Slow Relaxation of Magnetization in an Isostructural Series of Zinc-Lanthanide Complexes: An Integrated EPR and AC Susceptibility Study.

We report the synthesis, structure, and spectroscopic and dynamic magnetic properties of a series of heterodinuclear complexes, [ZnLn(LH4 )2 ](NO3 )3 ⋅6 H2 O (Ln=Nd, Tb, Dy, Ho, Er, and Yb), with the singly deprotonated form of a new compartmentalized Schiff-base ligand, LH5 . The Ln(III) ions in these systems show a distorted square-antiprism geometry with an LnO8 coordination sphere. EPR spectroscopy and DC magnetic studies have shown that the anisotropic nature of the complexes is far more complicated than predicted on the basis of a simple electrostatic model. Among the investigated systems, only the Dy(III) derivative showed single-ion magnet behavior, in zero and an applied magnetic field, both in pure polycrystalline samples and in a series of polycrystalline samples with different degrees of dilution at the single-crystal level in the isostructural Y(III) derivative. The rich dynamics observed as functions of frequency, field, and temperature reveals that multiple relaxation mechanisms are at play, resulting in a barrier of 189 cm(-1) , which is among the highest reported for a dinuclear Zn-Dy system. Analysis of the dynamic behavior as a function of dilution degree further evidenced the persistence of non-negligible intermolecular interactions, even at the lowest concentration of 1 %.

Water-stable manganese(IV) complex of a N2O4-donor non-Schiff-base ligand: synthesis, structure, and multifrequency high-field electron paramagnetic resonance studies.

A novel water-stable (t1/2 ∼ 6.8 days) mononuclear manganese(IV) complex of a hexacoordinating non-Schiff-base ligand (H4L) with N2O4-donor atoms has been synthesized and characterized crystallographically. High-frequency electron paramagnetic resonance experiments performed on a single crystal reveal a manganese(IV) ion with an S = 3/2 ground spin state that displays a large single-ion anisotropy, setting the record of mononuclear manganese(IV) complexes reported so far. In addition, spin-echo experiments reveal a spin-spin relaxation time T2 ∼ 500 ns.

Deep learning-based automatic contour quality assurance for auto-segmented abdominal MR-Linac contours.

OBJECTIVE: Deep-learning auto-segmentation (DLAS) aims to streamline contouring in clinical settings. Nevertheless, achieving clinical acceptance of DLAS remains a hurdle in abdominal MRI, hindering the implementation of efficient clinical workflows for MR-guided online adaptive radiotherapy (MRgOART). Integrating automated contour quality assurance (ACQA) with automatic contour correction (ACC) techniques could optimize the performance of ACC by concentrating on inaccurate contours. Furthermore, ACQA can facilitate the contour selection process from various DLAS tools and/or deformable contour propagation from a prior treatment session. Here, we present the performance of novel DL-based 3D ACQA models for evaluating DLAS contours acquired during MRgOART. Approach. The ACQA model, based on a 3D convolutional neural network (CNN), was trained using pancreas and duodenum contours obtained from a research DLAS tool on abdominal MRIs acquired from a 1.5T MR-Linac. The training dataset contained abdominal MR images, DL contours, and their corresponding quality ratings, from 103 datasets. The quality of DLAS contours was determined using an in-house contour classification tool, which categorizes contours as acceptable or edit-required based on the expected editing effort. The performance of the 3D ACQA model was evaluated using an independent dataset of 34 abdominal MRIs, utilizing confusion matrices for true and predicted classes. Main results. The ACQA predicted 'acceptable' and 'edit-required' contours at 72.2% (91/136) and 83.6% (726/868) accuracy for pancreas, and at 71.2% (79/111) and 89.6% (772/862) for duodenum contours, respectively. The model successfully identified false positive (extra) and false negative (missing) DLAS contours at 93.75% (15/16) and %99.7 (438/439) accuracy for pancreas, and at 95% (57/60) and 98.9% (91/99) for duodenum, respectively. Significance. We developed 3D-ACQA models capable of quickly evaluating the quality of DLAS pancreas and duodenum contours on abdominal MRI. These models can be integrated into clinical workflow, facilitating efficient and consistent contour evaluation process in MRgOART for abdominal malignancies. .

