Detecting molecules across different spatial scales remains a major challenge in biological samples, particularly in complex tissues such as brain, where linking molecular identification and nanoscale structural detail is essential for understanding function. Fluorescence microscopy provides selective labeling of specific molecules but cannot fully resolve many cellular and subcellular features, whereas electron microscopy reveals ultrastructure without molecular specificity. Bridging these modalities is difficult because commonly used fluorescent probes such as dyes and proteins cannot provide electron contrast. Quantum dots are particularly useful as they retain fluorescence while providing sufficient electron density for ultrastructural visualization. Here, we show that streptavidin-functionalized CdSe/CdS core/shell quantum dots can function as dual-modal probes for labeling γ-aminobutyric acid (GABA) neurons in brain tissue. The quantum dots produce bright and stable fluorescence signals for optical detection while their inorganic composition generates strong electron contrast in ultrathin sections examined by transmission electron microscopy. This work demonstrates that functionalized CdSe/CdS quantum dots can serve as stable dual-modal probes for fluorescence and electron microscopy in complex tissues, highlighting the potential of semiconductor nanocrystals for multifunctional imaging applications.

Reliable 3D reconstruction of tissue architecture from sequential 2D multiplex images is challenging due to the noise and distortions introduced by ultrathin (50 nm) slicing and complex alignment procedures. Conventional cell tracking methods often fail under such conditions, resulting in inaccurate linkage of cells across sections. To bridge this gap, we propose a Bayesian Transformer framework that incorporates uncertainty-aware feature embeddings and higher-order graph matching with belief propagation. By tracking cells across consecutive sections, our method facilitates the 3D reconstruction of volumetric tissue organization, even in highly noise-prone scenarios. The methodology begins with a standard segmentation step, followed by feature extraction that computes morphological, shape, and texture descriptors, as well as deep CNN embeddings. These rich, uncertainty-sensitive representations reduce errors caused by both registration artifacts and morphological variability. We validate the effectiveness of the proposed approach on a private multiplex dataset of fixed tissue sections and further demonstrate its generalizability on public time-lapse microscopy videos, showcasing adaptability to diverse datasets. Experimental comparisons reveal that our method outperforms baseline tracking techniques, achieving higher accuracy and more consistent cell linkages across multiple serial sections. The code used in this research with sample dataset are publicly available at https://github.com/NabaviLab/bayesian-transformer- cell- tracking

3D image registration is essential in computer vision, medical imaging, and robotics. By aligning images from different perspectives into a single coordinate system, this approach provides a consistent viewpoint for analysis. Using accurate image alignment, we may compare, evaluate, and integrate data from different contexts. This paper describes a new method to register 3D or z-stack microscopy and medical image. It uses a hybrid of traditional and deep learning methods for feature extraction and adaptive likelihood-based methods for finding outliers. The proposed method uses the Scale-invariant Feature Transform (SIFT) and the Residual Network with 50 layers (ResNet50) to extract effective features to obtain precise and accurate representations of image contents. The registration approach also relies on the adaptive Maximum Likelihood Estimation SAmple Consensus (MLESAC) method, which optimizes outlier detection and increases noise and distortion resistance to improve the efficacy of these combined extracted features. This concatenation approach demonstrates robustness, flexibility, and adaptability across a variety of imaging modalities, enabling the registration of complex images with higher precision. Results show that the proposed algorithm outperforms commonly used registration methods, including SIFT, KAZE, Oriented FAST and Rotated BRIEF (ORB), and also registration software tools such as bUnwarpJ, and TurboReg. The algorithm's effectiveness is evaluated in terms of Mutual Information (MI), Phase Congruency-Based (PCB), and Gradient-Based Metrics (GBM). These metrics are applied to two types of datasets, including a brain scan dataset and 3D serial sections of multiplex microscopy image datasets.

Despite known sex differences in brain function, female subjects are underrepresented in preclinical neuroscience research. This is driven in part by concerns about variability arising from estrous cycle-related hormone fluctuations, especially in fear- and anxiety-related research where there are conflicting reports as to whether and how the cycle influences behavior. The inconsistency may arise from a lack of common standards for tracking and reporting the cycle as opposed to inherent unpredictability in the cycle itself. The rat estrous cycle is conventionally tracked by assigning vaginal cytology smears to one of four qualitatively-defined stages. Although the cytology stages are of unequal length, the stage names are often, but not always, used to refer to the four cycle days. Subjective staging criteria and inconsistent use of terminology are not necessarily a problem in research on the cycle itself, but can lead to irreproducibility in neuroscience studies that treat the stages as independent grouping factors. We propose the explicit use of cycle days as independent variables, which we term Track-by-Day to differentiate it from traditional stage-based tracking, and that days be indexed to the only cytology feature that is a direct and rapid consequence of a hormonal event: a cornified cell layer formed in response to the pre-ovulatory 17β-estradiol peak. Here we demonstrate that cycle length is robustly regular with this method, and that the method outperforms traditional staging in detecting estrous cycle effects on Pavlovian fear conditioning and on a separate proxy for hormonal changes, uterine histology.

