur mechanical response to various load conditions is of major clinical importance in orthopaedics. This paper presents a novel, empirically validated high-order finite element method (FEM) for simulating the bone response to loads. A model of the bone geometry was constructed from a quantitative computerized tomography (QCT) scan using smooth surfaces for both the cortical and trabecular regions. Inhomogeneous isotropic elastic properties were assigned to the finite element model using distinct continuous spatial fields for each region. The Young's modulus was represented as a continuous function computed by a least mean squares method. p-FEMs were used to bound the simulation numerical error and to quantify the modeling assumptions. We validated the FE results with in-vitro experiments on a fresh-frozen femur loaded by a quasi-static force of up to 1500 N at four different angles. We measured the vertical displacement and strains at various locations and investigated the sensitivity of the simulation. Good agreement was found for the displacements, and a fair agreement found in the measured strain in some of the locations. The presented study is a first step toward a reliable p-FEM simulation of human femurs based on QCT data for clinical computer aided decision making.     

The finite cell method for bone simulations: verification and validation

Standard methods for predicting bone’s mechanical response from quantitative computer tomography (qCT) scans are mainly based on classical h-version finite element methods (FEMs). Due to the low-order polynomial approximation, the need for segmentation and the simplified approach to assign a constant material property to each element in h-FE models, these often compromise the accuracy and efficiency of h-FE solutions. Herein, a non-standard method, the finite cell method (FCM), is proposed for predicting the mechanical response of the human femur. The FCM is free of the above limitations associated with h-FEMs and is orders of magnitude more efficient, allowing its use in the setting of computational steering. This non-standard method applies a fictitious domain approach to simplify the modeling of a complex bone geometry obtained directly from a qCT scan and takes into consideration easily the heterogeneous material distribution of the various bone regions of the femur. The fundamental principles and properties of the FCM are briefly described in relation to bone analysis, providing a theoretical basis for the comparison with the p-FEM as a reference analysis and simulation method of high quality. Both p-FEM and FCM results are validated by comparison with an in vitro experiment on a fresh-frozen femur.     

The membrane proximal external region of the HIV-1 envelope glycoprotein gp41 contributes to the stabilization of the six-helix bundle formed with a matching N' peptide

The HIV-1 envelope glycoprotein gp41 undergoes a sequence of extensive conformational changes while participating in the fusion of the virus with the host cell. Since the discovery of its postfusion conformation, the structure and function of the protease-resistant six-helix bundle (6-HB) have been the subject of extensive investigation. In this work, we describe additional determinants (S528-Q540 and W666-N677) in the fusion peptide proximal region (FP-PR) and the membrane proximal external region (MPER) that stabilize the six-helix bundle and are involved in the interaction of T-20 (FUZEON, an anti-HIV-1 fusion inhibitor drug) with the gp41 FP-PR. Circular dichroism and sedimentation equilibrium measurements indicate that the 1:1 mixture of N' and C' peptides comprising residues A541-T569 and I635-K665 from the gp41 first and second helical repeats, HR1 and HR2, respectively, fail to form a stable six-helix bundle. Triglutamic acid and triarginine tags were added to these N' and C' peptides, respectively, at the termini distant from the FP-PR and the MPER to alter their pI and increase their solubility at pH 3.5. The tagged HR1 and HR2 peptides were elongated by addition of residues S528-Q540 from the FP-PR and residues W666-N677 from the MPER, respectively. A 1:1 complex of the elongated peptides formed a stable six-helix bundle which melted at 60 degrees C. These results underscore the importance of a detailed high-resolution characterization of MPER interactions, the results of which may improve our understanding of the structure-function relationship of gp41 and its role in HIV-1 fusion.     

Cutting edge: MHC class I-Ly49 interaction regulates neuronal function

MHC class I molecules (MHC-I) have been implicated in nervous system development in the mouse. In this study we present evidence for the interaction of MHC-I with the NK cell receptor Ly49 in primary cortical neuronal cultures. We show that MHC-I and Ly49 are expressed on neuronal soma and axon surfaces, with Ly49 also present on dendrites. Anti-MHC-I Abs reduce synapsin-I expression and enhance neurite outgrowth and neuronal death. Conversely, anti-Ly49 mAbs increase synapsin-I expression, reduce neurite outgrowth, and promote neuron viability. Because we show that Ly49 genes are selectively expressed in the adult brain, these findings suggest an unsuspected role for the MHC-I-Ly49 interaction in the development and function of the brain.     

Semaphorin 3A and neurotrophins: a balance between apoptosis and survival signaling in embryonic DRG neurons

Large numbers of neurons are eliminated by apoptosis during nervous system development. For instance, in the mouse dorsal root ganglion (DRG), the highest incidence of cell death occurs between embryonic days 12 and 14 (E12-E14). While the cause of cell death and its biological significance in the nervous system is not entirely understood, it is generally believed that limiting quantities of neurotrophins are responsible for neuronal death. Between E12 and E14, developing DRG neurons pass through tissues expressing high levels of axonal guidance molecules such as Semaphorin 3A (Sema3A) while navigating to their targets. Here, we demonstrate that Sema3A acts as a death-inducing molecule in neurotrophin-3 (NT-3)-, brain-derived neurotrophic factor (BDNF)- and nerve growth factor (NGF)-dependent E12 and E13 cultured DRG neurons. We show that Sema3A most probably induces cell death through activation of the c-Jun N-terminal kinase (JNK)/c-Jun signaling pathway, and that this cell death is blocked by a moderate increase in NGF concentration. Interestingly, increasing concentrations of other neurotrophic factors, such as NT-3 or BDNF, do not elicit similar effects. Our data suggest that the number of DRG neurons is determined by a fine balance between neurotrophins and Semaphorin 3A, and not only by neurotrophin levels.