Spatial atlas of the mouse central nervous system at molecular resolution

Control of sodium appetite by hindbrain aldosterone-sensitive neurons

Mineralocorticoids play a key role in hydromineral balance by regulating sodium retention and potassium wasting. Through favoring sodium, mineralocorticoids can cause hypertension from fluid overload under conditions of hyperaldosteronism, such as aldosterone-secreting tumors. An often-overlooked mechanism by which aldosterone functions to increase sodium is through stimulation of salt appetite. To drive sodium intake, aldosterone targets neurons in the hindbrain which uniquely express 11β-hydroxysteroid dehydrogenase type 2 (HSD2). This enzyme is a necessary precondition for aldosterone-sensing cells as it metabolizes glucocorticoids - preventing their activation of the mineralocorticoid receptor. In this review, we will consider the role of hindbrain HSD2 neurons in regulating sodium appetite by discussing HSD2 expression in the brain, regulation of hindbrain HSD2 neuron activity, and the circuitry mediating the effects of these aldosterone-sensitive neurons. Reducing the activity of hindbrain HSD2 neurons may be a viable strategy to reduce sodium intake and cardiovascular risk, particularly for conditions of hyperaldosteronism.

A specific olfactory bulb interneuron subtype Tpbg/5T4 generated at embryonic and neonatal stages

Various mammals have shown that sensory stimulation plays a crucial role in regulating the development of diverse structures, such as the olfactory bulb (OB), cerebral cortex, hippocampus, and retina. In the OB, the dendritic development of excitatory projection neurons like mitral/tufted cells is influenced by olfactory experiences. Odor stimulation is also essential for the dendritic development of inhibitory OB interneurons, such as granule and periglomerular cells, which are continuously produced in the ventricular-subventricular zone throughout life. Based on the morphological and molecular features, OB interneurons are classified into several subtypes. The role for each interneuron subtype in the control of olfactory behavior remains poorly understood due to lack of each specific marker. Among the several OB interneuron subtypes, a specific granule cell subtype, which expresses the oncofetal trophoblast glycoprotein (Tpbg or 5T4) gene, has been reported to be required for odor detection and discrimination behavior. This review will primarily focus on elucidating the contribution of different granule cell subtypes, including the Tpbg/5T4 subtype, to olfactory processing and behavior during the embryonic and adult stages.

Complete spatially resolved gene expression is not necessary for identifying spatial domains

Spatially resolved transcriptomics (SRT) technologies have revolutionized the study of tissue organization. We introduce a graph convolutional network with an attention and positive emphasis mechanism, termed BINARY, relying exclusively on binarized SRT data to accurately delineate spatial domains. BINARY outperforms existing methods across various SRT data types while using significantly less input information. Our study suggests that precise gene expression quantification may not always be essential, inspiring further exploration of the broader applications of spatially resolved binarized gene expression data.

A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images

Germline cells are critical for transmitting genetic information to subsequent generations in biological organisms. While their differentiation from somatic cells during embryonic development is well-documented in most animals, the regulatory mechanisms initiating plant germline cells are not well understood. To thoroughly investigate the complex morphological transformations of their ultrastructure over developmental time, nanoscale 3D reconstruction of entire plant tissues is necessary, achievable exclusively through electron microscopy imaging. This paper presents a full-process framework designed for reconstructing large-volume plant tissue from serial electron microscopy images. The framework ensures end-to-end direct output of reconstruction results, including topological networks and morphological analysis. The proposed 3D cell alignment, denoise, and instance segmentation pipeline (3DCADS) leverages deep learning to provide a cell instance segmentation workflow for electron microscopy image series, ensuring accurate and robust 3D cell reconstructions with high computational efficiency. The pipeline involves five stages: the registration of electron microscopy serial images; image enhancement and denoising; semantic segmentation using a Transformer-based neural network; instance segmentation through a supervoxel-based clustering algorithm; and an automated analysis and statistical assessment of the reconstruction results, with the mapping of topological connections. The 3DCADS model's precision was validated on a plant tissue ground-truth dataset, outperforming traditional baseline models and deep learning baselines in overall accuracy. The framework was applied to the reconstruction of early meiosis stages in the anthers of Arabidopsis thaliana, resulting in a topological connectivity network and analysis of morphological parameters and characteristics of cell distribution. The experiment underscores the 3DCADS model's potential for biological tissue identification and its significance in quantitative analysis of plant cell development, crucial for examining samples across different genetic phenotypes and mutations in plant development. Additionally, the paper discusses the regulatory mechanisms of Arabidopsis thaliana's germline cells and the development of stamen cells before meiosis, offering new insights into the transition from somatic to germline cell fate in plants.

