Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets

Single-cell analysis tools for drug discovery and development

The genetic, functional or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development. Such heterogeneity hinders the design of accurate disease models and can confound the interpretation of biomarker levels and of patient responses to specific therapies. The complex nature of virtually all tissues has motivated the development of tools for single-cell genomic, transcriptomic and multiplex proteomic analyses. Here, we review these tools and assess their advantages and limitations. Emerging applications of single cell analysis tools in drug discovery and development, particularly in the field of oncology, are discussed.

Design and Analysis of Single-Cell Sequencing Experiments

Recent advances in single-cell sequencing hold great potential for exploring biological systems with unprecedented resolution. Sequencing the genome of individual cells can reveal somatic mutations and allows the investigation of clonal dynamics. Single-cell transcriptome sequencing can elucidate the cell type composition of a sample. However, single-cell sequencing comes with major technical challenges and yields complex data output. In this Primer, we provide an overview of available methods and discuss experimental design and single-cell data analysis. We hope that these guidelines will enable a growing number of researchers to leverage the power of single-cell sequencing.

Globus Pallidus Externus Neurons Expressing parvalbumin Interconnect the Subthalamic Nucleus and Striatal Interneurons

The globus pallidus externus (GP) is a nucleus of the basal ganglia (BG), containing GABAergic projection neurons that arborize widely throughout the BG, thalamus and cortex. Ongoing work seeks to map axonal projection patterns from GP cell types, as defined by their electrophysiological and molecular properties. Here we use transgenic mice and recombinant viruses to characterize parvalbumin expressing (PV⁺) GP neurons within the BG circuit. We confirm that PV⁺ neurons 1) make up ~40% of the GP neurons 2) exhibit fast-firing spontaneous activity and 3) provide the major axonal arborization to the STN and substantia nigra reticulata/compacta (SNr/c). PV⁺ neurons also innervate the striatum. Retrograde labeling identifies ~17% of pallidostriatal neurons as PV⁺, at least a subset of which also innervate the STN and SNr. Optogenetic experiments in acute brain slices demonstrate that the PV⁺ pallidostriatal axons make potent inhibitory synapses on low threshold spiking (LTS) and fast-spiking interneurons (FS) in the striatum, but rarely on spiny projection neurons (SPNs). Thus PV⁺ GP neurons are synaptically positioned to directly coordinate activity between BG input nuclei, the striatum and STN, and thalamic-output from the SNr.

Miniaturization Technologies for Efficient Single-Cell Library Preparation for Next-Generation Sequencing

As the cost of next-generation sequencing has decreased, library preparation costs have become a more significant proportion of the total cost, especially for high-throughput applications such as single-cell RNA profiling. Here, we have applied novel technologies to scale down reaction volumes for library preparation. Our system consisted of in vitro differentiated human embryonic stem cells representing two stages of pancreatic differentiation, for which we prepared multiple biological and technical replicates. We used the Fluidigm (San Francisco, CA) C1 single-cell Autoprep System for single-cell complementary DNA (cDNA) generation and an enzyme-based tagmentation system (Nextera XT; Illumina, San Diego, CA) with a nanoliter liquid handler (mosquito HTS; TTP Labtech, Royston, UK) for library preparation, reducing the reaction volume down to 2 µL and using as little as 20 pg of input cDNA. The resulting sequencing data were bioinformatically analyzed and correlated among the different library reaction volumes. Our results showed that decreasing the reaction volume did not interfere with the quality or the reproducibility of the sequencing data, and the transcriptional data from the scaled-down libraries allowed us to distinguish between single cells. Thus, we have developed a process to enable efficient and cost-effective high-throughput single-cell transcriptome sequencing.

Single-cell transcriptome sequencing: recent advances and remaining challenges

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.

Classification of low quality cells from single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells.

An Emerging Technology Framework for the Neurobiology of Appetite

Advances in neuro-technology for mapping, manipulating, and monitoring molecularly defined cell types are rapidly advancing insight into neural circuits that regulate appetite. Here, we review these important tools and their applications in circuits that control food seeking and consumption. Technical capabilities provided by these tools establish a rigorous experimental framework for research into the neurobiology of hunger.