Comprehensive Clinical Usability-Oriented Contour Quality Evaluation for Deep Learning Auto-segmentation: Combining Multiple Quantitative Metrics Through Machine Learning.

PURPOSE: The current commonly used metrics for evaluating the quality of auto-segmented contours have limitations and do not always reflect the clinical usefulness of the contours. This work aims to develop a novel contour quality classification (CQC) method by combining multiple quantitative metrics for clinical usability-oriented contour quality evaluation for deep learning-based auto-segmentation (DLAS). METHODS AND MATERIALS: The CQC was designed to categorize contours on slices as acceptable, minor edit, or major edit based on the expected editing effort/time with supervised ensemble tree classification models using 7 quantitative metrics. Organ-specific models were trained for 5 abdominal organs (pancreas, duodenum, stomach, small, and large bowels) using 50 magnetic resonance imaging (MRI) data sets. Twenty additional MRI and 9 computed tomography (CT) data sets were employed for testing. Interobserver variation (IOV) was assessed among 6 observers and consensus labels were established through majority vote for evaluation. The CQC was also compared with a threshold-based baseline approach. RESULTS: For the 5 organs, the average area under the curve was 0.982 ± 0.01 and 0.979 ± 0.01, the mean accuracy was 95.8% ± 1.7% and 94.3% ± 2.1%, and the mean risk rate was 0.8% ± 0.4% and 0.7% ± 0.5% for MRI and CT testing data set, respectively. The CQC results closely matched the IOV results (mean accuracy of 94.2% ± 0.8% and 94.8% ± 1.7%) and were significantly higher than those obtained using the threshold-based method (mean accuracy of 80.0% ± 4.7%, 83.8% ± 5.2%, and 77.3% ± 6.6% using 1, 2, and 3 metrics). CONCLUSIONS: The CQC models demonstrated high performance in classifying the quality of contour slices. This method can address the limitations of existing metrics and offers an intuitive and comprehensive solution for clinically oriented evaluation and comparison of DLAS systems.

Deep learning based automatic contour refinement for inaccurate auto-segmentation in MR-guided adaptive radiotherapy.

Objective.Fast and accurate auto-segmentation is essential for magnetic resonance-guided adaptive radiation therapy (MRgART). Deep learning auto-segmentation (DLAS) is not always clinically acceptable, particularly for complex abdominal organs. We previously reported an automatic contour refinement (ACR) solution of using an active contour model (ACM) to partially correct the DLAS contours. This study aims to develop a DL-based ACR model to work in conjunction with ACM-ACR to further improve the contour accuracy.Approach.The DL-ACR model was trained and tested using bowel contours created by an in-house DLAS system from 160 MR sets (76 from MR-simulation and 84 from MR-Linac). The contours were classified into acceptable, minor-error and major-error groups using two approaches of contour quality classification (CQC), based on the AAPM TG-132 recommendation and an in-house classification model, respectively. For the major-error group, DL-ACR was applied subsequently after ACM-ACR to further refine the contours. For the minor-error group, contours were directly corrected by DL-ACR without applying an initial ACM-ACR. The ACR workflow was performed separately for the two CQC methods and was evaluated using contours from 25 image sets as independent testing data.Main results.The best ACR performance was observed in the MR-simulation testing set using CQC by TG-132: (1) for the major-error group, 44% (177/401) were improved to minor-error group and 5% (22/401) became acceptable by applying ACM-ACR; among these 177 contours that shifted from major-error to minor-error with ACM-ACR, DL-ACR further refined 49% (87/177) to acceptable; and overall, 36% (145/401) were improved to minor-error contours, and 30% (119/401) became acceptable after sequentially applying ACM-ACR and DL-ACR; (2) for the minor-error group, 43% (320/750) were improved to acceptable contours using DL-ACR.Significance.The obtained ACR workflow substantially improves the accuracy of DLAS bowel contours, minimizing the manual editing time and accelerating the segmentation process of MRgART.

