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Abstract BackgroundTo address the limitations of large-scale high quality microscopy image acquisition, PSSR (Point-Scanning Super-Resolution) was introduced to enhance easily acquired low quality microscopy data to a higher quality using deep learning-based methods. However, while PSSR was released as open-source, it was difficult for users to implement into their workflows due to an outdated codebase, limiting its usage by prospective users. Additionally, while the data enhancements provided by PSSR were significant, there was still potential for further improvement. MethodsTo overcome this, we introduce PSSR2, a redesigned implementation of PSSR workflows and methods built to put state-of-the-art technology into the hands of the general microscopy and biology research community. PSSR2 enables user-friendly implementation of super-resolution workflows for simultaneous super-resolution and denoising of undersampled microscopy data, especially through its integrated Command Line Interface and Napari plugin. PSSR2 improves and expands upon previously established PSSR algorithms, mainly through improvements in the semi-synthetic data generation (“crappification”) and training processes. ResultsIn benchmarking PSSR2 on a test dataset of paired high and low resolution electron microscopy images, PSSR2 super-resolves high-resolution images from low-resolution images to a significantly higher accuracy than PSSR. The super-resolved images are also more visually representative of real-world high-resolution images. DiscussionThe improvements in PSSR2, in providing higher quality images, should improve the performance of downstream analyses. We note that for accurate super-resolution, PSSR2 models should only be applied to super-resolve data sufficiently similar to training data and should be validated against real-world ground truth data. 

Abstract PSSR2 improves and expands on the previously established PSSR (Point-Scanning Super-Resolution) workflow for simultaneous super-resolution and denoising of undersampled microscopy data. PSSR2 is designed to put state-of-the-art technology into the hands of the general microscopy and biology research community, enabling user-friendly implementation of PSSR workflows with little to no programming experience required, especially through its integrated CLI and Napari plugin. 

Producing dense 3D reconstructions from biological imaging data is a challenging instance segmentation task that requires significant ground-truth training data for effective and accurate deep learning-based models. Generating training data requires intense human effort to annotate each instance of an object across serial section images. Our focus is on the especially complicated brain neuropil, comprising an extensive interdigitation of dendritic, axonal, and glial processes visualized through serial section electron microscopy. We developed a novel deep learning-based method to generate dense 3D segmentations rapidly from sparse 2D annotations of a few objects on single sections. Models trained on the rapidly generated segmentations achieved similar accuracy as those trained on expert dense ground-truth annotations. Human time to generate annotations was reduced by three orders of magnitude and could be produced by non-expert annotators. This capability will democratize generation of training data for large image volumes needed to achieve brain circuits and measures of circuit strengths. 

Abstract In this paper, we introduce a new, open-source software developed in Python for analyzing Auditory Brainstem Response (ABR) waveforms. ABRs are a far-field recording of synchronous neural activity generated by the auditory fibers in the ear in response to sound, and used to study acoustic neural information traveling along the ascending auditory pathway. Common ABR data analysis practices are subject to human interpretation and are labor-intensive, requiring manual annotations and visual estimation of hearing thresholds. The proposed new Auditory Brainstem Response Analyzer (ABRA) software is designed to facilitate the analysis of ABRs by supporting batch data import/export, waveform visualization, and statistical analysis. Techniques implemented in this software include algorithmic peak finding, threshold estimation, latency estimation, time warping for curve alignment, and 3D plotting of ABR waveforms over stimulus frequencies and decibels. The excellent performance on a large dataset of ABR collected from three labs in the field of hearing research that use different experimental recording settings illustrates the efficacy, flexibility, and wide utility of ABRA. 

Abstract Proper regulation of organelle dynamics and inter-organelle contacts is critical for cellular health and function. Both the endoplasmic reticulum (ER) and actin cytoskeleton are known to regulate organelle dynamics, but how, when, and where these two subcellular components are coordinated to control organelle dynamics remains unclear. Here, we show that ER-associated actin consistently marks mitochondrial, endosomal, and lysosomal fission sites. We also show that actin polymerization by the ER-anchored isoform of the formin protein INF2 is a key regulator of the morphology and mobility of these organelles. Together, our findings establish a mechanism by which INF2-mediated polymerization of ER-associated actin at ER-organelle contacts regulates organelle dynamics. 