The molecular organization of cells and tissue is challenging to study due to the inefficiency of multiplexed molecular labeling methods and the limited options for combining microscopy modalities in a single specimen, especially when high spatial resolution is needed. Here we describe ultraplex microscopy, which combines serial multiplexing, ultrathin sectioning, and reversible embedding to circumvent incompatibilities between labeling and imaging techniques, enhance resolution, and expand multiplexing capacity within and across modalities. Samples can be labeled with antibodies, RNA probes, and tissue stains for imaging by brightfield, epifluorescence, super-resolution, and electron microscopy without specialized reagents or materials. We demonstrate applications in brain tissue including molecular profiling of single cells and axonal boutons, high-resolution molecular colocalization, and correlative imaging of fluorescent proteins with confocal and ultraplex microscopy. The power and versatility of ultraplex microscopy will be valuable in addressing currently intractable experimental questions in many systems and contexts.

Image registration is important in biological image analysis; however, it is often challenged by distortions and non-linear transformations. In this paper, we present a novel patch-wise image registration method to address the mentioned issues. Our method begins with global registration to correct linear transformations, followed by a detailed examination of geometrical distortions. After that, each image is adaptively divided into patches to isolate and correct non-linear distortions, followed by reconstruction and combining patches using Otsu thresholding. We evaluated our method against state-of-the-art techniques using mutual information (MI), phase congruency-based (PCB), and gradient-based metrics (GBM) across four real biology datasets. Our results demonstrate superior feature alignment and image coherence, especially in serial-stack registrations. While the proposed method has longer processing times compared to linear registration methods, its enhanced accuracy and reliability to handle non-uniform distortion makes it beneficial for precision-demanding applications. We have created a public GitHub repository containing the code used in our research, available at https://github.com/NabaviLab/CAPTURE.

The exclusion of female subjects from preclinical neuroscience research has traditionally been justified in part by concerns about potential effects of cycling ovarian hormones on brain function. There is evidence that some behavioral and neurobiological measures do change over the estrous cycle and, as the use of female subjects becomes increasingly routine, there is a greater demand for accessible cycle‐tracking methods. Conventional estrous cycle staging requires expert training in the qualitative interpretation of vaginal cytology smears, which serves as a barrier for novice researchers. In addition, definitions and reporting practices are not standardized across laboratories, which makes it difficult to compare results across studies and likely contributes to a false perception of the cycle as ephemeral and inconsistent. Here, we describe a streamlined method for monitoring the estrous cycle in rats, which we term Track‐by‐Day. It is simple to implement and inherently produces consistent reporting. Our protocol should serve to demystify and facilitate adoption of cycle tracking for those new to the practice. © 2023 Wiley Periodicals LLC.

In various applications, such as computer vision, medical imaging, and robotics, three-dimensional (3D) image registration is a significant step. It enables the alignment of various datasets into a single coordinate system, consequently providing a consistent perspective that allows further analysis. By precisely aligning images, we can compare, analyze, and combine data collected in different situations. This paper presents a novel approach for 3D or z-stack microscopy and medical image registration, utilizing a combination of conventional and deep learning techniques for feature extraction and adaptive likelihood-based methods for outlier detection. The proposed method uses the Scale-invariant Feature Transform (SIFT) and the Residual Network (ResNet50) deep neural learning network to extract effective features and obtain precise and exhaustive representations of image contents. The registration approach also employs the adaptive Maximum Likelihood Estimation SAmple Consensus (MLESAC) method that optimizes outlier detection and increases noise and distortion resistance to improve the efficacy of these combined extracted features. This integrated approach demonstrates robustness, flexibility, and adaptability across a variety of imaging modalities, enabling the registration of complex images with higher precision. Experimental results show that the proposed algorithm outperforms state-of-the-art image registration methods, including conventional SIFT, SIFT with Random Sample Consensus (RANSAC), and Oriented FAST and Rotated BRIEF (ORB) methods, as well as registration software packages such as bUnwrapJ and TurboReg, in terms of Mutual Information (MI), Phase Congruency-Based (PCB) metrics, and Gradiant-based metrics (GBM), using 3D MRI and 3D serial sections of multiplex microscopy images.