Deep learning auto-segmentation on multi-sequence magnetic resonance images for upper abdominal organs.

Introduction: Multi-sequence multi-parameter MRIs are often used to define targets and/or organs at risk (OAR) in radiation therapy (RT) planning. Deep learning has so far focused on developing auto-segmentation models based on a single MRI sequence. The purpose of this work is to develop a multi-sequence deep learning based auto-segmentation (mS-DLAS) based on multi-sequence abdominal MRIs. Materials and methods: Using a previously developed 3DResUnet network, a mS-DLAS model using 4 T1 and T2 weighted MRI acquired during routine RT simulation for 71 cases with abdominal tumors was trained and tested. Strategies including data pre-processing, Z-normalization approach, and data augmentation were employed. Additional 2 sequence specific T1 weighted (T1-M) and T2 weighted (T2-M) models were trained to evaluate performance of sequence-specific DLAS. Performance of all models was quantitatively evaluated using 6 surface and volumetric accuracy metrics. Results: The developed DLAS models were able to generate reasonable contours of 12 upper abdomen organs within 21 seconds for each testing case. The 3D average values of dice similarity coefficient (DSC), mean distance to agreement (MDA mm), 95 percentile Hausdorff distance (HD95% mm), percent volume difference (PVD), surface DSC (sDSC), and relative added path length (rAPL mm/cc) over all organs were 0.87, 1.79, 7.43, -8.95, 0.82, and 12.25, respectively, for mS-DLAS model. Collectively, 71% of the auto-segmented contours by the three models had relatively high quality. Additionally, the obtained mS-DLAS successfully segmented 9 out of 16 MRI sequences that were not used in the model training. Conclusion: We have developed an MRI-based mS-DLAS model for auto-segmenting of upper abdominal organs on MRI. Multi-sequence segmentation is desirable in routine clinical practice of RT for accurate organ and target delineation, particularly for abdominal tumors. Our work will act as a stepping stone for acquiring fast and accurate segmentation on multi-contrast MRI and make way for MR only guided radiation therapy.

Automatic Contour Refinement for Deep Learning Auto-segmentation of Complex Organs in MRI-guided Adaptive Radiation Therapy.

Purpose: Fast and accurate auto-segmentation on daily images is essential for magnetic resonance imaging (MRI)-guided adaptive radiation therapy (ART). However, the state-of-the-art auto-segmentation based on deep learning still has limited success, particularly for complex structures in the abdomen. This study aimed to develop an automatic contour refinement (ACR) process to quickly correct for unacceptable auto-segmented contours. Methods and Materials: An improved level set-based active contour model (ACM) was implemented for the ACR process and was tested on the deep learning-based auto-segmentation of 80 abdominal MRI sets along with their ground truth contours. The performance of the ACR process was evaluated using 4 contour accuracy metrics: the Dice similarity coefficient (DSC), mean distance to agreement (MDA), surface DSC, and added path length (APL) on the auto-segmented contours of the small bowel, large bowel, combined bowels, pancreas, duodenum, and stomach. Results: A portion (3%-39%) of the corrected contours became practically acceptable per the American Association of Physicists in Medicine Task Group 132 (TG-132) recommendation (DSC >0.8 and MDA <3 mm). The best correction performance was seen in the combined bowels, where for the contours with major errors (initial DSC <0.5 or MDA >8 mm), the mean DSC increased from 0.34 to 0.59, the mean MDA decreased from 7.02 mm to 5.23 mm, and the APL reduced by almost 20 mm, whereas for the contours with minor errors, the mean DSC increased from 0.72 to 0.79, the mean MDA decreased from 3.35 mm to 3.29 mm, and more than one-third (39%) of the ACR contours became clinically acceptable. The execution time for the ACR process on one subregion was less than 2 seconds using an NVIDIA GTX 1060 GPU. Conclusions: The ACR process implemented based on the ACM was able to quickly correct for some inaccurate contours produced from MRI-based deep learning auto-segmentation of complex abdominal anatomy. The ACR method may be integrated into the auto-segmentation step to accelerate the process of MRI-guided ART.

