Improving replicability in single-cell RNA-Seq cell type discovery with Dune.

BACKGROUND: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. RESULTS: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . CONCLUSIONS: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

A Latent Activated Olfactory Stem Cell State Revealed by Single Cell Transcriptomic and Epigenomic Profiling.

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration after injury-induced cell death. In this study, we used single cell sequencing to identify major regulatory players in determining olfactory epithelial stem cell fate after acute injury. We combined gene expression and accessible chromatin profiles of individual lineage traced olfactory stem cells to predict transcription factor activity specific to different lineages and stages of recovery. We further identified a discrete stem cell state that appears poised for activation, characterized by accessible chromatin around wound response and lineage specific genes prior to their later expression in response to injury. Together these results provide evidence that a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

Continuing trial responsibilities for implantable neural devices.

Participants of neural implant studies have research-related posttrial care needs (e.g., hardware replacements). Gaps in plans for posttrial care are currently common, which can have major consequences for patients. Professionals and organizations involved should address important unmet posttrial needs.

Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects.

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of epigenetic regulation of gene expression by assessing chromatin configuration for an entire genome. Despite its popularity, there have been limited studies investigating the analytical challenges related to ATAC-seq data, with most studies leveraging tools developed for bulk transcriptome sequencing. Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins and evaluate it together with 11 different normalization procedures on 8 public ATAC-seq datasets. Accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, improving accuracy and interpretability. Through case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset.

BRAIN 2.0: Transforming neuroscience.

The NIH BRAIN Initiative is entering a new phase. Three large new projects-a comprehensive human brain cell atlas, a whole mammalian brain microconnectivity map, and tools for precision access to brain cell types-promise to transform neuroscience research and the treatment of human brain disorders.

Advancing scientific excellence through inclusivity in the NIH BRAIN Initiative.

Leveraging breadth and depth of the scientific workforce invites creativity, relevance, and differing views that directly tie into innovation and problem solving. The NIH BRAIN Initiative is using a multi-pronged strategy to enhance diversity and inclusion toward promoting the best science.

Generation of a DAT-P2A-Flpo mouse line for intersectional genetic targeting of dopamine neuron subpopulations.

Dopaminergic projections exert widespread influence over multiple brain regions and modulate various behaviors including movement, reward learning, and motivation. It is increasingly appreciated that dopamine neurons are heterogeneous in their gene expression, circuitry, physiology, and function. Current approaches to target dopamine neurons are largely based on single gene drivers, which either label all dopamine neurons or mark a subset but concurrently label non-dopaminergic neurons. Here, we establish a mouse line with Flpo recombinase expressed from the endogenous Slc6a3 (dopamine active transporter [DAT]) locus. DAT-P2A-Flpo mice can be used together with Cre-expressing mouse lines to efficiently and selectively label dopaminergic subpopulations using Cre/Flp-dependent intersectional strategies. We demonstrate the utility of this approach by generating DAT-P2A-Flpo;NEX-Cre mice that specifically label Neurod6-expressing dopamine neurons, which project to the nucleus accumbens medial shell. DAT-P2A-Flpo mice add to a growing toolbox of genetic resources that will help parse the diverse functions mediated by dopaminergic circuits.

The promise of the BRAIN initiative: NIH strategies for understanding neural circuit function.

New neurotechnologies fueled by the BRAIN Initiative now allow investigators to map, monitor and modulate complex neural circuits, enabling the pursuit of research questions previously considered unapproachable. Yet it is the convergence of molecular neuroscience with the new systems neuroscience that promises the greatest future advances. This is particularly true for our understanding of nervous system disorders, some of which have known molecular drivers or pathology but result in unknown perturbations in circuit function. NIH-supported research on "BRAIN Circuits" programs integrate experimental, analytic, and theoretical capabilities for analysis of specific neural circuits and their contributions to perceptions, motivations, and actions. In this review, we describe the BRAIN priority areas, review our strategy for balancing early feasibility with mature projects, and the balance of individual with team science for this 'BRAIN Circuits' program. We also highlight the diverse portfolio of techniques, species, and neural systems represented in these projects.

The cellular basis of distinct thirst modalities.

Fluid intake is an essential innate behaviour that is mainly caused by two distinct types of thirst1-3. Increased blood osmolality induces osmotic thirst that drives animals to consume pure water. Conversely, the loss of body fluid induces hypovolaemic thirst, in which animals seek both water and minerals (salts) to recover blood volume. Circumventricular organs in the lamina terminalis are critical sites for sensing both types of thirst-inducing stimulus4-6. However, how different thirst modalities are encoded in the brain remains unknown. Here we employed stimulus-to-cell-type mapping using single-cell RNA sequencing to identify the cellular substrates that underlie distinct types of thirst. These studies revealed diverse types of excitatory and inhibitory neuron in each circumventricular organ structure. We show that unique combinations of these neuron types are activated under osmotic and hypovolaemic stresses. These results elucidate the cellular logic that underlies distinct thirst modalities. Furthermore, optogenetic gain of function in thirst-modality-specific cell types recapitulated water-specific and non-specific fluid appetite caused by the two distinct dipsogenic stimuli. Together, these results show that thirst is a multimodal physiological state, and that different thirst states are mediated by specific neuron types in the mammalian brain.

Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia.

Altered olfactory function is a common symptom of COVID-19, but its etiology is unknown. A key question is whether SARS-CoV-2 (CoV-2) - the causal agent in COVID-19 - affects olfaction directly, by infecting olfactory sensory neurons or their targets in the olfactory bulb, or indirectly, through perturbation of supporting cells. Here we identify cell types in the olfactory epithelium and olfactory bulb that express SARS-CoV-2 cell entry molecules. Bulk sequencing demonstrated that mouse, non-human primate and human olfactory mucosa expresses two key genes involved in CoV-2 entry, ACE2 and TMPRSS2. However, single cell sequencing revealed that ACE2 is expressed in support cells, stem cells, and perivascular cells, rather than in neurons. Immunostaining confirmed these results and revealed pervasive expression of ACE2 protein in dorsally-located olfactory epithelial sustentacular cells and olfactory bulb pericytes in the mouse. These findings suggest that CoV-2 infection of non-neuronal cell types leads to anosmia and related disturbances in odor perception in COVID-19 patients.

Transcriptome analysis of the zebrafish atoh7-/- Mutant, lakritz, highlights Atoh7-dependent genetic networks with potential implications for human eye diseases.

Expression of the bHLH transcription protein Atoh7 is a crucial factor conferring competence to retinal progenitor cells for the development of retinal ganglion cells. Several studies have emerged establishing ATOH7 as a retinal disease gene. Remarkably, such studies uncovered ATOH7 variants associated with global eye defects including optic nerve hypoplasia, microphthalmia, retinal vascular disorders, and glaucoma. The complex genetic networks and cellular decisions arising downstream of atoh7 expression, and how their dysregulation cause development of such disease traits remains unknown. To begin to understand such Atoh7-dependent events in vivo, we performed transcriptome analysis of wild-type and atoh7 mutant (lakritz) zebrafish embryos at the onset of retinal ganglion cell differentiation. We investigated in silico interplays of atoh7 and other disease-related genes and pathways. By network reconstruction analysis of differentially expressed genes, we identified gene clusters enriched in retinal development, cell cycle, chromatin remodeling, stress response, and Wnt pathways. By weighted gene coexpression network, we identified coexpression modules affected by the mutation and enriched in retina development genes tightly connected to atoh7. We established the groundwork whereby Atoh7-linked cellular and molecular processes can be investigated in the dynamic multi-tissue environment of the developing normal and diseased vertebrate eye.

Cellular mechanisms of epithelial stem cell self-renewal and differentiation during homeostasis and repair.

Epithelia in adult mammals exhibit remarkable regenerative capacities owing to the presence of adult stem cells, which self-renew and differentiate to replace cells lost to normal turnover or injury. The mechanisms supporting tissue homeostasis and injury-induced repair often differ from each other as well as from those used in embryonic development. Recent studies have also highlighted the phenomenon of cellular plasticity in adult tissues, in which differentiated cells can change fate and even give rise to new stem cell populations to complement the canonical stem cells in promoting repair following injury. Signaling pathways such as WNT, bone morphogenetic protein, and Sonic Hedgehog play critical roles in stem cell maintenance and cell fate decisions across diverse epithelia and conditions, suggesting that conserved mechanisms underlie the regenerative capacity of adult epithelial structures. This article is categorized under: Gene Expression and Transcriptional Hierarchies > Regulatory Mechanisms Adult Stem Cells, Tissue Renewal, and Regeneration > Tissue Stem Cells and Niches Adult Stem Cells, Tissue Renewal, and Regeneration > Stem Cell Differentiation and Reversion Adult Stem Cells, Tissue Renewal, and Regeneration > Regeneration.

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq.

Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. We have developed "scone"- a flexible framework for assessing performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes trade-offs and ranks large numbers of normalization methods by panel performance. The method is implemented in the open-source Bioconductor R software package scone. We show that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets. scone can be downloaded at http://bioconductor.org/packages/scone/.

Complementary networks of cortical somatostatin interneurons enforce layer specific control.

The neocortex is functionally organized into layers. Layer four receives the densest bottom up sensory inputs, while layers 2/3 and 5 receive top down inputs that may convey predictive information. A subset of cortical somatostatin (SST) neurons, the Martinotti cells, gate top down input by inhibiting the apical dendrites of pyramidal cells in layers 2/3 and 5, but it is unknown whether an analogous inhibitory mechanism controls activity in layer 4. Using high precision circuit mapping, in vivo optogenetic perturbations, and single cell transcriptional profiling, we reveal complementary circuits in the mouse barrel cortex involving genetically distinct SST subtypes that specifically and reciprocally interconnect with excitatory cells in different layers: Martinotti cells connect with layers 2/3 and 5, whereas non-Martinotti cells connect with layer 4. By enforcing layer-specific inhibition, these parallel SST subnetworks could independently regulate the balance between bottom up and top down input.

