A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation

Leveraging the Cell Ontology to classify unseen cell types

Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology. A key advantage of OnClass is its capability to classify cells into cell types not present in the training data because it uses the Cell Ontology graph to infer cell type relationships. Furthermore, OnClass can be used to identify marker genes for all the cell ontology categories, regardless of whether the cell types are present or absent in the training data, suggesting that OnClass goes beyond a simple annotation tool for single cell datasets, being the first algorithm capable to identify marker genes specific to all terms of the Cell Ontology and offering the possibility of refining the Cell Ontology using a data-centric approach.

Cellular resolution anatomical and molecular atlases for prenatal human brains

Increasing interest in studies of prenatal human brain development, particularly using new single‐cell genomics and anatomical technologies to create cell atlases, creates a strong need for accurate and detailed anatomical reference atlases. In this study, we present two cellular‐resolution digital anatomical atlases for prenatal human brain at postconceptional weeks (PCW) 15 and 21. Both atlases were annotated on sequential Nissl‐stained sections covering brain‐wide structures on the basis of combined analysis of cytoarchitecture, acetylcholinesterase staining, and an extensive marker gene expression dataset. This high information content dataset allowed reliable and accurate demarcation of developing cortical and subcortical structures and their subdivisions. Furthermore, using the anatomical atlases as a guide, spatial expression of 37 and 5 genes from the brains, respectively, at PCW 15 and 21 was annotated, illustrating reliable marker genes for many developing brain structures. Finally, the present study uncovered several novel developmental features, such as the lack of an outer subventricular zone in the hippocampal formation and entorhinal cortex, and the apparent extension of both cortical (excitatory) and subcortical (inhibitory) progenitors into the prenatal olfactory bulb. These comprehensive atlases provide useful tools for visualization, segmentation, targeting, imaging, and interpretation of brain structures of prenatal human brain, and for guiding and interpreting the next generation of cell census and connectome studies.

Evaluation of some aspects in supervised cell type identification for single-cell RNA-seq: classifier, feature selection, and reference construction

BACKGROUND

Cell type identification is one of the most important questions in single-cell RNA sequencing (scRNA-seq) data analysis. With the accumulation of public scRNA-seq data, supervised cell type identification methods have gained increasing popularity due to better accuracy, robustness, and computational performance. Despite all the advantages, the performance of the supervised methods relies heavily on several key factors: feature selection, prediction method, and, most importantly, choice of the reference dataset.

RESULTS

In this work, we perform extensive real data analyses to systematically evaluate these strategies in supervised cell identification. We first benchmark nine classifiers along with six feature selection strategies and investigate the impact of reference data size and number of cell types in cell type prediction. Next, we focus on how discrepancies between reference and target datasets and how data preprocessing such as imputation and batch effect correction affect prediction performance. We also investigate the strategies of pooling and purifying reference data.

CONCLUSIONS

Based on our analysis results, we provide guidelines for using supervised cell typing methods. We suggest combining all individuals from available datasets to construct the reference dataset and use multi-layer perceptron (MLP) as the classifier, along with F-test as the feature selection method. All the code used for our analysis is available on GitHub ( https://github.com/marvinquiet/RefConstruction_supervisedCelltyping ).

PsiNorm: a scalable normalization for single-cell RNA-seq data

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) enables transcriptome-wide gene expression measurements at single-cell resolution providing a comprehensive view of the compositions and dynamics of tissue and organism development. The evolution of scRNA-seq protocols has led to a dramatic increase of cells throughput, exacerbating many of the computational and statistical issues that previously arose for bulk sequencing. In particular, with scRNA-seq data all the analyses steps, including normalization, have become computationally intensive, both in terms of memory usage and computational time. In this perspective, new accurate methods able to scale efficiently are desirable.