A practical guide to spatial transcriptomics

Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.

Multimodal techniques and strategies for chemical and metabolic imaging at the single-cell level

Single-cell chemical and metabolic imaging technologies provide unprecedented insights into individual cell dynamics, advancing our understanding of cellular processes, molecular interactions, and metabolic activities. Advances in fluorescence, Raman, optoacoustic (photoacoustic), or mass spectrometry methods have paved the way to characterize metabolites, signaling molecules, and other moieties within individual cells. These modalities can also lead to single-cell imaging capabilities by targeting endogenous cell contrast or by employing exogenous contrast generation techniques, including contrast agents that target specific cell structure or function. In this review, we present key developments, summarize recent applications in single-cell interrogation and imaging, and illustrate their advantages, limitations, and outlook.

Transcriptomic landscape of mammalian ventral pallidum at single-cell resolution

The ventral pallidum (VP) is critical for motivated behaviors. While contemporary work has begun to elucidate the functional diversity of VP neurons, the molecular heterogeneity underlying this functional diversity remains incompletely understood. We used snRNA-seq and in situ hybridization to define the transcriptional taxonomy of VP cell types in mice, macaques, and baboons. We found transcriptional conservation between all three species, within the broader neurochemical cell types. Unique dopaminoceptive and cholinergic subclusters were identified and conserved across both primate species but had no homolog in mice. This harmonized consensus VP cellular atlas will pave the way for understanding the structure and function of the VP and identified key neuropeptides, neurotransmitters, and neuro receptors that could be targeted within specific VP cell types for functional investigations.

Making Ramón y Cajal proud: Development of cell identity and diversity in the cerebral cortex

Since the beautiful images of Santiago Ramón y Cajal provided a first glimpse into the immense diversity and complexity of cell types found in the cerebral cortex, neuroscience has been challenged and inspired to understand how these diverse cells are generated and how they interact with each other to orchestrate the development of this remarkable tissue. Some fundamental questions drive the field's quest to understand cortical development: what are the mechanistic principles that govern the emergence of neuronal diversity? How do extrinsic and intrinsic signals integrate with physical forces and activity to shape cell identity? How do the diverse populations of neurons and glia influence each other during development to guarantee proper integration and function? The advent of powerful new technologies to profile and perturb cortical development at unprecedented resolution and across a variety of modalities has offered a new opportunity to integrate past knowledge with brand new data. Here, we review some of this progress using cortical excitatory projection neurons as a system to draw out general principles of cell diversification and the role of cell-cell interactions during cortical development.

Systematic development of immunohistochemistry protocol for large cryosections-specific to non-perfused fetal brain

BACKGROUND

Immunohistochemistry (IHC) is an important technique in understanding the expression of neurochemical molecules in the developing human brain. Despite its routine application in the research and clinical setup, the IHC protocol specific for soft fragile fetal brains that are fixed using the non-perfusion method is still limited in studying the whole brain.

NEW METHOD

This study shows that the IHC protocols, using a chromogenic detection system, used in animals and adult humans are not optimal in the fetal brains. We have optimized key steps from Antigen retrieval (AR) to chromogen visualization for formalin-fixed whole-brain cryosections (20 µm) mounted on glass slides.

RESULTS

We show the results from six validated, commonly used antibodies to study the fetal brain. We achieved optimal antigen retrieval with 0.1 M Boric Acid, pH 9.0 at 70°C for 20 minutes. We also present the optimal incubation duration and temperature for protein blocking and the primary antibody that results in specific antigen labeling with minimal tissue damage.

COMPARISON WITH EXISTING METHODS

The IHC protocol commonly used for adult human and animal brains results in significant tissue damage in the fetal brains with little or suboptimal antigen expression. Our new method with important modifications including the temperature, duration, and choice of the alkaline buffer for AR addresses these pitfalls and provides high-quality results.

CONCLUSION

The optimized IHC protocol for the developing human brain (13-22 GW) provides a high-quality, repeatable, and reliable method for studying chemoarchitecture in neurotypical and pathological conditions across different gestational ages.

Sex differences in the rodent medial prefrontal cortex - What Do and Don't we know?