Perspectives on defining cell types in the brain.

The diversity of brain cell types was one of the earliest observations in modern neuroscience and continues to be one of the central concerns of current neuroscience research. Despite impressive recent progress, including single cell transcriptome and epigenome profiling as well as anatomical methods, we still lack a complete census or taxonomy of brain cell types. We argue this is due partly to the conceptual difficulty in defining a cell type. By considering the biological drivers of cell identity, such as networks of genes and gene regulatory elements, we propose a definition of cell type that emphasizes self-stabilizing regulation. We explore the predictions and hypotheses that arise from this definition. Integration of data from multiple modalities, including molecular profiling of genes and gene products, epigenetic landscape, cellular morphology, connectivity, and physiology, will be essential for a meaningful and broadly useful definition of brain cell types.

clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets.

Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells. It is common in the detection of novel subtypes to run many clustering algorithms, as well as rely on subsampling and ensemble methods to improve robustness. We introduce a Bioconductor R package, clusterExperiment, that implements a general and flexible strategy we entitle Resampling-based Sequential Ensemble Clustering (RSEC). RSEC enables the user to easily create multiple, competing clusterings of the data based on different techniques and associated tuning parameters, including easy integration of resampling and sequential clustering, and then provides methods for consolidating the multiple clusterings into a final consensus clustering. The package is modular and allows the user to separately apply the individual components of the RSEC procedure, i.e., apply multiple clustering algorithms, create a consensus clustering or choose tuning parameters, and merge clusters. Additionally, clusterExperiment provides a variety of visualization tools for the clustering process, as well as methods for the identification of possible cluster signatures or biomarkers. The R package clusterExperiment is publicly available through the Bioconductor Project, with a detailed manual (vignette) as well as well documented help pages for each function.

Combinatorial Expression of Grp and Neurod6 Defines Dopamine Neuron Populations with Distinct Projection Patterns and Disease Vulnerability.

Midbrain dopamine neurons project to numerous targets throughout the brain to modulate various behaviors and brain states. Within this small population of neurons exists significant heterogeneity based on physiology, circuitry, and disease susceptibility. Recent studies have shown that dopamine neurons can be subdivided based on gene expression; however, the extent to which genetic markers represent functionally relevant dopaminergic subpopulations has not been fully explored. Here we performed single-cell RNA-sequencing of mouse dopamine neurons and validated studies showing that Neurod6 and Grp are selective markers for dopaminergic subpopulations. Using a combination of multiplex fluorescent in situ hybridization, retrograde labeling, and electrophysiology in mice of both sexes, we defined the anatomy, projection targets, physiological properties, and disease vulnerability of dopamine neurons based on Grp and/or Neurod6 expression. We found that the combinatorial expression of Grp and Neurod6 defines dopaminergic subpopulations with unique features. Grp+/Neurod6+ dopamine neurons reside in the ventromedial VTA, send projections to the medial shell of the nucleus accumbens, and have noncanonical physiological properties. Grp+/Neurod6- dopamine neurons are found in the VTA as well as in the ventromedial portion of the SNc, where they project selectively to the dorsomedial striatum. Grp-/Neurod6+ dopamine neurons represent a smaller VTA subpopulation, which is preferentially spared in a 6-OHDA model of Parkinson's disease. Together, our work provides detailed characterization of Neurod6 and Grp expression in the midbrain and generates new insights into how these markers define functionally relevant dopaminergic subpopulations.

Creating Lineage Trajectory Maps Via Integration of Single-Cell RNA-Sequencing and Lineage Tracing: Integrating transgenic lineage tracing and single-cell RNA-sequencing is a robust approach for mapping developmental lineage trajectories and cell fate changes.

Mapping the paths that stem and progenitor cells take en route to differentiate and elucidating the underlying molecular controls are key goals in developmental and stem cell biology. However, with population level analyses it is difficult - if not impossible - to define the transition states and lineage trajectory branch points within complex developmental lineages. Single-cell RNA-sequencing analysis can discriminate heterogeneity in a population of cells and even identify rare or transient intermediates. In this review, we propose that using these data, one can infer the lineage trajectories of individual stem cells and identify putative branch points. Clonal lineage tracing of stem cells allows one to define the outcome of differentiation. Integrating these single cell-based approaches provides a robust strategy for establishing and testing models of how an individual stem cell changes through time to differentiate and self-renew.

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.

BACKGROUND: Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. RESULTS: We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods. CONCLUSIONS: Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression.