Comparative cellular analysis of motor cortex in human, marmoset and mouse.

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

A guide to the BRAIN Initiative Cell Census Network data ecosystem.

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Conserved cell types with divergent features in human versus mouse cortex.

Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain.

A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing.

Single-cell genomics is rapidly advancing our knowledge of the diversity of cell phenotypes, including both cell types and cell states. Driven by single-cell/-nucleus RNA sequencing (scRNA-seq), comprehensive cell atlas projects characterizing a wide range of organisms and tissues are currently underway. As a result, it is critical that the transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell types by surface protein expression to defining diseases by their molecular drivers. Here, we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the nonlinear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that optimally capture the cell type identity represented in complete scRNA-seq transcriptional profiles. The marker genes selected provide an expression barcode that serves as both a useful tool for downstream biological investigation and the necessary and sufficient characteristics for semantic cell type definition. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and noncoding RNAs in neuronal cell type identity.

FR-Match: robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test.

Single cell/nucleus RNA sequencing (scRNAseq) is emerging as an essential tool to unravel the phenotypic heterogeneity of cells in complex biological systems. While computational methods for scRNAseq cell type clustering have advanced, the ability to integrate datasets to identify common and novel cell types across experiments remains a challenge. Here, we introduce a cluster-to-cluster cell type matching method-FR-Match-that utilizes supervised feature selection for dimensionality reduction and incorporates shared information among cells to determine whether two cell type clusters share the same underlying multivariate gene expression distribution. FR-Match is benchmarked with existing cell-to-cell and cell-to-cluster cell type matching methods using both simulated and real scRNAseq data. FR-Match proved to be a stringent method that produced fewer erroneous matches of distinct cell subtypes and had the unique ability to identify novel cell phenotypes in new datasets. In silico validation demonstrated that the proposed workflow is the only self-contained algorithm that was robust to increasing numbers of true negatives (i.e. non-represented cell types). FR-Match was applied to two human brain scRNAseq datasets sampled from cortical layer 1 and full thickness middle temporal gyrus. When mapping cell types identified in specimens isolated from these overlapping human brain regions, FR-Match precisely recapitulated the laminar characteristics of matched cell type clusters, reflecting their distinct neuroanatomical distributions. An R package and Shiny application are provided at https://github.com/JCVenterInstitute/FRmatch for users to interactively explore and match scRNAseq cell type clusters with complementary visualization tools.

FastMix: a versatile data integration pipeline for cell type-specific biomarker inference.

MOTIVATION: Flow cytometry (FCM) and transcription profiling are the two widely used assays in translational immunology research. However, there is no data integration pipeline for analyzing these two types of assays together with experiment variables for biomarker inference. Current FCM data analysis mainly relies on subjective manual gating analysis, which is difficult to be directly integrated with other automated computational methods. Existing deconvolutional analysis of bulk transcriptomics relies on predefined marker genes in the transcriptomics data, which are unavailable for novel cell types and does not utilize the FCM data that provide canonical phenotypic definitions of the cell types. RESULTS: We developed a novel analytics pipeline-FastMix-for computational immunology, which integrates flow cytometry, bulk transcriptomics and clinical covariates for identifying cell type-specific gene expression signatures and biomarker genes. FastMix addresses the 'large p, small n' problem in the gene expression and flow cytometry integration analysis via a linear mixed effects model (LMER) for both cross-sectional and longitudinal studies. Its novel moment-based estimator not only reduces bias in parameter estimation but also is more efficient than iterative optimization. The FastMix pipeline also includes a cutting-edge flow cytometry data analysis method-DAFi-for identifying cell populations of interest and their characteristics. Simulation studies showed that FastMix produced smaller type I/II errors than competing methods. Validation using real data of two vaccine studies showed that FastMix identified a consistent set of signature genes as in independent single-cell RNA-seq analysis, producing additional interesting findings. AVAILABILITY AND IMPLEMENTATION: Source code of FastMix is publicly available at https://github.com/terrysun0302/FastMix. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The genome and preliminary single-nuclei transcriptome of Lemna minuta reveals mechanisms of invasiveness.

