Asymmetric neurons are necessary for olfactory learning in the Drosophila brain.

Animals have complementary parallel memory systems that process signals from various sensory modalities. In the brain of the fruit fly Drosophila melanogaster, mushroom body (MB) circuitry is the primary associative neuropil, critical for all stages of olfactory memory. Here, our findings suggest that active signaling from specific asymmetric body (AB) neurons is also crucial for this process. These AB neurons respond to odors and electric shock separately and exhibit timing-sensitive neuronal activity in response to paired stimulation while leaving a decreased memory trace during retrieval. Our experiments also show that rutabaga-encoded adenylate cyclase, which mediates coincidence detection, is required for learning and short-term memory in both AB and MB. We observed additive effects when manipulating rutabaga co-expression in both structures. Together, these results implicate the AB in playing a critical role in associative olfactory learning and short-term memory.

Deep-computer-generated holography with temporal-focusing and a digital propagation matrix for rapid 3D multiphoton stimulation.

Deep learning-based computer-generated holography (DeepCGH) has the ability to generate three-dimensional multiphoton stimulation nearly 1,000 times faster than conventional CGH approaches such as the Gerchberg-Saxton (GS) iterative algorithm. However, existing DeepCGH methods cannot achieve axial confinement at the several-micron scale. Moreover, they suffer from an extended inference time as the number of stimulation locations at different depths (i.e., the number of input layers in the neural network) increases. Accordingly, this study proposes an unsupervised U-Net DeepCGH model enhanced with temporal focusing (TF), which currently achieves an axial resolution of around 5 µm. The proposed model employs a digital propagation matrix (DPM) in the data preprocessing stage, which enables stimulation at arbitrary depth locations and reduces the computation time by more than 35%. Through physical constraint learning using an improved loss function related to the TF excitation efficiency, the axial resolution and excitation intensity of the proposed TF-DeepCGH with DPM rival that of the optimal GS with TF method but with a greatly increased computational efficiency.

Isotropic multi-scale neuronal reconstruction from high-ratio expansion microscopy with contrastive unsupervised deep generative models.

BACKGROUND AND OBJECTIVE: Current methods for imaging reconstruction from high-ratio expansion microscopy (ExM) data are limited by anisotropic optical resolution and the requirement for extensive manual annotation, creating a significant bottleneck in the analysis of complex neuronal structures. METHODS: We devised an innovative approach called the IsoGAN model, which utilizes a contrastive unsupervised generative adversarial network to sidestep these constraints. This model leverages multi-scale and isotropic neuron/protein/blood vessel morphology data to generate high-fidelity 3D representations of these structures, eliminating the need for rigorous manual annotation and supervision. The IsoGAN model introduces simplified structures with idealized morphologies as shape priors to ensure high consistency in the generated neuronal profiles across all points in space and scalability for arbitrarily large volumes. RESULTS: The efficacy of the IsoGAN model in accurately reconstructing complex neuronal structures was quantitatively assessed by examining the consistency between the axial and lateral views and identifying a reduction in erroneous imaging artifacts. The IsoGAN model accurately reconstructed complex neuronal structures, as evidenced by the consistency between the axial and lateral views and a reduction in erroneous imaging artifacts, and can be further applied to various biological samples. CONCLUSION: With its ability to generate detailed 3D neurons/proteins/blood vessel structures using significantly fewer axial view images, IsoGAN can streamline the process of imaging reconstruction while maintaining the necessary detail, offering a transformative solution to the existing limitations in high-throughput morphology analysis across different structures.

Sparse-view synchrotron X-ray tomographic reconstruction with learning-based sinogram synthesis.

