Cochlear transcriptome analysis of an outbred mouse population (CFW).

Age-related hearing loss (ARHL) is the most common cause of hearing loss and one of the most prevalent conditions affecting the elderly worldwide. Despite evidence from our lab and others about its polygenic nature, little is known about the specific genes, cell types, and pathways involved in ARHL, impeding the development of therapeutic interventions. In this manuscript, we describe, for the first time, the complete cell-type specific transcriptome of the aging mouse cochlea using snRNA-seq in an outbred mouse model in relation to auditory threshold variation. Cochlear cell types were identified using unsupervised clustering and annotated via a three-tiered approach-first by linking to expression of known marker genes, then using the NSForest algorithm to select minimum cluster-specific marker genes and reduce dimensional feature space for statistical comparison of our clusters with existing publicly-available data sets on the gEAR website, and finally, by validating and refining the annotations using Multiplexed Error Robust Fluorescence In Situ Hybridization (MERFISH) and the cluster-specific marker genes as probes. We report on 60 unique cell-types expanding the number of defined cochlear cell types by more than two times. Importantly, we show significant specific cell type increases and decreases associated with loss of hearing acuity implicating specific subsets of hair cell subtypes, ganglion cell subtypes, and cell subtypes within the stria vascularis in this model of ARHL. These results provide a view into the cellular and molecular mechanisms responsible for age-related hearing loss and pathways for therapeutic targeting.

Application of machine learning models to animal health pharmacovigilance: A proof-of-concept study.

Machine learning (ML) models were applied to pharmacovigilance (PV) data in a two-component proof-of-concept study. PV data were partitioned into Training, Validation, and Holdout datasets for model training and selection. During the first component ML models were challenged to identify factors in individual case safety reports (ICSRs) involving spinosad and neurological and ocular clinical signs. The target feature for the models were these clinical signs that were disproportionately reported for spinosad. The endpoints were normalized coefficient values representing the relationship between the target feature and ICSR free text fields. The deployed model accurately identified the risk factors "demodectic," "demodicosis," and "ivomec." In the second component, the ML models were trained to identify high quality and complete ICSRs free of confounders. The deployed model was presented with an external Test dataset of six ICSRs, one that was complete, of high quality, and devoid of confounders, and five that were not. The endpoints were model-generated probabilities for the ICSRs. The deployed ML model accurately identified the ICSR of interest with a greater than 10-fold higher probability score. Although narrow in scope, the study supports further investigation and potential application of ML models to animal health PV data.

Cochlear transcriptome analysis of an outbred mouse population (CFW).

Age-related hearing loss (ARHL) is the most common cause of hearing loss and one of the most prevalent conditions affecting the elderly worldwide. Despite evidence from our lab and others about its polygenic nature, little is known about the specific genes, cell types and pathways involved in ARHL, impeding the development of therapeutic interventions. In this manuscript, we describe, for the first time, the complete cell-type specific transcriptome of the aging mouse cochlea using snRNA-seq in an outbred mouse model in relation to auditory threshold variation. Cochlear cell types were identified using unsupervised clustering and annotated via a three-tiered approach - first by linking to expression of known marker genes, then using the NS-Forest algorithm to select minimum cluster-specific marker genes and reduce dimensional feature space for statistical comparison of our clusters with existing publicly-available data sets on the gEAR website (https://umgear.org/), and finally, by validating and refining the annotations using Multiplexed Error Robust Fluorescence In Situ Hybridization (MERFISH) and the cluster-specific marker genes as probes. We report on 60 unique cell-types expanding the number of defined cochlear cell types by more than two times. Importantly, we show significant specific cell type increases and decreases associated with loss of hearing acuity implicating specific subsets of hair cell subtypes, ganglion cell subtypes, and cell subtypes withing the stria vascularis in this model of ARHL. These results provide a view into the cellular and molecular mechanisms responsible for age-related hearing loss and pathways for therapeutic targeting.

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.

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.

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.

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.

Evaluation of signal detection algorithms within the Elanco Animal Health Pharmacovigilance database.

Statistical algorithms for detecting safety signals are beginning to be applied to Animal Health Pharmacovigilance (PV) databases. How these signal detection algorithms (SDAs) perform in an animal health PV database is the subject of this report. Statistical methods and SDAs were assessed against a set of known signals in order to identify which SDAs were most appropriate for signal detection using the Elanco Animal Health PV database. A reference set of adverse events that should signal was created for 31 products across four species. Nine SDAs based on five disproportionality statistical methods were evaluated against the reference set. The performance metrics were sensitivity, precision, specificity, accuracy, and F score. For bovine and porcine products, the Observed-to-Expected (O/E) SDA was the closest in terms of geometric distance to 100% sensitivity and 100% precision. For canine and feline products, the Information Component (IC) SDA was geometrically closest to 100% sensitivity and 100% precision. Principal Component Analysis confirmed that the O/E and IC SDAs were unique performers with respect to one another and other SDAs. The performance of the SDAs was dependent on the choice of the statistical method with differences seen between animal species.