Single cells in confined volumes: microchambers and microdroplets

Microfluidic devices capable of manipulating and guiding small fluid volumes open new methodical approaches in the fields of biology, pharmacy, and medicine. They have already proven their extraordinary value for cell analysis. The emergence of microfluidic platforms has paved the way to novel analytical strategies for the positioning, treatment and observation of living cells, for the creation of chemically defined liquid environments, and for tailoring biomechanical or physical conditions in small volumes. In this article, we particularly focus on two complementary approaches: (i) the isolation of cells in small chambers defined by microchannels and integrated valves and (ii) the encapsulation of cells in microdroplets. We review the advantages and limitations of both approaches and discuss their potential for single-cell analysis and related fields. Our intention is also to give a recommendation on which platform is most appropriate for a new question, i.e., a guideline to choose the most suitable platform.

Lateral hypothalamic circuits for feeding and reward

In experiments conducted over 60 years ago, the lateral hypothalamic area (LHA) was identified as a critical neuroanatomical substrate for motivated behavior. Electrical stimulation of the LHA induces voracious feeding even in well-fed animals. In the absence of food, animals will work tirelessly, often lever-pressing thousands of times per hour, for electrical stimulation at the same site that provokes feeding, drinking and other species-typical motivated behaviors. Here we review the classic findings from electrical stimulation studies and integrate them with more recent work that has used contemporary circuit-based approaches to study the LHA. We identify specific anatomically and molecularly defined LHA elements that integrate diverse information arising from cortical, extended amygdala and basal forebrain networks to ultimately generate a highly specified and invigorated behavioral state conveyed via LHA projections to downstream reward and feeding-specific circuits.

Haplotyping germline and cancer genomes with high-throughput linked-read sequencing

Haplotyping of human chromosomes is a prerequisite for cataloguing the full repertoire of genetic variation. We present a microfluidics-based, linked-read sequencing technology that can phase and haplotype germline and cancer genomes using nanograms of input DNA. This high-throughput platform prepares barcoded libraries for short-read sequencing and computationally reconstructs long-range haplotype and structural variant information. We generate haplotype blocks in a nuclear trio that are concordant with expected inheritance patterns and phase a set of structural variants. We also resolve the structure of the EML4-ALK gene fusion in the NCI-H2228 cancer cell line using phased exome sequencing. Finally, we assign genetic aberrations to specific megabase-scale haplotypes generated from whole-genome sequencing of a primary colorectal adenocarcinoma. This approach resolves haplotype information using up to 100 times less genomic DNA than some methods and enables the accurate detection of structural variants.

Advancing drug discovery for neuropsychiatric disorders using patient-specific stem cell models

Compelling clinical, social, and economic reasons exist to innovate in the process of drug discovery for neuropsychiatric disorders. The use of patient-specific, induced pluripotent stem cells (iPSCs) now affords the ability to generate neuronal cell-based models that recapitulate key aspects of human disease. In the context of neuropsychiatric disorders, where access to physiologically active and relevant cell types of the central nervous system for research is extremely limiting, iPSC-derived in vitro culture of human neurons and glial cells is transformative. Potential applications relevant to early stage drug discovery, include support of quantitative biochemistry, functional genomics, proteomics, and perhaps most notably, high-throughput and high-content chemical screening. While many phenotypes in human iPSC-derived culture systems may prove adaptable to screening formats, addressing the question of which in vitro phenotypes are ultimately relevant to disease pathophysiology and therefore more likely to yield effective pharmacological agents that are disease-modifying treatments requires careful consideration. Here, we review recent examples of studies of neuropsychiatric disorders using human stem cell models where cellular phenotypes linked to disease and functional assays have been reported. We also highlight technical advances using genome-editing technologies in iPSCs to support drug discovery efforts, including the interpretation of the functional significance of rare genetic variants of unknown significance and for the purpose of creating cell type- and pathway-selective functional reporter assays. Additionally, we evaluate the potential of in vitro stem cell models to investigate early events of disease pathogenesis, in an effort to understand the underlying molecular mechanism, including the basis of selective cell-type vulnerability, and the potential to create new cell-based diagnostics to aid in the classification of patients and subsequent selection for clinical trials. A number of key challenges remain, including the scaling of iPSC models to larger cohorts and integration with rich clinicopathological information and translation of phenotypes. Still, the overall use of iPSC-based human cell models with functional cellular and biochemical assays holds promise for supporting the discovery of next-generation neuropharmacological agents for the treatment and ultimately prevention of a range of severe mental illnesses.