Synchrotron radiation can be used as a light source in X-ray microscopy to acquire a high-resolution image of a microscale object for tomography. However, numerous projections must be captured for a high-quality tomographic image to be reconstructed; thus, image acquisition is time consuming. Such dense imaging is not only expensive and time consuming but also results in the target receiving a large dose of radiation. To resolve these problems, sparse acquisition techniques have been proposed; however, the generated images often have many artefacts and are noisy. In this study, a deep-learning-based approach is proposed for the tomographic reconstruction of sparse-view projections that are acquired with a synchrotron light source; this approach proceeds as follows. A convolutional neural network (CNN) is used to first interpolate sparse X-ray projections and then synthesize a sufficiently large set of images to produce a sinogram. After the sinogram is constructed, a second CNN is used for error correction. In experiments, this method successfully produced high-quality tomography images from sparse-view projections for two data sets comprising Drosophila and mouse tomography images. However, the initial results for the smaller mouse data set were poor; therefore, transfer learning was used to apply the Drosophila model to the mouse data set, greatly improving the quality of the reconstructed sinogram. The method could be used to achieve high-quality tomography while reducing the radiation dose to imaging subjects and the imaging time and cost.

A subset of cholinergic mushroom body neurons blocks long-term memory formation in Drosophila.

Long-term memory (LTM) requires learning-induced synthesis of new proteins allocated to specific neurons and synapses in a neural circuit. Not all learned information, however, becomes permanent memory. How the brain gates relevant information into LTM remains unclear. In Drosophila adults, weak learning after a single training session in an olfactory aversive task typically does not induce protein-synthesis-dependent LTM. Instead, strong learning after multiple spaced training sessions is required. Here, we report that pre-synaptic active-zone protein synthesis and cholinergic signaling from the early α/ß subset of mushroom body (MB) neurons produce a downstream inhibitory effect on LTM formation. When we eliminated inhibitory signaling from these neurons, weak learning was then sufficient to form LTM. This bidirectional circuit mechanism modulates the transition between distinct memory phase functions in different subpopulations of MB neurons in the olfactory memory circuit.

Electronic Preresonance Stimulated Raman Scattering Spectromicroscopy Using Multiple-Plate Continuum.

Stimulated Raman scattering (SRS) spectromicroscopy is a powerful technique that enables label-free detection of chemical bonds with high specificity. However, the low Raman cross section due to typical far-electronic resonance excitation seriously restricts the sensitivity and undermines its application to bio-imaging. To address this bottleneck, the electronic preresonance (EPR) SRS technique has been developed to enhance the Raman signals by shifting the excitation frequency toward the molecular absorption. A fundamental weakness of the previous demonstration is the lack of dual-wavelength tunability, making EPR-SRS only applicable to a limited number of species in the proof-of-concept experiment. Here, we demonstrate the EPR-SRS spectromicroscopy using a multiple-plate continuum (MPC) light source able to examine a single vibration mode with independently adjustable pump and Stokes wavelengths. In our experiments, the C═C vibration mode of Alexa 635 is interrogated by continuously scanning the pump-to-absorption frequency detuning throughout the entire EPR region enabled by MPC. The results exhibit 150-fold SRS signal enhancement and good agreement with the Albrecht A-term preresonance model. Signal enhancement is also observed in EPR-SRS images of the whole Drosophila brain stained with Alexa 635. With the improved sensitivity and potential to implement hyperspectral measurement, we envision that MPC-EPR-SRS spectromicroscopy can bring the Raman techniques closer to a routine in bio-imaging.

Temporal focusing multiphoton microscopy with cross-modality multi-stage 3D U-Net for fast and clear bioimaging.

Temporal focusing multiphoton excitation microscopy (TFMPEM) enables fast widefield biotissue imaging with optical sectioning. However, under widefield illumination, the imaging performance is severely degraded by scattering effects, which induce signal crosstalk and a low signal-to-noise ratio in the detection process, particularly when imaging deep layers. Accordingly, the present study proposes a cross-modality learning-based neural network method for performing image registration and restoration. In the proposed method, the point-scanning multiphoton excitation microscopy images are registered to the TFMPEM images by an unsupervised U-Net model based on a global linear affine transformation process and local VoxelMorph registration network. A multi-stage 3D U-Net model with a cross-stage feature fusion mechanism and self-supervised attention module is then used to infer in-vitro fixed TFMPEM volumetric images. The experimental results obtained for in-vitro drosophila mushroom body (MB) images show that the proposed method improves the structure similarity index measures (SSIMs) of the TFMPEM images acquired with a 10-ms exposure time from 0.38 to 0.93 and 0.80 for shallow- and deep-layer images, respectively. A 3D U-Net model, pretrained on in-vitro images, is further trained using a small in-vivo MB image dataset. The transfer learning network improves the SSIMs of in-vivo drosophila MB images captured with a 1-ms exposure time to 0.97 and 0.94 for shallow and deep layers, respectively.