The ability to trace every cell in some model organisms has led to the fundamental understanding of development and cellular function. However, in plants the complexity of cell number, organ size, and developmental time makes this a challenge even in the diminutive model plant Arabidopsis (Arabidopsis thaliana). Duckweed, basal nongrass aquatic monocots, provide an opportunity to follow every cell of an entire plant due to their small size, reduced body plan, and fast clonal growth habit. Here we present a chromosome-resolved genome for the highly invasive Lesser Duckweed (Lemna minuta) and generate a preliminary cell atlas leveraging low cell coverage single-nuclei sequencing. We resolved the 360 megabase genome into 21 chromosomes, revealing a core nonredundant gene set with only the ancient tau whole-genome duplication shared with all monocots, and paralog expansion as a result of tandem duplications related to phytoremediation. Leveraging SMARTseq2 single-nuclei sequencing, which provided higher gene coverage yet lower cell count, we profiled 269 nuclei covering 36.9% (8,457) of the L. minuta transcriptome. Since molecular validation was not possible in this nonmodel plant, we leveraged gene orthology with model organism single-cell expression datasets, gene ontology, and cell trajectory analysis to define putative cell types. We found that the tissue that we computationally defined as mesophyll expressed high levels of elemental transport genes consistent with this tissue playing a role in L. minuta wastewater detoxification. The L. minuta genome and preliminary cell map provide a paradigm to decipher developmental genes and pathways for an entire plant.

Cell type discovery using single-cell transcriptomics: implications for ontological representation.

Cells are fundamental function units of multicellular organisms, with different cell types playing distinct physiological roles in the body. The recent advent of single-cell transcriptional profiling using RNA sequencing is producing 'big data', enabling the identification of novel human cell types at an unprecedented rate. In this review, we summarize recent work characterizing cell types in the human central nervous and immune systems using single-cell and single-nuclei RNA sequencing, and discuss the implications that these discoveries are having on the representation of cell types in the reference Cell Ontology (CL). We propose a method, based on random forest machine learning, for identifying sets of necessary and sufficient marker genes, which can be used to assemble consistent and reproducible cell type definitions for incorporation into the CL. The representation of defined cell type classes and their relationships in the CL using this strategy will make the cell type classes being identified by high-throughput/high-content technologies findable, accessible, interoperable and reusable (FAIR), allowing the CL to serve as a reference knowledgebase of information about the role that distinct cellular phenotypes play in human health and disease.

Influenza Research Database: An integrated bioinformatics resource for influenza virus research.

The Influenza Research Database (IRD) is a U.S. National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Bioinformatics Resource Center dedicated to providing bioinformatics support for influenza virus research. IRD facilitates the research and development of vaccines, diagnostics and therapeutics against influenza virus by providing a comprehensive collection of influenza-related data integrated from various sources, a growing suite of analysis and visualization tools for data mining and hypothesis generation, personal workbench spaces for data storage and sharing, and active user community support. Here, we describe the recent improvements in IRD including the use of cloud and high performance computing resources, analysis and visualization of user-provided sequence data with associated metadata, predictions of novel variant proteins, annotations of phenotype-associated sequence markers and their predicted phenotypic effects, hemagglutinin (HA) clade classifications, an automated tool for HA subtype numbering conversion, linkouts to disease event data and the addition of host factor and antiviral drug components. All data and tools are freely available without restriction from the IRD website at https://www.fludb.org.

Cell type discovery and representation in the era of high-content single cell phenotyping.

BACKGROUND: A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology. RESULTS: In this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including "context annotations" in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations. CONCLUSION: The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.

Diversifying Selection Analysis Predicts Antigenic Evolution of 2009 Pandemic H1N1 Influenza A Virus in Humans.

UNLABELLED: Although a large number of immune epitopes have been identified in the influenza A virus (IAV) hemagglutinin (HA) protein using various experimental systems, it is unclear which are involved in protective immunity to natural infection in humans. We developed a data mining approach analyzing natural H1N1 human isolates to identify HA protein regions that may be targeted by the human immune system and can predict the evolution of IAV. We identified 16 amino acid sites experiencing diversifying selection during the evolution of prepandemic seasonal H1N1 strains and found that 11 sites were located in experimentally determined B-cell/antibody (Ab) epitopes, including three distinct neutralizing Caton epitopes: Sa, Sb, and Ca2 [A. J. Caton, G. G. Brownlee, J. W. Yewdell, and W. Gerhard, Cell 31:417-427, 1982, http://dx.doi.org/10.1016/0092-8674(82)90135-0]. We predicted that these diversified epitope regions would be the targets of mutation as the 2009 H1N1 pandemic (pH1N1) lineage evolves in response to the development of population-level protective immunity in humans. Using a chi-squared goodness-of-fit test, we identified 10 amino acid sites that significantly differed between the pH1N1 isolates and isolates from the recent 2012-2013 and 2013-2014 influenza seasons. Three of these sites were located in the same diversified B-cell/Ab epitope regions as identified in the analysis of prepandemic sequences, including Sa and Sb. As predicted, hemagglutination inhibition (HI) assays using human sera from subjects vaccinated with the initial pH1N1 isolate demonstrated reduced reactivity against 2013-2014 isolates. Taken together, these results suggest that diversifying selection analysis can identify key immune epitopes responsible for protective immunity to influenza virus in humans and thereby predict virus evolution. IMPORTANCE: The WHO estimates that approximately 5 to 10% of adults and 20 to 30% of children in the world are infected by influenza virus each year. While an adaptive immune response helps eliminate the virus following acute infection, the virus rapidly evolves to evade the established protective memory immune response, thus allowing for the regular seasonal cycles of influenza virus infection. The analytical approach described here, which combines an analysis of diversifying selection with an integration of immune epitope data, has allowed us to identify antigenic regions that contribute to protective immunity and are therefore the key targets of immune evasion by the virus. This information can be used to determine when sequence variations in seasonal influenza virus strains have affected regions responsible for protective immunity in order to decide when new vaccine formulations are warranted.