A survey of best practices for RNA-seq data analysis

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.

Single-cell genome sequencing: current state of the science

The field of single-cell genomics is advancing rapidly and is generating many new insights into complex biological systems, ranging from the diversity of microbial ecosystems to the genomics of human cancer. In this Review, we provide an overview of the current state of the field of single-cell genome sequencing. First, we focus on the technical challenges of making measurements that start from a single molecule of DNA, and then explore how some of these recent methodological advancements have enabled the discovery of unexpected new biology. Areas highlighted include the application of single-cell genomics to interrogate microbial dark matter and to evaluate the pathogenic roles of genetic mosaicism in multicellular organisms, with a focus on cancer. We then attempt to predict advances we expect to see in the next few years.

Serum-Based Culture Conditions Provoke Gene Expression Variability in Mouse Embryonic Stem Cells as Revealed by Single-Cell Analysis

Variation in gene expression is an important feature of mouse embryonic stem cells (ESCs). However, the mechanisms responsible for global gene expression variation in ESCs are not fully understood. We performed single-cell mRNA-seq analysis of mouse ESCs and uncovered significant heterogeneity in ESCs cultured in serum. We define highly variable gene clusters with distinct chromatin states and show that bivalent genes are prone to expression variation. At the same time, we identify an ESC-priming pathway that initiates the exit from the naive ESC state. Finally, we provide evidence that a large proportion of intracellular network variability is due to the extracellular culture environment. Serum-free culture reduces cellular heterogeneity and transcriptome variation in ESCs.

Genomic Views of Transcriptional Enhancers: Essential Determinants of Cellular Identity and Activity-Dependent Responses in the CNS

Sprinkled throughout the genome are a million regulatory sequences called transcriptional enhancers that activate gene promoters in the right cells, at the right time. Enhancers endow the brain with its incredible diversity of cell types and also translate neural activity into gene induction. Thanks to rapid advances in genomic technologies, it is now possible to identify thousands of enhancers rapidly, test their transcriptional function en masse, and address their neurobiological functions via genome editing. Enhancers also promise to be a great technological opportunity for neuroscience, offering the potential for cell-type-specific genetic labeling and manipulation without the need for transgenesis. The objective of this review and the accompanying 2015 SfN mini-symposium is to highlight the use of new and emerging genomic technologies to probe enhancer function in the nervous system.

SIGNIFICANCE STATEMENT

Transcriptional enhancers turn on genes in the right cells, at the right time. Enhancers are also the genomic sequences that encode the incredible diversity of cell types in the brain and enable the brain to turn genes on in response to new experiences. New technology enables enhancers to be found and manipulated. The study of enhancers promises to inform our understanding of brain development and function. The application of enhancer technology holds promise in accelerating basic neuroscience research and enabling gene therapies to be targeted to specific cell types in the brain.

Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis

The transcriptional state of a cell reflects a variety of biological factors, from persistent cell-type specific features to transient processes such as cell cycle. Depending on biological context, all such aspects of transcriptional heterogeneity may be of interest, but detecting them from noisy single-cell RNA-seq data remains challenging. We developed PAGODA to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability amongst measured cells.

Systems biology: A tool for charting the antiviral landscape

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Conventional approaches overlook the complexity of the antiviral response.

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Systems biology approaches provide a comprehensive and unbiased analysis.

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The Collaborative Cross studies how host genetics influences antiviral immunity.

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Transcriptomics is a powerful tool to study tissue and cellular antiviral responses.

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Single cell analysis allows for discrimination between bystander and infected cells.

The host antiviral programs that are initiated following viral infection form a dynamic and complex web of responses that we have collectively termed as “the antiviral landscape”. Conventional approaches to studying antiviral responses have primarily used reductionist systems to assess the function of a single or a limited subset of molecules. Systems biology is a holistic approach that considers the entire system as a whole, rather than individual components or molecules. Systems biology based approaches facilitate an unbiased and comprehensive analysis of the antiviral landscape, while allowing for the discovery of emergent properties that are missed by conventional approaches. The antiviral landscape can be viewed as a hierarchy of complexity, beginning at the whole organism level and progressing downward to isolated tissues, populations of cells, and single cells. In this review, we will discuss how systems biology has been applied to better understand the antiviral landscape at each of these layers. At the organismal level, the Collaborative Cross is an invaluable genetic resource for assessing how genetic diversity influences the antiviral response. Whole tissue and isolated bulk cell transcriptomics serves as a critical tool for the comprehensive analysis of antiviral responses at both the tissue and cellular levels of complexity. Finally, new techniques in single cell analysis are emerging tools that will revolutionize our understanding of how individual cells within a bulk infected cell population contribute to the overall antiviral landscape.

Single neuron transcriptome analysis can reveal more than cell type classification: Does it matter if every neuron is unique?

A recent single cell mRNA sequencing study by Dueck et al. compares neuronal transcriptomes to the transcriptomes of adipocytes and cardiomyocytes. Single cell omic approaches such as those used by the authors are at the leading edge of molecular and biophysical measurement. Many groups are currently employing single cell sequencing approaches to understand cellular heterogeneity in cancer and during normal development. These single cell approaches also are beginning to address long-standing questions regarding nervous system diversity. Beyond an innate interest in cataloging cell type diversity in the brain, single cell neuronal diversity has important implications for neurotypic neural circuit function and for neurological disease. Herein, we review the authors' methods and findings, which most notably include evidence of unique expression profiles in some single neurons.

A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages

We introduce a microfluidic platform that enables off-chip single-cell RNA-seq after multi-generational lineage tracking under controlled culture conditions. We use this platform to generate whole-transcriptome profiles of primary, activated murine CD8+ T-cell and lymphocytic leukemia cell line lineages. Here we report that both cell types have greater intra- than inter-lineage transcriptional similarity. For CD8+ T-cells, genes with functional annotation relating to lymphocyte differentiation and function--including Granzyme B--are enriched among the genes that demonstrate greater intra-lineage expression level similarity. Analysis of gene expression covariance with matched measurements of time since division reveals cell type-specific transcriptional signatures that correspond with cell cycle progression. We believe that the ability to directly measure the effects of lineage and cell cycle-dependent transcriptional profiles of single cells will be broadly useful to fields where heterogeneous populations of cells display distinct clonal trajectories, including immunology, cancer, and developmental biology.

The functional diversity of retinal ganglion cells in the mouse

In the vertebrate visual system, all output of the retina is carried by retinal ganglion cells. Each type encodes distinct visual features in parallel for transmission to the brain. How many such “output channels” exist and what each encodes is an area of intense debate. In mouse, anatomical estimates range between 15–20 channels, and only a handful are functionally understood. Combining two-photon calcium imaging to obtain dense retinal recordings and unsupervised clustering of the resulting sample of >11,000 cells, we here show that the mouse retina harbours substantially more than 30 functional output channels. These include all known and several new ganglion cell types, as verified by genetic and anatomical criteria. Therefore, information channels from the mouse’s eye to the mouse’s brain are considerably more diverse than shown thus far by anatomical studies, suggesting an encoding strategy resembling that used in state-of-the-art artificial vision systems.

ANISEED 2015: a digital framework for the comparative developmental biology of ascidians

Ascidians belong to the tunicates, the sister group of vertebrates and are recognized model organisms in the field of embryonic development, regeneration and stem cells. ANISEED is the main information system in the field of ascidian developmental biology. This article reports the development of the system since its initial publication in 2010. Over the past five years, we refactored the system from an initial custom schema to an extended version of the Chado schema and redesigned all user and back end interfaces. This new architecture was used to improve and enrich the description of Ciona intestinalis embryonic development, based on an improved genome assembly and gene model set, refined functional gene annotation, and anatomical ontologies, and a new collection of full ORF cDNAs. The genomes of nine ascidian species have been sequenced since the release of the C. intestinalis genome. In ANISEED 2015, all nine new ascidian species can be explored via dedicated genome browsers, and searched by Blast. In addition, ANISEED provides full functional gene annotation, anatomical ontologies and some gene expression data for the six species with highest quality genomes. ANISEED is publicly available at: http://www.aniseed.cnrs.fr.

Adult mouse cortical cell taxonomy revealed by single cell transcriptomics

Nervous systems are composed of various cell types, but the extent of cell type diversity is poorly understood. Here, we construct a cellular taxonomy of one cortical region, primary visual cortex, in adult mice based on single cell RNA-sequencing. We identify 49 transcriptomic cell types including 23 GABAergic, 19 glutamatergic and seven non-neuronal types. We also analyze cell-type specific mRNA processing and characterize genetic access to these transcriptomic types by many transgenic Cre lines. Finally, we show that some of our transcriptomic cell types display specific and differential electrophysiological and axon projection properties, thereby confirming that the single cell transcriptomic signatures can be associated with specific cellular properties.

Droplet-microarray on superhydrophobic–superhydrophilic patterns for high-throughput live cell screenings

Droplet-microarray platform based on superhydrophobic–superhydrophilic patterning allows for miniaturized high throughput drug and transfection screenings of live cells in separated nanoliter droplets.

Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data

We introduce an iterative normalization and clustering method for single-cell gene expression data. The emerging technology of single-cell RNA-seq gives access to gene expression measurements for thousands of cells, allowing discovery and characterization of cell types. However, the data is confounded by technical variation emanating from experimental errors and cell type-specific biases. Current approaches perform a global normalization prior to analyzing biological signals, which does not resolve missing data or variation dependent on latent cell types. Our model is formulated as a hierarchical Bayesian mixture model with cell-specific scalings that aid the iterative normalization and clustering of cells, teasing apart technical variation from biological signals. We demonstrate that this approach is superior to global normalization followed by clustering. We show identifiability and weak convergence guarantees of our method and present a scalable Gibbs inference algorithm. This method improves cluster inference in both synthetic and real single-cell data compared with previous methods, and allows easy interpretation and recovery of the underlying structure and cell types.

COMPUTATIONAL APPROACHES TO STUDY MICROBES AND MICROBIOMES

Technological advances are making large-scale measurements of microbial communities commonplace. These newly acquired datasets are allowing researchers to ask and answer questions about the composition of microbial communities, the roles of members in these communities, and how genes and molecular pathways are regulated in individual community members and communities as a whole to effectively respond to diverse and changing environments. In addition to providing a more comprehensive survey of the microbial world, this new information allows for the development of computational approaches to model the processes underlying microbial systems. We anticipate that the field of computational microbiology will continue to grow rapidly in the coming years. In this manuscript we highlight both areas of particular interest in microbiology as well as computational approaches that begin to address these challenges.

Mouse embryonic fibroblasts exhibit extensive developmental and phenotypic diversity

Analysis of embryonic fibroblasts from GFP reporter mice indicates that the fibroblast cell type harbors a large collection of developmentally and phenotypically heterogeneous subtypes. Some of these cells exhibit multipotency, whereas others do not. Multiparameter flow cytometry analysis shows that a large number of distinct populations of fibroblast-like cells can be found in cultures initiated from different embryonic organs, and cells sorted according to their surface phenotype typically retain their characteristics on continued propagation in culture. Similarly, surface phenotypes of individual cloned fibroblast-like cells exhibit significant variation. The fibroblast cell class appears to contain a very large number of denumerable subtypes.

Hierarchical deconstruction of mouse olfactory sensory neurons: from whole mucosa to single-cell RNA-seq

The mouse olfactory mucosa is a complex chemosensory tissue composed of multiple cell types, neuronal and non-neuronal. We have here applied RNA-seq hierarchically, in three steps of decreasing cellular heterogeneity: starting with crude tissue samples dissected from the nose, proceeding to flow-cytometrically sorted pools of mature olfactory sensory neurons (OSNs), and finally arriving at single mature OSNs. We show that 98.9% of intact olfactory receptor (OR) genes are expressed in mature OSNs. We uncover a hitherto unknown bipartition among mature OSNs. We find that 19 of 21 single mature OSNs each express a single intact OR gene abundantly, consistent with the one neuron-one receptor rule. For the 9 single OSNs where the two alleles of the abundantly expressed OR gene exhibit single-nucleotide polymorphisms, we demonstrate that monoallelic expression of the abundantly expressed OR gene is extremely tight. The remaining two single mature OSNs lack OR gene expression but express Trpc2 and Gucy1b2. We establish these two cells as a neuronal cell type that is fundamentally distinct from canonical, OR-expressing OSNs and that is defined by the differential, higher expression of 55 genes. We propose this tiered experimental approach as a paradigm to unravel gene expression in other cellularly heterogeneous systems.

Implications of Epigenetic Variability within a Cell Population for "Cell Type" Classification

Here, we propose a new approach to defining nerve “cell types” in reaction to recent advances in single cell analysis. Among cells previously thought to be equivalent, considerable differences in global gene expression and biased tendencies among differing developmental fates have been demonstrated within multiple lineages. The model of classifying cells into distinct types thus has to be revised to account for this intrinsic variability. A “cell type” could be a group of cells that possess similar, but not necessarily identical properties, variable within a spectrum of epigenetic adjustments that permit its developmental path toward a specific function to be achieved. Thus, the definition of a cell type is becoming more similar to the definition of a species: sharing essential properties with other members of its group, but permitting a certain amount of deviation in aspects that do not seriously impact function. This approach accommodates, even embraces the spectrum of natural variation found in various cell populations and consequently avoids the fallacy of false equivalence. For example, developing neurons will react to their microenvironments with epigenetic changes resulting in slight changes in gene expression and morphology. Addressing the new questions implied here will have significant implications for developmental neurobiology.

What cell biologists should know about the National Institutes of Health BRAIN Initiative

The BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative is an ambitious project to develop innovative tools for a deeper understanding of how the brain functions in health and disease. Early programs in the National Institutes of Health BRAIN Initiative focus on tools for next-generation imaging and recording, studies of cell diversity and cell census, and integrative approaches to circuit function. In all of these efforts, cell biologists can play a leading role.

Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding

Rapid progress in single-cell analysis methods allow for exploration of cellular diversity at unprecedented depth and throughput. Visualizing and understanding these large, high-dimensional datasets poses a major analytical challenge. Mass cytometry allows for simultaneous measurement of >40 different proteins, permitting in-depth analysis of multiple aspects of cellular diversity. In this article, we present one-dimensional soli-expression by nonlinear stochastic embedding (One-SENSE), a dimensionality reduction method based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm, for categorical analysis of mass cytometry data. With One-SENSE, measured parameters are grouped into predefined categories, and cells are projected onto a space composed of one dimension for each category. In contrast with higher-dimensional t-SNE, each dimension (plot axis) in One-SENSE has biological meaning that can be easily annotated with binned heat plots. We applied One-SENSE to probe relationships between categories of human T cell phenotypes and observed previously unappreciated cellular populations within an orchestrated view of immune cell diversity. The presentation of high-dimensional cytometric data using One-SENSE showed a significant improvement in distinguished T cell diversity compared with the original t-SNE algorithm and could be useful for any high-dimensional dataset.

Human cerebral organoids recapitulate gene expression programs of fetal neocortex development

Cerebral organoids-3D cultures of human cerebral tissue derived from pluripotent stem cells-have emerged as models of human cortical development. However, the extent to which in vitro organoid systems recapitulate neural progenitor cell proliferation and neuronal differentiation programs observed in vivo remains unclear. Here we use single-cell RNA sequencing (scRNA-seq) to dissect and compare cell composition and progenitor-to-neuron lineage relationships in human cerebral organoids and fetal neocortex. Covariation network analysis using the fetal neocortex data reveals known and previously unidentified interactions among genes central to neural progenitor proliferation and neuronal differentiation. In the organoid, we detect diverse progenitors and differentiated cell types of neuronal and mesenchymal lineages and identify cells that derived from regions resembling the fetal neocortex. We find that these organoid cortical cells use gene expression programs remarkably similar to those of the fetal tissue to organize into cerebral cortex-like regions. Our comparison of in vivo and in vitro cortical single-cell transcriptomes illuminates the genetic features underlying human cortical development that can be studied in organoid cultures.

Nanotechnologies for biomedical science and translational medicine

In 2000 the United States launched the National Nanotechnology Initiative and, along with it, a well-defined set of goals for nanomedicine. This Perspective looks back at the progress made toward those goals, within the context of the changing landscape in biomedicine that has occurred over the past 15 years, and considers advances that are likely to occur during the next decade. In particular, nanotechnologies for health-related genomics and single-cell biology, inorganic and organic nanoparticles for biomedicine, and wearable nanotechnologies for wellness monitoring are briefly covered.

Single-cell sequencing in cancer research

Genome-wide single-cell sequencing investigations have the potential to classify individual cells within a tumor mass. In recent years, various single-cell DNA and RNA quantification techniques have facilitated significant advances in our ability to classify subpopulations of cells within a heterogeneous population. These approaches provide the possibility of unraveling the complex variability in genetic, epigenetic and transcriptional interactions that occur within identical cells in a tumor. This should enhance our knowledge of the underlying biological phenotypes and could have a huge impact in designing more precise anticancer treatments in order to improve outcomes and avoid tumor resistance. In addition, single-cell sequencing analysis has the potential to allow the development of better diagnostic and prognostic biomarkers, and thus aid the delivery of more personalized targeted cancer therapy. Nevertheless, further research is still required to overcome technical, biological and computational problems before clinical application.

Deformation of double emulsions under conditions of flow cytometry hydrodynamic focusing

Water-in-oil-in-water (w/o/w) microfluidics double emulsions offer a new route to compartmentalise reagents into isolated aqueous microenvironments while maintaining an aqueous carrier fluid phase; this enables compatibility with commercial flow cytometry systems such as fluorescence-activated cell sorting (FACS). Double emulsion (inner core) deformation under hydrodynamic focusing conditions that mimic the environment double emulsions experience in flow cytometry applications is of particular importance for droplet stability and cell viability. This paper reports on an experimental study of the dynamic deformation of aqueous cores of w/o/w double emulsions under hydrodynamic focusing, with the sheath flow directed at 45° to the sample flow. A number of factors affecting the inner core deformation and recovery were examined. Deformation was found to depend significantly on the core or shell viscosity, the droplet-to-sheath flow velocity ratio, and core and shell sizes. Core deformation was found to depend more on the type of surfactant rather concentration with high molecular weight surfactant exhibiting a negligible effect on deformation whereas low molecular weight surfactant enhancing deformation at low concentrations due to their lateral mobility at the interface.

Practical guidelines for B-cell receptor repertoire sequencing analysis

High-throughput sequencing of B-cell immunoglobulin repertoires is increasingly being applied to gain insights into the adaptive immune response in healthy individuals and in those with a wide range of diseases. Recent applications include the study of autoimmunity, infection, allergy, cancer and aging. As sequencing technologies continue to improve, these repertoire sequencing experiments are producing ever larger datasets, with tens- to hundreds-of-millions of sequences. These data require specialized bioinformatics pipelines to be analyzed effectively. Numerous methods and tools have been developed to handle different steps of the analysis, and integrated software suites have recently been made available. However, the field has yet to converge on a standard pipeline for data processing and analysis. Common file formats for data sharing are also lacking. Here we provide a set of practical guidelines for B-cell receptor repertoire sequencing analysis, starting from raw sequencing reads and proceeding through pre-processing, determination of population structure, and analysis of repertoire properties. These include methods for unique molecular identifiers and sequencing error correction, V(D)J assignment and detection of novel alleles, clonal assignment, lineage tree construction, somatic hypermutation modeling, selection analysis, and analysis of stereotyped or convergent responses. The guidelines presented here highlight the major steps involved in the analysis of B-cell repertoire sequencing data, along with recommendations on how to avoid common pitfalls.