RESULTS

Here, we propose PsiNorm, a between-sample normalization method based on the power-law Pareto distribution parameter estimate. Here, we show that the Pareto distribution well resembles scRNA-seq data, especially those coming from platforms that use unique molecular identifiers. Motivated by this result, we implement PsiNorm, a simple and highly scalable normalization method. We benchmark PsiNorm against seven other methods in terms of cluster identification, concordance and computational resources required. We demonstrate that PsiNorm is among the top performing methods showing a good trade-off between accuracy and scalability. Moreover, PsiNorm does not need a reference, a characteristic that makes it useful in supervised classification settings, in which new out-of-sample data need to be normalized.

AVAILABILITY AND IMPLEMENTATION

PsiNorm is implemented in the scone Bioconductor package and available at https://bioconductor.org/packages/scone/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies. However, large-scale integrative analysis of scRNA-seq data remains a challenge largely due to unwanted batch effects and the limited transferabilty, interpretability, and scalability of the existing computational methods. We present single-cell Embedded Topic Model (scETM). Our key contribution is the utilization of a transferable neural-network-based encoder while having an interpretable linear decoder via a matrix tri-factorization. In particular, scETM simultaneously learns an encoder network to infer cell type mixture and a set of highly interpretable gene embeddings, topic embeddings, and batch-effect linear intercepts from multiple scRNA-seq datasets. scETM is scalable to over 106 cells and confers remarkable cross-tissue and cross-species zero-shot transfer-learning performance. Using gene set enrichment analysis, we find that scETM-learned topics are enriched in biologically meaningful and disease-related pathways. Lastly, scETM enables the incorporation of known gene sets into the gene embeddings, thereby directly learning the associations between pathways and topics via the topic embeddings.

Development, Diversity, and Death of MGE-Derived Cortical Interneurons

In the mammalian brain, cortical interneurons (INs) are a highly diverse group of cells. A key neurophysiological question concerns how each class of INs contributes to cortical circuit function and whether specific roles can be attributed to a selective cell type. To address this question, researchers are integrating knowledge derived from transcriptomic, histological, electrophysiological, developmental, and functional experiments to extensively characterise the different classes of INs. Our hope is that such knowledge permits the selective targeting of cell types for therapeutic endeavours. This review will focus on two of the main types of INs, namely the parvalbumin (PV⁺) or somatostatin (SOM⁺)-containing cells, and summarise the research to date on these classes.

Probing subicular inputs to the medial prefrontal cortex

The hippocampal formation is anatomically and functionally divided into a dorsal and a ventral part, being involved in processing cognitive tasks and emotional stimuli, respectively. The ventral subiculum as part of the hippocampal formation projects to the medial prefrontal cortex (mPFC), but only very little is known about connections arising from the dorsal SUB (dSUB). Here, we investigate the dSUB to mPFC connectivity in acute brain slices using electrophysiology and optogenetics. We show that the anterior cingulate cortex (ACC) is the main target of dorsal subicular projections to the mPFC, with no preference between excitatory or inhibitory neurons. In addition to superficial neurons in the ACC, the prelimbic and infralimbic PFC are also targeted by subicular fibers. Thus, these novel region- and layer-specific connections between the dSUB and the prefrontal cortices challenge existing anatomical data and refine the hippocampocortical wiring diagram.

Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization

The Patch-seq approach is a powerful variation of the patch-clamp technique that allows for the combined electrophysiological, morphological, and transcriptomic characterization of individual neurons. To generate Patch-seq datasets at scale, we identified and refined key factors that contribute to the efficient collection of high-quality data. We developed patch-clamp electrophysiology software with analysis functions specifically designed to automate acquisition with online quality control. We recognized the importance of extracting the nucleus for transcriptomic success and maximizing membrane integrity during nucleus extraction for morphology success. The protocol is generalizable to different species and brain regions, as demonstrated by capturing multimodal data from human and macaque brain slices. The protocol, analysis and acquisition software are compiled at https://githubcom/AllenInstitute/patchseqtools. This resource can be used by individual labs to generate data across diverse mammalian species and that is compatible with large publicly available Patch-seq datasets.

