BRAIN 2.0: Transforming neuroscience

Green-to-red spectral labeling: A novel polysynaptic retrograde tracing strategy in the marker footprint mouse model

BACKGROUND

Rabies virus (RABV)-derived neuronal tracing tools are extensively applied in retrograde tracing due to their strict retrograde transsynaptic transfer property and low neurotoxicity. However, the RABV infection and expression of fluorescence products would be gradually cleared while the infected neurons still survive, a phenomenon known as non-cytolytic immune clearance (NCLIC). This phenomenon introduced the risk of fluorescence loss and led to the omission of a subset of neurons that should be labeled, thereby interfering in the analysis of tracing results.

METHODS

To compensate for the fluorescence loss problem, in this study, we developed a novel marker footprints (MF) mouse, involving a Cre recombinase-dependent red fluorescent reporter system and systemic expression of glycoprotein (G) and ASLV-A receptor (TVA). Using this mouse model combined with the well-developed RABV-EnvA-ΔG-GFP-Cre viral tool, we developed a novel green-to-red spectral labeling strategy.

RESULTS

Neurons in the MF mouse could be co-labeled with green fluorescence from the very quick expression of the viral tool and with red fluorescence from the relatively slow expression of the neuron itself, so neurons undergoing NCLIC with green fluorescence loss could be relabeled red. Furthermore, newly infected neurons could be labeled green and other neurons could be labeled yellow due to the temporal expression difference between the two fluorescent proteins.

CONCLUSIONS

This is the first polysynaptic retrograde tracing labeling strategy that could label neurons using spectral fluorescence colors with only one injection of the viral tool, enabling its application in recognizing the labeling sequence of neurons in brain regions and enhancing the spatiotemporal resolution of neuronal tracing.

Sialic acid and PirB are not required for targeting of neural circuits by neurotropic mammalian orthoreovirus

Serotype 3 (T3) strains of mammalian orthoreovirus (reovirus) spread to the central nervous system to infect the brain and cause lethal encephalitis in newborn mice. Although reovirus targets several regions in the brain, susceptibility to infection is not uniformly distributed. The neuronal subtypes and anatomic sites targeted throughout the brain are not precisely known. Reovirus binds several attachment factors and entry receptors, including sialic acid (SA)-containing glycans and paired immunoglobulin-like receptor B (PirB). While these receptors are not required for infection of some types of neurons, reovirus engagement of these receptors can influence neuronal infection in certain contexts. To identify patterns of T3 neurotropism, we used microbial identification after passive tissue clearance and hybridization chain reaction to stain reovirus-infected cells throughout intact, optically transparent brains of newborn mice. Three-dimensional reconstructions revealed in detail the sites targeted by reovirus throughout the brain volume, including dense infection of the midbrain and hindbrain. Using reovirus mutants incapable of binding SA and mice lacking PirB expression, we found that neither SA nor PirB is required for the infection of various brain regions. However, SA may confer minor differences in infection that vary by region. Collectively, these studies indicate that many regions in the brain of newborn mice are susceptible to reovirus and that patterns of reovirus infection are not dependent on reovirus receptors SA and PirB.IMPORTANCENeurotropic viruses invade the central nervous system (CNS) and target various cell types to cause disease manifestations, such as meningitis, myelitis, or encephalitis. Infections of the CNS are often difficult to treat and can lead to lasting sequelae or death. Mammalian orthoreovirus (reovirus) causes age-dependent lethal encephalitis in many young mammals. Reovirus infects neurons in several different regions of the brain. However, the complete pattern of CNS infection is not understood. We found that reovirus targets almost all regions of the brain and that patterns of tropism are not dependent on receptors sialic acid and paired immunoglobulin-like receptor B. These studies confirm that two known reovirus receptors do not completely explain the cell types infected in brain tissue and establish strategies that can be used to understand complete patterns of viral tropism in an intact brain.

