Fluorescence microscopy is essential for biological research, offering high-contrast imaging of microscopic structures. However, the quality of these images is often compromised by optical aberrations and noise, particularly in low signal-to-noise ratio (SNR) conditions. While adaptive optics (AO) can correct aberrations, it requires costly hardware and slows down imaging; whereas current denoising approaches boost the SNR but leave out the aberration compensation. To address these limitations, we introduce HD2Net, a deep learning framework that enhances image quality by simultaneously denoising and suppressing the effect of aberrations without the need for additional hardware. Building on our previous work, HD2Net incorporates noise estimation and aberration removal modules, effectively restoring images degraded by noise and aberrations. Through comprehensive evaluation of synthetic phantoms and biological data, we demonstrate that HD2Net outperforms existing methods, significantly improving image resolution and contrast. This framework offers a promising solution for enhancing biological imaging, particularly in challenging aberrating and low-light conditions.

Fluorescence microscopy is essential for biological research, offering high-contrast imaging of microscopic structures. However, the quality of these images is often compromised by optical aberrations and noise, particularly in low signal-to-noise ratio (SNR) conditions. While adaptive optics (AO) can correct aberrations, it requires costly hardware and slows down imaging; whereas current denoising approaches boost the SNR but leave out the aberration compensation. To address these limitations, we introduce HD2Net, a deep learning framework that enhances image quality by simultaneously denoising and suppressing the effect of aberrations without the need for additional hardware. Building on our previous work, HD2Net incorporates noise estimation and aberration removal modules, effectively restoring images degraded by noise and aberrations. Through comprehensive evaluation of synthetic phantoms and biological data, we demonstrate that HD2Net outperforms existing methods, significantly improving image resolution and contrast. This framework offers a promising solution for enhancing biological imaging, particularly in challenging aberrating and low-light conditions.

Zebrafish larvae are used to model the pathogenesis of multiple bacteria. This transparent model offers the unique advantage of allowing quantification of fluorescent bacterial burdens (fluorescent pixel counts [FPC]) by facile microscopical methods, replacing enumeration of bacteria using time-intensive plating of lysates on bacteriological media. Accurate FPC measurements require laborious manual image processing to mark the outside borders of the animals so as to delineate the bacteria inside the animals from those in the culture medium that they are in. Here, we have developed an automated ImageJ/Fiji-based macro that accurately detects the outside borders of -infected larvae.

Animals need to rapidly learn to recognize and avoid predators. This ability may be especially important for young animals due to their increased vulnerability. It is unknown whether, and how, nascent vertebrates are capable of such rapid learning. Here, we used a robotic predator-prey interaction assay to show that 1 week after fertilization-a developmental stage where they have approximately 1% the number of neurons of adults-zebrafish larvae rapidly and robustly learn to recognize a stationary object as a threat after the object pursues the fish for ∼1 min. Larvae continue to avoid the threatening object after it stops moving and can learn to distinguish threatening from non-threatening objects of a different color. Whole-brain functional imaging revealed the multi-timescale activity of noradrenergic neurons and forebrain circuits that encoded the threat. Chemogenetic ablation of those populations prevented the learning. Thus, a noradrenergic and forebrain multiregional network underlies the ability of young vertebrates to rapidly learn to recognize potential predators within their first week of life.

Effective classification of neuronal cell types requires both molecular and morphological descriptors to be collected in situ at single cell resolution. However, current spatial transcriptomics techniques are not compatible with imaging workflows that successfully reconstruct the morphology of complete axonal projections. Here, we introduce a new methodology that combines tissue clearing, submicron whole-brain two photon imaging, and Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to assign molecular identities to fully reconstructed neurons in the mouse brain, which we call morphoFISH. We used morphoFISH to molecularly identify a previously unknown population of cingulate neurons projecting ipsilaterally to the dorsal striatum and contralaterally to higher-order thalamus. By pairing whole-brain morphometry, improved techniques for nucleic acid preservation and spatial gene expression, morphoFISH offers a quantitative solution for discovery of multimodal cell types and complements existing techniques for characterization of increasingly fine-grained cellular heterogeneity in brain circuits.Competing Interest StatementThe authors have declared no competing interest.