Cellular and Behavioral Characterization of Pcdh19 Mutant Mice: subtle Molecular Changes, Increased Exploratory Behavior and an Impact of Social Environment

Mutations in the X-linked cell adhesion protein PCDH19 lead to seizures, cognitive impairment, and other behavioral comorbidities when present in a mosaic pattern. Neither the molecular mechanisms underpinning this disorder nor the function of PCDH19 itself are well understood. By combining RNA in situ hybridization with immunohistochemistry and analyzing single-cell RNA sequencing datasets, we reveal Pcdh19 expression in cortical interneurons and provide a first account of the subtypes of neurons expressing Pcdh19/PCDH19, both in the mouse and the human cortex. Our quantitative analysis of the Pcdh19 mutant mouse exposes subtle changes in cortical layer composition, with no major alterations of the main axonal tracts. In addition, Pcdh19 mutant animals, particularly females, display preweaning behavioral changes, including reduced anxiety and increased exploratory behavior. Importantly, our experiments also reveal an effect of the social environment on the behavior of wild-type littermates of Pcdh19 mutant mice, which show alterations when compared with wild-type animals not housed with mutants.

Neuronal replacement: Concepts, achievements, and call for caution

Regenerative approaches have made such a great progress, now aiming toward replacing the exact neurons lost upon injury or neurodegeneration. Transplantation and direct reprogramming approaches benefit from identification of molecular programs for neuronal subtype specification, allowing engineering of more precise neuronal subtypes. Disentangling subtype diversity from dynamic transcriptional states presents a challenge now. Adequate identity and connectivity is a prerequisite to restore neuronal network function, which is achieved by transplanted neurons generating the correct output and input, depending on the location and injury condition. Direct neuronal reprogramming of local glial cells has also made great progress in achieving high efficiency of conversion, with adequate output connectivity now aiming toward the goal of replacing neurons in a noninvasive approach.

Evolution of glutamatergic signaling and synapses

Glutamate (Glu) is the primary excitatory transmitter in the mammalian brain. But, we know little about the evolutionary history of this adaptation, including the selection of l-glutamate as a signaling molecule in the first place. Here, we used comparative metabolomics and genomic data to reconstruct the genealogy of glutamatergic signaling. The origin of Glu-mediated communications might be traced to primordial nitrogen and carbon metabolic pathways. The versatile chemistry of L-Glu placed this molecule at the crossroad of cellular biochemistry as one of the most abundant metabolites. From there, innovations multiplied. Many stress factors or injuries could increase extracellular glutamate concentration, which led to the development of modular molecular systems for its rapid sensing in bacteria and archaea. More than 20 evolutionarily distinct families of ionotropic glutamate receptors (iGluRs) have been identified in eukaryotes. The domain compositions of iGluRs correlate with the origins of multicellularity in eukaryotes. Although L-Glu was recruited as a neuro-muscular transmitter in the early-branching metazoans, it was predominantly a non-neuronal messenger, with a possibility that glutamatergic synapses evolved more than once. Furthermore, the molecular secretory complexity of glutamatergic synapses in invertebrates (e.g., Aplysia) can exceed their vertebrate counterparts. Comparative genomics also revealed 15+ subfamilies of iGluRs across Metazoa. However, most of this ancestral diversity had been lost in the vertebrate lineage, preserving AMPA, Kainate, Delta, and NMDA receptors. The widespread expansion of glutamate synapses in the cortical areas might be associated with the enhanced metabolic demands of the complex brain and compartmentalization of Glu signaling within modular neuronal ensembles.

Long-term stability of cortical ensembles

Neuronal ensembles, coactive groups of neurons found in spontaneous and evoked cortical activity, are causally related to memories and perception, but it is still unknown how stable or flexible they are over time. We used two-photon multiplane calcium imaging to track over weeks the activity of the same pyramidal neurons in layer 2/3 of the visual cortex from awake mice and recorded their spontaneous and visually evoked responses. Less than half of the neurons remained active across any two imaging sessions. These stable neurons formed ensembles that lasted weeks, but some ensembles were also transient and appeared only in one single session. Stable ensembles preserved most of their neurons for up to 46 days, our longest imaged period, and these 'core' cells had stronger functional connectivity. Our results demonstrate that neuronal ensembles can last for weeks and could, in principle, serve as a substrate for long-lasting representation of perceptual states or memories.