Collaborative augmented reconstruction of 3D neuron morphology in mouse and human brains

Digital reconstruction of the intricate 3D morphology of individual neurons from microscopic images is a crucial challenge in both individual laboratories and large-scale projects focusing on cell types and brain anatomy. This task often fails in both conventional manual reconstruction and state-of-the-art artificial intelligence (AI)-based automatic reconstruction algorithms. It is also challenging to organize multiple neuroanatomists to generate and cross-validate biologically relevant and mutually agreed upon reconstructions in large-scale data production. Based on collaborative group intelligence augmented by AI, we developed a collaborative augmented reconstruction (CAR) platform for neuron reconstruction at scale. This platform allows for immersive interaction and efficient collaborative editing of neuron anatomy using a variety of devices, such as desktop workstations, virtual reality headsets and mobile phones, enabling users to contribute anytime and anywhere and to take advantage of several AI-based automation tools. We tested CAR's applicability for challenging mouse and human neurons toward scaled and faithful data production.

MBV-Pipe: A One-Stop Toolbox for Assessing Mouse Brain Morphological Changes for Cross-Scale Studies

Mouse models are crucial for neuroscience research, yet discrepancies arise between macro- and meso-scales due to sample preparation altering brain morphology. The absence of an accessible toolbox for magnetic resonance imaging (MRI) data processing presents a challenge for assessing morphological changes in the mouse brain. To address this, we developed the MBV-Pipe (Mouse Brain Volumetric Statistics-Pipeline) toolbox, integrating the methods of Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL)-Voxel-based morphometry (VBM) and Tract-Based Spatial Statistics (TBSS) to evaluate brain tissue volume and white matter integrity. To validate the reliability of MBV-Pipe, brain MRI data from seven mice at three time points (in vivo, post-perfusion, and post-fixation) were acquired using a 9.4T ultra-high MRI system. Employing the MBV-Pipe toolbox, we discerned substantial volumetric changes in the mouse brain following perfusion relative to the in vivo condition, with the fixation process inducing only negligible variations. Importantly, the white matter integrity was found to be largely stable throughout the sample preparation procedures. The MBV-Pipe source code is publicly available and includes a user-friendly GUI for facilitating quality control and experimental protocol optimization, which holds promise for advancing mouse brain research in the future.

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative1-6, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 107 chemical synapses7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster8,9. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities10-12. Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome-a map of projections between regions-from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.

BRAIN @ 10: A decade of innovation

Now entering its second decade, the National Institutes of Health Brain Research Through Advancing Innovative Neurotechnologies Initiative, or the NIH BRAIN Initiative, has yielded remarkable success, accelerating research on the neural circuit basis of behavior and breaking new ground toward the treatment of complex human brain disorders.

Small data methods in omics: the power of one

Over the last decade, biology has begun utilizing 'big data' approaches, resulting in large, comprehensive atlases in modalities ranging from transcriptomics to neural connectomics. However, these approaches must be complemented and integrated with 'small data' approaches to efficiently utilize data from individual labs. Integration of smaller datasets with major reference atlases is critical to provide context to individual experiments, and approaches toward integration of large and small data have been a major focus in many fields in recent years. Here we discuss progress in integration of small data with consortium-sized atlases across multiple modalities, and its potential applications. We then examine promising future directions for utilizing the power of small data to maximize the information garnered from small-scale experiments. We envision that, in the near future, international consortia comprising many laboratories will work together to collaboratively build reference atlases and foundation models using small data methods.

Modeling and Analysis of Environmental Electromagnetic Interference in Multiple-Channel Neural Recording Systems for High Common-Mode Interference Rejection Performance

Environmental electromagnetic interference (EMI) has always been a major interference source for multiple-channel neural recording systems, and little theoretical work has been attempted to address it. In this paper, equivalent circuit models are proposed to model both electromagnetic interference sources and neural signals in such systems, and analysis has been performed to generate the design guidelines for neural probes and the subsequent recording circuit towards higher common-mode interference (CMI) rejection performance while maintaining the recorded neural action potential (AP) signal quality. In vivo animal experiments with a configurable 32-channel neural recording system are carried out to validate the proposed models and design guidelines. The results show the power spectral density (PSD) of environmental 50 Hz EMI interference is reduced by three orders from 4.43 × 10-3 V2/Hz to 4.04 × 10-6 V2/Hz without affecting the recorded AP signal quality in an unshielded experiment environment.