The prefrontal cortex, particularly its medial subregions (mPFC), mediates critical functions such as executive control, behavioral inhibition, and memory formation, with relevance for everyday functioning and psychopathology. Despite broad characterization of the mPFC in multiple model organisms, the extent to which mPFC structure and function vary according to an individual's sex is unclear - a knowledge gap that can be attributed to a historical bias for male subjects in neuroscience research. Recent efforts to consider sex as a biological variable in basic science highlight the great need to close this gap. Here we review the knowns and unknowns about how rodents categorized as male or female compare in mPFC neuroanatomy, pharmacology, as well as in aversive, appetitive, and goal- or habit-directed behaviors that recruit the mPFC. We propose that long-standing dogmatic concepts of mPFC structure and function may not remain supported when we move beyond male-only studies, and that empirical challenges to these dogmas are warranted. Additionally, we note some common pitfalls in this work. Most preclinical studies operationalize sex as a binary categorization, and while this approach has furthered the inclusion of non-male rodents it is not as such generalizable to what we know of sex as a multidimensional, dynamic variable. Exploration of sex variability may uncover both sex differences and sex similarities, but care must be taken in their interpretation. Including females in preclinical research needs to go beyond the investigation of sex differences, improving our knowledge of how this brain region and its subregions mediate behavior and health. This article is part of the Special Issue on "PFC circuit function in psychiatric disease and relevant models".

Pan-striatal reduction in the expression of the astrocytic dopamine transporter precedes the development of dorsolateral striatum dopamine-dependent incentive heroin seeking habits

The emergence of compulsive drug-seeking habits, a hallmark feature of substance use disorder, has been shown to be predicated on the engagement of dorsolateral striatal control over behaviour. This process involves the dopamine-dependent functional coupling of the anterior dorsolateral striatum (aDLS) with the nucleus accumbens core, but the mechanisms by which this coupling occurs have not been fully elucidated. The striatum is tiled by a syncytium of astrocytes that express the dopamine transporter (DAT), the level of which is altered in individuals with heroin use disorder. Astrocytes are therefore uniquely placed functionally to bridge dopamine-dependent mechanisms across the striatum. Here we tested the hypothesis that exposure to heroin influences the expression of DAT in striatal astrocytes across the striatum before the development of DLS-dependent incentive heroin seeking habits. Using Western-blot, qPCR, and RNAscope™, we measured DAT protein and mRNA levels in whole tissue, culture and in situ astrocytes from striatal territories of rats with a well-established cue-controlled heroin seeking habit and rats trained to respond for heroin or food under continuous reinforcement. Incentive heroin seeking habits were associated with a reduction in DAT protein levels in the anterior aDLS that was preceded by a heroin-induced reduction in DAT mRNA and protein in astrocytes across the striatum. Striatal astrocytes were also shown to be susceptible to direct dopamine- and opioid-induced downregulation of DAT expression. These results suggest that astrocytes may critically regulate the striatal dopaminergic adaptations that lead to the development of incentive heroin seeking habits.

Molecular and cellular mechanisms of selective vulnerability in neurodegenerative diseases

The selective vulnerability of specific neuronal subtypes is a hallmark of neurodegenerative diseases. In this Review, I summarize our current understanding of the brain regions and cell types that are selectively vulnerable in different neurodegenerative diseases and describe the proposed underlying cell-autonomous and non-cell-autonomous mechanisms. I highlight how recent methodological innovations - including single-cell transcriptomics, CRISPR-based screens and human cell-based models of disease - are enabling new breakthroughs in our understanding of selective vulnerability. An understanding of the molecular mechanisms that determine selective vulnerability and resilience would shed light on the key processes that drive neurodegeneration and point to potential therapeutic strategies to protect vulnerable cell populations.

Spot-Based Global Registration for Subpixel Stitching of Single-Molecule Resolution Images for Tissue-Scale Spatial Transcriptomics

Single-molecule imaging at the tissue scale has revolutionized our understanding of biology by providing unprecedented insight into the molecular expression of individual cells and their spatial organization within tissues. However, achieving precise image stitching at the single-molecule level remains a challenge, primarily due to heterogeneous background signals and dim labeling signals in single-molecule images. This paper introduces Spot-Based Global Registration (SBGR), a novel strategy that shifts the focus from raw images to identified molecular spots for high-resolution image alignment. The use of spot-based data enables straightforward and robust evaluation of the credibility of estimated translations and stitching performance. The method outperforms existing image-based stitching methods, achieving subpixel accuracy (83 ± 36 nm) with exceptional consistency. Furthermore, SBGR incorporates a mechanism to surgically remove duplicate spots in overlapping regions, maximizing information recovery from duplicate measurements. In conclusion, SBGR emerges as a robust and accurate solution for stitching single-molecule resolution images in tissue-scale spatial transcriptomics, offering versatility and potential for high-resolution spatial analysis.