Multi-parametric magnetic resonance imaging for radiation treatment planning.

In recent years, multi-parametric magnetic resonance imaging (MpMRI) has played a major role in radiation therapy treatment planning. The superior soft tissue contrast, functional or physiological imaging capabilities, and the flexibility of site-specific image sequence development has placed MpMRI at the forefront. In this article, the present status of MpMRI for external beam radiation therapy planning is reviewed. Common MpMRI sequences, preprocessing, and quality assurance strategies are briefly discussed, and various image registration techniques and strategies are addressed. Image segmentation methods including automatic segmentation and deep learning techniques for organs at risk and target delineation are reviewed. Due to the advancement in MRI-guided online adaptive radiotherapy, treatment planning considerations addressing MRI only planning are also discussed.

General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis.

PURPOSE: To reduce workload and inconsistencies in organ segmentation for radiation treatment planning, we developed and evaluated general and custom autosegmentation models on computed tomography (CT) for three major tumor sites using a well-established deep convolutional neural network (DCNN). METHODS: Five CT-based autosegmentation models for 42 organs at risk (OARs) in head and neck (HN), abdomen (ABD), and male pelvis (MP) were developed using a full three-dimensional (3D) DCNN architecture. Two types of deep learning (DL) models were separately trained using either general diversified multi-institutional datasets or custom well-controlled single-institution datasets. To improve segmentation accuracy, an adaptive spatial resolution approach for small and/or narrow OARs and a pseudo scan extension approach, when CT scan length is too short to cover entire organs, were implemented. The performance of the obtained models was evaluated based on accuracy and clinical applicability of the autosegmented contours using qualitative visual inspection and quantitative calculation of dice similarity coefficient (DSC), mean distance to agreement (MDA), and time efficiency. RESULTS: The five DL autosegmentation models developed for the three anatomical sites were found to have high accuracy (DSC ranging from 0.8 to 0.98) for 74% OARs and marginally acceptable for 26% OARs. The custom models performed slightly better than the general models, even with smaller custom datasets used for the custom model training. The organ-based approaches improved autosegmentation accuracy for small or complex organs (e.g., eye lens, optic nerves, inner ears, and bowels). Compared with traditional manual contouring times, the autosegmentation times, including subsequent manual editing, if necessary, were substantially reduced by 88% for MP, 80% for HN, and 65% for ABD models. CONCLUSIONS: The obtained autosegmentation models, incorporating organ-based approaches, were found to be effective and accurate for most OARs in the male pelvis, head and neck, and abdomen. We have demonstrated that our multianatomical DL autosegmentation models are clinically useful for radiation treatment planning.

A Prior Knowledge-Guided, Deep Learning-Based Semiautomatic Segmentation for Complex Anatomy on Magnetic Resonance Imaging.