Computational Tools for Neuronal Morphometric Analysis: A Systematic Search and Review

Morphometry is fundamental for studying and correlating neuronal morphology with brain functions. With increasing computational power, it is possible to extract morphometric characteristics automatically, including features such as length, volume, and number of neuron branches. However, to the best of our knowledge, there is no mapping of morphometric tools yet. In this context, we conducted a systematic search and review to identify and analyze tools within the scope of neuron analysis. Thus, the work followed a well-defined protocol and sought to answer the following research questions: What open-source tools are available for neuronal morphometric analysis? What morphometric characteristics are extracted by these tools? For this, aiming for greater robustness and coverage, the study was based on the paper analysis as well as the study of documentation and tests with the tools available in repositories. We analyzed 1,586 papers and mapped 23 tools, where NeuroM, L-Measure, and NeuroMorphoVis extract the most features. Furthermore, we contribute to the body of knowledge with the unprecedented presentation of 150 unique morphometric features whose terminologies were categorized and standardized. Overall, the study contributes to advancing the understanding of the complex mechanisms underlying the brain.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Large-scale neurophysiology and single-cell profiling in human neuroscience

Advances in large-scale single-unit human neurophysiology, single-cell RNA sequencing, spatial transcriptomics and long-term ex vivo tissue culture of surgically resected human brain tissue have provided an unprecedented opportunity to study human neuroscience. In this Perspective, we describe the development of these paradigms, including Neuropixels and recent brain-cell atlas efforts, and discuss how their convergence will further investigations into the cellular underpinnings of network-level activity in the human brain. Specifically, we introduce a workflow in which functionally mapped samples of human brain tissue resected during awake brain surgery can be cultured ex vivo for multi-modal cellular and functional profiling. We then explore how advances in human neuroscience will affect clinical practice, and conclude by discussing societal and ethical implications to consider. Potential findings from the field of human neuroscience will be vast, ranging from insights into human neurodiversity and evolution to providing cell-type-specific access to study and manipulate diseased circuits in pathology. This Perspective aims to provide a unifying framework for the field of human neuroscience as we welcome an exciting era for understanding the functional cytoarchitecture of the human brain.

Development of wafer-scale multifunctional nanophotonic neural probes for brain activity mapping

Optical techniques, such as optogenetic stimulation and functional fluorescence imaging, have been revolutionary for neuroscience by enabling neural circuit analysis with cell-type specificity. To probe deep brain regions, implantable light sources are crucial. Silicon photonics, commonly used for data communications, shows great promise in creating implantable devices with complex optical systems in a compact form factor compatible with high volume manufacturing practices. This article reviews recent developments of wafer-scale multifunctional nanophotonic neural probes. The probes can be realized on 200 or 300 mm wafers in commercial foundries and integrate light emitters for photostimulation, microelectrodes for electrophysiological recording, and microfluidic channels for chemical delivery and sampling. By integrating active optical devices to the probes, denser emitter arrays, enhanced on-chip biosensing, and increased ease of use may be realized. Silicon photonics technology makes possible highly versatile implantable neural probes that can transform neuroscience experiments.

Molecular tools for recording and intervention of neuronal activity

Observing the activity of neural networks is critical for the identification of learning and memory processes, as well as abnormal activities of neural circuits in disease, particularly for the purpose of tracking disease progression. Methodologies for describing the activity history of neural networks using molecular biology techniques first utilized genes expressed by active neurons, followed by the application of recently developed techniques including optogenetics and incorporation of insights garnered from other disciplines, including chemistry and physics. In this review, we will discuss ways in which molecular biological techniques used to describe the activity of neural networks have evolved along with the potential for future development.

Data science and its future in large neuroscience collaborations

The rise of large scientific collaborations in neuroscience requires systematic, scalable, and reliable data management. How this is best done in practice remains an open question. To address this, we conducted a data science survey among currently active U19 grants, funded through the NIH's BRAIN Initiative. The survey was answered by both data science liaisons and Principal Investigators, speaking for ~500 researchers across 21 nation-wide collaborations. We describe the tools, technologies, and methods currently in use, and identify several shortcomings of current data science practice. Building on this survey, we develop plans and propose policies to improve data collection, use, publication, reuse and training in the neuroscience community.