Projection-TAGs enable multiplex projection tracing and multi-modal profiling of projection neurons

Single-cell multiomic techniques have sparked immense interest in developing a comprehensive multi-modal map of diverse neuronal cell types and their brain wide projections. However, investigating the spatial organization, transcriptional and epigenetic landscapes of brain wide projection neurons is hampered by the lack of efficient and easily adoptable tools. Here we introduce Projection-TAGs, a retrograde AAV platform that allows multiplex tagging of projection neurons using RNA barcodes. By using Projection-TAGs, we performed multiplex projection tracing of the mouse cortex and high-throughput single-cell profiling of the transcriptional and epigenetic landscapes of the cortical projection neurons. Projection-TAGs can be leveraged to obtain a snapshot of activity-dependent recruitment of distinct projection neurons and their molecular features in the context of a specific stimulus. Given its flexibility, usability, and compatibility, we envision that Projection-TAGs can be readily applied to build a comprehensive multi-modal map of brain neuronal cell types and their projections.

Benchmarking spatial clustering methods with spatially resolved transcriptomics data

Spatial clustering, which shares an analogy with single-cell clustering, has expanded the scope of tissue physiology studies from cell-centroid to structure-centroid with spatially resolved transcriptomics (SRT) data. Computational methods have undergone remarkable development in recent years, but a comprehensive benchmark study is still lacking. Here we present a benchmark study of 13 computational methods on 34 SRT data (7 datasets). The performance was evaluated on the basis of accuracy, spatial continuity, marker genes detection, scalability, and robustness. We found existing methods were complementary in terms of their performance and functionality, and we provide guidance for selecting appropriate methods for given scenarios. On testing additional 22 challenging datasets, we identified challenges in identifying noncontinuous spatial domains and limitations of existing methods, highlighting their inadequacies in handling recent large-scale tasks. Furthermore, with 145 simulated data, we examined the robustness of these methods against four different factors, and assessed the impact of pre- and postprocessing approaches. Our study offers a comprehensive evaluation of existing spatial clustering methods with SRT data, paving the way for future advancements in this rapidly evolving field.

AI-assisted mass spectrometry imaging with in situ image segmentation for subcellular metabolomics analysis

Subcellular metabolomics analysis is crucial for understanding intracellular heterogeneity and accurate drug-cell interactions. Unfortunately, the ultra-small size and complex microenvironment inside the cell pose a great challenge to achieving this goal. To address this challenge, we propose an artificial intelligence-assisted subcellular mass spectrometry imaging (AI-SMSI) strategy with in situ image segmentation. Based on the nanometer-resolution MSI technique, the protonated guanine and threonine ions were respectively employed as the nucleus and cytoplasmic markers to complete image segmentation at the subcellular level, avoiding mutual interference of signals from various compartments in the cell. With advanced AI models, the metabolites within the different regions could be further integrated and profiled. Through this method, we decrypted the distinct action mechanism of isomeric drugs, doxorubicin (DOX) and epirubicin (EPI), only with a stereochemical inversion at C-4'. Within the cytoplasmic region, fifteen specific metabolites were discovered as biomarkers for distinguishing the drug action difference between DOX and EPI. Moreover, we identified that the downregulations of glutamate and aspartate in the malate-aspartate shuttle pathway may contribute to the higher paratoxicity of DOX. Our current AI-SMSI approach has promising applications for subcellular metabolomics analysis and thus opens new opportunities to further explore drug-cell specific interactions for the long-term pursuit of precision medicine.