Cross-Comparison of Inflammatory Skin Disease Transcriptomics Identifies PTEN as a Pathogenic Disease Classifier in Cutaneous Lupus Erythematosus.

Tissue transcriptomics is used to uncover molecular dysregulations underlying diseases. However, the majority of transcriptomics studies focus on single diseases with limited relevance for understanding the molecular relationship between diseases or for identifying disease-specific markers. In this study, we used a normalization approach to compare gene expression across nine inflammatory skin diseases. The normalized datasets were found to retain differential expression signals that allowed unsupervised disease clustering and identification of disease-specific gene signatures. Using the NS-Forest algorithm, we identified a minimal set of biomarkers and validated their use as diagnostic disease classifier. Among them, PTEN was identified as being a specific marker for cutaneous lupus erythematosus and found to be strongly expressed by lesional keratinocytes in association with pathogenic type I IFNs. In fact, PTEN facilitated the expression of IFN-ß and IFN-κ in keratinocytes by promoting activation and nuclear translocation of IRF3. Thus, cross-comparison of tissue transcriptomics is a valid strategy to establish a molecular disease classification and to identify pathogenic disease biomarkers.

Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain.

With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly, providing spatial location and gene expression information about cells in tissue sections at single cell resolution. Cell type classification of these spatially-resolved cells can be inferred by matching the spatial transcriptomics data to reference atlases derived from single cell RNA-sequencing (scRNA-seq) in which cell types are defined by differences in their gene expression profiles. However, robust cell type matching of the spatially-resolved cells to reference scRNA-seq atlases is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four image-based spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that many cells are assigned as the same type by multiple cell type matching algorithms and are present in spatial patterns previously reported from scRNA-seq studies in VISp. Furthermore, by combining the results of individual matching strategies into consensus cell type assignments, we see even greater alignment with biological expectations. We present two ensemble meta-analysis strategies used in this study and share the consensus cell type matching results in the Cytosplore Viewer ( https://viewer.cytosplore.org ) for interactive visualization and data exploration. The consensus matching can also guide spatial data analysis using SSAM, allowing segmentation-free cell type assignment.

Brain Data Standards - A method for building data-driven cell-type ontologies.

Large-scale single-cell 'omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.

Cell type matching in single-cell RNA-sequencing data using FR-Match.

Reference cell atlases powered by single cell and spatial transcriptomics technologies are becoming available to study healthy and diseased tissue at single cell resolution. One important use of these data resources is to compare cell types from new dataset with cell types in the reference atlases to evaluate their phenotypic similarities and differences, for example, for identifying novel cell types under disease conditions. For this purpose, rigorously-validated computational algorithms are needed to perform these cell type matching tasks that can compare datasets from different experiment platforms and sample types. Here, we present significant enhancements to FR-Match (v2.0)-a multivariate nonparametric statistical testing approach for matching cell types in query datasets to reference atlases. FR-Match v2.0 includes a normalization procedure to facilitate cross-platform cluster-level comparisons (e.g., plate-based SMART-seq and droplet-based 10X Chromium single cell and single nucleus RNA-seq and spatial transcriptomics) and extends the pipeline to also allow cell-level matching. In the use cases evaluated, FR-Match showed robust and accurate performance for identifying common and novel cell types across tissue regions, for discovering sub-optimally clustered cell types, and for cross-platform and cross-sample cell type matching.

Machine learning for cell type classification from single nucleus RNA sequencing data.