NSF DARE-transforming modeling in neurorehabilitation: perspectives and opportunities from US funding agencies

In recognition of the importance and timeliness of computational models for accelerating progress in neurorehabilitation, the U.S. National Science Foundation (NSF) and the National Institutes of Health (NIH) sponsored a conference in March 2023 at the University of Southern California that drew global participation from engineers, scientists, clinicians, and trainees. This commentary highlights promising applications of computational models to understand neurorehabilitation ("Using computational models to understand complex mechanisms in neurorehabilitation" section), improve rehabilitation care in the context of digital twin frameworks ("Using computational models to improve delivery and implementation of rehabilitation care" section), and empower future interdisciplinary workforces to deliver higher-quality clinical care using computational models ("Using computational models in neurorehabilitation requires an interdisciplinary workforce" section). The authors describe near-term gaps and opportunities, all of which encourage interdisciplinary team science. Four major opportunities were identified including (1) deciphering the relationship between engineering figures of merit-a term commonly used by engineers to objectively quantify the performance of a device, system, method, or material relative to existing state of the art-and clinical outcome measures, (2) validating computational models from engineering and patient perspectives, (3) creating and curating datasets that are made publicly accessible, and (4) developing new transdisciplinary frameworks, theories, and models that incorporate the complexities of the nervous and musculoskeletal systems. This commentary summarizes U.S. funding opportunities by two Federal agencies that support computational research in neurorehabilitation. The NSF has funding programs that support high-risk/high-reward research proposals on computational methods in neurorehabilitation informed by theory- and data-driven approaches. The NIH supports the development of new interventions and therapies for a wide range of nervous system injuries and impairments informed by the field of computational modeling. The conference materials can be found at https://dare2023.usc.edu/ .

Precision psychiatry and Research Domain Criteria: Implications for clinical trials and future practice

Psychiatric disorders are associated with significant social and economic burdens, many of which are related to issues with current diagnosis and treatments. The coronavirus (COVID-19) pandemic is estimated to have increased the prevalence and burden of major depressive and anxiety disorders, indicating an urgent need to strengthen mental health systems globally. To date, current approaches adopted in drug discovery and development for psychiatric disorders have been relatively unsuccessful. Precision psychiatry aims to tailor healthcare more closely to the needs of individual patients and, when informed by neuroscience, can offer the opportunity to improve the accuracy of disease classification, treatment decisions, and prevention efforts. In this review, we highlight the growing global interest in precision psychiatry and the potential for the National Institute of Health-devised Research Domain Criteria (RDoC) to facilitate the implementation of transdiagnostic and improved treatment approaches. The need for current psychiatric nosology to evolve with recent scientific advancements and increase awareness in emerging investigators/clinicians of the value of this approach is essential. Finally, we examine current challenges and future opportunities of adopting the RDoC-associated translational and transdiagnostic approaches in clinical studies, acknowledging that the strength of RDoC is that they form a dynamic framework of guiding principles that is intended to evolve continuously with scientific developments into the future. A collaborative approach that recruits expertise from multiple disciplines, while also considering the patient perspective, is needed to pave the way for precision psychiatry that can improve the prognosis and quality of life of psychiatric patients.

Special Issue-Biosensors and Neuroscience: Is Biosensors Engineering Ready to Embrace Design Principles from Neuroscience?

In partnership with the Air Force Office of Scientific Research (AFOSR), the National Science Foundation's (NSF) Emerging Frontiers and Multidisciplinary Activities (EFMA) office of the Directorate for Engineering (ENG) launched an Emerging Frontiers in Research and Innovation (EFRI) topic for the fiscal years FY22 and FY23 entitled "Brain-inspired Dynamics for Engineering Energy-Efficient Circuits and Artificial Intelligence" (BRAID) [...].

A full-body transcription factor expression atlas with completely resolved cell identities in C. elegans

Invariant cell lineage in C. elegans enables spatiotemporal resolution of transcriptional regulatory mechanisms controlling the fate of each cell. Here, we develop RAPCAT (Robust-point-matching- And Piecewise-affine-based Cell Annotation Tool) to automate cell identity assignment in three-dimensional image stacks of L1 larvae and profile reporter expression of 620 transcription factors in every cell. Transcription factor profile-based clustering analysis defines 80 cell types distinct from conventional phenotypic cell types and identifies three general phenotypic modalities related to these classifications. First, transcription factors are broadly downregulated in quiescent stage Hermaphrodite Specific Neurons, suggesting stage- and cell type-specific variation in transcriptome size. Second, transcription factor expression is more closely associated with morphology than other phenotypic modalities in different pre- and post-differentiation developmental stages. Finally, embryonic cell lineages can be associated with specific transcription factor expression patterns and functions that persist throughout postembryonic life. This study presents a comprehensive transcription factor atlas for investigation of intra-cell type heterogeneity.