Systems Biology Methods Applied to Blood and Tissue for a Comprehensive Analysis of Immune Response to Hepatitis B Vaccine in Adults.

Conventional vaccine design has been based on trial-and-error approaches, which have been generally successful. However, there have been some major failures in vaccine development and we still do not have highly effective licensed vaccines for tuberculosis, HIV, respiratory syncytial virus, and other major infections of global significance. Approaches at rational vaccine design have been limited by our understanding of the immune response to vaccination at the molecular level. Tools now exist to undertake in-depth analysis using systems biology approaches, but to be fully realized, studies are required in humans with intensive blood and tissue sampling. Methods that support this intensive sampling need to be developed and validated as feasible. To this end, we describe here a detailed approach that was applied in a study of 15 healthy adults, who were immunized with hepatitis B vaccine. Sampling included ~350 mL of blood, 12 microbiome samples, and lymph node fine needle aspirates obtained over a ~7-month period, enabling comprehensive analysis of the immune response at the molecular level, including single cell and tissue sample analysis. Samples were collected for analysis of immune phenotyping, whole blood and single cell gene expression, proteomics, lipidomics, epigenetics, whole blood response to key immune stimuli, cytokine responses, in vitro T cell responses, antibody repertoire analysis and the microbiome. Data integration was undertaken using different approaches-NetworkAnalyst and DIABLO. Our results demonstrate that such intensive sampling studies are feasible in healthy adults, and data integration tools exist to analyze the vast amount of data generated from a multi-omics systems biology approach. This will provide the basis for a better understanding of vaccine-induced immunity and accelerate future rational vaccine design.

Integrated Single-Cell Atlases Reveal an Oral SARS-CoV-2 Infection and Transmission Axis.

Despite signs of infection, the involvement of the oral cavity in COVID-19 is poorly understood. To address this, single-cell RNA sequencing data-sets were integrated from human minor salivary glands and gingiva to identify 11 epithelial, 7 mesenchymal, and 15 immune cell clusters. Analysis of SARS-CoV-2 viral entry factor expression showed enrichment in epithelia including the ducts and acini of the salivary glands and the suprabasal cells of the mucosae. COVID-19 autopsy tissues confirmed in vivo SARS-CoV-2 infection in the salivary glands and mucosa. Saliva from SARS-CoV-2-infected individuals harbored epithelial cells exhibiting ACE2 expression and SARS-CoV-2 RNA. Matched nasopharyngeal and saliva samples found distinct viral shedding dynamics and viral burden in saliva correlated with COVID-19 symptoms including taste loss. Upon recovery, this cohort exhibited salivary antibodies against SARS-CoV-2 proteins. Collectively, the oral cavity represents a robust site for COVID-19 infection and implicates saliva in viral 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.

Erratum: Evolutionary dynamics and molecular epidemiology of West Nile virus in New York State: 1999-2015.

[This corrects the article DOI: 10.1093/ve/vez020.].

A niche-dependent myeloid transcriptome signature defines dormant myeloma cells.

The era of targeted therapies has seen significant improvements in depth of response, progression-free survival, and overall survival for patients with multiple myeloma. Despite these improvements in clinical outcome, patients inevitably relapse and require further treatment. Drug-resistant dormant myeloma cells that reside in specific niches within the skeleton are considered a basis of disease relapse but remain elusive and difficult to study. Here, we developed a method to sequence the transcriptome of individual dormant myeloma cells from the bones of tumor-bearing mice. Our analyses show that dormant myeloma cells express a distinct transcriptome signature enriched for immune genes and, unexpectedly, genes associated with myeloid cell differentiation. These genes were switched on by coculture with osteoblastic cells. Targeting AXL, a gene highly expressed by dormant cells, using small-molecule inhibitors released cells from dormancy and promoted their proliferation. Analysis of the expression of AXL and coregulated genes in human cohorts showed that healthy human controls and patients with monoclonal gammopathy of uncertain significance expressed higher levels of the dormancy signature genes than patients with multiple myeloma. Furthermore, in patients with multiple myeloma, the expression of this myeloid transcriptome signature translated into a twofold increase in overall survival, indicating that this dormancy signature may be a marker of disease progression. Thus, engagement of myeloma cells with the osteoblastic niche induces expression of a suite of myeloid genes that predicts disease progression and that comprises potential drug targets to eradicate dormant myeloma cells.

Contemporary Circulating Enterovirus D68 Strains Have Acquired the Capacity for Viral Entry and Replication in Human Neuronal Cells.

