Three-Dimensional Segmentation and Reconstruction of Neuronal Nuclei in Confocal Microscopic Images

Neuronal activation affects the organization and protein composition of the nuclear speckles

Nuclear speckles, also known as interchromatin granule clusters (IGCs), are subnuclear domains highly enriched in proteins involved in transcription and mRNA metabolism and, until recently, have been regarded primarily as their storage and modification hubs. However, several recent studies on non-neuronal cell types indicate that nuclear speckles may directly contribute to gene expression as some of the active genes have been shown to associate with these structures. Neuronal activity is one of the key transcriptional regulators and may lead to the rearrangement of some nuclear bodies. Notably, the impact of neuronal activation on IGC/nuclear speckles organization and function remains unexplored. To address this research gap, we examined whether and how neuronal stimulation affects the organization of these bodies in granular neurons from the rat hippocampal formation. Our findings demonstrate that neuronal stimulation induces morphological and proteomic remodelling of the nuclear speckles under both in vitro and in vivo conditions. Importantly, these changes are not associated with cellular stress or cell death but are dependent on transcription and splicing.

Optical Diffraction Tomography and Raman Confocal Microscopy for the Investigation of Vacuoles Associated with Cancer Senescent Engulfing Cells

Wild-type p53 cancer therapy-induced senescent cells frequently engulf and degrade neighboring ones inside a massive vacuole in their cytoplasm. After clearance of the internalized cell, the vacuole persists, seemingly empty, for several hours. Despite large vacuoles being associated with cell death, this process is known to confer a survival advantage to cancer engulfing cells, leading to therapy resistance and tumor relapse. Previous attempts to resolve the vacuolar structure and visualize their content using dyes were unsatisfying for lack of known targets and ineffective dye penetration and/or retention. Here, we overcame this problem by applying optical diffraction tomography and Raman spectroscopy to MCF7 doxorubicin-induced engulfing cells. We demonstrated a real ability of cell tomography and Raman to phenotype complex microstructures, such as cell-in-cells and vacuoles, and detect chemical species in extremely low concentrations within live cells in a completely label-free fashion. We show that vacuoles had a density indistinguishable to the medium, but were not empty, instead contained diluted cell-derived macromolecules, and we could discern vacuoles from medium and cells using their Raman fingerprint. Our approach is useful for the noninvasive investigation of senescent engulfing (and other peculiar) cells in unperturbed conditions, crucial for a better understanding of complex biological processes.

Efficient automatic 3D segmentation of cell nuclei for high-content screening

BACKGROUND

High-content screening (HCS) is a pre-clinical approach for the assessment of drug efficacy. On modern platforms, it involves fluorescent image capture using three-dimensional (3D) scanning microscopy. Segmentation of cell nuclei in 3D images is an essential prerequisite to quantify captured fluorescence in cells for screening. However, this segmentation is challenging due to variabilities in cell confluency, drug-induced alterations in cell morphology, and gradual degradation of fluorescence with the depth of scanning. Despite advances in algorithms for segmenting nuclei for HCS, robust 3D methods that are insensitive to these conditions are still lacking.

RESULTS

We have developed an algorithm which first generates a 3D nuclear mask in the original images. Next, an iterative 3D marker-controlled watershed segmentation is applied to downsized images to segment adjacent nuclei under the mask. In the last step, borders of segmented nuclei are adjusted in the original images based on local nucleus and background intensities. The method was developed using a set of 10 3D images. Extensive tests on a separate set of 27 3D images containing 2,367 nuclei demonstrated that our method, in comparison with 6 reference methods, achieved the highest precision (PR = 0.97), recall (RE = 0.88) and F1-score (F1 = 0.93) of nuclei detection. The Jaccard index (JI = 0.83), which reflects the accuracy of nuclei delineation, was similar to that yielded by all reference approaches. Our method was on average more than twice as fast as the reference method that produced the best results. Additional tests carried out on three stacked 3D images comprising heterogenous nuclei yielded average PR = 0.96, RE = 0.84, F1 = 0.89, and JI = 0.80.

CONCLUSIONS

The high-performance metrics yielded by the proposed approach suggest that it can be used to reliably delineate nuclei in 3D images of monolayered and stacked cells exposed to cytotoxic drugs.

Multimode Gesture Recognition Algorithm Based on Convolutional Long Short-Term Memory Network

Gesture recognition utilizes deep learning network model to automatically extract deep features of data; however, traditional machine learning algorithms rely on manual feature extraction and poor model generalization ability. In this paper, a multimodal gesture recognition algorithm based on convolutional long-term memory network is proposed. First, a convolutional neural network (CNN) is employed to automatically extract the deeply hidden features of multimodal gesture data. Then, a time series model is constructed using a long short-term memory (LSTM) network to learn the long-term dependence of multimodal gesture features on the time series. On this basis, the classification of multimodal gestures is realized by the SoftMax classifier. Finally, the method is experimented and evaluated on two dynamic gesture datasets, VIVA and NVGesture. Experimental results indicate that the accuracy rates of the proposed method on the VIVA and NVGesture datasets are 92.55% and 87.38%, respectively, and its recognition accuracy and convergence performance are better than those of other comparison algorithms.