Spaco: A comprehensive tool for coloring spatial data at single-cell resolution

Understanding tissue architecture and niche-specific microenvironments in spatially resolved transcriptomics (SRT) requires in situ annotation and labeling of cells. Effective spatial visualization of these data demands appropriate colorization of numerous cell types. However, current colorization frameworks often inadequately account for the spatial relationships between cell types. This results in perceptual ambiguity in neighboring cells of biological distinct types, particularly in complex environments such as brain or tumor. To address this, we introduce Spaco, a potent tool for spatially aware colorization. Spaco utilizes the Degree of Interlacement metric to construct a weighted graph that evaluates the spatial relationships among different cell types, refining color assignments. Furthermore, Spaco incorporates an adaptive palette selection approach to amplify chromatic distinctions. When benchmarked on four diverse datasets, Spaco outperforms existing solutions, capturing complex spatial relationships and boosting visual clarity. Spaco ensures broad accessibility by accommodating color vision deficiency and offering open-accessible code in both Python and R.

Comparative analysis of multiplexed in situ gene expression profiling technologies

The burgeoning interest in in situ multiplexed gene expression profiling technologies has opened new avenues for understanding cellular behavior and interactions. In this study, we present a comparative benchmark analysis of six in situ gene expression profiling methods, including both commercially available and academically developed methods, using publicly accessible mouse brain datasets. We find that standard sensitivity metrics, such as the number of unique molecules detected per cell, are not directly comparable across datasets due to substantial differences in the incidence of off-target molecular artifacts impacting specificity. To address these challenges, we explored various potential sources of molecular artifacts, developed novel metrics to control for them, and utilized these metrics to evaluate and compare different in situ technologies. Finally, we demonstrate how molecular false positives can seriously confound spatially-aware differential expression analysis, requiring caution in the interpretation of downstream results. Our analysis provides guidance for the selection, processing, and interpretation of in situ spatial technologies.

Revisiting the development of cerebellar inhibitory interneurons in the light of single-cell genetic analyses

The present review aims to provide a short update of our understanding of the inhibitory interneurons of the cerebellum. While these cells constitute but a minority of all cerebellar neurons, their functional significance is increasingly being recognized. For one, inhibitory interneurons of the cerebellar cortex are now known to constitute a clearly more diverse group than their traditional grouping as stellate, basket, and Golgi cells suggests, and this diversity is now substantiated by single-cell genetic data. The past decade or so has also provided important information about interneurons in cerebellar nuclei. Significantly, developmental studies have revealed that the specification and formation of cerebellar inhibitory interneurons fundamentally differ from, say, the cortical interneurons, and define a mode of diversification critically dependent on spatiotemporally patterned external signals. Last, but not least, in the past years, dysfunction of cerebellar inhibitory interneurons could also be linked with clinically defined deficits. I hope that this review, however fragmentary, may stimulate interest and help focus research towards understanding the cerebellum.

CRISPRmap: Sequencing-free optical pooled screens mapping multi-omic phenotypes in cells and tissue

Pooled genetic screens are powerful tools to study gene function in a high-throughput manner. Typically, sequencing-based screens require cell lysis, which limits the examination of critical phenotypes such as cell morphology, protein subcellular localization, and cell-cell/tissue interactions. In contrast, emerging optical pooled screening methods enable the investigation of these spatial phenotypes in response to targeted CRISPR perturbations. In this study, we report a multi-omic optical pooled CRISPR screening method, which we have named CRISPRmap. Our method combines a novel in situ CRISPR guide identifying barcode readout approach with concurrent multiplexed immunofluorescence and in situ RNA detection. CRISPRmap barcodes are detected and read out through combinatorial hybridization of DNA oligos, enhancing barcode detection efficiency, while reducing both dependency on third party proprietary sequencing reagents and assay cost. Notably, we conducted a multi-omic base-editing screen in a breast cancer cell line on core DNA damage repair genes involved in the homologous recombination and Fanconi anemia pathways investigating how nucleotide variants in those genes influence DNA damage signaling and cell cycle regulation following treatment with ionizing radiation or DNA damaging agents commonly used for cancer therapy. Approximately a million cells were profiled with our multi-omic approach, providing a comprehensive phenotypic assessment of the functional consequences of the studied variants. CRISPRmap enabled us to pinpoint likely-pathogenic patient-derived mutations that were previously classified as variants of unknown clinical significance. Furthermore, our approach effectively distinguished barcodes of a pooled library in tumor tissue, and we coupled it with cell-type and molecular phenotyping by cyclic immunofluorescence. Multi-omic spatial analysis of how CRISPR-perturbed cells respond to various environmental cues in the tissue context offers the potential to significantly expand our understanding of tissue biology in both health and disease.

STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping

Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we develop STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit at https://github.com/JEFworks-Lab/STalign and as Supplementary Software with additional documentation and tutorials available at https://jef.works/STalign .