PURPOSE: Despite recent substantial improvement in autosegmentation using deep learning (DL) methods, labor-intensive and time-consuming slice-by-slice manual editing is often needed, particularly for complex anatomy (eg, abdominal organs). This work aimed to develop a fast, prior knowledge-guided DL semiautomatic segmentation (DL-SAS) method for complex structures on abdominal magnetic resonance imaging (MRI) scans. METHODS AND MATERIALS: A novel application using contours on an adjacent slice as a prior knowledge informant in a 2-dimensional UNet DL model to guide autosegmentation for a subsequent slice was implemented for DL-SAS. A generalized, instead of organ-specific, DL-SAS model was trained and tested for abdominal organs on T2-weighted MRI scans collected from 75 patients (65 for training and 10 for testing). The DL-SAS model performance was compared with 3 common autocontouring methods (linear interpolation, rigid propagation, and a full 3-dimensional DL autosegmentation model trained with the same training data set) based on various quantitative metrics including the Dice similarity coefficient (DSC) and ratio of acceptable slices (ROA) using paired t tests. RESULTS: For the 10 testing cases, the DL-SAS model performed best with the slice interval (SI) of 1, resulting in an average DSC of 0.93 ± 0.02, 0.92 ± 0.02, 0.91 ± 0.02, 0.88 ± 0.03, and 0.87 ± 0.02 for the large bowel, stomach, small bowel, duodenum, and pancreas, respectively. The performance decreased with increased SIs from the guidance slice. The DL-SAS method performed significantly better (P < .05) than the other 3 methods. The ROA values were in the range of 48% to 66% for all the organs with an SI of 1 for DL-SAS, higher than those for linear interpolation (31%-57% for an SI of 1) and DL auto-segmentation (16%-51%). CONCLUSIONS: The developed DL-SAS model segmented complex abdominal structures on MRI with high accuracy and efficiency and may be implemented as an interactive manual contouring tool or a contour editing tool in conjunction with a full autosegmentation process, facilitating fast and accurate segmentation for MRI-guided online adaptive radiation therapy.

Sequestration of CaMKII in dendritic spines in silico.

Calcium calmodulin dependent kinase II (CaMKII) is sequestered in dendritic spines within seconds upon synaptic stimulation. The program Smoldyn was used to develop scenarios of single molecule CaMKII diffusion and binding in virtual dendritic spines. We first validated simulation of diffusion as a function of spine morphology. Additional cellular structures were then incorporated to simulate binding of CaMKII to the post-synaptic density (PSD); binding to cytoskeleton; or their self-aggregation. The distributions of GFP tagged native and mutant constructs in dissociated hippocampal neurons were measured to guide quantitative analysis. Intra-spine viscosity was estimated from fluorescence recovery after photo-bleach (FRAP) of red fluorescent protein. Intra-spine mobility of the GFP-CaMKIIα constructs was measured, with hundred-millisecond or better time resolution, from FRAP of distal spine tips in conjunction with fluorescence loss (FLIP) from proximal regions. Different FRAP \ FLIP profiles were predicted from our Scenarios and provided a means to differentiate binding to the PSDs from self-aggregation. The predictions were validated by experiments. Simulated fits of the Scenarios provided estimates of binding and rate constants. We utilized these values to assess the role of self-aggregation during the initial response of native CaMKII holoenzymes to stimulation. The computations revealed that self-aggregation could provide a concentration-dependent switch to amplify CaMKII sequestration and regulate its activity depending on its occupancy of the actin cytoskeleton.

Brain-derived neurotrophic factor promotes immune reconstitution following radiation injury via activation of bone marrow mesenchymal stem cells.

Brain-derived neurotrophic factor (BDNF) is a member of the nerve growth factor family which has been extensively studied for its roles in neural development, long-term memory, brain injury, and neurodegenerative diseases. BDNF signaling through tropomyosin receptor kinase B (TrkB) stimulates neuronal cell survival. For this reason, small molecule TrkB agonists are under pre-clinical develoment for the treatment of a range of neurodegenerative diseases and injuries. Our laboratory recently reported BDNF is secreted by pro-regenerative endothelial progenitor cells (EPCs) which support hematopoietic reconstitution following total body irradiation (TBI). Here we report BDNF-TrkB signaling plays a novel regenerative role in bone marrow and thymic regeneration following radiation injury. Exogenous administration of BDNF or TrkB agonist 7,8-dihydroxyflavone (7,8-DHF) following myelosuppressive radiation injury promoted faster recovery of mature blood cells and hematopoietic stem cells capable of multi-lineage reconstitution. BDNF promotes hematopoietic regeneration via activation of PDGFRα+ bone marrow mesenchymal stem cells (MSCs) which increase secretion of hematopoietic cytokines interleukin 6 (IL-6) and leukemia inhibitory factor (LIF) in response to TrkB activation. These data suggest pharmacologic activation of the BDNF pathway with either BDNF or 7,8-DHF may be beneficial for treatment of radiation or chemotherapy induced myelosuppression.