With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into specific, well-defined categories with the empirical data provided by sc/snRNA-seq remains nontrivial due to the difficulty in determining specific differences between related cell types with close transcriptional similarities, resulting in challenges with matching cell types identified in separate experiments. To investigate possible approaches to overcome these obstacles, we explored the use of supervised machine learning methods-logistic regression, support vector machines, random forests, neural networks, and light gradient boosting machine (LightGBM)-as approaches to classify cell types using snRNA-seq datasets from human brain middle temporal gyrus (MTG) and human kidney. Classification accuracy was evaluated using an F-beta score weighted in favor of precision to account for technical artifacts of gene expression dropout. We examined the impact of hyperparameter optimization and feature selection methods on F-beta score performance. We found that the best performing model for granular cell type classification in both datasets is a multinomial logistic regression classifier and that an effective feature selection step was the most influential factor in optimizing the performance of the machine learning pipelines.

Machine Learning-Based Single Cell and Integrative Analysis Reveals That Baseline mDC Predisposition Correlates With Hepatitis B Vaccine Antibody Response.

Vaccination to prevent infectious disease is one of the most successful public health interventions ever developed. And yet, variability in individual vaccine effectiveness suggests that a better mechanistic understanding of vaccine-induced immune responses could improve vaccine design and efficacy. We have previously shown that protective antibody levels could be elicited in a subset of recipients with only a single dose of the hepatitis B virus (HBV) vaccine and that a wide range of antibody levels were elicited after three doses. The immune mechanisms responsible for this vaccine response variability is unclear. Using single cell RNA sequencing of sorted innate immune cell subsets, we identified two distinct myeloid dendritic cell subsets (NDRG1-expressing mDC2 and CDKN1C-expressing mDC4), the ratio of which at baseline (pre-vaccination) correlated with the immune response to a single dose of HBV vaccine. Our results suggest that the participants in our vaccine study were in one of two different dendritic cell dispositional states at baseline - an NDRG2-mDC2 state in which the vaccine elicited an antibody response after a single immunization or a CDKN1C-mDC4 state in which the vaccine required two or three doses for induction of antibody responses. To explore this correlation further, genes expressed in these mDC subsets were used for feature selection prior to the construction of predictive models using supervised canonical correlation machine learning. The resulting models showed an improved correlation with serum antibody titers in response to full vaccination. Taken together, these results suggest that the propensity of circulating dendritic cells toward either activation or suppression, their "dispositional endotype" at pre-vaccination baseline, could dictate response to vaccination.

SARS-CoV-2 infection of the oral cavity and saliva.

Despite signs of infection-including taste loss, dry mouth and mucosal lesions such as ulcerations, enanthema and macules-the involvement of the oral cavity in coronavirus disease 2019 (COVID-19) is poorly understood. To address this, we generated and analyzed two single-cell RNA sequencing datasets of the human minor salivary glands and gingiva (9 samples, 13,824 cells), identifying 50 cell clusters. Using integrated cell normalization and annotation, we classified 34 unique cell subpopulations between glands and gingiva. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral entry factors such as ACE2 and TMPRSS members were broadly enriched in epithelial cells of the glands and oral mucosae. Using orthogonal RNA and protein expression assessments, we confirmed SARS-CoV-2 infection in the glands and mucosae. Saliva from SARS-CoV-2-infected individuals harbored epithelial cells exhibiting ACE2 and TMPRSS expression and sustained SARS-CoV-2 infection. Acellular and cellular salivary fractions from asymptomatic individuals were found to transmit SARS-CoV-2 ex vivo. Matched nasopharyngeal and saliva samples displayed distinct viral shedding dynamics, and salivary viral burden correlated with COVID-19 symptoms, including taste loss. Upon recovery, this asymptomatic cohort exhibited sustained salivary IgG antibodies against SARS-CoV-2. Collectively, these data show that the oral cavity is an important site for SARS-CoV-2 infection and implicate saliva as a potential route of SARS-CoV-2 transmission.

Transcriptomic evidence that von Economo neurons are regionally specialized extratelencephalic-projecting excitatory neurons.

von Economo neurons (VENs) are bipolar, spindle-shaped neurons restricted to layer 5 of human frontoinsula and anterior cingulate cortex that appear to be selectively vulnerable to neuropsychiatric and neurodegenerative diseases, although little is known about other VEN cellular phenotypes. Single nucleus RNA-sequencing of frontoinsula layer 5 identifies a transcriptomically-defined cell cluster that contained VENs, but also fork cells and a subset of pyramidal neurons. Cross-species alignment of this cell cluster with a well-annotated mouse classification shows strong homology to extratelencephalic (ET) excitatory neurons that project to subcerebral targets. This cluster also shows strong homology to a putative ET cluster in human temporal cortex, but with a strikingly specific regional signature. Together these results suggest that VENs are a regionally distinctive type of ET neuron. Additionally, we describe the first patch clamp recordings of VENs from neurosurgically-resected tissue that show distinctive intrinsic membrane properties relative to neighboring pyramidal neurons.