Genetics of human brain development

Brain development in humans is achieved through precise spatiotemporal genetic control, the mechanisms of which remain largely elusive. Recently, integration of technological advances in human stem cell-based modelling with genome editing has emerged as a powerful platform to establish causative links between genotypes and phenotypes directly in the human system. Here, we review our current knowledge of complex genetic regulation of each key step of human brain development through the lens of evolutionary specialization and neurodevelopmental disorders and highlight the use of human stem cell-derived 2D cultures and 3D brain organoids to investigate human-enriched features and disease mechanisms. We also discuss opportunities and challenges of integrating new technologies to reveal the genetic architecture of human brain development and disorders.

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain

The mammalian brain consists of millions to billions of cells that are organized into many cell types with specific spatial distribution patterns and structural and functional properties1-3. Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing quality control), and a spatial transcriptomic dataset of approximately 4.3 million cells using multiplexed error-robust fluorescence in situ hybridization (MERFISH). The atlas is hierarchically organized into 4 nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, to visualize the mouse whole-brain cell-type atlas along with the single-cell RNA-sequencing and MERFISH datasets. We systematically analysed the neuronal and non-neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell-type organization in different brain regions-in particular, a dichotomy between the dorsal and ventral parts of the brain. The dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. Our study also uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types. Finally, we found that transcription factors are major determinants of cell-type classification and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole mouse brain transcriptomic and spatial cell-type atlas establishes a benchmark reference atlas and a foundational resource for integrative investigations of cellular and circuit function, development and evolution of the mammalian brain.

Probing neural circuit mechanisms in Alzheimer's disease using novel technologies

The study of Alzheimer's Disease (AD) has traditionally focused on neuropathological mechanisms that has guided therapies that attenuate neuropathological features. A new direction is emerging in AD research that focuses on the progressive loss of cognitive function due to disrupted neural circuit mechanisms. Evidence from humans and animal models of AD show that dysregulated circuits initiate a cascade of pathological events that culminate in functional loss of learning, memory, and other aspects of cognition. Recent progress in single-cell, spatial, and circuit omics informs this circuit-focused approach by determining the identities, locations, and circuitry of the specific cells affected by AD. Recently developed neuroscience tools allow for precise access to cell type-specific circuitry so that their functional roles in AD-related cognitive deficits and disease progression can be tested. An integrated systems-level understanding of AD-associated neural circuit mechanisms requires new multimodal and multi-scale interrogations that longitudinally measure and/or manipulate the ensemble properties of specific molecularly-defined neuron populations first susceptible to AD. These newly developed technological and conceptual advances present new opportunities for studying and treating circuits vulnerable in AD and represent the beginning of a new era for circuit-based AD research.

A Novel Mouse Model for Polysynaptic Retrograde Tracing and Rabies Pathological Research

Retrograde tracing is an important method for dissecting neuronal connections and mapping neural circuits. Over the past decades, several virus-based retrograde tracers have been developed and have contributed to display multiple neural circuits in the brain. However, most of the previously widely used viral tools have focused on mono-transsynaptic neural tracing within the central nervous system, with very limited options for achieving polysynaptic tracing between the central and peripheral nervous systems. In this study, we generated a novel mouse line, GT mice, in which both glycoprotein (G) and ASLV-A receptor (TVA) were expressed throughout the body. Using this mouse model, in combination with the well-developed rabies virus tools (RABV-EnvA-ΔG) for monosynaptic retrograde tracing, polysynaptic retrograde tracing can be achieved. This allows functional forward mapping and long-term tracing. Furthermore, since the G-deleted rabies virus can travel upstream against the nervous system as the original strain, this mouse model can also be used for rabies pathological studies. Schematic illustrations about the application principles of GT mice in polysynaptic retrograde tracing and rabies pathological research.