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

Ecdysone signaling determines lateral polarity and remodels neurites to form Drosophila's left-right brain asymmetry.

Left-right (LR) asymmetry of the brain is fundamental to its higher-order functions. The Drosophila brain's asymmetrical body (AB) consists of a structural pair arborized from AB neurons and is larger on the right side than the left. We find that the AB initially forms LR symmetrically and then develops LR asymmetrically by neurite remodeling that is specific to the left AB and is dynamin dependent. Additionally, neuronal ecdysone signaling inhibition randomizes AB laterality, suggesting that ecdysone signaling determines AB's LR polarity. Given that AB's LR asymmetry relates to memory formation, our research establishes AB as a valuable model for studying LR asymmetry and higher-order brain function relationships.

CREBB repression of protein synthesis in mushroom body gates long-term memory formation in Drosophila.

Learned experiences are not necessarily consolidated into long-term memory (LTM) unless they are periodic and meaningful. LTM depends on de novo protein synthesis mediated by cyclic AMP response element-binding protein (CREB) activity. In Drosophila, two creb genes (crebA, crebB) and multiple CREB isoforms have reported influences on aversive olfactory LTM in response to multiple cycles of spaced conditioning. How CREB isoforms regulate LTM effector genes in various neural elements of the memory circuit is unclear, especially in the mushroom body (MB), a prominent associative center in the fly brain that has been shown to participate in LTM formation. Here, we report that i) spaced training induces crebB expression in MB α-lobe neurons and ii) elevating specific CREBB isoform levels in the early α/ß subpopulation of MB neurons enhances LTM formation. By contrast, learning from weak training iii) induces 5-HT1A serotonin receptor synthesis, iv) activates 5-HT1A in early α/ß neurons, and v) inhibits LTM formation. vi) LTM is enhanced when this inhibitory effect is relieved by down-regulating 5-HT1A or overexpressing CREBB. Our findings show that spaced training-induced CREBB antagonizes learning-induced 5-HT1A in early α/ß MB neurons to modulate LTM consolidation.

Towards stimulated Raman scattering spectro-microscopy across the entire Raman active region using a multiple-plate continuum.

Stimulated Raman scattering (SRS) has attracted increasing attention in bio-imaging because of the ability toward background-free molecular-specific acquisitions without fluorescence labeling. Nevertheless, the corresponding sensitivity and specificity remain far behind those of fluorescence techniques. Here, we demonstrate SRS spectro-microscopy driven by a multiple-plate continuum (MPC), whose octave-spanning bandwidth (600-1300 nm) and high spectral energy density (∼1 nJ/cm-1) enable spectroscopic interrogation across the entire Raman active region (0-4000 cm-1), SRS imaging of a Drosophila brain, and electronic pre-resonance (EPR) detection of a fluorescent dye. We envision that utilizing MPC light source will substantially enhance the sensitivity and specificity of SRS by implementing EPR mode and spectral multiplexing via accessing three or more coherent wavelengths.

The new X-ray/visible microscopy MAXWELL technique for fast three-dimensional nanoimaging with isotropic resolution.

Microscopy by Achromatic X-rays With Emission of Laminar Light (MAXWELL) is a new X-ray/visible technique with attractive characteristics including isotropic resolution in all directions, large-volume imaging and high throughput. An ultrathin, laminar X-ray beam produced by a Wolter type I mirror irradiates the sample stimulating the emission of visible light by scintillating nanoparticles, captured by an optical system. Three-dimensional (3D) images are obtained by scanning the specimen with respect to the laminar beam. We implemented and tested the technique with a high-brightness undulator at SPring-8, demonstrating its validity for a variety of specimens. This work was performed under the Synchrotrons for Neuroscience-an Asia-Pacific Strategic Enterprise (SYNAPSE) collaboration.

Three-dimensional-generator U-net for dual-resonant scanning multiphoton microscopy image inpainting and denoising.