Neurochemical signals like dopamine (DA) play a crucial role in a variety of brain functions through intricate interactions with other neuromodulators and intracellular signaling pathways. However, studying these complex networks has been hindered by the challenge of detecting multiple neurochemicals in vivo simultaneously. To overcome this limitation, we developed a single-protein chemigenetic DA sensor, HaloDA1.0, which combines a cpHaloTag-chemical dye approach with the G protein-coupled receptor activation-based (GRAB) strategy, providing high sensitivity for DA, sub-second response kinetics, and an extensive spectral range from far-red to near-infrared. When used together with existing green and red fluorescent neuromodulator sensors, Ca2+ indicators, cAMP sensors, and optogenetic tools, HaloDA1.0 provides high versatility for multiplex imaging in cultured neurons, brain slices, and behaving animals, facilitating in-depth studies of dynamic neurochemical networks.Competing Interest StatementThe authors have declared no competing interest.

The regular distribution of mitochondrial DNA-containing nucleoids is essential for mitochondrial function and genome inheritance; however, the underlying mechanisms remain unknown. Our data reveal that mitochondria frequently undergo spontaneous and reversible pearling - a biophysical instability in which tubules undulate into regularly spaced beads. We discovered that pearling imposes a characteristic length scale, simultaneously mediating nucleoid disaggregation and establishing inter-nucleoid distancing with near-maximally achievable precision. Cristae invaginations play a dual role: lamellar cristae density determines pearling frequency and duration, and preserves the resulting nucleoid spacing after recovery. The distribution of mitochondrial genomes is thus fundamentally governed by the interplay between spontaneous pearling and cristae ultrastructure.

Neuronal dendrites must relay synaptic inputs over long distances, but the mechanisms by which activity-evoked intracellular signals propagate over macroscopic distances remain unclear. Here, we discovered a system of periodically arranged endoplasmic reticulum-plasma membrane (ER-PM) junctions tiling the plasma membrane of dendrites at ∼1 μm intervals, interlinked by a meshwork of ER tubules patterned in a ladder-like array. Populated with Junctophilin-linked plasma membrane voltage-gated Ca channels and ER Ca-release channels (ryanodine receptors), ER-PM junctions are hubs for ER-PM crosstalk, fine-tuning of Ca homeostasis, and local activation of the Ca/calmodulin-dependent protein kinase II. Local spine stimulation activates the Ca modulatory machinery, facilitating signal transmission and ryanodine-receptor-dependent Ca release at ER-PM junctions over 20 μm away. Thus, interconnected ER-PM junctions support signal propagation and Ca release from the spine-adjacent ER. The capacity of this subcellular architecture to modify both local and distant membrane-proximal biochemistry potentially contributes to dendritic computations.

Brain neurons utilize the primary cilium as a privileged compartment to detect and respond to extracellular ligands such as Sonic hedgehog (SHH). However, cilia in cerebellar granule cell (GC) neurons disassemble during differentiation through ultrastructurally unique intermediates, a process we refer to as cilia deconstruction. In addition, mature neurons do not reciliate despite having docked centrioles. Here, we identify molecular changes that accompany cilia deconstruction and centriole docking in GC neurons. We used single cell transcriptomic and immunocytological analyses to compare the transcript levels and subcellular localization of proteins between progenitor, differentiating, and mature GCs. Differentiating GCs lacked transcripts for key activators of premitotic cilia resorption, indicating that cilia disassembly in differentiating cells is distinct from premitotic cilia resorption. Instead, during differentiation, transcripts of many genes required for cilia maintenance-specifically those encoding components of intraflagellar transport, pericentrosomal material, and centriolar satellites-decreased. The abundance of several corresponding proteins in and around cilia and centrosomes also decreased. These changes coincided with downregulation of SHH signaling prior to differentiation, even in a mutant with excessive SHH activation. Finally, mother centrioles in maturing granule neurons recruited the cap complex protein, CEP97. These data suggest that a global, developmentally programmed decrease in cilium maintenance in differentiating GCs mediates cilia deconstruction, while capping of docked mother centrioles prevents cilia regrowth and dysregulated SHH signaling. Our study provides mechanistic insights expanding our understanding of permanent cilia loss in multiple tissue-specific contexts.