Adeno-associated viral vectors for functional intravenous gene transfer throughout the non-human primate brain

Crossing the blood-brain barrier in primates is a major obstacle for gene delivery to the brain. Adeno-associated viruses (AAVs) promise robust, non-invasive gene delivery from the bloodstream to the brain. However, unlike in rodents, few neurotropic AAVs efficiently cross the blood-brain barrier in non-human primates. Here we report on AAV.CAP-Mac, an engineered variant identified by screening in adult marmosets and newborn macaques, which has improved delivery efficiency in the brains of multiple non-human primate species: marmoset, rhesus macaque and green monkey. CAP-Mac is neuron biased in infant Old World primates, exhibits broad tropism in adult rhesus macaques and is vasculature biased in adult marmosets. We demonstrate applications of a single, intravenous dose of CAP-Mac to deliver functional GCaMP for ex vivo calcium imaging across multiple brain areas, or a cocktail of fluorescent reporters for Brainbow-like labelling throughout the macaque brain, circumventing the need for germline manipulations in Old World primates. As such, CAP-Mac is shown to have potential for non-invasive systemic gene transfer in the brains of non-human primates.

High-volume hybridoma sequencing on the NeuroMabSeq platform enables efficient generation of recombinant monoclonal antibodies and scFvs for neuroscience research

The Neuroscience Monoclonal Antibody Sequencing Initiative (NeuroMabSeq) is a concerted effort to determine and make publicly available hybridoma-derived sequences of monoclonal antibodies (mAbs) valuable to neuroscience research. Over 30 years of research and development efforts including those at the UC Davis/NIH NeuroMab Facility have resulted in the generation of a large collection of mouse mAbs validated for neuroscience research. To enhance dissemination and increase the utility of this valuable resource, we applied a high-throughput DNA sequencing approach to determine immunoglobulin heavy and light chain variable domain sequences from source hybridoma cells. The resultant set of sequences was made publicly available as a searchable DNA sequence database (neuromabseq.ucdavis.edu) for sharing, analysis and use in downstream applications. We enhanced the utility, transparency, and reproducibility of the existing mAb collection by using these sequences to develop recombinant mAbs. This enabled their subsequent engineering into alternate forms with distinct utility, including alternate modes of detection in multiplexed labeling, and as miniaturized single chain variable fragments or scFvs. The NeuroMabSeq website and database and the corresponding recombinant antibody collection together serve as a public DNA sequence repository of mouse mAb heavy and light chain variable domain sequences and as an open resource for enhancing dissemination and utility of this valuable collection of validated mAbs.

Cortical somatostatin interneuron subtypes form cell-type-specific circuits

The cardinal classes are a useful simplification of cortical interneuron diversity, but such broad subgroupings gloss over the molecular, morphological, and circuit specificity of interneuron subtypes, most notably among the somatostatin interneuron class. Although there is evidence that this diversity is functionally relevant, the circuit implications of this diversity are unknown. To address this knowledge gap, we designed a series of genetic strategies to target the breadth of somatostatin interneuron subtypes and found that each subtype possesses a unique laminar organization and stereotyped axonal projection pattern. Using these strategies, we examined the afferent and efferent connectivity of three subtypes (two Martinotti and one non-Martinotti) and demonstrated that they possess selective connectivity with intratelecephalic or pyramidal tract neurons. Even when two subtypes targeted the same pyramidal cell type, their synaptic targeting proved selective for particular dendritic compartments. We thus provide evidence that subtypes of somatostatin interneurons form cell-type-specific cortical circuits.

Padlock Probe-Based Targeted In Situ Sequencing: Overview of Methods and Applications

Elucidating spatiotemporal changes in gene expression has been an essential goal in studies of health, development, and disease. In the emerging field of spatially resolved transcriptomics, gene expression profiles are acquired with the tissue architecture maintained, sometimes at cellular resolution. This has allowed for the development of spatial cell atlases, studies of cell-cell interactions, and in situ cell typing. In this review, we focus on padlock probe-based in situ sequencing, which is a targeted spatially resolved transcriptomic method. We summarize recent methodological and computational tool developments and discuss key applications. We also discuss compatibility with other methods and integration with multiomic platforms for future applications.