A dual-resonant scanning multiphoton (DRSM) microscope incorporating a tunable acoustic gradient index of refraction lens and a resonant mirror is developed for rapid volumetric bioimaging. It is shown that the microscope achieves a volumetric imaging rate up to 31.25 volumes per second (vps) for a scanning volume of up to 200 × 200 × 100 µm3 with 256 × 256 × 128 voxels. However, the volumetric images have a severe negative signal-to-noise ratio (SNR) as a result of a large number of missing voxels for a large scanning volume and the presence of Lissajous patterning residuals. Thus, a modified three-dimensional (3D)-generator U-Net model trained using simulated microbead images is proposed and used to inpaint and denoise the images. The performance of the 3D U-Net model for bioimaging applications is enhanced by training the model with high-SNR in-vitro drosophila brain images captured using a conventional point scanning multiphoton microscope. The trained model shows the ability to produce clear in-vitro drosophila brain images at a rate of 31.25 vps with a SNR improvement of approximately 20 dB over the original images obtained by the DRSM microscope. The training convergence time of the modified U-Net model is just half that of a general 3D U-Net model. The model thus has significant potential for 3D in-vivo bioimaging transfer learning. Through the assistance of transfer learning, the model can be extended to the restoration of in-vivo drosophila brain images with a high image quality and a rapid training time.

Light-field microscopy with temporal focusing multiphoton illumination for scanless volumetric bioimaging.

A temporal focusing multiphoton illumination (TFMI) method is proposed for achieving selective volume illumination (SVI) (i.e., illuminating only the volume of interest) in light-field microscopy (LFM). The proposed method minimizes the background noise of the LFM images and enhances the contrast, and thus improves the imaging quality. Three-dimensional (3D) volumetric imaging is achieved by reconstructing the LFM images using a phase-space deconvolution algorithm. The experimental results obtained using 100-nm fluorescent beads show that the proposed TFMI-LFM system achieves lateral and axial resolutions of 1.2 µm and 1.1 µm, respectively, at the focal plane. Furthermore, the TFMI-LFM system enables 3D images of the single lobe of the drosophila mushroom body with GFP biomarker (OK-107) to be reconstructed in a one-snapshot record.

Dihydroceramide desaturase promotes the formation of intraluminal vesicles and inhibits autophagy to increase exosome production.

Exosomes are important for cell-cell communication. Deficiencies in the human dihydroceramide desaturase gene, DEGS1, increase the dihydroceramide-to-ceramide ratio and cause hypomyelinating leukodystrophy. However, the disease mechanism remains unknown. Here, we developed an in vivo assay with spatially controlled expression of exosome markers in Drosophila eye imaginal discs and showed that the level and activity of the DEGS1 ortholog, Ifc, correlated with exosome production. Knocking out ifc decreased the density of the exosome precursor intraluminal vesicles (ILVs) in the multivesicular endosomes (MVEs) and reduced the number of exosomes released. While ifc overexpression and autophagy inhibition both enhanced exosome production, combining the two had no additive effect. Moreover, DEGS1 activity was sufficient to drive ILV formation in vitro. Together, DEGS1/Ifc controls the dihydroceramide-to-ceramide ratio and enhances exosome secretion by promoting ILV formation and preventing the autophagic degradation of MVEs. These findings provide a potential cause for the neuropathy associated with DEGS1-deficient mutations.

Neuropeptide F inhibits dopamine neuron interference of long-term memory consolidation in Drosophila.

Long-term memory (LTM) formation requires consolidation processes to overcome interfering signals that erode memory formation. Olfactory memory in Drosophila involves convergent projection neuron (PN; odor) and dopaminergic neuron (DAN; reinforcement) input to the mushroom body (MB). How post-training DAN activity in the posterior lateral protocerebrum (PPL1) continues to regulate memory consolidation remains unknown. Here we address this question using targeted transgenes in behavior and electrophysiology experiments to show that (1) persistent post-training activity of PPL1-α2α'2 and PPL1-α3 DANs interferes with aversive LTM formation; (2) neuropeptide F (NPF) signaling blocks this interference in PPL1-α2α'2 and PPL1-α3 DANs after spaced training to enable LTM formation; and (3) training-induced NPF release and neurotransmission from two upstream dorsal-anterior-lateral (DAL2) neurons are required to form LTM. Thus, NPF signals from DAL2 neurons to specific PPL1 DANs disinhibit the memory circuit, ensuring that periodic events are remembered as consolidated LTM.