ABSTRACT Cochlear hair cell stereocilia bundles are key organelles required for normal hearing. Often, deafness mutations cause aberrant stereocilia heights or morphology that are visually apparent but challenging to quantify. Actin-based structures, stereocilia are easily and most often labeled with phalloidin then imaged with 3D confocal microscopy. Unfortunately, phalloidin non-specifically labels all the actin in the tissue and cells and therefore results in a challenging segmentation task wherein the stereocilia phalloidin signal must be separated from the rest of the tissue. This can require many hours of manual human effort for each 3D confocal image stack. Currently, there are no existing software pipelines that provide an end-to-end automated solution for 3D stereocilia bundle instance segmentation. Here we introduce VASCilia, a Napari plugin designed to automatically generate 3D instance segmentation and analysis of 3D confocal images of cochlear hair cell stereocilia bundles stained with phalloidin. This plugin combines user-friendly manual controls with advanced deep learning-based features to streamline analyses. With VASCilia, users can begin their analysis by loading image stacks. The software automatically preprocesses these samples and displays them in Napari. At this stage, users can select their desired range of z-slices, adjust their orientation, and initiate 3D instance segmentation. After segmentation, users can remove any undesired regions and obtain measurements including volume, centroids, and surface area. VASCilia introduces unique features that measures bundle heights, determines their orientation with respect to planar polarity axis, and quantifies the fluorescence intensity within each bundle. The plugin is also equipped with trained deep learning models that differentiate between inner hair cells and outer hair cells and predicts their tonotopic position within the cochlea spiral. Additionally, the plugin includes a training section that allows other laboratories to fine-tune our model with their own data, provides responsive mechanisms for manual corrections through event-handlers that check user actions, and allows users to share their analyses by uploading a pickle file containing all intermediate results. We believe this software will become a valuable resource for the cochlea research community, which has traditionally lacked specialized deep learning-based tools for obtaining high-throughput image quantitation. Furthermore, we plan to release our code along with a manually annotated dataset that includes approximately 55 3D stacks featuring instance segmentation. This dataset comprises a total of 1,870 instances of hair cells, distributed between 410 inner hair cells and 1,460 outer hair cells, all annotated in 3D. As the first open-source dataset of its kind, we aim to establish a foundational resource for constructing a comprehensive atlas of cochlea hair cell images. Together, this open-source tool will greatly accelerate the analysis of stereocilia bundles and demonstrates the power of deep learning-based algorithms for challenging segmentation tasks in biological imaging research. Ultimately, this initiative will support the development of foundational models adaptable to various species, markers, and imaging scales to advance and accelerate research within the cochlea research community. 

Decreased dendritic spine density in the cortex is a key pathological feature of neuropsychiatric diseases including depression, addiction, and schizophrenia (SCZ). Psychedelics possess a remarkable ability to promote cortical neuron growth and increase spine density; however, these compounds are contraindicated for patients with SCZ or a family history of psychosis. Here, we report the molecular design and de novo total synthesis of (+)-JRT, a structural analogue of lysergic acid diethylamide (LSD) with lower hallucinogenic potential and potent neuroplasticity-promoting properties. In addition to promoting spinogenesis in the cortex, (+)-JRT produces therapeutic effects in behavioral assays relevant to depression and cognition without exacerbating behavioral and gene expression signatures relevant to psychosis. This work underscores the potential of nonhallucinogenic psychoplastogens for treating diseases where the use of psychedelics presents significant safety concerns. 

Age-related hearing loss (ARHL) is a common sensory impairment with complex underlying mechanisms. In our previous study, we performed a meta-analysis of genome-wide association studies (GWAS) in mice and identified a novel locus on chromosome 18 associated with ARHL specifically linked to a 32 kHz tone burst stimulus. Consequently, we investigated the role of Formin Homology 2 Domain Containing 3 (Fhod3), a newly discovered candidate gene for ARHL based on the GWAS results. We observed Fhod3 expression in auditory hair cells (HCs) primarily localized at the cuticular plate (CP). To understand the functional implications of Fhod3 in the cochlea, we generated Fhod3 overexpression mice (Pax2-Cre^+/-; Fhod3^Tg/+) (TG) and HC-specific conditional knockout mice (Atoh1-Cre^+/-; Fhod3^fl/fl) (KO). Audiological assessments in TG mice demonstrated progressive high-frequency hearing loss, characterized by predominant loss of outer hair cells, and a decreased phalloidin intensities of CP. Ultrastructural analysis revealed loss of the shortest row of stereocilia in the basal turn of the cochlea, and alterations in the cuticular plate surrounding stereocilia rootlets. Importantly, the hearing and HC phenotype in TG mice phenocopied that of the KO mice. These findings suggest that balanced expression of Fhod3 is critical for proper CP and stereocilia structure and function. Further investigation of Fhod3 related hearing impairment mechanisms may lend new insight towards the myriad mechanisms underlying ARHL, which in turn could facilitate the development of therapeutic strategies for ARHL. 

Abstract Mitochondria play a crucial role in the regulation of cellular metabolism and signalling. Mitochondrial activity is modulated by the processes of mitochondrial fission and fusion, which are required to properly balance respiratory and metabolic functions, transfer material between mitochondria, and remove defective mitochondria. Mitochondrial fission occurs at sites of contact between the endoplasmic reticulum (ER) and mitochondria, and is dependent on the formation of actin filaments that drive mitochondrial constriction and the recruitment and activation of the dynamin-related GTPase fission protein DRP1. The requirement for mitochondria- and ER-associated actin filaments in mitochondrial fission remains unclear, and the role of actin in mitochondrial fusion remains entirely unexplored. Here we show that preventing the formation of actin filaments on either mitochondria or the ER disrupts both mitochondrial fission and fusion. We show that fusion but not fission is dependent on Arp2/3, whereas both fission and fusion are dependent on INF2 formin-dependent actin polymerization. We also show that mitochondria-associated actin marks fusion sites prior to the dynamin family GTPase fusion protein MFN2. Together, our work introduces a novel method for perturbing organelle-associated actin filaments, and demonstrates a previously unknown role for actin in mitochondrial fusion.