Ultraplex microscopy: versatile highly-multiplexed molecular labeling and imaging across scale and resolution.

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.

Track-by-Day: A standardized approach to estrous cycle monitoring in biobehavioral research.

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 17ß-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 Track-by-Day Method for Monitoring the Rodent Estrous Cycle.

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. Basic Protocol 1: Collection and staining of vaginal smears Basic Protocol 2: Track-by-Day classification of vaginal smears Support Protocol: Preparation of gelatin-subbed slides.

3D Biological/Biomedical Image Registration with enhanced Feature Extraction and Outlier Detection.

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.

Automated classification of estrous stage in rodents using deep learning.

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.