Three-Dimensional Tracking of Multiple Small Insects by a Single Camera.

Many systems to monitor insect behavior have been developed recently. Yet most of these can only detect two-dimensional behavior for convenient analysis and exclude other activities, such as jumping or flying. Therefore, the development of a three-dimensional (3D) monitoring system is necessary to investigate the 3D behavior of insects. In such a system, multiple-camera setups are often used to accomplish this purpose. Here, a system with a single camera for tracking small insects in a 3D space is proposed, eliminating the synchronization problems that typically occur when multiple cameras are instead used. With this setup, two other images are obtained via mirrors fixed at other viewing angles. Using the proposed algorithms, the tracking accuracy of five individual drain flies, Clogmia albipunctata (Williston) (Diptera: Psychodidae), flitting about in a spherical arena (78 mm in diameter) is as high as 98.7%, whereas the accuracy of 10 individuals is 96.3%. With this proposed method, the 3D trajectory monitoring experiments of insects can be performed more efficiently.

CREBA and CREBB in two identified neurons gate long-term memory formation in Drosophila.

Episodic events are frequently consolidated into labile memory but are not necessarily transferred to persistent long-term memory (LTM). Regulatory mechanisms leading to LTM formation are poorly understood, however, especially at the resolution of identified neurons. Here, we demonstrate enhanced LTM following aversive olfactory conditioning in Drosophila when the transcription factor cyclic AMP response element binding protein A (CREBA) is induced in just two dorsal-anterior-lateral (DAL) neurons. Our experiments show that this process is regulated by protein-gene interactions in DAL neurons: (1) crebA transcription is induced by training and repressed by crebB overexpression, (2) CREBA bidirectionally modulates LTM formation, (3) crebA overexpression enhances training-induced gene transcription, and (4) increasing membrane excitability enhances LTM formation and gene expression. These findings suggest that activity-dependent gene expression in DAL neurons during LTM formation is regulated by CREB proteins.

NeuroRetriever: Automatic Neuron Segmentation for Connectome Assembly.

Segmenting individual neurons from a large number of noisy raw images is the first step in building a comprehensive map of neuron-to-neuron connections for predicting information flow in the brain. Thousands of fluorescence-labeled brain neurons have been imaged. However, mapping a complete connectome remains challenging because imaged neurons are often entangled and manual segmentation of a large population of single neurons is laborious and prone to bias. In this study, we report an automatic algorithm, NeuroRetriever, for unbiased large-scale segmentation of confocal fluorescence images of single neurons in the adult Drosophila brain. NeuroRetriever uses a high-dynamic-range thresholding method to segment three-dimensional morphology of single neurons based on branch-specific structural features. Applying NeuroRetriever to automatically segment single neurons in 22,037 raw brain images, we successfully retrieved 28,125 individual neurons validated by human segmentation. Thus, automated NeuroRetriever will greatly accelerate 3D reconstruction of the single neurons for constructing the complete connectomes.

Light field microscopy based on structured light illumination.

In this Letter, we present the modeling, design, and characterization of a light sheet-based structured light illumination (SLI) light field microscopy (LFM) system for fast 3D imaging, where a digital micromirror device is employed to rapidly generate designed sinusoidal patterns in the imaging field. Specifically, we sequentially obtain uniformly illuminated and structured light field images, followed by post-processing with a new, to the best of our knowledge, algorithm that combines the deconvolution and HiLo algorithms. This enables fast volumetric imaging with improved optical cross-sectioning capability at a speed of 50 volumes per second over an imaging field of 250×250×80µm3 in the x, y, and z axis, respectively. Mathematical models have been derived to explain the performance enhancement due to suppressed background noises. To verify the results, imaging experiments on fluorescence beads, fern spore, and Drosophila brain samples, have been performed. The results indicate that the light sheet-based SLI-LFM presents a fast 3D imaging solution with substantially improved optical cross-sectioning capability in comparison with a standard light sheet-based LFM. The new light field imaging method may find important applications in the field of biophotonics.