SCRaMbLE generates designed combinatorial stochastic diversity in synthetic chromosomes

Synthetic chromosome rearrangement and modification by loxP-mediated evolution (SCRaMbLE) generates combinatorial genomic diversity through rearrangements at designed recombinase sites. We applied SCRaMbLE to yeast synthetic chromosome arm synIXR (43 recombinase sites) and then used a computational pipeline to infer or unscramble the sequence of recombinations that created the observed genomes. Deep sequencing of 64 synIXR SCRaMbLE strains revealed 156 deletions, 89 inversions, 94 duplications, and 55 additional complex rearrangements; several duplications are consistent with a double rolling circle mechanism. Every SCRaMbLE strain was unique, validating the capability of SCRaMbLE to explore a diverse space of genomes. Rearrangements occurred exclusively at designed loxPsym sites, with no significant evidence for ectopic rearrangements or mutations involving synthetic regions, the 99% nonsynthetic nuclear genome, or the mitochondrial genome. Deletion frequencies identified genes required for viability or fast growth. Replacement of 3′ UTR by non-UTR sequence had surprisingly little effect on fitness. SCRaMbLE generates genome diversity in designated regions, reveals fitness constraints, and should scale to simultaneous evolution of multiple synthetic chromosomes.

Research highlights: microfluidic-enabled single-cell epigenetics

Individual cells are the fundamental unit of life with diverse functions from metabolism to motility. In multicellular organisms, a single genome can give rise to tremendous variability across tissues at the single-cell level due to epigenetic differences in the genes that are expressed. Signals from the local environment or a history of signals can drive these variations, and tissues have many cell types that play separate roles. This epigenetic heterogeneity is of biological importance in normal functions such as tissue morphogenesis and can contribute to development or resistance of cancer, or other disease states. Therefore, an improved understanding of variations at the single cell level are fundamental to understanding biology and developing new approaches to combating disease. Traditional approaches to characterize epigenetic modifications of chromatin or the transcriptome of cells have often focused on blended responses of many cells in a tissue; however, such bulk measures lose spatial and temporal differences that occur from cell to cell, and cannot uncover novel or rare populations of cells. Here we highlight a flurry of recent activity to identify the mRNA profiles from thousands of single-cells as well as chromatin accessibility and histone marks on single to few hundreds of cells. Microfluidics and microfabrication have played a central role in the range of new techniques, and will likely continue to impact their further development towards routine single-cell epigenetic analysis.

Modeling developmental neuropsychiatric disorders with iPSC technology: challenges and opportunities

The development of cellular reprogramming methods to generate human induced pluripotent stem cells (iPSC) has led to the establishment of lines from hundreds of patients with a variety of neurologic and psychiatric diseases. One of the fundamental powers of iPSC technology lies in the competency of these cells to be directed to become any cell type in the body, thus allowing researchers to examine disease mechanisms and identify and test novel therapeutics in relevant cell types. The field has now exited the phase of 'proof-of-principle' studies showing the potential of the model systems, and it has now entered an exciting new era where iPSC studies are contributing to the field's understanding of mechanisms of disease. Here, we describe the challenges of iPSC modeling of neuropsychiatric disorders, and highlight studies where some of these challenges have been addressed to provide novel insights into disease mechanisms.

Molecular heterogeneity of midbrain dopaminergic neurons--Moving toward single cell resolution

Since their discovery, midbrain dopamine (DA) neurons have been researched extensively, in part because of their diverse functions and involvement in various neuropsychiatric disorders. Over the last few decades, reports have emerged that midbrain DA neurons were not a homogeneous group, but that DA neurons located in distinct anatomical locations within the midbrain had distinctive properties in terms of physiology, function, and vulnerability. Accordingly, several studies focused on identifying heterogeneous gene expression across DA neuron clusters. Here we review the importance of understanding DA neuron heterogeneity at the molecular level, previous studies detailing heterogeneous gene expression in DA neurons, and finally recent work which brings together previous heterogeneous gene expression profiles in a coordinated manner, at single cell resolution.

Asymmetric Cell Division in T Lymphocyte Fate Diversification

Immunological protection against microbial pathogens is dependent on robust generation of functionally diverse T lymphocyte subsets. Upon microbial infection, naïve CD4(+) or CD8(+) T lymphocytes can give rise to effector- and memory-fated progeny that together mediate a potent immune response. Recent advances in single-cell immunological and genomic profiling technologies have helped elucidate early and late diversification mechanisms that enable the generation of heterogeneity from single T lymphocytes. We discuss these findings here and argue that one such mechanism, asymmetric cell division, creates an early divergence in T lymphocyte fates by giving rise to daughter cells with a propensity towards the terminally differentiated effector or self-renewing memory lineages, with cell-intrinsic and -extrinsic cues from the microenvironment driving the final maturation steps.