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

NeuroMabSeq: high volume acquisition, processing, and curation of hybridoma sequences and their use in generating recombinant monoclonal antibodies and scFvs for neuroscience research

The Neuroscience Monoclonal Antibody Sequencing Initiative (NeuroMabSeq) is a concerted effort to determine and make publicly available hybridoma-derived sequences of monoclonal antibodies (mAbs) valuable to neuroscience research. Over 30 years of research and development efforts including those at the UC Davis/NIH NeuroMab Facility have resulted in the generation of a large collection of mouse mAbs validated for neuroscience research. To enhance dissemination and increase the utility of this valuable resource, we applied a high-throughput DNA sequencing approach to determine immunoglobulin heavy and light chain variable domain sequences from source hybridoma cells. The resultant set of sequences was made publicly available as searchable DNA sequence database ( neuromabseq.ucdavis.edu ) for sharing, analysis and use in downstream applications. We enhanced the utility, transparency, and reproducibility of the existing mAb collection by using these sequences to develop recombinant mAbs. This enabled their subsequent engineering into alternate forms with distinct utility, including alternate modes of detection in multiplexed labeling, and as miniaturized single chain variable fragments or scFvs. The NeuroMabSeq website and database and the corresponding recombinant antibody collection together serve as a public DNA sequence repository of mouse mAb heavy and light chain variable domain sequences and as an open resource for enhancing dissemination and utility of this valuable collection of validated mAbs.

Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease

In the past decade, single-cell technologies have proliferated and improved from their technically challenging beginnings to become common laboratory methods capable of determining the expression of thousands of genes in thousands of cells simultaneously. The field has progressed by taking the CNS as a primary research subject - the cellular complexity and multiplicity of neuronal cell types provide fertile ground for the increasing power of single-cell methods. Current single-cell RNA sequencing methods can quantify gene expression with sufficient accuracy to finely resolve even subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the CNS and its disorders. However, single-cell RNA sequencing requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss how single-cell and spatially resolved transcriptomics have been contributing to unravelling the pathomechanisms underlying brain disorders. We focus on three areas where we feel these new technologies have provided particularly useful insights: selective neuronal vulnerability, neuroimmune dysfunction and cell-type-specific treatment response. We also discuss the limitations and future directions of single-cell and spatial RNA sequencing technologies.

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain

The mammalian brain is composed of millions to billions of cells that are organized into numerous cell types with specific spatial distribution patterns and structural and functional properties. An essential step towards understanding brain function is to obtain a parts list, i.e., a catalog of cell types, of the brain. Here, we report a comprehensive and high-resolution transcriptomic and spatial cell type atlas for the whole adult mouse brain. The cell type atlas was created based on the combination of two single-cell-level, whole-brain-scale datasets: a single-cell RNA-sequencing (scRNA-seq) dataset of ~7 million cells profiled, and a spatially resolved transcriptomic dataset of ~4.3 million cells using MERFISH. The atlas is hierarchically organized into five nested levels of classification: 7 divisions, 32 classes, 306 subclasses, 1,045 supertypes and 5,200 clusters. We systematically analyzed the neuronal, non-neuronal, and immature neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell type organization in different brain regions, in particular, a dichotomy between the dorsal and ventral parts of the brain: the dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. We also systematically characterized cell-type specific expression of neurotransmitters, neuropeptides, and transcription factors. The study uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types across the brain, suggesting they mediate a myriad of modes of intercellular communications. Finally, we found that transcription factors are major determinants of cell type classification in the adult mouse brain and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole-mouse-brain transcriptomic and spatial cell type atlas establishes a benchmark reference atlas and a foundational resource for deep and integrative investigations of cell type and circuit function, development, and evolution of the mammalian brain.

Single-cell transcriptome profiles of Drosophila fruitless-expressing neurons from both sexes

Drosophila melanogaster reproductive behaviors are orchestrated by fruitless neurons. We performed single-cell RNA-sequencing on pupal neurons that produce sex-specifically spliced fru transcripts, the fru P1-expressing neurons. Uniform Manifold Approximation and Projection (UMAP) with clustering generates an atlas containing 113 clusters. While the male and female neurons overlap in UMAP space, more than half the clusters have sex differences in neuron number, and nearly all clusters display sex-differential expression. Based on an examination of enriched marker genes, we annotate clusters as circadian clock neurons, mushroom body Kenyon cell neurons, neurotransmitter- and/or neuropeptide-producing, and those that express doublesex. Marker gene analyses also show that genes that encode members of the immunoglobulin superfamily of cell adhesion molecules, transcription factors, neuropeptides, neuropeptide receptors, and Wnts have unique patterns of enriched expression across the clusters. In vivo spatial gene expression links to the clusters are examined. A functional analysis of fru P1 circadian neurons shows they have dimorphic roles in activity and period length. Given that most clusters are comprised of male and female neurons indicates that the sexes have fru P1 neurons with common gene expression programs. Sex-specific expression is overlaid on this program, to build the potential for vastly different sex-specific behaviors.