Timing and localization of myasthenia gravis-related gene expression

Myasthenia gravis (MG) is an acquired autoimmune disorder caused by autoantibodies binding acetylcholine receptors (AChR), muscle‐specific kinase (MuSK), agrin or low‐density lipoprotein receptor‐related protein 4 (Lrp4). These autoantibodies inhibit neuromuscular transmission by blocking the function of these proteins and thereby cause fluctuating skeletal muscle weakness. Several reports suggest that these autoantibodies might also affect the central nervous system (CNS) in MG patients. A comprehensive overview of the timing and localization of the expression of MG‐related antigens in other organs is currently lacking. To investigate the spatio‐temporal expression of MG‐related genes outside skeletal muscle, we used in silico tools to assess public expression databases. Acetylcholine esterase, nicotinic AChR α1 subunit, agrin, collagen Q, downstream of kinase‐7, Lrp4, MuSK and rapsyn were included as MG‐related genes because of their well‐known involvement in either congenital or autoimmune MG. We investigated expression of MG‐related genes in (1) all human tissues using GTEx data, (2) specific brain regions, (3) neurodevelopmental stages, and (4) cell types using datasets from the Allen Institute for Brain Sciences. MG‐related genes show heterogenous spatio‐temporal expression patterns in the human body as well as in the CNS. For each of these genes, several (new) tissues, brain areas and cortical cell types with (relatively) high expression were identified suggesting a potential role for these genes outside skeletal muscle. The possible presence of MG‐related antigens outside skeletal muscle suggests that autoimmune MG, congenital MG or treatments targeting the same proteins may affect MG‐related protein function in other organs.

Single-cell transcriptomics of the early developing mouse cerebral cortex disentangle the spatial and temporal components of neuronal fate acquisition

In the developing cerebral cortex, how progenitors that seemingly display limited diversity end up producing a vast array of neurons remains a puzzling question. The prevailing model suggests that temporal maturation of progenitors is a key driver in the diversification of the neuronal output. However, temporal constraints are unlikely to account for all diversity, especially in the ventral and lateral pallium where neuronal types significantly differ from their dorsal neocortical counterparts born at the same time. In this study, we implemented single-cell RNAseq to sample the diversity of progenitors and neurons along the dorso-ventral axis of the early developing pallium. We first identified neuronal types, mapped them on the tissue and determined their origin through genetic tracing. We characterised progenitor diversity and disentangled the gene modules underlying temporal versus spatial regulations of neuronal specification. Finally, we reconstructed the developmental trajectories followed by ventral and dorsal pallial neurons to identify lineage-specific gene waves. Our data suggest a model by which discrete neuronal fate acquisition from a continuous gradient of progenitors results from the superimposition of spatial information and temporal maturation.

Heterogeneous Expression of Nuclear Encoded Mitochondrial Genes Distinguishes Inhibitory and Excitatory Neurons

Mitochondrial composition varies by organ and their constituent cell types. This mitochondrial diversity likely determines variations in mitochondrial function. However, the heterogeneity of mitochondria in the brain remains underexplored despite the large diversity of cell types in neuronal tissue. Here, we used molecular systems biology tools to address whether mitochondrial composition varies by brain region and neuronal cell type in mice. We reasoned that proteomics and transcriptomics of microdissected brain regions combined with analysis of single-cell mRNA sequencing (scRNAseq) could reveal the extent of mitochondrial compositional diversity. We selected nuclear encoded gene products forming complexes of fixed stoichiometry, such as the respiratory chain complexes and the mitochondrial ribosome, as well as molecules likely to perform their function as monomers, such as the family of SLC25 transporters. We found that the proteome encompassing these nuclear-encoded mitochondrial genes and obtained from microdissected brain tissue segregated the hippocampus, striatum, and cortex from each other. Nuclear-encoded mitochondrial transcripts could only segregate cell types and brain regions when the analysis was performed at the single-cell level. In fact, single-cell mitochondrial transcriptomes were able to distinguish glutamatergic and distinct types of GABAergic neurons from one another. Within these cell categories, unique SLC25A transporters were able to identify distinct cell subpopulations. Our results demonstrate heterogeneous mitochondrial composition across brain regions and cell types. We postulate that mitochondrial heterogeneity influences regional and cell type-specific mechanisms in health and disease.

Seq-ing out cell types across the isocortex and hippocampal formation

A central quest in neuroscience is to gain a holistic understanding of all cell types in the brain. In this issue of Cell, Yao et al. establish a molecular architectural view of cell types across the entire adult mouse isocortex and hippocampal formation and reveal surprising similarities of cell types in these two brain regions.

Dissociable Roles of Pallidal Neuron Subtypes in Regulating Motor Patterns

We have previously established that PV+ neurons and Npas1+ neurons are distinct neuron classes in the external globus pallidus (GPe): they have different topographical, electrophysiological, circuit, and functional properties. Aside from Foxp2+ neurons, which are a unique subclass within the Npas1+ class, we lack driver lines that effectively capture other GPe neuron subclasses. In this study, we examined the utility of Kcng4-Cre, Npr3-Cre, and Npy2r-Cre mouse lines (both males and females) for the delineation of GPe neuron subtypes. By using these novel driver lines, we have provided the most exhaustive investigation of electrophysiological studies of GPe neuron subtypes to date. Corroborating our prior studies, GPe neurons can be divided into two statistically distinct clusters that map onto PV+ and Npas1+ classes. By combining optogenetics and machine learning-based tracking, we showed that optogenetic perturbation of GPe neuron subtypes generated unique behavioral structures. Our findings further highlighted the dissociable roles of GPe neurons in regulating movement and anxiety-like behavior. We concluded that Npr3+ neurons and Kcng4+ neurons are distinct subclasses of Npas1+ neurons and PV+ neurons, respectively. Finally, by examining local collateral connectivity, we inferred the circuit mechanisms involved in the motor patterns observed with optogenetic perturbations. In summary, by identifying mouse lines that allow for manipulations of GPe neuron subtypes, we created new opportunities for interrogations of cellular and circuit substrates that can be important for motor function and dysfunction.SIGNIFICANCE STATEMENT Within the basal ganglia, the external globus pallidus (GPe) has long been recognized for its involvement in motor control. However, we lacked an understanding of precisely how movement is controlled at the GPe level as a result of its cellular complexity. In this study, by using transgenic and cell-specific approaches, we showed that genetically-defined GPe neuron subtypes have distinct roles in regulating motor patterns. In addition, the in vivo contributions of these neuron subtypes are in part shaped by the local, inhibitory connections within the GPe. In sum, we have established the foundation for future investigations of motor function and disease pathophysiology.

Regional, Layer, and Cell-Type-Specific Connectivity of the Mouse Default Mode Network

The evolutionarily conserved default mode network (DMN) is a distributed set of brain regions coactivated during resting states that is vulnerable to brain disorders. How disease affects the DMN is unknown, but detailed anatomical descriptions could provide clues. Mice offer an opportunity to investigate structural connectivity of the DMN across spatial scales with cell-type resolution. We co-registered maps from functional magnetic resonance imaging and axonal tracing experiments into the 3D Allen mouse brain reference atlas. We find that the mouse DMN consists of preferentially interconnected cortical regions. As a population, DMN layer 2/3 (L2/3) neurons project almost exclusively to other DMN regions, whereas L5 neurons project in and out of the DMN. In the retrosplenial cortex, a core DMN region, we identify two L5 projection types differentiated by in- or out-DMN targets, laminar position, and gene expression. These results provide a multi-scale description of the anatomical correlates of the mouse DMN.

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Mouse resting-state default mode network anatomy described at high resolution in 3D

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Systematic axon tracing shows cortical DMN regions are preferentially interconnected

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Layer 2/3 DMN neurons project mostly in the DMN; layer 5 neurons project in and out

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Retrosplenial cortex contains distinct types of in- and out-DMN projection neurons