Large spin-state changes in isostructural cyanate- and azide-bridged Mn(3)(III)Mn(2)(II) single-molecule magnets.

We prepared three structurally related Mn(3)(III)Mn(2)(II) complexes that possess S approximately 1-11 spin ground states as a result of variations in the geometry and identity of mu(2)-eta(1):eta(1) bridging groups. These complexes function as single-molecule magnets yet demonstrate other interesting behavior such as quasi-classical magnetization hysteresis and comparable magnetization reversal barriers (U(eff)).

Identification of novel bacterial urease inhibitors through molecular shape and structure based virtual screening approaches.

The enzyme urease is an essential colonizing factor of the notorious carcinogenic pathogen Helicobacter pylori (H. pylori), conferring acid resistance to the bacterium. Recently, antibiotic resistant strains have emerged globally with little to no alternative treatment available. In this study we propose novel urease inhibitors capable of controlling infection by H. pylori and other pathogenic bacteria. We employed hierarchal computational approaches to screen new urease inhibitors from commercial chemical databases followed by in vitro anti-urease assays. Initially ROCS shape-based screening was performed using o-chloro-hippurohydroxamic acid followed by molecular docking studies. Out of 1.83 million compounds, 1700 compounds were retrieved based on having a ROCS Tanimoto combo score in the range of values from 1.216 to 1.679. These compounds were further screened using molecular docking simulations and the 100 top ranked compounds were selected based on their Glide score. After structural classification of the top ranked compounds, eight compounds were selected and purchased for biological assays. The plausible binding modes of the most active compounds were also confirmed using molecular dynamics (MD) simulations. Compounds 1, 2 and 3 demonstrated good urease inhibitory properties (IC50 = 0.32, 0.68 and 0.42 µM) compared to the other compounds. Enzyme kinetic studies revealed that compounds 1 and 3 are competitive inhibitors while 2 is a mixed type inhibitor of the urease enzyme. Cell based urease inhibition and MTT assay showed that these compounds blocked H. pylori urease activity, affecting bacterial growth and acid tolerance.

Synthesis, Crystal Structure, and Magnetic Properties of a New Mixed Metal (Co(II), Ni(II)) Cubane.

The mixed Co(II)/Ni(II) complex, [Co2.67Ni1.33L4(CH3COO)2][BPh4]2·0.75H2O where HL = 4-(salicylaldimine)antipyrine, was isolated as an orange solid from the reaction of 4-(salicylaldimine)antipyrine, with mixed cobalt(II) acetate and nickel(II) acetate in ethanol. The complex was characterized by Frustrated Total Internal Reflection (FTIR), UltraViolet Visible spectroscopy (UV-Vis), X-ray single crystal diffraction, and by elemental analysis. The complex is composed of two symmetry independent cationic units, A and B. The two units are essentially isostructural; nevertheless, small differences exist between them. The units contain four metal atoms, arranged at the corners of a distorted cubane-like core alternately with phenoxy oxygen of the Schiff base. The overall eight corners occupied by metal ions in the asymmetric unit are shared between cobalt and nickel in a 5.33:2.67 ratio. Each metal divalent cation binds three coordinated sites from the corresponding tridentate Schiff base ligand, the fourth one is bound by the acetate oxygen, the fifth and the sixth donor sites come from the phenolate oxygens of other Schiff base ligands. Intermolecular hydrogen bonds join the complexes to the water molecules present in the crystal packing. The magnetic characterization was carried out for this new complex and for its isostructural counterpart containing only cobalt ions. The magnetic measurements for the cobalt(II)/nickel(II) mixed compound indicate either antiferromagnetic interactions among the two cubanes or an anisotropic contribution, whereas a ferromagnetic interaction is observed within the cubane, for both the complexes, as expected by geometrical considerations. A comparison between the magnetic properties of the pure cobalt(II) derivative and similar systems discussed in literature, is presented.