Perspectives on single-nucleus RNA sequencing in different cell types and tissues

Single-cell RNA sequencing has become a powerful and essential tool for delineating cellular diversity in normal tissues and alterations in disease states. For certain cell types and conditions, there are difficulties in isolating intact cells for transcriptome profiling due to their fragility, large size, tight interconnections, and other factors. Single-nucleus RNA sequencing (snRNA-seq) is an alternative or complementary approach for cells that are difficult to isolate. In this review, we will provide an overview of the experimental and analysis steps of snRNA-seq to understand the methods and characteristics of general and tissue-specific snRNA-seq data. Knowing the advantages and limitations of snRNA-seq will increase its use and improve the biological interpretation of the data generated using this technique.

Decoding psychosis: from national genome project to national brain project

The mind has puzzled humans for centuries, and its disorders, such as psychoses, have caused tremendous difficulties. However, relatively recent biotechnological breakthroughs, such as DNA technology and neuroimaging, have empowered scientists to explore the more fundamental aspects of psychosis. From searching for psychosis-causing genes to imaging the depths of the brain, scientists worldwide seek novel methods to understand the mind and the causes of its disorders. This article will briefly review the history of understanding and managing psychosis and the main findings of modern genetic research and then attempt to stimulate thought for decoding the biological mechanisms of psychosis in the present era of brain science.

What is a cell type and how to define it?

Cell types are the basic functional units of an organism. Cell types exhibit diverse phenotypic properties at multiple levels, making them challenging to define, categorize, and understand. This review provides an overview of the basic principles of cell types rooted in evolution and development and discusses approaches to characterize and classify cell types and investigate how they contribute to the organism's function, using the mammalian brain as a primary example. I propose a roadmap toward a conceptual framework and knowledge base of cell types that will enable a deeper understanding of the dynamic changes of cellular function under healthy and diseased conditions.

Transcriptome, connectome and neuromodulation of the primate brain

Single-cell transcriptomic analysis has facilitated cell type identification in the brain and mapping of cell type-specific connectomes, helping to elucidate neural circuits underlying brain functions and to treat brain disorders by neuromodulation. Yet, we lack a consensual definition of neuronal types/subtypes and clear distinction between cause and effect within interconnected networks.

Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function

Psychiatric and neurological disorders are influenced by an undetermined number of genes and molecular pathways that may differ among afflicted individuals. Functionally testing and characterizing biological systems is essential to discovering the interrelationship among candidate genes and understanding the neurobiology of behavior. Recent advancements in genetic, genomic, and behavioral approaches are revolutionizing modern neuroscience. Although these tools are often used separately for independent experiments, combining these areas of research will provide a viable avenue for multidimensional studies on the brain. Herein we will briefly review some of the available tools that have been developed for characterizing novel cellular and animal models of human disease. A major challenge will be openly sharing resources and datasets to effectively integrate seemingly disparate types of information and how these systems impact human disorders. However, as these emerging technologies continue to be developed and adopted by the scientific community, they will bring about unprecedented opportunities in our understanding of molecular neuroscience and behavior.

The dark side of synaptic proteins in tumours

Research in the past decade has uncovered the essential role of the nervous system in the tumour microenvironment. The recent advances in cancer neuroscience, especially the discovery of neuron-tumour synaptic/perisynaptic structures, have revealed the dark side of synaptic proteins in the progression of brain tumours. Here, we provide an overview of the synaptic proteins expressed by tumour cells and analyse their molecular functions and organisation by comparing them with neuronal synaptic proteins. We focus on the studies of neuroligin-3, the glutamate receptors AMPAR and NMDAR and the synaptic scaffold protein DLGAP1, for their newly discovered regulatory role in the proliferation and progression of tumours. Progress in cancer neuroscience has brought novel insights into the treatment of cancers. In the last part of this review, we discuss the therapeutical strategies targeting synaptic proteins and the current challenges and possible toolkits regarding their clinical application in cancer treatment. Our understanding of cancer neuroscience is still in its infancy; deeper investigation of how tumour cells co-opt synaptic signaling will help fulfil the therapeutical potential of the synaptic proteins as promising anti-tumour targets.