Enterovirus D68 (EV-D68) has historically been associated with respiratory illnesses. However, in the summers of 2014 and 2016, EV-D68 outbreaks coincided with a spike in polio-like acute flaccid myelitis/paralysis (AFM/AFP) cases. This raised concerns that EV-D68 could be the causative agent of AFM during these recent outbreaks. To assess the potential neurotropism of EV-D68, we utilized the neuroblastoma-derived neuronal cell line SH-SY5Y as a cell culture model to determine if differential infection is observed for different EV-D68 strains. In contrast to HeLa and A549 cells, which support viral infection of all EV-D68 strains tested, SH-SY5Y cells only supported infection by a subset of contemporary EV-D68 strains, including isolates from the 2014 outbreak. Viral replication and infectivity in SH-SY5Y were assessed using multiple assays: virus production, cytopathic effects, cellular ATP release, and VP1 capsid protein production. Similar differential neurotropism was also observed in differentiated SH-SY5Y cells, primary human neuron cultures, and a mouse paralysis model. Using the SH-SY5Y cell culture model, we determined that barriers to viral binding and entry were at least partly responsible for the differential infectivity phenotype. Transfection of genomic RNA into SH-SY5Y generated virions for all EV-D68 isolates, but only a single round of replication was observed from strains that could not directly infect SH-SY5Y. In addition to supporting virus replication and other functional studies, this cell culture model may help identify the signatures of virulence to confirm epidemiological associations between EV-D68 strains and AFM and allow for the rapid identification and characterization of emerging neurotropic strains.IMPORTANCE Since the EV-D68 outbreak during the summer of 2014, evidence of a causal link to a type of limb paralysis (AFM) has been mounting. In this article, we describe a neuronal cell culture model (SH-SY5Y cells) in which a subset of contemporary 2014 outbreak strains of EV-D68 show infectivity in neuronal cells, or neurotropism. We confirmed the difference in neurotropism in vitro using primary human neuron cell cultures and in vivo with a mouse paralysis model. Using the SH-SY5Y cell model, we determined that a barrier to viral entry is at least partly responsible for neurotropism. SH-SY5Y cells may be useful in determining if specific EV-D68 genetic determinants are associated with neuropathogenesis, and replication in this cell line could be used as rapid screening tool for identification of neurotropic EV-D68 strains. This may assist with better understanding of pathogenesis and epidemiology and with the development of potential therapies.

Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type.

We describe convergent evidence from transcriptomics, morphology, and physiology for a specialized GABAergic neuron subtype in human cortex. Using unbiased single-nucleus RNA sequencing, we identify ten GABAergic interneuron subtypes with combinatorial gene signatures in human cortical layer 1 and characterize a group of human interneurons with anatomical features never described in rodents, having large 'rosehip'-like axonal boutons and compact arborization. These rosehip cells show an immunohistochemical profile (GAD1+CCK+, CNR1-SST-CALB2-PVALB-) matching a single transcriptomically defined cell type whose specific molecular marker signature is not seen in mouse cortex. Rosehip cells in layer 1 make homotypic gap junctions, predominantly target apical dendritic shafts of layer 3 pyramidal neurons, and inhibit backpropagating pyramidal action potentials in microdomains of the dendritic tuft. These cells are therefore positioned for potent local control of distal dendritic computation in cortical pyramidal neurons.

Single-Cell Transcriptomes Distinguish Stem Cell State Changes and Lineage Specification Programs in Early Mammary Gland Development.

The mammary gland consists of cells with gene expression patterns reflecting their cellular origins, function, and spatiotemporal context. However, knowledge of developmental kinetics and mechanisms of lineage specification is lacking. We address this significant knowledge gap by generating a single-cell transcriptome atlas encompassing embryonic, postnatal, and adult mouse mammary development. From these data, we map the chronology of transcriptionally and epigenetically distinct cell states and distinguish fetal mammary stem cells (fMaSCs) from their precursors and progeny. fMaSCs show balanced co-expression of factors associated with discrete adult lineages and a metabolic gene signature that subsides during maturation but reemerges in some human breast cancers and metastases. These data provide a useful resource for illuminating mammary cell heterogeneity, the kinetics of differentiation, and developmental correlates of tumorigenesis.

Author Correction: L1-associated genomic regions are deleted in somatic cells of the healthy human brain.

In the version of this article initially published, NIH grant U01 MH106882 to F.H.G. was missing from the Acknowledgments. The error has been corrected in the HTML and PDF versions of the article.

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.

A Whole Blood Molecular Signature for Acute Myocardial Infarction.

Chest pain is a leading reason patients seek medical evaluation. While assays to detect myocyte death are used to diagnose a heart attack (acute myocardial infarction, AMI), there is no biomarker to indicate an impending cardiac event. Transcriptional patterns present in circulating endothelial cells (CEC) may provide a window into the plaque rupture process and identify a proximal biomarker for AMI. Thus, we aimed to identify a transcriptomic signature of AMI present in whole blood, but derived from CECs. Candidate genes indicative of AMI were nominated from microarray of enriched CEC samples, and then verified for detectability and predictive potential via qPCR in whole blood. This signature was validated in an independent cohort. Our findings suggest that a whole blood CEC-derived molecular signature identifies patients with AMI and sets the framework to potentially identify the earlier stages of an impending cardiac event when used in concert with clinical history and other diagnostics where conventional biomarkers indicative of myonecrosis remain undetected.