Conditional knockout of TMEM16A/anoctamin1 abolishes the calcium-activated chloride current in mouse vomeronasal sensory neurons.

Pheromones are substances released from animals that, when detected by the vomeronasal organ of other individuals of the same species, affect their physiology and behavior. Pheromone binding to receptors on microvilli on the dendritic knobs of vomeronasal sensory neurons activates a second messenger cascade to produce an increase in intracellular Ca(2+) concentration. Here, we used whole-cell and inside-out patch-clamp analysis to provide a functional characterization of currents activated by Ca(2+) in isolated mouse vomeronasal sensory neurons in the absence of intracellular K(+). In whole-cell recordings, the average current in 1.5 µM Ca(2+) and symmetrical Cl(-) was -382 pA at -100 mV. Ion substitution experiments and partial blockade by commonly used Cl(-) channel blockers indicated that Ca(2+) activates mainly anionic currents in these neurons. Recordings from inside-out patches from dendritic knobs of mouse vomeronasal sensory neurons confirmed the presence of Ca(2+)-activated Cl(-) channels in the knobs and/or microvilli. We compared the electrophysiological properties of the native currents with those mediated by heterologously expressed TMEM16A/anoctamin1 or TMEM16B/anoctamin2 Ca(2+)-activated Cl(-) channels, which are coexpressed in microvilli of mouse vomeronasal sensory neurons, and found a closer resemblance to those of TMEM16A. We used the Cre-loxP system to selectively knock out TMEM16A in cells expressing the olfactory marker protein, which is found in mature vomeronasal sensory neurons. Immunohistochemistry confirmed the specific ablation of TMEM16A in vomeronasal neurons. Ca(2+)-activated currents were abolished in vomeronasal sensory neurons of TMEM16A conditional knockout mice, demonstrating that TMEM16A is an essential component of Ca(2+)-activated Cl(-) currents in mouse vomeronasal sensory neurons.

Calcium-activated chloride channels in the apical region of mouse vomeronasal sensory neurons.

The rodent vomeronasal organ plays a crucial role in several social behaviors. Detection of pheromones or other emitted signaling molecules occurs in the dendritic microvilli of vomeronasal sensory neurons, where the binding of molecules to vomeronasal receptors leads to the influx of sodium and calcium ions mainly through the transient receptor potential canonical 2 (TRPC2) channel. To investigate the physiological role played by the increase in intracellular calcium concentration in the apical region of these neurons, we produced localized, rapid, and reproducible increases in calcium concentration with flash photolysis of caged calcium and measured calcium-activated currents with the whole cell voltage-clamp technique. On average, a large inward calcium-activated current of -261 pA was measured at -50 mV, rising with a time constant of 13 ms. Ion substitution experiments showed that this current is anion selective. Moreover, the chloride channel blockers niflumic acid and 4,4'-diisothiocyanatostilbene-2,2'-disulfonic acid partially inhibited the calcium-activated current. These results directly demonstrate that a large chloride current can be activated by calcium in the apical region of mouse vomeronasal sensory neurons. Furthermore, we showed by immunohistochemistry that the calcium-activated chloride channels TMEM16A/anoctamin1 and TMEM16B/anoctamin2 are present in the apical layer of the vomeronasal epithelium, where they largely colocalize with the TRPC2 transduction channel. Immunocytochemistry on isolated vomeronasal sensory neurons showed that TMEM16A and TMEM16B coexpress in the neuronal microvilli. Therefore, we conclude that microvilli of mouse vomeronasal sensory neurons have a high density of calcium-activated chloride channels that may play an important role in vomeronasal transduction.