Electron microscopy (EM) volume reconstruction is a powerful tool for investigating the fundamental structure of brain circuits, but the full potential of this technique is limited by the difficulty of integrating molecular information. High quality ultrastructural preservation is necessary for EM reconstruction, and intact, highly contrasted cell membranes are essential for following small neuronal processes through serial sections. Unfortunately, the antibody labeling methods used to identify most endogenous molecules result in compromised morphology, especially of membranes. Cryofixation can produce superior morphological preservation and has the additional advantage of allowing indefinite storage of valuable samples. We have developed a method based on cryofixation that allows sensitive immunolabeling of endogenous molecules, preserves excellent ultrastructure, and is compatible with high-contrast staining for serial EM reconstruction.

Journal Article Click Chemistry for Visualization of Newly Synthesized RNA and Antibody Labeling on Ultrathin Tissue Sections Get access Janeth Pérez-Garza, Janeth Pérez-Garza Department of Physiology and Neurobiology. University of Connecticut, Storrs, CT, United States Search for other works by this author on: Oxford Academic Google Scholar Jairo Orea, Jairo Orea Department of Physiology and Neurobiology. University of Connecticut, Storrs, CT, United States Search for other works by this author on: Oxford Academic Google Scholar Linnaea Ostroff Linnaea Ostroff Department of Physiology and Neurobiology. University of Connecticut, Storrs, CT, United StatesConnecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, United StatesInstitute of Materials Science, University of Connecticut, Storrs, CT, United States Corresponding author: linnaea.ostroff@uconn.edu Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 1075–1076, https://doi.org/10.1093/micmic/ozad067.552 Published: 22 July 2023

With advances in cellular neuroanatomy in the mid-nineteenth century came the idea that neurons are interconnected, and from the very beginning there were theories that these connections – later called synapses – were structurally and functionally plastic in response to experience. The discovery of long-term potentiation in 1973 not only confirmed activity-dependent functional plasticity, it provided a powerful platform for investigating the relationship between structure and function at the brain's most fundamental level. This presentation will cover the various experimental techniques that have been used to study LTP-associated structural plasticity over the past 50 years, from electron microscopy of intact brain to targeted stimulation of individual dendritic spines, as well as the phenomena observed, including dendritic spine expansion, synaptogenesis, and synaptic scaling. None

Local protein synthesis at synapses can provide a rapid supply of proteins to support synaptic changes during consolidation of new memories, but its role in the maintenance or updating of established memories is unknown. Consolidation requires new protein synthesis in the period immediately following learning, whereas established memories are resistant to protein synthesis inhibitors. We have previously reported that polyribosomes are up-regulated in the lateral amygdala (LA) during consolidation of aversive-cued Pavlovian conditioning. In this study, we used serial section electron microscopy reconstructions to determine whether the distribution of dendritic polyribosomes returns to baseline during the long-term memory phase. Relative to control groups, long-term memory was associated with up-regulation of polyribosomes throughout dendrites, including in dendritic spines of all sizes. Retrieval of a consolidated memory by presentation of a small number of cues induces a new, transient requirement for protein synthesis to maintain the memory, while presentation of a large number of cues results in extinction learning, forming a new memory. One hour after retrieval or extinction training, the distribution of dendritic polyribosomes was similar except in the smallest spines, which had more polyribosomes in the extinction group. Our results demonstrate that the effects of learning on dendritic polyribosomes are not restricted to the transient translation-dependent phase of memory formation. Cued Pavlovian conditioning induces persistent synapse strengthening in the LA that is not reversed by retrieval or extinction, and dendritic polyribosomes may therefore correlate generally with synapse strength as opposed to recent activity or transient translational processes.

The rodent estrous cycle modulates a range of biological functions, from gene expression to behavior. The cycle is typically divided into four stages, each characterized by distinct hormone concentration profiles. Given the difficulty of repeatedly sampling plasma steroid hormones from rodents, the primary method for classifying estrous stage is by identifying vaginal epithelial cell types. However, manual classification of epithelial cell samples is time-intensive and variable, even amongst expert investigators. Here, we use a deep learning approach to achieve classification accuracy at expert level. Due to the heterogeneity and breadth of our input dataset, our deep learning approach ("EstrousNet") is highly generalizable across rodent species, stains, and subjects. The EstrousNet algorithm exploits the temporal dimension of the hormonal cycle by fitting classifications to an archetypal cycle, highlighting possible misclassifications and flagging anestrus phases (e.g., pseudopregnancy). EstrousNet allows for rapid estrous cycle staging, improving the ability of investigators to consider endocrine state in their rodent studies.