Improved Quantification of Cell Density in the Arterial Wall-A Novel Nucleus Splitting Approach Applied to 3D Two-Photon Laser-Scanning Microscopy

Accurate information on vascular smooth muscle cell (VSMC) content, orientation, and distribution in blood vessels is indispensable to increase understanding of arterial remodeling and to improve modeling of vascular biomechanics. We have previously proposed an analysis method to automatically characterize VSMC orientation and transmural distribution in murine carotid arteries under well-controlled biomechanical conditions. However, coincident nuclei, erroneously detected as one large nucleus, were excluded from the analysis, hampering accurate VSMC content characterization and distorting transmural distributions. In the present study, therefore, we aim to (1) improve the previous method by adding a "nucleus splitting" procedure to split coinciding nuclei, (2) evaluate the accuracy of this novel method, and (3) test this method in a mouse model of VSMC apoptosis. After euthanasia, carotid arteries from SM22α-hDTR Apoe -/- and control Apoe -/- mice were bluntly dissected, excised, mounted in a biaxial biomechanical tester and brought to in vivo axial stretch and a pressure of 100 mmHg. Nuclei and elastin fibers were then stained using Syto-41 and Eosin-Y, respectively, and imaged using 3D two-photon laser scanning microscopy. Nuclei were segmented from images and coincident nuclei were split. The nucleus splitting procedure determines the likelihood that voxel pairs within coincident nuclei belong to the same nucleus and utilizes these likelihoods to identify individual nuclei using spectral clustering. Manual nucleus counts were used as a reference to assess the performance of our splitting procedure. Before and after splitting, automatic nucleus counts differed -26.6 ± 9.90% (p < 0.001) and -1.44 ± 7.05% (p = 0.467) from the manual reference, respectively. Whereas the slope of the relative difference between the manual and automated counts as a function of the manual count was significantly negative before splitting (p = 0.008), this slope became insignificant after splitting (p = 0.653). Smooth muscle apoptosis led to a 33.7% decrease in VSMC density (p = 0.008). Nucleus splitting improves the accuracy of automated cell content quantification in murine carotid arteries and overcomes the progressively worsening problem of coincident nuclei with increasing cell content in vessels. The presented image analysis framework provides a robust tool to quantify cell content, orientation, shape, and distribution in vessels to inform experimental and advanced computational studies on vascular structure and function.

The interplay of seizures-induced axonal sprouting and transcription-dependent Bdnf repositioning in the model of temporal lobe epilepsy

The Brain-Derived Neurotrophic Factor is one of the most important trophic proteins in the brain. The role of this growth factor in neuronal plasticity, in health and disease, has been extensively studied. However, mechanisms of epigenetic regulation of Bdnf gene expression in epilepsy are still elusive. In our previous work, using a rat model of neuronal activation upon kainate-induced seizures, we observed a repositioning of Bdnf alleles from the nuclear periphery towards the nuclear center. This change of Bdnf intranuclear position was associated with transcriptional gene activity. In the present study, using the same neuronal activation model, we analyzed the relation between the percentage of the Bdnf allele at the nuclear periphery and clinical and morphological traits of epilepsy. We observed that the decrease of the percentage of the Bdnf allele at the nuclear periphery correlates with stronger mossy fiber sprouting-an aberrant form of excitatory circuits formation. Moreover, using in vitro hippocampal cultures we showed that Bdnf repositioning is a consequence of transcriptional activity. Inhibition of RNA polymerase II activity in primary cultured neurons with Actinomycin D completely blocked Bdnf gene transcription and repositioning occurring after neuronal excitation. Interestingly, we observed that histone deacetylases inhibition with Trichostatin A induced a slight increase of Bdnf gene transcription and its repositioning even in the absence of neuronal excitation. Presented results provide novel insight into the role of BDNF in epileptogenesis. Moreover, they strengthen the statement that this particular gene is a good candidate to search for a new generation of antiepileptic therapies.

Basic quantitative morphological methods applied to the central nervous system

Generating numbers has become an almost inevitable task associated with studies of the morphology of the nervous system. Numbers serve a desire for clarity and objectivity in the presentation of results and are a prerequisite for the statistical evaluation of experimental outcomes. Clarity, objectivity, and statistics make demands on the quality of the numbers that are not met by many methods. This review provides a refresher of problems associated with generating numbers that describe the nervous system in terms of the volumes, surfaces, lengths, and numbers of its components. An important aim is to provide comprehensible descriptions of the methods that address these problems. Collectively known as design-based stereology, these methods share two features critical to their application. First, they are firmly based in mathematics and its proofs. Second and critically underemphasized, an understanding of their mathematical background is not necessary for their informed and productive application. Understanding and applying estimators of volume, surface, length or number does not require more of an organizational mastermind than an immunohistochemical protocol. And when it comes to calculations, square roots are the gravest challenges to overcome. Sampling strategies that are combined with stereological probes are efficient and allow a rational assessment if the numbers that have been generated are "good enough." Much may be unfamiliar, but very little is difficult. These methods can no longer be scapegoats for discrepant results but faithfully produce numbers on the material that is assessed. They also faithfully reflect problems that associated with the histological material and the anatomically informed decisions needed to generate numbers that are not only valid in theory. It is within reach to generate practically useful numbers that must integrate with qualitative knowledge to understand the function of neural systems.