SMDB: a Spatial Multimodal Data Browser.

Understanding the relationship between fine-scale spatial organization and biological function necessitates a tool that effectively combines spatial positions, morphological information, and spatial transcriptomics (ST) data. We introduce the Spatial Multimodal Data Browser (SMDB, https://www.biosino.org/smdb), a robust visualization web service for interactively exploring ST data. By integrating multimodal data, such as hematoxylin and eosin (H&E) images, gene expression-based molecular clusters, and more, SMDB facilitates the analysis of tissue composition through the dissociation of two-dimensional (2D) sections and the identification of gene expression-profiled boundaries. In a digital three-dimensional (3D) space, SMDB allows researchers to reconstruct morphology visualizations based on manually filtered spots or expand anatomical structures using high-resolution molecular subtypes. To enhance user experience, it offers customizable workspaces for interactive exploration of ST spots in tissues, providing features like smooth zooming, panning, 360-degree rotation in 3D and adjustable spot scaling. SMDB is particularly valuable in neuroscience and spatial histology studies, as it incorporates Allen's mouse brain anatomy atlas for reference in morphological research. This powerful tool provides a comprehensive and efficient solution for examining the intricate relationships between spatial morphology, and biological function in various tissues.

Cryo-fluorescence micro-optical sectioning tomography for volumetric imaging of various whole organs with subcellular resolution.

Optical visualization of complex microstructures in the entire organ is essential for biomedical research. However, the existing methods fail to accurately acquire the detailed microstructures of whole organs with good morphological and biochemical preservation. This study proposes a cryo-fluorescence micro-optical sectioning tomography (cryo-fMOST) to image whole organs in three dimensions (3D) with submicron resolution. The system comprises a line-illumination microscope module, cryo-microtome, three-stage refrigeration module, and heat insulation device. To demonstrate the imaging capacity and wide applicability of the system, we imaged and reconstructed various organs of mice in 3D, including the healthy tongue, kidney, and brain, as well as the infarcted heart. More importantly, imaged brain slices were performed sugar phosphates determination and fluorescence in situ hybridization imaging to verify the compatibility of multi-omics measurements. The results demonstrated that cryo-fMOST is capable of acquiring high-resolution morphological details of various whole organs and may be potentially useful for spatial multi-omics.

Distinct neural networks derived from galanin-containing nociceptors and neurotensin-expressing pruriceptors.

Pain and itch are distinct sensations arousing evasion and compulsive desire for scratching, respectively. It's unclear whether they could invoke different neural networks in the brain. Here, we use the type 1 herpes simplex virus H129 strain to trace the neural networks derived from two types of dorsal root ganglia (DRG) neurons: one kind of polymodal nociceptors containing galanin (Gal) and one type of pruriceptors expressing neurotensin (Nts). The DRG microinjection and immunosuppression were performed in transgenic mice to achieve a successful tracing from specific types of DRG neurons to the primary sensory cortex. About one-third of nuclei in the brain were labeled. More than half of them were differentially labeled in two networks. For the ascending pathways, the spinothalamic tract was absent in the network derived from Nts-expressing pruriceptors, and the two networks shared the spinobulbar projections but occupied different subnuclei. As to the motor systems, more neurons in the primary motor cortex and red nucleus of the somatic motor system participated in the Gal-containing nociceptor-derived network, while more neurons in the nucleus of the solitary tract (NST) and the dorsal motor nucleus of vagus nerve (DMX) of the emotional motor system was found in the Nts-expressing pruriceptor-derived network. Functional validation of differentially labeled nuclei by c-Fos test and chemogenetic inhibition suggested the red nucleus in facilitating the response to noxious heat and the NST/DMX in regulating the histamine-induced scratching. Thus, we reveal the organization of neural networks in a DRG neuron type-dependent manner for processing pain and itch.

DeepBrainSeg: Automated Brain Region Segmentation for Micro-Optical Images With a Convolutional Neural Network.

The segmentation of brain region contours in three dimensions is critical for the analysis of different brain structures, and advanced approaches are emerging continuously within the field of neurosciences. With the development of high-resolution micro-optical imaging, whole-brain images can be acquired at the cellular level. However, brain regions in microscopic images are aggregated by discrete neurons with blurry boundaries, the complex and variable features of brain regions make it challenging to accurately segment brain regions. Manual segmentation is a reliable method, but is unrealistic to apply on a large scale. Here, we propose an automated brain region segmentation framework, DeepBrainSeg, which is inspired by the principle of manual segmentation. DeepBrainSeg incorporates three feature levels to learn local and contextual features in different receptive fields through a dual-pathway convolutional neural network (CNN), and to provide global features of localization by image registration and domain-condition constraints. Validated on biological datasets, DeepBrainSeg can not only effectively segment brain-wide regions with high accuracy (Dice ratio > 0.9), but can also be applied to various types of datasets and to datasets with noises. It has the potential to automatically locate information in the brain space on the large scale.

A Robust Image Registration Interface for Large Volume Brain Atlas.

Accurately mapping brain structures in three-dimensions is critical for an in-depth understanding of brain functions. Using the brain atlas as a hub, mapping detected datasets into a standard brain space enables efficient use of various datasets. However, because of the heterogeneous and nonuniform brain structure characteristics at the cellular level introduced by recently developed high-resolution whole-brain microscopy techniques, it is difficult to apply a single standard to robust registration of various large-volume datasets. In this study, we propose a robust Brain Spatial Mapping Interface (BrainsMapi) to address the registration of large-volume datasets by introducing extracted anatomically invariant regional features and a large-volume data transformation method. By performing validation on model data and biological images, BrainsMapi achieves accurate registration on intramodal, individual, and multimodality datasets and can also complete the registration of large-volume datasets (approximately 20 TB) within 1 day. In addition, it can register and integrate unregistered vectorized datasets into a common brain space. BrainsMapi will facilitate the comparison, reuse and integration of a variety of brain datasets.