Local translation can provide a rapid, spatially targeted supply of new proteins in distal dendrites to support synaptic changes that underlie learning. Learning and memory are especially sensitive to manipulations of translational control mechanisms, particularly those that target the initiation step, and translation initiation at synapses could be a means of maintaining synapse specificity during plasticity. Initiation predominantly occurs via recruitment of ribosomes to the 5’ mRNA cap by complexes of eukaryotic initiation factors (eIFs), and the interaction between eIF4E and eIF4G1 is a particularly important target of translational control pathways. Pharmacological inhibition of eIF4E-eIF4G1 binding impairs consolidation of memory for aversive Pavlovian conditioning as well as the accompanying increase in polyribosomes in the heads of dendritic spines in the lateral amygdala (LA). This is consistent with a role for initiation at synapses in memory formation, but whether eIFs are even present near synapses is unknown. To determine whether dendritic spines contain eIFs and whether eIF distribution is affected by learning, we combined immunolabeling with serial section transmission electron microscopy (ssTEM) volume reconstructions of LA dendrites after Pavlovian conditioning. Labeling for eIF4E, eIF4G1, and eIF2α – another key target of regulation – occurred in roughly half of dendritic spines, but learning effects were only found for eIF4E, which was upregulated in the heads of dendritic spines. Our results support the possibility of regulated translation initiation as a means of synapse-specific protein targeting during learning and are consistent with the model of eIF4E availability as a central point of control.

Local translation can support memory consolidation by supplying new proteins to synapses undergoing plasticity. Translation in adult forebrain dendrites is an established mechanism of synaptic plasticity and is regulated by learning, yet there is no evidence for learning-regulated protein synthesis in adult forebrain axons, which have traditionally been believed to be incapable of translation. Here, we show that axons in the adult rat amygdala contain translation machinery, and use translating ribosome affinity purification (TRAP) with RNASeq to identify mRNAs in cortical axons projecting to the amygdala, over 1200 of which were regulated during consolidation of associative memory. Mitochondrial and translation-related genes were upregulated, whereas synaptic, cytoskeletal, and myelin-related genes were downregulated; the opposite effects were observed in the cortex. Our results demonstrate that axonal translation occurs in the adult forebrain and is altered after learning, supporting the likelihood that local translation is more a rule than an exception in neuronal processes.

Local translation can support memory consolidation by supplying new proteins to synapses undergoing plasticity. Translation in adult forebrain dendrites is an established mechanism of synaptic plasticity and is regulated by learning, yet there is no evidence for learning-regulated protein synthesis in adult forebrain axons, which have traditionally been believed to be incapable of translation. Here we show that axons in the adult rat amygdala contain translation machinery, and use translating ribosome affinity purification (TRAP) with RNASeq to identify mRNAs in cortical axons projecting to the amygdala, over 1200 of which were regulated during consolidation of associative memory. Mitochondrial and translation-related genes were upregulated, whereas synaptic, cytoskeletal, and myelin-related genes were downregulated; the opposite effects were observed in the cortex. Our results demonstrate that learning-regulated axonal translation occurs in the adult forebrain, and support the likelihood that local translation is more a rule than an exception in neuronal processes.

Hippocampal long-term potentiation (LTP) is a cellular memory mechanism. For LTP to endure, new protein synthesis is required immediately after induction and some of these proteins must be delivered to specific, presumably potentiated, synapses. Local synthesis in dendrites could rapidly provide new proteins to synapses, but the spatial distribution of translation following induction of LTP is not known. Here, we quantified polyribosomes, the sites of local protein synthesis, in CA1 stratum radiatum dendrites and spines from postnatal day 15 rats. Hippocampal slices were rapidly fixed at 5, 30, or 120 min after LTP induction by theta-burst stimulation (TBS). Dendrites were reconstructed through serial section electron microscopy from comparable regions near the TBS or control electrodes in the same slice, and in unstimulated hippocampus that was perfusion-fixed in vivo. At 5 min after induction of LTP, polyribosomes were elevated in dendritic shafts and spines, especially near spine bases and in spine heads. At 30 min, polyribosomes remained elevated only in spine bases. At 120 min, both spine bases and spine necks had elevated polyribosomes. Polyribosomes accumulated in spines with larger synapses at 5 and 30 min, but not at 120 min. Small spines, meanwhile, proliferated dramatically by 120 min, but these largely lacked polyribosomes. The number of ribosomes per polyribosome is variable and may reflect differences in translation regulation. In dendritic spines, but not shafts, there were fewer ribosomes per polyribosome in the slice conditions relative to in vivo, but this recovered transiently in the 5 min LTP condition. Overall, our data show that LTP induces a rapid, transient upregulation of large polyribosomes in larger spines, and a persistent upregulation of small polyribosomes in the bases and necks of small spines. This is consistent with local translation supporting enlargement of potentiated synapses within minutes of LTP induction.

Translation in dendrites is believed to support synaptic changes during memory consolidation. Although translational control mechanisms are fundamental mediators of memory, little is known about their role in local translation. We previously found that polyribosomes accumulate in dendritic spines of the adult rat lateral amygdala (LA) during consolidation of aversive pavlovian conditioning and that this memory requires cap-dependent initiation, a primary point of translational control in eukaryotic cells. Here we used serial electron microscopy reconstructions to quantify polyribosomes in LA dendrites when consolidation was blocked by the cap-dependent initiation inhibitor 4EGI-1. We found that 4EGI-1 depleted polyribosomes in dendritic shafts and selectively prevented their upregulation in spine heads, but not bases and necks, during consolidation. Cap-independent upregulation was specific to spines with small, astrocyte-associated synapses. Our results reveal that cap-dependent initiation is involved in local translation during learning and that local translational control varies with synapse type.SIGNIFICANCE STATEMENT Translation initiation is a central regulator of long-term memory formation. Local translation in dendrites supports memory by providing necessary proteins at synaptic sites, but it is unknown whether this requires initiation or bypasses it. We used serial electron microscopy reconstructions to examine polyribosomes in dendrites when memory formation was blocked by an inhibitor of translation initiation. This revealed two major pools of polyribosomes that were upregulated during memory formation: one pool in dendritic spine heads that was initiation dependent and another pool in the bases and necks of small spines that was initiation independent. Thus, translation regulation differs between spine types and locations, and translation that occurs closest to individual synapses during memory formation is initiation dependent.

Mitochondria support synaptic transmission through production of ATP, sequestration of calcium, synthesis of glutamate, and other vital functions. Surprisingly, less than 50% of hippocampal CA1 presynaptic boutons contain mitochondria, raising the question of whether synapses without mitochondria can sustain changes in efficacy. To address this question, we analyzed synapses from postnatal day 15 (P15) and adult rat hippocampus that had undergone theta-burst stimulation to produce long-term potentiation (TBS-LTP) and compared them to control or no stimulation. At 30 and 120 min after TBS-LTP, vesicles were decreased only in presynaptic boutons that contained mitochondria at P15, and vesicle decrement was greatest in adult boutons containing mitochondria. Presynaptic mitochondrial cristae were widened, suggesting a sustained energy demand. Thus, mitochondrial proximity reflected enhanced vesicle mobilization well after potentiation reached asymptote, in parallel with the apparently silent addition of new dendritic spines at P15 or the silent enlargement of synapses in adults.

ABSTRACT In adult hippocampus, long‐term potentiation (LTP) produces synapse enlargement while preventing the formation of new small dendritic spines. Here, we tested how LTP affects structural synaptic plasticity in hippocampal area CA1 of Long‐Evans rats at postnatal day 15 (P15). P15 is an age of robust synaptogenesis when less than 35% of dendritic spines have formed. We hypothesized that LTP might therefore have a different effect on synapse structure than in adults. Theta‐burst stimulation (TBS) was used to induce LTP at one site and control stimulation was delivered at an independent site, both within s. radiatum of the same hippocampal slice. Slices were rapidly fixed at 5, 30, and 120 min after TBS, and processed for analysis by three‐dimensional reconstruction from serial section electron microscopy (3DEM). All findings were compared to hippocampus that was perfusion‐fixed (PF) in vivo at P15. Excitatory and inhibitory synapses on dendritic spines and shafts were distinguished from synaptic precursors, including filopodia and surface specializations. The potentiated response plateaued between 5 and 30 min and remained potentiated prior to fixation. TBS resulted in more small spines relative to PF by 30 min. This TBS‐related spine increase lasted 120 min, hence, there were substantially more small spines with LTP than in the control or PF conditions. In contrast, control test pulses resulted in spine loss relative to PF by 120 min, but not earlier. The findings provide accurate new measurements of spine and synapse densities and sizes. The added or lost spines had small synapses, took time to form or disappear, and did not result in elevated potentiation or depression at 120 min. Thus, at P15 the spines formed following TBS, or lost with control stimulation, appear to be functionally silent. With TBS, existing synapses were awakened and then new spines formed as potential substrates for subsequent plasticity. © 2015 The Authors Hippocampus Published by Wiley Periodicals, Inc.